Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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edbeeching/prj_gia_dataset_atari_2B_atari_phoenix_1111
--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the atari_phoenix environment, sample for the policy atari_2B_atari_phoenix_1111 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
thur071/thjovemdx
--- license: openrail ---
andersonbcdefg/MEDI
--- license: mit ---
pkarypis/ultrachat_filtered_0.9
--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: test_gen num_bytes: 148276089 num_examples: 28304 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: train_sft num_bytes: 1257349338.1050777 num_examples: 187078 download_size: 1531111421 dataset_size: 2907717898.1050777 --- # Dataset Card for "ultrachat_filtered_0.9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tasksource/HYPO-L
--- license: mit task_categories: - text-classification language: - en tags: - hyperbola - exageration --- https://github.com/yunx-z/MOVER ``` @inproceedings{zhang-wan-2022-mover, title = "{MOVER}: Mask, Over-generate and Rank for Hyperbole Generation", author = "Zhang, Yunxiang and Wan, Xiaojun", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.440", doi = "10.18653/v1/2022.naacl-main.440", pages = "6018--6030", abstract = "Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing. In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way. Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs. During training, we fine-tune BART to infill masked hyperbolic spans of sentences from HYPO-XL. During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions. Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality. Automatic and human evaluation results show that our model is effective at generating hyperbolic paraphrase sentences and outperforms several baseline systems.", } ```
somosnlp/dib-translation-for-es-bench
--- dataset_info: features: - name: input dtype: string - name: target sequence: 'null' - name: target-suggestion dtype: 'null' - name: target-suggestion-metadata struct: - name: agent dtype: 'null' - name: score dtype: 'null' - name: type dtype: 'null' - name: external_id dtype: string - name: metadata dtype: string - name: generation_model sequence: string - name: generation_prompt list: list: - name: content dtype: string - name: role dtype: string - name: raw_generation_responses sequence: string - name: generations sequence: string - name: labelling_model dtype: string - name: labelling_prompt list: - name: content dtype: string - name: role dtype: string - name: raw_labelling_response dtype: string - name: score dtype: float64 - name: critique dtype: string splits: - name: train num_bytes: 3593965 num_examples: 501 download_size: 1760427 dataset_size: 3593965 configs: - config_name: default data_files: - split: train path: data/train-* ---
PengLx/DeepstashArticle
--- license: gpl-3.0 task_categories: - text-classification language: - en tags: - art - finance - webdataset pretty_name: DeepstashArticle size_categories: - 100K<n<1M ---
CyberHarem/mary_nikke
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mary/メアリー/梅里/메어리 (Nikke: Goddess of Victory) This is the dataset of mary/メアリー/梅里/메어리 (Nikke: Goddess of Victory), containing 83 images and their tags. The core tags of this character are `breasts, long_hair, black_hair, bangs, braid, large_breasts, hat, sun_hat, white_headwear, multicolored_hair, streaked_hair, huge_breasts, hairband`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 83 | 182.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 83 | 77.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 224 | 181.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 83 | 147.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 224 | 302.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mary_nikke/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mary_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, white_bikini, bare_shoulders, closed_eyes, closed_mouth, smile, solo, collarbone, earrings, blush, outdoors, thigh_strap, choker, day, dress, necklace, official_alternate_costume, see-through, beach, blue_hair, blue_rose, blue_sky, hair_over_shoulder, navel, thighlet, thighs | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, closed_eyes, long_sleeves, white_hairband, blush, blue_sweater, smile, dress, open_jacket, turtleneck_sweater, parted_lips, thighs, white_jacket, armband, white_gloves, hair_between_eyes, panties, ribbed_sweater | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | white_bikini | bare_shoulders | closed_eyes | closed_mouth | smile | solo | collarbone | earrings | blush | outdoors | thigh_strap | choker | day | dress | necklace | official_alternate_costume | see-through | beach | blue_hair | blue_rose | blue_sky | hair_over_shoulder | navel | thighlet | thighs | long_sleeves | white_hairband | blue_sweater | open_jacket | turtleneck_sweater | parted_lips | white_jacket | armband | white_gloves | hair_between_eyes | panties | ribbed_sweater | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------------|:-----------------|:--------------|:---------------|:--------|:-------|:-------------|:-----------|:--------|:-----------|:--------------|:---------|:------|:--------|:-----------|:-----------------------------|:--------------|:--------|:------------|:------------|:-----------|:---------------------|:--------|:-----------|:---------|:---------------|:-----------------|:---------------|:--------------|:---------------------|:--------------|:---------------|:----------|:---------------|:--------------------|:----------|:-----------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | | X | | X | X | | | X | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
rooftopcoder/coqa-squad-1.0-fixed
--- language: en dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 187243501 num_examples: 108647 - name: test num_bytes: 13255826 num_examples: 7983 - name: validation num_bytes: 13255826 num_examples: 7983 download_size: 17359756 dataset_size: 213755153 --- # Dataset Card for "coqa-squad-1.0-fixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rainerberger/Mri_segmentation
--- dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 34791415.8 num_examples: 400 - name: test num_bytes: 8485825.2 num_examples: 100 - name: valid num_bytes: 20015103.0 num_examples: 200 download_size: 63164131 dataset_size: 63292344.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
weqweasdas/rsf_pi0_iter1_with_len
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: type dtype: string - name: instances list: - name: prompt dtype: string - name: responses sequence: string - name: rewards sequence: float64 - name: len sequence: int64 splits: - name: train num_bytes: 150364452 num_examples: 1 download_size: 73615313 dataset_size: 150364452 --- # Dataset Card for "rsf_pi0_iter1_with_len" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ilybelly/trmsg
--- license: bigscience-openrail-m ---
amandyk/kazakh_wiki_articles
--- license: afl-3.0 task_categories: - text-generation language: - kk --- Source: https://dumps.wikimedia.org/kkwiki/latest/ [kwiki-latest-pages-articles.xml.bz2]
open-llm-leaderboard/details_UCLA-AGI__zephyr-7b-sft-full-SPIN-iter0
--- pretty_name: Evaluation run of UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0](https://huggingface.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_UCLA-AGI__zephyr-7b-sft-full-SPIN-iter0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T15:47:16.037619](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__zephyr-7b-sft-full-SPIN-iter0/blob/main/results_2024-01-13T15-47-16.037619.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6119702436885257,\n\ \ \"acc_stderr\": 0.03283940141095218,\n \"acc_norm\": 0.6171401402100634,\n\ \ \"acc_norm_stderr\": 0.03350745031301805,\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5034226817976417,\n\ \ \"mc2_stderr\": 0.015916803874695535\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735567,\n\ \ \"acc_norm\": 0.6356655290102389,\n \"acc_norm_stderr\": 0.014063260279882417\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6543517227643896,\n\ \ \"acc_stderr\": 0.00474607219107258,\n \"acc_norm\": 0.8442541326428998,\n\ \ \"acc_norm_stderr\": 0.0036187316588377175\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\ \ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5063829787234042,\n \"acc_stderr\": 0.03268335899936337,\n\ \ \"acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.03268335899936337\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\ acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7451612903225806,\n\ \ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n\ \ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176088,\n \ \ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176088\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\ \ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7981651376146789,\n \"acc_stderr\": 0.01720857935778758,\n \"\ acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.01720857935778758\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696042,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696042\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n \"\ acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\ \ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.024414947304543674,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.024414947304543674\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388677006,\n\ \ \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388677006\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39217877094972065,\n\ \ \"acc_stderr\": 0.016329061073207442,\n \"acc_norm\": 0.39217877094972065,\n\ \ \"acc_norm_stderr\": 0.016329061073207442\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.026173908506718576,\n\ \ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.026173908506718576\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.026082700695399665,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.026082700695399665\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.02570264026060374,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.02570264026060374\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45436766623207303,\n\ \ \"acc_stderr\": 0.012716941720734806,\n \"acc_norm\": 0.45436766623207303,\n\ \ \"acc_norm_stderr\": 0.012716941720734806\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6388888888888888,\n \"acc_stderr\": 0.019431775677037313,\n \ \ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.019431775677037313\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6816326530612244,\n \"acc_stderr\": 0.029822533793982055,\n\ \ \"acc_norm\": 0.6816326530612244,\n \"acc_norm_stderr\": 0.029822533793982055\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5034226817976417,\n\ \ \"mc2_stderr\": 0.015916803874695535\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.011646276755089686\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36315390447308565,\n \ \ \"acc_stderr\": 0.013246614539839866\n }\n}\n```" repo_url: https://huggingface.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|arc:challenge|25_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|arc:challenge|25_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-13T15-47-16.037619.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|gsm8k|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|gsm8k|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hellaswag|10_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hellaswag|10_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-46-28.796344.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-47-16.037619.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-47-16.037619.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T15-47-16.037619.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T15_46_28.796344 path: - '**/details_harness|winogrande|5_2024-01-13T15-46-28.796344.parquet' - split: 2024_01_13T15_47_16.037619 path: - '**/details_harness|winogrande|5_2024-01-13T15-47-16.037619.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-13T15-47-16.037619.parquet' - config_name: results data_files: - split: 2024_01_13T15_46_28.796344 path: - results_2024-01-13T15-46-28.796344.parquet - split: 2024_01_13T15_47_16.037619 path: - results_2024-01-13T15-47-16.037619.parquet - split: latest path: - results_2024-01-13T15-47-16.037619.parquet --- # Dataset Card for Evaluation run of UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0](https://huggingface.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_UCLA-AGI__zephyr-7b-sft-full-SPIN-iter0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T15:47:16.037619](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__zephyr-7b-sft-full-SPIN-iter0/blob/main/results_2024-01-13T15-47-16.037619.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6119702436885257, "acc_stderr": 0.03283940141095218, "acc_norm": 0.6171401402100634, "acc_norm_stderr": 0.03350745031301805, "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756547, "mc2": 0.5034226817976417, "mc2_stderr": 0.015916803874695535 }, "harness|arc:challenge|25": { "acc": 0.6015358361774744, "acc_stderr": 0.014306946052735567, "acc_norm": 0.6356655290102389, "acc_norm_stderr": 0.014063260279882417 }, "harness|hellaswag|10": { "acc": 0.6543517227643896, "acc_stderr": 0.00474607219107258, "acc_norm": 0.8442541326428998, "acc_norm_stderr": 0.0036187316588377175 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.631578947368421, "acc_stderr": 0.03925523381052932, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.03925523381052932 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.660377358490566, "acc_stderr": 0.02914690474779833, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.02914690474779833 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.03724249595817731, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.03724249595817731 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5063829787234042, "acc_stderr": 0.03268335899936337, "acc_norm": 0.5063829787234042, "acc_norm_stderr": 0.03268335899936337 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3994708994708995, "acc_stderr": 0.02522545028406788, "acc_norm": 0.3994708994708995, "acc_norm_stderr": 0.02522545028406788 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7451612903225806, "acc_stderr": 0.024790118459332208, "acc_norm": 0.7451612903225806, "acc_norm_stderr": 0.024790118459332208 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885417, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885417 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479049, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479049 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5948717948717949, "acc_stderr": 0.024890471769938145, "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.29259259259259257, "acc_stderr": 0.027738969632176088, "acc_norm": 0.29259259259259257, "acc_norm_stderr": 0.027738969632176088 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7981651376146789, "acc_stderr": 0.01720857935778758, "acc_norm": 0.7981651376146789, "acc_norm_stderr": 0.01720857935778758 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.03350991604696042, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.03350991604696042 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588667, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588667 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.038808483010823944, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.038808483010823944 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.039418975265163025, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.039418975265163025 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615623, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615623 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7475728155339806, "acc_stderr": 0.04301250399690878, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.04301250399690878 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8333333333333334, "acc_stderr": 0.024414947304543674, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.024414947304543674 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7023121387283237, "acc_stderr": 0.024617055388677006, "acc_norm": 0.7023121387283237, "acc_norm_stderr": 0.024617055388677006 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.39217877094972065, "acc_stderr": 0.016329061073207442, "acc_norm": 0.39217877094972065, "acc_norm_stderr": 0.016329061073207442 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7026143790849673, "acc_stderr": 0.026173908506718576, "acc_norm": 0.7026143790849673, "acc_norm_stderr": 0.026173908506718576 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.026082700695399665, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.026082700695399665 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.691358024691358, "acc_stderr": 0.02570264026060374, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.02570264026060374 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.45436766623207303, "acc_stderr": 0.012716941720734806, "acc_norm": 0.45436766623207303, "acc_norm_stderr": 0.012716941720734806 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681397, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681397 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.019431775677037313, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.019431775677037313 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6816326530612244, "acc_stderr": 0.029822533793982055, "acc_norm": 0.6816326530612244, "acc_norm_stderr": 0.029822533793982055 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756547, "mc2": 0.5034226817976417, "mc2_stderr": 0.015916803874695535 }, "harness|winogrande|5": { "acc": 0.7797947908445146, "acc_stderr": 0.011646276755089686 }, "harness|gsm8k|5": { "acc": 0.36315390447308565, "acc_stderr": 0.013246614539839866 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
lang-uk/uk150m
--- license: mit ---
Maxx0/llamamprofy
--- license: apache-2.0 ---
open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test
--- pretty_name: Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7658118587114254,\n\ \ \"acc_stderr\": 0.02808039655994379,\n \"acc_norm\": 0.7696925363139744,\n\ \ \"acc_norm_stderr\": 0.02861463324453946,\n \"mc1\": 0.44920440636474906,\n\ \ \"mc1_stderr\": 0.017412941986115305,\n \"mc2\": 0.6119893427851197,\n\ \ \"mc2_stderr\": 0.014925989149943244\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620456,\n\ \ \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173306\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6566421031666999,\n\ \ \"acc_stderr\": 0.0047385929002801905,\n \"acc_norm\": 0.8538139812786297,\n\ \ \"acc_norm_stderr\": 0.003525705773353417\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066652,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066652\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.02564834125169361,\n\ \ \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.02564834125169361\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.02426297983937228,\n\ \ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.02426297983937228\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05000000000000001,\n \ \ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05000000000000001\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7398843930635838,\n\ \ \"acc_stderr\": 0.033450369167889904,\n \"acc_norm\": 0.7398843930635838,\n\ \ \"acc_norm_stderr\": 0.033450369167889904\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n\ \ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.026947483121496217,\n\ \ \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496217\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.708994708994709,\n \"acc_stderr\": 0.023393826500484875,\n \"\ acc_norm\": 0.708994708994709,\n \"acc_norm_stderr\": 0.023393826500484875\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n\ \ \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n\ \ \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\ acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n \"\ acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\"\ : 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865387,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865387\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9191919191919192,\n \"acc_stderr\": 0.019417681889724536,\n \"\ acc_norm\": 0.9191919191919192,\n \"acc_norm_stderr\": 0.019417681889724536\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.01028141701190903,\n\ \ \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.01028141701190903\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8128205128205128,\n \"acc_stderr\": 0.01977660108655004,\n \ \ \"acc_norm\": 0.8128205128205128,\n \"acc_norm_stderr\": 0.01977660108655004\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.45555555555555555,\n \"acc_stderr\": 0.03036486250482443,\n \ \ \"acc_norm\": 0.45555555555555555,\n \"acc_norm_stderr\": 0.03036486250482443\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.0237933539975288,\n \ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.0237933539975288\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862088,\n \"\ acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862088\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6388888888888888,\n \"acc_stderr\": 0.032757734861009996,\n \"\ acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.032757734861009996\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9029535864978903,\n \"acc_stderr\": 0.01926932302564026,\n \ \ \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.01926932302564026\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597446,\n\ \ \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597446\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881348,\n\ \ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881348\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9358974358974359,\n\ \ \"acc_stderr\": 0.016046261631673137,\n \"acc_norm\": 0.9358974358974359,\n\ \ \"acc_norm_stderr\": 0.016046261631673137\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9042145593869731,\n\ \ \"acc_stderr\": 0.01052403107905584,\n \"acc_norm\": 0.9042145593869731,\n\ \ \"acc_norm_stderr\": 0.01052403107905584\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.815028901734104,\n \"acc_stderr\": 0.020903975842083027,\n\ \ \"acc_norm\": 0.815028901734104,\n \"acc_norm_stderr\": 0.020903975842083027\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7754189944134078,\n\ \ \"acc_stderr\": 0.01395680366654464,\n \"acc_norm\": 0.7754189944134078,\n\ \ \"acc_norm_stderr\": 0.01395680366654464\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.020823758837580912,\n\ \ \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.020823758837580912\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8360128617363344,\n\ \ \"acc_stderr\": 0.0210295764646627,\n \"acc_norm\": 0.8360128617363344,\n\ \ \"acc_norm_stderr\": 0.0210295764646627\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\ \ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.028947338851614098,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.028947338851614098\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.605606258148631,\n\ \ \"acc_stderr\": 0.01248214166563118,\n \"acc_norm\": 0.605606258148631,\n\ \ \"acc_norm_stderr\": 0.01248214166563118\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.023886881922440335,\n\ \ \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.023886881922440335\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8218954248366013,\n \"acc_stderr\": 0.015478369653108568,\n \ \ \"acc_norm\": 0.8218954248366013,\n \"acc_norm_stderr\": 0.015478369653108568\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n\ \ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\ \ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.02366169917709861,\n\ \ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.02366169917709861\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\ \ \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n\ \ \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072867,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072867\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.44920440636474906,\n\ \ \"mc1_stderr\": 0.017412941986115305,\n \"mc2\": 0.6119893427851197,\n\ \ \"mc2_stderr\": 0.014925989149943244\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237433\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6929492039423806,\n \ \ \"acc_stderr\": 0.012705685723131702\n }\n}\n```" repo_url: https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|arc:challenge|25_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-13T18-09-57.188193.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|gsm8k|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hellaswag|10_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T18-09-57.188193.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-09-57.188193.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T18-09-57.188193.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T18_09_57.188193 path: - '**/details_harness|winogrande|5_2024-01-13T18-09-57.188193.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-13T18-09-57.188193.parquet' - config_name: results data_files: - split: 2024_01_13T18_09_57.188193 path: - results_2024-01-13T18-09-57.188193.parquet - split: latest path: - results_2024-01-13T18-09-57.188193.parquet --- # Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7658118587114254, "acc_stderr": 0.02808039655994379, "acc_norm": 0.7696925363139744, "acc_norm_stderr": 0.02861463324453946, "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6119893427851197, "mc2_stderr": 0.014925989149943244 }, "harness|arc:challenge|25": { "acc": 0.6527303754266212, "acc_stderr": 0.013913034529620456, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173306 }, "harness|hellaswag|10": { "acc": 0.6566421031666999, "acc_stderr": 0.0047385929002801905, "acc_norm": 0.8538139812786297, "acc_norm_stderr": 0.003525705773353417 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066652, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066652 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8881578947368421, "acc_stderr": 0.02564834125169361, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.02564834125169361 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8075471698113208, "acc_stderr": 0.02426297983937228, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.02426297983937228 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.55, "acc_stderr": 0.05000000000000001, "acc_norm": 0.55, "acc_norm_stderr": 0.05000000000000001 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7398843930635838, "acc_stderr": 0.033450369167889904, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.033450369167889904 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7829787234042553, "acc_stderr": 0.026947483121496217, "acc_norm": 0.7829787234042553, "acc_norm_stderr": 0.026947483121496217 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7586206896551724, "acc_stderr": 0.03565998174135302, "acc_norm": 0.7586206896551724, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.708994708994709, "acc_stderr": 0.023393826500484875, "acc_norm": 0.708994708994709, "acc_norm_stderr": 0.023393826500484875 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5793650793650794, "acc_stderr": 0.04415438226743745, "acc_norm": 0.5793650793650794, "acc_norm_stderr": 0.04415438226743745 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865387, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865387 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9191919191919192, "acc_stderr": 0.019417681889724536, "acc_norm": 0.9191919191919192, "acc_norm_stderr": 0.019417681889724536 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.01028141701190903, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.01028141701190903 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8128205128205128, "acc_stderr": 0.01977660108655004, "acc_norm": 0.8128205128205128, "acc_norm_stderr": 0.01977660108655004 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.45555555555555555, "acc_stderr": 0.03036486250482443, "acc_norm": 0.45555555555555555, "acc_norm_stderr": 0.03036486250482443 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8403361344537815, "acc_stderr": 0.0237933539975288, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.0237933539975288 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9192660550458716, "acc_stderr": 0.011680172292862088, "acc_norm": 0.9192660550458716, "acc_norm_stderr": 0.011680172292862088 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6388888888888888, "acc_stderr": 0.032757734861009996, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.032757734861009996 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9029535864978903, "acc_stderr": 0.01926932302564026, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.01926932302564026 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8854961832061069, "acc_stderr": 0.027927473753597446, "acc_norm": 0.8854961832061069, "acc_norm_stderr": 0.027927473753597446 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.029239272675632748, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.029239272675632748 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881348, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881348 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9358974358974359, "acc_stderr": 0.016046261631673137, "acc_norm": 0.9358974358974359, "acc_norm_stderr": 0.016046261631673137 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9042145593869731, "acc_stderr": 0.01052403107905584, "acc_norm": 0.9042145593869731, "acc_norm_stderr": 0.01052403107905584 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.815028901734104, "acc_stderr": 0.020903975842083027, "acc_norm": 0.815028901734104, "acc_norm_stderr": 0.020903975842083027 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7754189944134078, "acc_stderr": 0.01395680366654464, "acc_norm": 0.7754189944134078, "acc_norm_stderr": 0.01395680366654464 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8431372549019608, "acc_stderr": 0.020823758837580912, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.020823758837580912 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8360128617363344, "acc_stderr": 0.0210295764646627, "acc_norm": 0.8360128617363344, "acc_norm_stderr": 0.0210295764646627 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8703703703703703, "acc_stderr": 0.018689725721062072, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.018689725721062072 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6205673758865248, "acc_stderr": 0.028947338851614098, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.028947338851614098 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.605606258148631, "acc_stderr": 0.01248214166563118, "acc_norm": 0.605606258148631, "acc_norm_stderr": 0.01248214166563118 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8088235294117647, "acc_stderr": 0.023886881922440335, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.023886881922440335 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8218954248366013, "acc_stderr": 0.015478369653108568, "acc_norm": 0.8218954248366013, "acc_norm_stderr": 0.015478369653108568 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7454545454545455, "acc_stderr": 0.041723430387053825, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.041723430387053825 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8367346938775511, "acc_stderr": 0.02366169917709861, "acc_norm": 0.8367346938775511, "acc_norm_stderr": 0.02366169917709861 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700643, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700643 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072867, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072867 }, "harness|truthfulqa:mc|0": { "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6119893427851197, "mc2_stderr": 0.014925989149943244 }, "harness|winogrande|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237433 }, "harness|gsm8k|5": { "acc": 0.6929492039423806, "acc_stderr": 0.012705685723131702 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
result-kand2-sdxl-wuerst-karlo/1b9c0c11
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 159 num_examples: 10 download_size: 1316 dataset_size: 159 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "1b9c0c11" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/beehunter_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of beehunter/ビーハンター/猎蜂 (Arknights) This is the dataset of beehunter/ビーハンター/猎蜂 (Arknights), containing 42 images and their tags. The core tags of this character are `short_hair, animal_ears, white_hair, multicolored_hair, purple_eyes, brown_hair, hair_over_one_eye, streaked_hair, bear_ears, two-tone_hair, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 42 | 57.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/beehunter_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 42 | 47.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/beehunter_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 99 | 92.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/beehunter_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/beehunter_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, long_sleeves, looking_at_viewer, solo, black_jacket, smile, bandages, yellow_thighhighs, hand_wraps, tongue_out, nail_polish, shirt, short_shorts, black_shorts | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, long_sleeves, looking_at_viewer, smile, animal_hood, holding, hood_up, honey, hoodie, open_mouth, simple_background, white_background, animal_costume, fang, fruit | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | looking_at_viewer | solo | black_jacket | smile | bandages | yellow_thighhighs | hand_wraps | tongue_out | nail_polish | shirt | short_shorts | black_shorts | animal_hood | holding | hood_up | honey | hoodie | open_mouth | simple_background | white_background | animal_costume | fang | fruit | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:---------------|:--------|:-----------|:--------------------|:-------------|:-------------|:--------------|:--------|:---------------|:---------------|:--------------|:----------|:----------|:--------|:---------|:-------------|:--------------------|:-------------------|:-----------------|:-------|:--------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
awalesushil/DBLP-QuAD
--- annotations_creators: - expert-generated language: - en language_creators: - machine-generated license: - cc-by-4.0 multilinguality: - monolingual pretty_name: 'DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph' size_categories: - 1K<n<10K source_datasets: - original tags: - knowledge-base-qa task_categories: - question-answering task_ids: [] --- # Dataset Card for DBLP-QuAD ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [DBLP-QuAD Homepage]() - **Repository:** [DBLP-QuAD Repository](https://github.com/awalesushil/DBLP-QuAD) - **Paper:** DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph - **Point of Contact:** [Sushil Awale](mailto:sushil.awale@web.de) ### Dataset Summary DBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions. ## Dataset Structure ### Data Instances An example of a question is given below: ``` { "id": "Q0577", "query_type": "MULTI_FACT", "question": { "string": "What are the primary affiliations of the authors of the paper 'Graphical Partitions and Graphical Relations'?" }, "paraphrased_question": { "string": "List the primary affiliations of the authors of 'Graphical Partitions and Graphical Relations'." }, "query": { "sparql": "SELECT DISTINCT ?answer WHERE { <https://dblp.org/rec/journals/fuin/ShaheenS19> <https://dblp.org/rdf/schema#authoredBy> ?x . ?x <https://dblp.org/rdf/schema#primaryAffiliation> ?answer }" }, "template_id": "TP11", "entities": [ "<https://dblp.org/rec/journals/fuin/ShaheenS19>" ], "relations": [ "<https://dblp.org/rdf/schema#authoredBy>", "<https://dblp.org/rdf/schema#primaryAffiliation>" ], "temporal": false, "held_out": true } ``` ### Data Fields - `id`: the id of the question - `question`: a string containing the question - `paraphrased_question`: a paraphrased version of the question - `query`: a SPARQL query that answers the question - `query_type`: the type of the query - `query_template`: the template of the query - `entities`: a list of entities in the question - `relations`: a list of relations in the question - `temporal`: a boolean indicating whether the question contains a temporal expression - `held_out`: a boolean indicating whether the question is held out from the training set ### Data Splits The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions. ## Additional Information ### Licensing Information DBLP-QuAD is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information In review. ### Contributions Thanks to [@awalesushil](https://github.com/awalesushil) for adding this dataset.
alexshengzhili/SciCapInstructed-graph-only-stage1
--- dataset_info: features: - name: image_file dtype: string - name: id dtype: string - name: caption dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: first_mention dtype: string - name: response dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: forty2seventy num_bytes: 546502923 num_examples: 105606 - name: first_twenty num_bytes: 363824537 num_examples: 70404 - name: twenty_to_forty num_bytes: 364128099 num_examples: 70403 - name: seventy2ninty num_bytes: 364417544 num_examples: 70403 - name: ninty2onehundred num_bytes: 181984295 num_examples: 35202 download_size: 921991197 dataset_size: 1820857398 --- # Dataset Card for "SciCapInstructed-graph-only-stage1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ali444/MIS
--- dataset_info: features: - name: passing_semester dtype: string - name: college_name dtype: string - name: prog_name dtype: string - name: cms_no dtype: int64 - name: reg_no dtype: string - name: name dtype: string - name: guardian_name dtype: string - name: Intake_semester dtype: string - name: earned_cr_hrs dtype: float64 - name: inter_max_12 dtype: float64 - name: inter_obt_12 dtype: float64 - name: inter_max_11 dtype: float64 - name: inter_obt_11 dtype: float64 splits: - name: train num_bytes: 625269 num_examples: 3126 download_size: 147966 dataset_size: 625269 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MIS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-inverse-scaling__quote-repetition-inverse-scaling__quot-3aff83-1695059596
--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/quote-repetition eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: inverse-scaling/quote-repetition dataset_config: inverse-scaling--quote-repetition dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: inverse-scaling/quote-repetition * Config: inverse-scaling--quote-repetition * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
HuggingFaceH4/code_evaluation_prompts
--- dataset_info: features: - name: prompt dtype: string - name: type dtype: string - name: bug dtype: string - name: language dtype: string - name: meta struct: - name: id dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 30363 num_examples: 115 download_size: 15622 dataset_size: 30363 language: - en tags: - code - rlhf size_categories: - n<1K --- # Dataset Card for H4 Code Evaluation Prompts These are a filtered set of prompts for evaluating code instruction models. It will contain a variety of languages and task types. Currently, we used ChatGPT (GPT-3.5-tubro) to generate these, so we encourage using them only for qualatative evaluation and not to train your models. The generation of this data is similar to something like [CodeAlpaca](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20khttps://github.com/sahil280114/codealpaca#data-generation-process), which you can download [here](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k), but we intend to make these tasks both a) more challenging, and b) more curated. These two things hopefully give a meaningful evaluation, but is not enough data to train an entire model. The data corresponds to the following: * 20 simple python instruction following, * 20 intermediate python instruction following, * 10 advanced python instruciton following, * 15 python machine learning questions, * 20 C++ instruction following, * 10 html instruction following, * 20 misc language code feedback questions. Or, on a per language basis: * Python: 81 * C++: 21 * html: 10 * Ruby: 1 * Bash: 1 * MATLAB: 1 * React: 1 * Scala: 1 * JavaScript: 1 * Java: 1 * PHP: 1 Or, per instruction type: * Code completion / instruction following: 95 * Bug fixing: 20 To get the current information on the tasks, you can use the following snippet: ``` from datasets import load_dataset d = load_dataset("HuggingFaceH4/code_evaluation_prompts") language_list = d['train']['language'] language_count = {ele:language_list.count(ele) for ele in language_list} ``` Similar code can be run for the type of instruction (code generation vs. bug advice). Interested in contributing? Open a PR with a specific language and question content. Here are the ChatGPT prompts used to initiate the responses (which are then filtered), May 3rd 2023 version: * Generate a bunch of instructions for coding questions in python (in the format of {"prompt": instruction}) * These have been useful, can you generate the last few that are the hardest and most Pythonic that you can think of? * Taking a step back, can you generate 20 for me that don't need to be as hard, but are machine learning focused (e.g. a mix of PyTorch and Jax). * Generate a bunch of instructions for coding questions in C++ (in the format of {"prompt": instruction}) * Can you generate 5 examples of instructions, with the same format {"prompt": text}, where the instruction has a piece of code with a bug, and you're asking for feedback on your code as if you wrote it?
farnhua/MEDQA_CP
--- dataset_info: features: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 6063870 num_examples: 10178 download_size: 3382946 dataset_size: 6063870 configs: - config_name: default data_files: - split: train path: data/train-* ---
camenduru/ine-dataset-test
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 105057652.0 num_examples: 369 download_size: 103726194 dataset_size: 105057652.0 --- # Dataset Card for "ine-dataset-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_mahiatlinux__MasherAI-v6.1-7B-checkpoint6-pro
--- pretty_name: Evaluation run of mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro](https://huggingface.co/mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mahiatlinux__MasherAI-v6.1-7B-checkpoint6-pro\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-03T10:28:33.737621](https://huggingface.co/datasets/open-llm-leaderboard/details_mahiatlinux__MasherAI-v6.1-7B-checkpoint6-pro/blob/main/results_2024-04-03T10-28-33.737621.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6327029624788667,\n\ \ \"acc_stderr\": 0.03244896513206761,\n \"acc_norm\": 0.6330284162227182,\n\ \ \"acc_norm_stderr\": 0.033118306720219995,\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.589720638696192,\n\ \ \"mc2_stderr\": 0.015374163217375555\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5955631399317406,\n \"acc_stderr\": 0.014342036483436177,\n\ \ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.01410457836649189\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6409081856203943,\n\ \ \"acc_stderr\": 0.004787537385153002,\n \"acc_norm\": 0.8327026488747261,\n\ \ \"acc_norm_stderr\": 0.0037247833892533307\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\ \ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\ \ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7483870967741936,\n\ \ \"acc_stderr\": 0.02468597928623996,\n \"acc_norm\": 0.7483870967741936,\n\ \ \"acc_norm_stderr\": 0.02468597928623996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8497409326424871,\n \"acc_stderr\": 0.02578772318072386,\n\ \ \"acc_norm\": 0.8497409326424871,\n \"acc_norm_stderr\": 0.02578772318072386\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059278,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059278\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.015990154885073403,\n \"\ acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.015990154885073403\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233497,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233497\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462469,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\ \ \"acc_stderr\": 0.013853724170922534,\n \"acc_norm\": 0.8160919540229885,\n\ \ \"acc_norm_stderr\": 0.013853724170922534\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\ \ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2871508379888268,\n\ \ \"acc_stderr\": 0.015131608849963748,\n \"acc_norm\": 0.2871508379888268,\n\ \ \"acc_norm_stderr\": 0.015131608849963748\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195448,\n\ \ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195448\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4485006518904824,\n\ \ \"acc_stderr\": 0.012702317490559806,\n \"acc_norm\": 0.4485006518904824,\n\ \ \"acc_norm_stderr\": 0.012702317490559806\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824873,\n\ \ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824873\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6535947712418301,\n \"acc_stderr\": 0.01924978569171721,\n \ \ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.01924978569171721\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.589720638696192,\n\ \ \"mc2_stderr\": 0.015374163217375555\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938275\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6679302501895376,\n \ \ \"acc_stderr\": 0.01297246503436187\n }\n}\n```" repo_url: https://huggingface.co/mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|arc:challenge|25_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-03T10-28-33.737621.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|gsm8k|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hellaswag|10_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T10-28-33.737621.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T10-28-33.737621.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T10-28-33.737621.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_03T10_28_33.737621 path: - '**/details_harness|winogrande|5_2024-04-03T10-28-33.737621.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-03T10-28-33.737621.parquet' - config_name: results data_files: - split: 2024_04_03T10_28_33.737621 path: - results_2024-04-03T10-28-33.737621.parquet - split: latest path: - results_2024-04-03T10-28-33.737621.parquet --- # Dataset Card for Evaluation run of mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro](https://huggingface.co/mahiatlinux/MasherAI-v6.1-7B-checkpoint6-pro) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mahiatlinux__MasherAI-v6.1-7B-checkpoint6-pro", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-03T10:28:33.737621](https://huggingface.co/datasets/open-llm-leaderboard/details_mahiatlinux__MasherAI-v6.1-7B-checkpoint6-pro/blob/main/results_2024-04-03T10-28-33.737621.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6327029624788667, "acc_stderr": 0.03244896513206761, "acc_norm": 0.6330284162227182, "acc_norm_stderr": 0.033118306720219995, "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.589720638696192, "mc2_stderr": 0.015374163217375555 }, "harness|arc:challenge|25": { "acc": 0.5955631399317406, "acc_stderr": 0.014342036483436177, "acc_norm": 0.6305460750853242, "acc_norm_stderr": 0.01410457836649189 }, "harness|hellaswag|10": { "acc": 0.6409081856203943, "acc_stderr": 0.004787537385153002, "acc_norm": 0.8327026488747261, "acc_norm_stderr": 0.0037247833892533307 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105653, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105653 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7483870967741936, "acc_stderr": 0.02468597928623996, "acc_norm": 0.7483870967741936, "acc_norm_stderr": 0.02468597928623996 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586808, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586808 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8497409326424871, "acc_stderr": 0.02578772318072386, "acc_norm": 0.8497409326424871, "acc_norm_stderr": 0.02578772318072386 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6410256410256411, "acc_stderr": 0.024321738484602354, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602354 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059278, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059278 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8330275229357799, "acc_stderr": 0.015990154885073403, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.015990154885073403 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 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0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8160919540229885, "acc_stderr": 0.013853724170922534, "acc_norm": 0.8160919540229885, "acc_norm_stderr": 0.013853724170922534 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7138728323699421, "acc_stderr": 0.02433214677913413, "acc_norm": 0.7138728323699421, "acc_norm_stderr": 0.02433214677913413 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2871508379888268, "acc_stderr": 0.015131608849963748, "acc_norm": 0.2871508379888268, "acc_norm_stderr": 0.015131608849963748 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7006172839506173, "acc_stderr": 0.025483115601195448, "acc_norm": 0.7006172839506173, "acc_norm_stderr": 0.025483115601195448 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4485006518904824, "acc_stderr": 0.012702317490559806, "acc_norm": 0.4485006518904824, "acc_norm_stderr": 0.012702317490559806 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6507352941176471, "acc_stderr": 0.028959755196824873, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.028959755196824873 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6535947712418301, "acc_stderr": 0.01924978569171721, "acc_norm": 0.6535947712418301, "acc_norm_stderr": 0.01924978569171721 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness|truthfulqa:mc|0": { "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.589720638696192, "mc2_stderr": 0.015374163217375555 }, "harness|winogrande|5": { "acc": 0.8050513022888713, "acc_stderr": 0.011134099415938275 }, "harness|gsm8k|5": { "acc": 0.6679302501895376, "acc_stderr": 0.01297246503436187 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### 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VIM-Bench/VIM-MMMU
--- dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 62608667.0 num_examples: 380 - name: vim_mmmu_validation num_bytes: 4748968.0 num_examples: 30 download_size: 191478930 dataset_size: 67357635.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 2016529888.0 num_examples: 287 - name: vim_mmmu_validation num_bytes: 242175033.0 num_examples: 30 download_size: 4766236496 dataset_size: 2258704921.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 52713217.0 num_examples: 551 - name: vim_mmmu_validation num_bytes: 3050033.0 num_examples: 30 download_size: 157425983 dataset_size: 55763250.0 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 461908712.0 num_examples: 231 - name: vim_mmmu_validation num_bytes: 57443048.0 num_examples: 30 download_size: 1083821871 dataset_size: 519351760.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 976250892.0 num_examples: 429 - name: vim_mmmu_validation num_bytes: 61395024.0 num_examples: 30 download_size: 1806124497 dataset_size: 1037645916.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 103732655.0 num_examples: 326 - name: vim_mmmu_validation num_bytes: 9063826.0 num_examples: 30 download_size: 226890073 dataset_size: 112796481.0 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 247875336.0 num_examples: 345 - name: vim_mmmu_validation num_bytes: 17944064.0 num_examples: 30 download_size: 512073989 dataset_size: 265819400.0 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 93198875.0 num_examples: 603 - name: vim_mmmu_validation num_bytes: 3813390.0 num_examples: 30 download_size: 177874466 dataset_size: 97012265.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 210931338.0 num_examples: 325 - name: vim_mmmu_validation num_bytes: 22908635.0 num_examples: 30 download_size: 476024799 dataset_size: 233839973.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 78837323.0 num_examples: 371 - name: vim_mmmu_validation num_bytes: 5372759.0 num_examples: 30 download_size: 146715613 dataset_size: 84210082.0 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 157430264.0 num_examples: 169 - name: vim_mmmu_validation num_bytes: 35721292.0 num_examples: 30 download_size: 378770457 dataset_size: 193151556.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 308954785.0 num_examples: 162 - name: vim_mmmu_validation num_bytes: 73084929.0 num_examples: 30 download_size: 818432545 dataset_size: 382039714.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 40466148.0 num_examples: 267 - name: vim_mmmu_validation num_bytes: 4425178.0 num_examples: 30 download_size: 73933956 dataset_size: 44891326.0 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 22679116.0 num_examples: 256 - name: vim_mmmu_validation num_bytes: 2517065.0 num_examples: 30 download_size: 46954536 dataset_size: 25196181.0 - config_name: Energy_and_Power features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 44090223.0 num_examples: 432 - name: vim_mmmu_validation num_bytes: 4412192.0 num_examples: 30 download_size: 94525159 dataset_size: 48502415.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 48412685.0 num_examples: 355 - name: vim_mmmu_validation num_bytes: 3939375.0 num_examples: 30 download_size: 91959691 dataset_size: 52352060.0 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 264102755.0 num_examples: 565 - name: vim_mmmu_validation num_bytes: 12721501.0 num_examples: 30 download_size: 544690081 dataset_size: 276824256.0 - config_name: History features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 254846725.0 num_examples: 278 - name: vim_mmmu_validation num_bytes: 19409959.0 num_examples: 30 download_size: 514283822 dataset_size: 274256684.0 - config_name: Literature features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 103498681.0 num_examples: 112 - name: vim_mmmu_validation num_bytes: 28860172.0 num_examples: 30 download_size: 287165454 dataset_size: 132358853.0 - config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 78879664.0 num_examples: 245 - name: vim_mmmu_validation num_bytes: 9524026.0 num_examples: 30 download_size: 164443416 dataset_size: 88403690.0 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 38496912.0 num_examples: 181 - name: vim_mmmu_validation num_bytes: 5767224.0 num_examples: 30 download_size: 75577785 dataset_size: 44264136.0 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 64953415.0 num_examples: 458 - name: vim_mmmu_validation num_bytes: 5237760.0 num_examples: 30 download_size: 134906715 dataset_size: 70191175.0 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 80547516.0 num_examples: 505 - name: vim_mmmu_validation num_bytes: 4137828.0 num_examples: 30 download_size: 158373001 dataset_size: 84685344.0 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 54930870.0 num_examples: 429 - name: vim_mmmu_validation num_bytes: 2932439.0 num_examples: 30 download_size: 101884728 dataset_size: 57863309.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 264459930.0 num_examples: 334 - name: vim_mmmu_validation num_bytes: 17472521.0 num_examples: 30 download_size: 464816759 dataset_size: 281932451.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 71919971.0 num_examples: 430 - name: vim_mmmu_validation num_bytes: 4073281.0 num_examples: 30 download_size: 140647171 dataset_size: 75993252.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 46761653.0 num_examples: 408 - name: vim_mmmu_validation num_bytes: 3496181.0 num_examples: 30 download_size: 95128064 dataset_size: 50257834.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 202510858.0 num_examples: 305 - name: vim_mmmu_validation num_bytes: 14354801.0 num_examples: 30 download_size: 383636002 dataset_size: 216865659.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 116987697.0 num_examples: 509 - name: vim_mmmu_validation num_bytes: 5771783.0 num_examples: 30 download_size: 209918156 dataset_size: 122759480.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string - name: vim_image dtype: image splits: - name: vim_mmmu_test num_bytes: 301375838.0 num_examples: 252 - name: vim_mmmu_validation num_bytes: 37670258.0 num_examples: 30 download_size: 681606364 dataset_size: 339046096.0 configs: - config_name: Accounting data_files: - split: vim_mmmu_validation path: Accounting/vim_mmmu_validation-* - split: vim_mmmu_test path: Accounting/vim_mmmu_test-* - config_name: Agriculture data_files: - split: vim_mmmu_validation path: Agriculture/vim_mmmu_validation-* - split: vim_mmmu_test path: Agriculture/vim_mmmu_test-* - config_name: Architecture_and_Engineering data_files: - split: vim_mmmu_validation path: Architecture_and_Engineering/vim_mmmu_validation-* - split: vim_mmmu_test path: Architecture_and_Engineering/vim_mmmu_test-* - config_name: Art data_files: - split: vim_mmmu_validation path: Art/vim_mmmu_validation-* - split: vim_mmmu_test path: Art/vim_mmmu_test-* - config_name: Art_Theory data_files: - split: vim_mmmu_validation path: Art_Theory/vim_mmmu_validation-* - split: vim_mmmu_test path: Art_Theory/vim_mmmu_test-* - config_name: Basic_Medical_Science data_files: - split: vim_mmmu_validation path: Basic_Medical_Science/vim_mmmu_validation-* - split: vim_mmmu_test path: Basic_Medical_Science/vim_mmmu_test-* - config_name: Biology data_files: - split: vim_mmmu_validation path: Biology/vim_mmmu_validation-* - split: vim_mmmu_test path: Biology/vim_mmmu_test-* - config_name: Chemistry data_files: - split: vim_mmmu_validation path: Chemistry/vim_mmmu_validation-* - split: vim_mmmu_test path: Chemistry/vim_mmmu_test-* - config_name: Clinical_Medicine data_files: - split: vim_mmmu_validation path: Clinical_Medicine/vim_mmmu_validation-* - split: vim_mmmu_test path: Clinical_Medicine/vim_mmmu_test-* - config_name: Computer_Science data_files: - split: vim_mmmu_validation path: Computer_Science/vim_mmmu_validation-* - split: vim_mmmu_test path: Computer_Science/vim_mmmu_test-* - config_name: Design data_files: - split: vim_mmmu_validation path: Design/vim_mmmu_validation-* - split: vim_mmmu_test path: Design/vim_mmmu_test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: vim_mmmu_validation path: Diagnostics_and_Laboratory_Medicine/vim_mmmu_validation-* - split: vim_mmmu_test path: Diagnostics_and_Laboratory_Medicine/vim_mmmu_test-* - config_name: Economics data_files: - split: vim_mmmu_validation path: Economics/vim_mmmu_validation-* - split: vim_mmmu_test path: Economics/vim_mmmu_test-* - config_name: Electronics data_files: - split: vim_mmmu_validation path: Electronics/vim_mmmu_validation-* - split: vim_mmmu_test path: Electronics/vim_mmmu_test-* - config_name: Energy_and_Power data_files: - split: vim_mmmu_validation path: Energy_and_Power/vim_mmmu_validation-* - split: vim_mmmu_test path: Energy_and_Power/vim_mmmu_test-* - config_name: Finance data_files: - split: vim_mmmu_validation path: Finance/vim_mmmu_validation-* - split: vim_mmmu_test path: Finance/vim_mmmu_test-* - config_name: Geography data_files: - split: vim_mmmu_validation path: Geography/vim_mmmu_validation-* - split: vim_mmmu_test path: Geography/vim_mmmu_test-* - config_name: History data_files: - split: vim_mmmu_validation path: History/vim_mmmu_validation-* - split: vim_mmmu_test path: History/vim_mmmu_test-* - config_name: Literature data_files: - split: vim_mmmu_validation path: Literature/vim_mmmu_validation-* - split: vim_mmmu_test path: Literature/vim_mmmu_test-* - config_name: Manage data_files: - split: vim_mmmu_validation path: Manage/vim_mmmu_validation-* - split: vim_mmmu_test path: Manage/vim_mmmu_test-* - config_name: Marketing data_files: - split: vim_mmmu_validation path: Marketing/vim_mmmu_validation-* - split: vim_mmmu_test path: Marketing/vim_mmmu_test-* - config_name: Materials data_files: - split: vim_mmmu_validation path: Materials/vim_mmmu_validation-* - split: vim_mmmu_test path: Materials/vim_mmmu_test-* - config_name: Math data_files: - split: vim_mmmu_validation path: Math/vim_mmmu_validation-* - split: vim_mmmu_test path: Math/vim_mmmu_test-* - config_name: Mechanical_Engineering data_files: - split: vim_mmmu_validation path: Mechanical_Engineering/vim_mmmu_validation-* - split: vim_mmmu_test path: Mechanical_Engineering/vim_mmmu_test-* - config_name: Music data_files: - split: vim_mmmu_validation path: Music/vim_mmmu_validation-* - split: vim_mmmu_test path: Music/vim_mmmu_test-* - config_name: Pharmacy data_files: - split: vim_mmmu_validation path: Pharmacy/vim_mmmu_validation-* - split: vim_mmmu_test path: Pharmacy/vim_mmmu_test-* - config_name: Physics data_files: - split: vim_mmmu_validation path: Physics/vim_mmmu_validation-* - split: vim_mmmu_test path: Physics/vim_mmmu_test-* - config_name: Psychology data_files: - split: vim_mmmu_validation path: Psychology/vim_mmmu_validation-* - split: vim_mmmu_test path: Psychology/vim_mmmu_test-* - config_name: Public_Health data_files: - split: vim_mmmu_validation path: Public_Health/vim_mmmu_validation-* - split: vim_mmmu_test path: Public_Health/vim_mmmu_test-* - config_name: Sociology data_files: - split: vim_mmmu_validation path: Sociology/vim_mmmu_validation-* - split: vim_mmmu_test path: Sociology/vim_mmmu_test-* ---
slushily/autotrain-data-hannah-demo
--- task_categories: - image-classification --- # AutoTrain Dataset for project: hannah-demo ## Dataset Description This dataset has been automatically processed by AutoTrain for project hannah-demo. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<11744x7026 RGBA PIL image>", "target": 0 }, { "image": "<11744x7026 RGBA PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['hannah'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 7 | | valid | 7 |
Katharinelw/Kk
--- license: creativeml-openrail-m ---
james-burton/textual-explanations
--- dataset_info: features: - name: model_name dtype: string - name: predicted_class dtype: string - name: task_name dtype: string - name: narration dtype: string - name: values sequence: string - name: sign sequence: string - name: narrative_id dtype: int32 - name: unique_id dtype: int32 - name: classes_dict dtype: string - name: narrative_questions sequence: string - name: feature_nums sequence: string - name: ft_num2name dtype: string - name: old2new_ft_nums dtype: string - name: old2new_classes dtype: string - name: predicted_class_label dtype: string - name: class2name dtype: string splits: - name: train num_bytes: 994784 num_examples: 375 - name: validation num_bytes: 121591 num_examples: 47 - name: test num_bytes: 122830 num_examples: 47 download_size: 0 dataset_size: 1239205 --- # Dataset Card for "textual-explanations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tingchih/classification_claims
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 5314830278 num_examples: 570692 - name: test num_bytes: 2277898100 num_examples: 244583 download_size: 4391700574 dataset_size: 7592728378 --- # Dataset Card for "classification_claims" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
monomer/program_peace
--- license: apache-2.0 task_categories: - text-classification language: - en tags: - health - Dr.JaredReser - mind-health - posture-improvement - mindset pretty_name: book size_categories: - 10K<n<100K ---
IndicRAGware/mistral-rag-4bit-test
--- license: apache-2.0 ---
Deojoandco/capstone_fromgpt_without_gold_v9_all
--- dataset_info: features: - name: dialog_id dtype: int64 - name: dialogue dtype: string - name: summary dtype: string - name: gold_tags dtype: string - name: gpt_success dtype: bool - name: gpt_response dtype: string - name: gold_tags_tokens_count dtype: int64 - name: GPT_TAGS_FOUND dtype: bool - name: gpt_output_tags dtype: string - name: gpt_output_tag_tokens_count dtype: int64 - name: GPT_MI_FOUND dtype: bool - name: gpt_tags_token_count dtype: int64 - name: gpt_tags dtype: string - name: tag_token_count_match dtype: bool splits: - name: train num_bytes: 124191 num_examples: 76 - name: validation num_bytes: 23023 num_examples: 12 - name: test num_bytes: 14536 num_examples: 12 download_size: 82277 dataset_size: 161750 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "capstone_fromgpt_without_gold_v9_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
uhhlt/amharichatespeechranlp
--- language: - amh pretty_name: "Amharic Hate Speech Dataset" tags: - am size_categories: - 10K<n<100K task_categories: - text-classification configs: - config_name: default column_names: ["label", "text"] data_files: - split: train path: "train.tsv" - split: test path: "test.tsv" - split: dev path: "dev.tsv" --- # Introduction The Amharic Hate Speech data is collected using the Twitter API spanning from October 1, 2020 - November 30, 2022, considering the socio-political dynamics of Ethiopia in Twitter space. We used [WebAnno](http://ltdemos.informatik.uni-hamburg.de/codebookanno-cba/) tool for data annotation; each tweet is annotated by two native speakers and curated by one more experienced adjudicator to determine the gold labels. A total of 15.1k tweets consisting of three class labels namely: Hate, Offensive and Normal are presented. Read our papers for more details about the dataset (see below). # Amharic Hate Speech Data Annotation: Lab-Controlled Annotation The dataset is annotated by two annotators and a curator to determine the gold labels. The annotation guideline can be found [here](https://github.com/uhh-lt/AmharicHateSpeech/blob/main/Data/RANLP2023/Annotation%20Guideline.pdf) # Dataset Details The prefix `__label__` has been removed from the labels in the uploaded version. When using the training script linked below the prefix must be added manually. # Citation Information and Links For more details, You can read our paper entitled: 1. [Exploring Amharic Hate Speech data Collection and Classification Approaches](https://aclanthology.org/2023.ranlp-1.6/) Or visit our [GitHub Repository](https://github.com/uhh-lt/AmharicHateSpeech) for the papers, models and training code. ``` @inproceedings{ayele-etal-2023-exploring, title = "Exploring {A}mharic Hate Speech Data Collection and Classification Approaches", author = "Ayele, Abinew Ali and Yimam, Seid Muhie and Belay, Tadesse Destaw and Asfaw, Tesfa and Biemann, Chris", editor = "Mitkov, Ruslan and Angelova, Galia", booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing", month = sep, year = "2023", address = "Varna, Bulgaria", publisher = "INCOMA Ltd., Shoumen, Bulgaria", url = "https://aclanthology.org/2023.ranlp-1.6", pages = "49--59", abstract = "In this paper, we present a study of efficient data selection and annotation strategies for Amharic hate speech. We also build various classification models and investigate the challenges of hate speech data selection, annotation, and classification for the Amharic language. From a total of over 18 million tweets in our Twitter corpus, 15.1k tweets are annotated by two independent native speakers, and a Cohen{'}s kappa score of 0.48 is achieved. A third annotator, a curator, is also employed to decide on the final gold labels. We employ both classical machine learning and deep learning approaches, which include fine-tuning AmFLAIR and AmRoBERTa contextual embedding models. Among all the models, AmFLAIR achieves the best performance with an F1-score of 72{\%}. We publicly release the annotation guidelines, keywords/lexicon entries, datasets, models, and associated scripts with a permissive license.", } ```
rouskinlab/pri_miRNA
--- license: mit language: - en tags: - chemistry - biology` author: Silvi Rouskin source: data.json date: 2024-01-11-09-57-28 --- # Data types - **sequence**: 1098 datapoints - **structure**: 1098 datapoints - **dms**: 1098 datapoints
Nexdata/Infant_Laugh_Speech_Data_by_Mobile_Phone
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Infant_Laugh_Speech_Data_by_Mobile_Phone ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/1090?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Laugh sound of 20 infants and young children aged 0~3 years old, a number of paragraphs from each of them; It provides data support for detecting children's laugh sound in smart home projects. For more details, please refer to the link: https://www.nexdata.ai/datasets/1090?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Infant Cry ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
open-llm-leaderboard/details_nisten__shqiponja-59b-v1
--- pretty_name: Evaluation run of nisten/shqiponja-59b-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [nisten/shqiponja-59b-v1](https://huggingface.co/nisten/shqiponja-59b-v1) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nisten__shqiponja-59b-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T05:56:39.495831](https://huggingface.co/datasets/open-llm-leaderboard/details_nisten__shqiponja-59b-v1/blob/main/results_2024-01-14T05-56-39.495831.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7432378535506855,\n\ \ \"acc_stderr\": 0.02859899074099913,\n \"acc_norm\": 0.7559556232571321,\n\ \ \"acc_norm_stderr\": 0.029186017628606568,\n \"mc1\": 0.5373317013463892,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.7043324455434049,\n\ \ \"mc2_stderr\": 0.014572093049489886\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6757679180887372,\n \"acc_stderr\": 0.01367881039951882,\n\ \ \"acc_norm\": 0.7005119453924915,\n \"acc_norm_stderr\": 0.01338502163731357\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6440948018323043,\n\ \ \"acc_stderr\": 0.004778081784542404,\n \"acc_norm\": 0.8405696076478789,\n\ \ \"acc_norm_stderr\": 0.0036532880435558015\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.725925925925926,\n\ \ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n\ \ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930387,\n\ \ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930387\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372274,\n\ \ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372274\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8819444444444444,\n\ \ \"acc_stderr\": 0.026983346503309382,\n \"acc_norm\": 0.8819444444444444,\n\ \ \"acc_norm_stderr\": 0.026983346503309382\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7341040462427746,\n\ \ \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.7341040462427746,\n\ \ \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.027678452578212394,\n\ \ \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.027678452578212394\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7034482758620689,\n \"acc_stderr\": 0.03806142687309992,\n\ \ \"acc_norm\": 0.7034482758620689,\n \"acc_norm_stderr\": 0.03806142687309992\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5846560846560847,\n \"acc_stderr\": 0.0253795249107784,\n \"acc_norm\"\ : 0.5846560846560847,\n \"acc_norm_stderr\": 0.0253795249107784\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.0437588849272706,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.0437588849272706\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9,\n\ \ \"acc_stderr\": 0.01706640371965727,\n \"acc_norm\": 0.9,\n \ \ \"acc_norm_stderr\": 0.01706640371965727\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280458,\n\ \ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280458\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\"\ : 0.76,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9585492227979274,\n \"acc_stderr\": 0.014385432857476444,\n\ \ \"acc_norm\": 0.9585492227979274,\n \"acc_norm_stderr\": 0.014385432857476444\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8076923076923077,\n \"acc_stderr\": 0.0199823472086373,\n \ \ \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.0199823472086373\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476668,\n \ \ \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476668\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8361344537815126,\n \"acc_stderr\": 0.02404405494044049,\n \ \ \"acc_norm\": 0.8361344537815126,\n \"acc_norm_stderr\": 0.02404405494044049\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\ acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9174311926605505,\n \"acc_stderr\": 0.01180036136301657,\n \"\ acc_norm\": 0.9174311926605505,\n \"acc_norm_stderr\": 0.01180036136301657\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6759259259259259,\n \"acc_stderr\": 0.03191923445686185,\n \"\ acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.03191923445686185\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316945,\n \"\ acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316945\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9071729957805907,\n \"acc_stderr\": 0.01888975055095671,\n \ \ \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.01888975055095671\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.0277901770643836,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.0277901770643836\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.9007633587786259,\n \"acc_stderr\": 0.02622223517147737,\n\ \ \"acc_norm\": 0.9007633587786259,\n \"acc_norm_stderr\": 0.02622223517147737\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622793,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622793\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553838,\n\ \ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553838\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5535714285714286,\n\ \ \"acc_stderr\": 0.047184714852195865,\n \"acc_norm\": 0.5535714285714286,\n\ \ \"acc_norm_stderr\": 0.047184714852195865\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9487179487179487,\n\ \ \"acc_stderr\": 0.014450181176872726,\n \"acc_norm\": 0.9487179487179487,\n\ \ \"acc_norm_stderr\": 0.014450181176872726\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8991060025542784,\n\ \ \"acc_stderr\": 0.01077047201488671,\n \"acc_norm\": 0.8991060025542784,\n\ \ \"acc_norm_stderr\": 0.01077047201488671\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575266,\n\ \ \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575266\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7854748603351955,\n\ \ \"acc_stderr\": 0.013728923407828853,\n \"acc_norm\": 0.7854748603351955,\n\ \ \"acc_norm_stderr\": 0.013728923407828853\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.826797385620915,\n \"acc_stderr\": 0.0216684002565143,\n\ \ \"acc_norm\": 0.826797385620915,\n \"acc_norm_stderr\": 0.0216684002565143\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478026,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478026\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999850994,\n\ \ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999850994\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6170212765957447,\n \"acc_stderr\": 0.02899908090480618,\n \ \ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.02899908090480618\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5788787483702738,\n\ \ \"acc_stderr\": 0.012610325733489905,\n \"acc_norm\": 0.5788787483702738,\n\ \ \"acc_norm_stderr\": 0.012610325733489905\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8014705882352942,\n \"acc_stderr\": 0.024231013370541093,\n\ \ \"acc_norm\": 0.8014705882352942,\n \"acc_norm_stderr\": 0.024231013370541093\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8055555555555556,\n \"acc_stderr\": 0.016011237996336938,\n \ \ \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.016011237996336938\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.0250002560395462,\n\ \ \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.0250002560395462\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\ \ \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n\ \ \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5373317013463892,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.7043324455434049,\n\ \ \"mc2_stderr\": 0.014572093049489886\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8026835043409629,\n \"acc_stderr\": 0.011185026389050366\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15466262319939347,\n \ \ \"acc_stderr\": 0.009959786220917203\n }\n}\n```" repo_url: https://huggingface.co/nisten/shqiponja-59b-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|arc:challenge|25_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-14T05-56-39.495831.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|gsm8k|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hellaswag|10_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T05-56-39.495831.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T05-56-39.495831.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T05-56-39.495831.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_14T05_56_39.495831 path: - '**/details_harness|winogrande|5_2024-01-14T05-56-39.495831.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-14T05-56-39.495831.parquet' - config_name: results data_files: - split: 2024_01_14T05_56_39.495831 path: - results_2024-01-14T05-56-39.495831.parquet - split: latest path: - results_2024-01-14T05-56-39.495831.parquet --- # Dataset Card for Evaluation run of nisten/shqiponja-59b-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [nisten/shqiponja-59b-v1](https://huggingface.co/nisten/shqiponja-59b-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_nisten__shqiponja-59b-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T05:56:39.495831](https://huggingface.co/datasets/open-llm-leaderboard/details_nisten__shqiponja-59b-v1/blob/main/results_2024-01-14T05-56-39.495831.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7432378535506855, "acc_stderr": 0.02859899074099913, "acc_norm": 0.7559556232571321, "acc_norm_stderr": 0.029186017628606568, "mc1": 0.5373317013463892, "mc1_stderr": 0.017454645150970588, "mc2": 0.7043324455434049, "mc2_stderr": 0.014572093049489886 }, "harness|arc:challenge|25": { "acc": 0.6757679180887372, "acc_stderr": 0.01367881039951882, "acc_norm": 0.7005119453924915, "acc_norm_stderr": 0.01338502163731357 }, "harness|hellaswag|10": { "acc": 0.6440948018323043, "acc_stderr": 0.004778081784542404, "acc_norm": 0.8405696076478789, "acc_norm_stderr": 0.0036532880435558015 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.725925925925926, "acc_stderr": 0.03853254836552003, "acc_norm": 0.725925925925926, "acc_norm_stderr": 0.03853254836552003 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8552631578947368, "acc_stderr": 0.028631951845930387, "acc_norm": 0.8552631578947368, "acc_norm_stderr": 0.028631951845930387 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372274, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372274 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8819444444444444, "acc_stderr": 0.026983346503309382, "acc_norm": 0.8819444444444444, "acc_norm_stderr": 0.026983346503309382 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7341040462427746, "acc_stderr": 0.03368762932259431, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.03368762932259431 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5196078431372549, "acc_stderr": 0.04971358884367406, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.04971358884367406 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7659574468085106, "acc_stderr": 0.027678452578212394, "acc_norm": 0.7659574468085106, "acc_norm_stderr": 0.027678452578212394 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7034482758620689, "acc_stderr": 0.03806142687309992, "acc_norm": 0.7034482758620689, "acc_norm_stderr": 0.03806142687309992 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5846560846560847, "acc_stderr": 0.0253795249107784, "acc_norm": 0.5846560846560847, "acc_norm_stderr": 0.0253795249107784 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.0437588849272706, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.0437588849272706 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9, "acc_stderr": 0.01706640371965727, "acc_norm": 0.9, "acc_norm_stderr": 0.01706640371965727 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280458, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280458 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.76, "acc_stderr": 0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9585492227979274, "acc_stderr": 0.014385432857476444, "acc_norm": 0.9585492227979274, "acc_norm_stderr": 0.014385432857476444 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8076923076923077, "acc_stderr": 0.0199823472086373, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.0199823472086373 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3888888888888889, "acc_stderr": 0.029723278961476668, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.029723278961476668 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8361344537815126, "acc_stderr": 0.02404405494044049, "acc_norm": 0.8361344537815126, "acc_norm_stderr": 0.02404405494044049 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.48344370860927155, "acc_stderr": 0.040802441856289715, "acc_norm": 0.48344370860927155, "acc_norm_stderr": 0.040802441856289715 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9174311926605505, "acc_stderr": 0.01180036136301657, "acc_norm": 0.9174311926605505, "acc_norm_stderr": 0.01180036136301657 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6759259259259259, "acc_stderr": 0.03191923445686185, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.03191923445686185 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9117647058823529, "acc_stderr": 0.019907399791316945, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.019907399791316945 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9071729957805907, "acc_stderr": 0.01888975055095671, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.01888975055095671 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7802690582959642, "acc_stderr": 0.0277901770643836, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.0277901770643836 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.9007633587786259, "acc_stderr": 0.02622223517147737, "acc_norm": 0.9007633587786259, "acc_norm_stderr": 0.02622223517147737 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622793, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622793 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563275, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563275 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8650306748466258, "acc_stderr": 0.026845765054553838, "acc_norm": 0.8650306748466258, "acc_norm_stderr": 0.026845765054553838 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5535714285714286, "acc_stderr": 0.047184714852195865, "acc_norm": 0.5535714285714286, "acc_norm_stderr": 0.047184714852195865 }, "harness|hendrycksTest-management|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9487179487179487, "acc_stderr": 0.014450181176872726, "acc_norm": 0.9487179487179487, "acc_norm_stderr": 0.014450181176872726 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8991060025542784, "acc_stderr": 0.01077047201488671, "acc_norm": 0.8991060025542784, "acc_norm_stderr": 0.01077047201488671 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8092485549132948, "acc_stderr": 0.021152676966575266, "acc_norm": 0.8092485549132948, "acc_norm_stderr": 0.021152676966575266 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7854748603351955, "acc_stderr": 0.013728923407828853, "acc_norm": 0.7854748603351955, "acc_norm_stderr": 0.013728923407828853 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.826797385620915, "acc_stderr": 0.0216684002565143, "acc_norm": 0.826797385620915, "acc_norm_stderr": 0.0216684002565143 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478026, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478026 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.845679012345679, "acc_stderr": 0.020100830999850994, "acc_norm": 0.845679012345679, "acc_norm_stderr": 0.020100830999850994 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6170212765957447, "acc_stderr": 0.02899908090480618, "acc_norm": 0.6170212765957447, "acc_norm_stderr": 0.02899908090480618 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5788787483702738, "acc_stderr": 0.012610325733489905, "acc_norm": 0.5788787483702738, "acc_norm_stderr": 0.012610325733489905 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8014705882352942, "acc_stderr": 0.024231013370541093, "acc_norm": 0.8014705882352942, "acc_norm_stderr": 0.024231013370541093 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8055555555555556, "acc_stderr": 0.016011237996336938, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.016011237996336938 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8122448979591836, "acc_stderr": 0.0250002560395462, "acc_norm": 0.8122448979591836, "acc_norm_stderr": 0.0250002560395462 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700643, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700643 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015577, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015577 }, "harness|truthfulqa:mc|0": { "mc1": 0.5373317013463892, "mc1_stderr": 0.017454645150970588, "mc2": 0.7043324455434049, "mc2_stderr": 0.014572093049489886 }, "harness|winogrande|5": { "acc": 0.8026835043409629, "acc_stderr": 0.011185026389050366 }, "harness|gsm8k|5": { "acc": 0.15466262319939347, "acc_stderr": 0.009959786220917203 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
epaolinos/septuagint
--- dataset_info: features: - name: Book dtype: string - name: Chapter dtype: int64 - name: Verse Number dtype: int64 - name: Verse Text dtype: string - name: Genre dtype: string splits: - name: train num_bytes: 9101054 num_examples: 30568 download_size: 3421032 dataset_size: 9101054 --- # Dataset Card for "septuagint" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_cloudyu__Qwen-72Bx2-MoE-120B
--- pretty_name: Evaluation run of cloudyu/Qwen-72Bx2-MoE-120B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cloudyu/Qwen-72Bx2-MoE-120B](https://huggingface.co/cloudyu/Qwen-72Bx2-MoE-120B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__Qwen-72Bx2-MoE-120B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-15T10:51:00.615971](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Qwen-72Bx2-MoE-120B/blob/main/results_2024-01-15T10-51-00.615971.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.23280937126754725,\n\ \ \"acc_stderr\": 0.030031934560283337,\n \"acc_norm\": 0.2333859951011877,\n\ \ \"acc_norm_stderr\": 0.030826897864000263,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4891376724889372,\n\ \ \"mc2_stderr\": 0.016320771330589307\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2090443686006826,\n \"acc_stderr\": 0.011882746987406453,\n\ \ \"acc_norm\": 0.2593856655290102,\n \"acc_norm_stderr\": 0.012808273573927099\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2590121489743079,\n\ \ \"acc_stderr\": 0.004371969542814558,\n \"acc_norm\": 0.24905397331208923,\n\ \ \"acc_norm_stderr\": 0.004315812968431582\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.1925925925925926,\n\ \ \"acc_stderr\": 0.03406542058502653,\n \"acc_norm\": 0.1925925925925926,\n\ \ \"acc_norm_stderr\": 0.03406542058502653\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.025447863825108632,\n\ \ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.025447863825108632\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03718489006818115,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03718489006818115\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\ acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2358974358974359,\n \"acc_stderr\": 0.021525965407408733,\n\ \ \"acc_norm\": 0.2358974358974359,\n \"acc_norm_stderr\": 0.021525965407408733\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.3106796116504854,\n \"acc_stderr\": 0.04582124160161549,\n\ \ \"acc_norm\": 0.3106796116504854,\n \"acc_norm_stderr\": 0.04582124160161549\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2863247863247863,\n\ \ \"acc_stderr\": 0.029614323690456645,\n \"acc_norm\": 0.2863247863247863,\n\ \ \"acc_norm_stderr\": 0.029614323690456645\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.22860791826309068,\n\ \ \"acc_stderr\": 0.015016884698539897,\n \"acc_norm\": 0.22860791826309068,\n\ \ \"acc_norm_stderr\": 0.015016884698539897\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.27485380116959063,\n\ \ \"acc_stderr\": 0.034240429246915824,\n \"acc_norm\": 0.27485380116959063,\n\ \ \"acc_norm_stderr\": 0.034240429246915824\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.22766217870257038,\n \"mc1_stderr\": 0.01467925503211107,\n\ \ \"mc2\": 0.4891376724889372,\n \"mc2_stderr\": 0.016320771330589307\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.47198105761641673,\n\ \ \"acc_stderr\": 0.014030404213405786\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/cloudyu/Qwen-72Bx2-MoE-120B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|arc:challenge|25_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-15T10-51-00.615971.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|gsm8k|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hellaswag|10_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-15T10-51-00.615971.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-management|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T10-51-00.615971.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|truthfulqa:mc|0_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-15T10-51-00.615971.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_15T10_51_00.615971 path: - '**/details_harness|winogrande|5_2024-01-15T10-51-00.615971.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-15T10-51-00.615971.parquet' - config_name: results data_files: - split: 2024_01_15T10_51_00.615971 path: - results_2024-01-15T10-51-00.615971.parquet - split: latest path: - results_2024-01-15T10-51-00.615971.parquet --- # Dataset Card for Evaluation run of cloudyu/Qwen-72Bx2-MoE-120B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cloudyu/Qwen-72Bx2-MoE-120B](https://huggingface.co/cloudyu/Qwen-72Bx2-MoE-120B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cloudyu__Qwen-72Bx2-MoE-120B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-15T10:51:00.615971](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Qwen-72Bx2-MoE-120B/blob/main/results_2024-01-15T10-51-00.615971.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.23280937126754725, "acc_stderr": 0.030031934560283337, "acc_norm": 0.2333859951011877, "acc_norm_stderr": 0.030826897864000263, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4891376724889372, "mc2_stderr": 0.016320771330589307 }, "harness|arc:challenge|25": { "acc": 0.2090443686006826, "acc_stderr": 0.011882746987406453, "acc_norm": 0.2593856655290102, "acc_norm_stderr": 0.012808273573927099 }, "harness|hellaswag|10": { "acc": 0.2590121489743079, "acc_stderr": 0.004371969542814558, "acc_norm": 0.24905397331208923, "acc_norm_stderr": 0.004315812968431582 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.1925925925925926, "acc_stderr": 0.03406542058502653, "acc_norm": 0.1925925925925926, "acc_norm_stderr": 0.03406542058502653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2188679245283019, "acc_stderr": 0.025447863825108632, "acc_norm": 0.2188679245283019, "acc_norm_stderr": 0.025447863825108632 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03718489006818115, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03718489006818115 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2358974358974359, "acc_stderr": 0.021525965407408733, "acc_norm": 0.2358974358974359, "acc_norm_stderr": 0.021525965407408733 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.1527777777777778, "acc_stderr": 0.024536326026134224, "acc_norm": 0.1527777777777778, "acc_norm_stderr": 0.024536326026134224 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.28703703703703703, "acc_stderr": 0.043733130409147614, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness|hendrycksTest-management|5": { "acc": 0.3106796116504854, "acc_stderr": 0.04582124160161549, "acc_norm": 0.3106796116504854, "acc_norm_stderr": 0.04582124160161549 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2863247863247863, "acc_stderr": 0.029614323690456645, "acc_norm": 0.2863247863247863, "acc_norm_stderr": 0.029614323690456645 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.22860791826309068, "acc_stderr": 0.015016884698539897, "acc_norm": 0.22860791826309068, "acc_norm_stderr": 0.015016884698539897 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.27485380116959063, "acc_stderr": 0.034240429246915824, "acc_norm": 0.27485380116959063, "acc_norm_stderr": 0.034240429246915824 }, "harness|truthfulqa:mc|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4891376724889372, "mc2_stderr": 0.016320771330589307 }, "harness|winogrande|5": { "acc": 0.47198105761641673, "acc_stderr": 0.014030404213405786 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
DBQ/Gucci.Product.prices.Romania
--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Romania - Gucci - Product-level price list tags: - webscraping - ecommerce - Gucci - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 2466121 num_examples: 5169 download_size: 723248 dataset_size: 2466121 --- # Gucci web scraped data ## About the website Operating within the **luxury fashion industry**, **Gucci** marks its prominent presence in the EMEA region, specifically in **Romania**. The luxury fashion industry in Romania has shown considerable growth, owing to increased consumer spending and changing lifestyle trends. The industry is characterized by high-income consumers with a taste for luxury fashion products displaying their status and personality. The industry, driven by premium quality and exclusiveness, demonstrates a good deal of opportunities for luxury brands like Gucci. The dataset observed provides insightful **Ecommerce product-list page (PLP) data** region specific information on Gucci in the Romanian marketplace. ## Link to **dataset** [Romania - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Romania/r/recfSStZ86lHvxQZi)
saadalafalcon/gg
--- license: unknown ---
heliosprime/twitter_dataset_1712952895
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 3264 num_examples: 7 download_size: 7745 dataset_size: 3264 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712952895" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ali-C137/Guanaco-oasst1_Originals_Arabic_pairs
--- dataset_info: features: - name: text dtype: string - name: translated_text dtype: string splits: - name: train num_bytes: 38713258 num_examples: 10364 download_size: 20094755 dataset_size: 38713258 --- # Dataset Card for "Guanaco-oasst1_Originals_Arabic_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/ads_corporate_prompts
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2970085 num_examples: 10000 download_size: 312601 dataset_size: 2970085 --- # Dataset Card for "ads_corporate_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shidowake/cosmopedia-japanese-subset_from_aixsatoshi_filtered-sharegpt-format-with-system-prompt_split_0
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 3998622.7045075125 num_examples: 500 download_size: 2425624 dataset_size: 3998622.7045075125 configs: - config_name: default data_files: - split: train path: data/train-* ---
bibidentuhanoi/BMO_vicuna_function
--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 161113 num_examples: 149 download_size: 82761 dataset_size: 161113 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BMO_vicuna_function" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_eng_scaffolding
--- dataset_info: features: - name: id dtype: int32 - name: source_lang dtype: string - name: target_lang dtype: string - name: source dtype: string - name: target dtype: string - name: eng_source dtype: string splits: - name: train num_bytes: 5588764908 num_examples: 10240000 download_size: 4223075178 dataset_size: 5588764908 --- # Dataset Card for "flores200_eng_scaffolding" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
onlycaps/flickr8k-sau-pace-annotated
--- license: mit task_categories: - image-classification --- # Annotation Annotated this [dataset](https://www.kaggle.com/datasets/srbhshinde/flickr8k-sau) by clasifying the images into slow, medium or fast depending on the suitable paced background music.
felipesampaio/gumballcantando
--- license: openrail ---
Trelis/function_calling_extended
--- task_categories: - question-answering - conversational - text-generation language: - en tags: - function call - function calling - function-calling size_categories: - n<1K extra_gated_prompt: "Access to this dataset requires the purchase of a license [here](https://buy.stripe.com/fZeeVG5tP2Hxg7ecNj)" extra_gated_fields: Name: text Affiliation: text Email: text I have purchased a license (access will be granted once your payment clears): checkbox I agree to the terms of the license described on the dataset card: checkbox --- # Trelis Function Calling Dataset UPDATE: As of Dec 5th 2023, there is a v3 of this dataset now available from [here](https://huggingface.co/datasets/Trelis/function_calling_v3). - Allows models to be fine-tuned for function-calling. - The dataset is human generated and does not make use of Llama 2 or OpenAI! - Contains 59 training and 17 test rows - Based on eight functions: search_bing, search_arxiv, save_chat, read_json_file, list_files, get_current_weather, delete_file, clear_chat Access this dataset by purchasing a license [HERE](https://buy.stripe.com/fZeeVG5tP2Hxg7ecNj). Alternatively, you can find pre-trained function calling models for Llama 2 and Mistral [HERE](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-v2) --Change-log-- 11Oct2023: Minor update adding in short prompts like "duck" to which the LLM should respond with a description of a duck or ducks, not a function call. 22Aug2023: Major updates to the main branch: - The 'systemPrompt' column is now replaced by 'functionList', which contains a raw list of function metadata without any guidance. - The previous dataset, with 'systemPrompt' - containing specific instructions - has been moved to the 'explicit' branch. - The 'implicit' branch is a copy of the 'explicit' branch, but with slightly less instruction provided to the LLM in the systemPrompt column. The reason for these updates are: - For one-shot model prompting, it is helpful to provide as much description as possible to the LLM. - For fine-tuning, is is desirable to minimise the length of any added context to describe functions, especially if not necessary. Users can play around with the different levels of instruction provided. In summary: - 'main' - provides the lowest level of instruction on how to use the functions - 'implicit' - moderate instructions - 'explicit' - detailed instructions 18Aug2023: Added new 'implicit' branch with a shorter system prompt. Performs similarly to main branch, but uses less tokens for prompting. 15Aug2023: Added datasets to fine-tune models for awareness of available functions. ## Fine-Tuning Notes and Scripts The objective of function calling is for the model to return a structured json object *and nothing else*. The performance of fine-tuning depends **strongly** on how the attention mask and loss mask are set. For further details see the [Youtube Video Here](https://youtu.be/OQdp-OeG1as) ### QLoRa Training Notebook for Llama 2 (FREE) - Access a basic Google Colab script for fine-tuning [here](https://colab.research.google.com/drive/1uMSS1o_8YOPyG1X_4k6ENEE3kJfBGGhH?usp=sharing). ### ADVANCED Fine-tuning Notebook for Structured Responses (incl. function calling) (PAID) - Fine-tune models for function calling or other structured responses. - Includes a prompt loss-mask for improved performance when structured responses are required. - Includes a stop token after responses - allowing the model to provide a short reponse (e.g. a function call) and then stop. - Request [access here](https://buy.stripe.com/5kAfZK6xT2Hxg7e8wW). ## Licensing The Function Calling Extended dataset is commercially licensed. Users can purchase a license per seat/user from [here](https://buy.stripe.com/fZeeVG5tP2Hxg7ecNj). Further terms: - Licenses are not transferable to other users/entities. ### Attribution of data sources This project includes data from the TruthfulQA dataset, which is available at: https://huggingface.co/datasets/truthful_qa. The truthful_qa dataset is licensed under the Apache License 2.0, Copyright (C) 2023, Stephanie Lin, Jacob Hilton, and Owain Evans. ## Dataset Structure The datasets (train and test) contain three prompt types: 1. The first portion provides function metadata in the systemPrompt but then has userPrompt and assistantResponse values that do not require function calling. This is to get the language model accustomed to having function metadata available, but not using it. Questions and answers for these prompts are generated by running addBlank.py and the questions and answers come from [truthful_qa](https://huggingface.co/datasets/truthful_qa) - see below for license details. 2. The second portion of the train and test datasets provide examples where a function call is necessary. 3. The third portion (new as of August 13th 2023) acclimatises the model to recognising what functions it has available from the system prompt, and sharing that with the user when appropriate. Further extended on October 11th to add one and two word prompts not requiring function calls as responses. ## Branches Specify the branch using: ``` data = load_dataset( "Trelis/function_calling_extended", revision="implicit" # optionally specify a branch ) ``` The 'main' branch uses short system/function prompt, with no instruction on usage (see the other branches for prompts with stronger instruction): ``` { "function": "search_bing", "description": "Search the web for content on Bing. This allows users to search online/the internet/the web for content.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } { "function": "list_files", "description": "This function provides a list of files in the user's directory. It can be useful when the user wants to check what files they have. This function requires no parameters and returns no values.", "arguments": [] } ``` The 'explicit' branch provides detailed instructions to the language model on how to call functions: ``` You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant: { "function": "search_bing", "description": "Search the web for content on Bing. This allows users to search online/the internet/the web for content.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } { "function": "list_files", "description": "This function provides a list of files in the user's directory. It can be useful when the user wants to check what files they have. This function requires no parameters and returns no values.", "arguments": [] } To call a function, respond - immediately and only - with a JSON object of the following format: { "function": "function_name", "arguments": { "argument1": value1, "argument2": value2 } } ``` The 'implicit' branch uses a shorter, less explicit branch that performs similarly and is therefore recommended as it reduces the length of the system prompt: ``` You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant: { "function": "search_bing", "description": "Search the web for content on Bing. This allows users to search online/the internet/the web for content.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } { "function": "list_files", "description": "This function provides a list of files in the user's directory. It can be useful when the user wants to check what files they have. This function requires no parameters and returns no values.", "arguments": [] } ``` Said differently, the 'implicit' branch omits the following portion of the prompt: ``` To call a function, respond - immediately and only - with a JSON object of the following format: { "function": "function_name", "arguments": { "argument1": value1, "argument2": value2 } } ``` ## Training and Inference Syntax Here is sample prompt syntax for Llama. This will depend on the language model you use and also how to wish to fine-tune the model: ``` # Define the roles and markers B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n" system_prompt = data['test'][index]['systemPrompt'] user_prompt = data['test'][index]['userPrompt'] correct_answer = data['test'][index]['assistantResponse'] # Format your prompt template prompt = f"{B_INST} {B_SYS}{system_prompt.strip()}{E_SYS}{user_prompt.strip()} {E_INST}\n\n" ``` The `\n\n` after E_INST is important as it prevents E_INST from sometimes being tokenized with the ']' attached to the next characters. Using `\n\n` also provides the best chance for the model correctly telling whether to call a function or provide a usual response. Alternatively, you may prefer to stay away from the system prompt and create a separate wrapper for function descriptions (as an example for the data on 'main'): ``` # Define the roles and markers B_INST, E_INST = "[INST]", "[/INST]" B_FUNC, E_FUNC = "<FUNCTIONS>", "</FUNCTIONS>\n\n" functionList = data['test'][index]['functionList'] user_prompt = data['test'][index]['userPrompt'] correct_answer = data['test'][index]['assistantResponse'] # Format your prompt template prompt = f"{B_FUNC}{functionList.strip()}{E_FUNC}{B_INST} {user_prompt.strip()} {E_INST}\n\n" ``` ## File Structure (for prompt dataset generation) - `functions/`: This directory contains function files, each of which is a JSON file with a specific structure that describes a function and its sample prompts and responses. - `generate_dataset.py`: This Python script generates the base training and testing dataset CSV files. - `addBlank.py`: This adds in truthfulqa questions and answers after system prompts with functions - `hello.py`: adds in prompts to accustomise the model to the presence of functions in the system prompt. ### JSON File Structure Each function file should be a JSON file with the following structure: ```json { "functionMetaData": { "function": "function_name", "description": "function_description", "arguments": [ { "name": "argument_name", "type": "argument_type", "description": "argument_description" }, ... ] }, "samplePromptResponsePairs": [ { "prompt": "sample_prompt", "response": { "arguments": { "argument_name": "argument_value", ... } } }, ... ] } ``` The `functionMetaData` object describes the function. The `samplePromptResponsePairs` array contains sample prompts and responses for the function. ## Dataset Generation To generate the dataset, run the `generate_dataset.py` script. This script will iterate over each function file and generate a CSV row for each sample prompt-response pair. ## CSV File Structure The generated CSV file has the following columns: 'main' branches: - `functionList`: Descriptions of two functions (the current function and a randomly selected other function). - `userPrompt`: The user's prompt. - `assistantResponse`: The assistant's response. 'explicit' and 'implicit' branches: - `systemPrompt`: The system's prompt, which includes the descriptions of two functions (the current function and a randomly selected other function) and instructions on how to call a function ('explicit branch only'). - `userPrompt`: The user's prompt. - `assistantResponse`: The assistant's response. ## Testing JSON Structure A script named `validate.py` can be used to validate the structure of a function JSON file. It checks for the presence and correct types of all necessary keys in the JSON structure. To use the script, call it from the command line with the name of the function file as an argument: ``` python validate.py my_function.json ```
joshswartz/hellaswag_train_d1_wikihow
--- dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string - name: full_text dtype: string - name: text dtype: string splits: - name: train num_bytes: 39851138.84323465 num_examples: 12582 download_size: 23181790 dataset_size: 39851138.84323465 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "hellaswag_train_wikihow_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MichaelR207/MultiSim
--- license: mit language: - en - fr - ru - ja - it - da - es - de - pt - sl - ur - eu task_categories: - summarization - text2text-generation - text-generation pretty_name: MultiSim tags: - medical - legal - wikipedia - encyclopedia - science - literature - news - websites size_categories: - 1M<n<10M --- # Dataset Card for MultiSim Benchmark ## Dataset Description - **Repository:https://github.com/XenonMolecule/MultiSim/tree/main** - **Paper:https://aclanthology.org/2023.acl-long.269/ https://arxiv.org/pdf/2305.15678.pdf** - **Point of Contact: michaeljryan@stanford.edu** ### Dataset Summary The MultiSim benchmark is a growing collection of text simplification datasets targeted at sentence simplification in several languages. Currently, the benchmark spans 12 languages. ![Figure showing four complex and simple sentence pairs. One pair in English, one in Japanese, one in Urdu, and one in Russian. The English complex sentence reads "He settled in London, devoting himself chiefly to practical teaching." which is paired with the simple sentence "He lived in London. He was a teacher."](MultiSimEx.png "MultiSim Example") ### Supported Tasks - Sentence Simplification ### Usage ```python from datasets import load_dataset dataset = load_dataset("MichaelR207/MultiSim") ``` ### Citation If you use this benchmark, please cite our [paper](https://aclanthology.org/2023.acl-long.269/): ``` @inproceedings{ryan-etal-2023-revisiting, title = "Revisiting non-{E}nglish Text Simplification: A Unified Multilingual Benchmark", author = "Ryan, Michael and Naous, Tarek and Xu, Wei", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.269", pages = "4898--4927", abstract = "Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been done on multilingual text simplification due to the lack of a diverse evaluation benchmark that covers complex-simple sentence pairs in many languages. This paper introduces the MultiSim benchmark, a collection of 27 resources in 12 distinct languages containing over 1.7 million complex-simple sentence pairs. This benchmark will encourage research in developing more effective multilingual text simplification models and evaluation metrics. Our experiments using MultiSim with pre-trained multilingual language models reveal exciting performance improvements from multilingual training in non-English settings. We observe strong performance from Russian in zero-shot cross-lingual transfer to low-resource languages. We further show that few-shot prompting with BLOOM-176b achieves comparable quality to reference simplifications outperforming fine-tuned models in most languages. We validate these findings through human evaluation.", } ``` ### Contact **Michael Ryan**: [Scholar](https://scholar.google.com/citations?user=8APGEEkAAAAJ&hl=en) | [Twitter](http://twitter.com/michaelryan207) | [Github](https://github.com/XenonMolecule) | [LinkedIn](https://www.linkedin.com/in/michael-ryan-207/) | [Research Gate](https://www.researchgate.net/profile/Michael-Ryan-86) | [Personal Website](http://michaelryan.tech/) | [michaeljryan@stanford.edu](mailto://michaeljryan@stanford.edu) ### Languages - English - French - Russian - Japanese - Italian - Danish (on request) - Spanish (on request) - German - Brazilian Portuguese - Slovene - Urdu (on request) - Basque (on request) ## Dataset Structure ### Data Instances MultiSim is a collection of 27 existing datasets: - AdminIT - ASSET - CBST - CLEAR - DSim - Easy Japanese - Easy Japanese Extended - GEOLino - German News - Newsela EN/ES - PaCCSS-IT - PorSimples - RSSE - RuAdapt Encyclopedia - RuAdapt Fairytales - RuAdapt Literature - RuWikiLarge - SIMPITIKI - Simple German - Simplext - SimplifyUR - SloTS - Teacher - Terence - TextComplexityDE - WikiAuto - WikiLargeFR ![Table 1: Important properties of text simplification parallel corpora](Table1.png "Table 1") ### Data Fields In the train set, you will only find `original` and `simple` sentences. In the validation and test sets you may find `simple1`, `simple2`, ... `simpleN` because a given sentence can have multiple reference simplifications (useful in SARI and BLEU calculations) ### Data Splits The dataset is split into a train, validation, and test set. ![Table 2: MultiSim splits. *Original splits preserved](Table2.png "Table 2") ## Dataset Creation ### Curation Rationale I hope that collecting all of these independently useful resources for text simplification together into one benchmark will encourage multilingual work on text simplification! ### Source Data #### Initial Data Collection and Normalization Data is compiled from the 27 existing datasets that comprise the MultiSim Benchmark. For details on each of the resources please see Appendix A in the [paper](https://aclanthology.org/2023.acl-long.269.pdf). #### Who are the source language producers? Each dataset has different sources. At a high level the sources are: Automatically Collected (ex. Wikipedia, Web data), Manually Collected (ex. annotators asked to simplify sentences), Target Audience Resources (ex. Newsela News Articles), or Translated (ex. Machine translations of existing datasets). These sources can be seen in Table 1 pictured above (Section: `Dataset Structure/Data Instances`) and further discussed in section 3 of the [paper](https://aclanthology.org/2023.acl-long.269.pdf). Appendix A of the paper has details on specific resources. ### Annotations #### Annotation process Annotators writing simplifications (only for some datasets) typically follow an annotation guideline. Some example guidelines come from [here](https://dl.acm.org/doi/10.1145/1410140.1410191), [here](https://link.springer.com/article/10.1007/s11168-006-9011-1), and [here](https://link.springer.com/article/10.1007/s10579-017-9407-6). #### Who are the annotators? See Table 1 (Section: `Dataset Structure/Data Instances`) for specific annotators per dataset. At a high level the annotators are: writers, translators, teachers, linguists, journalists, crowdworkers, experts, news agencies, medical students, students, writers, and researchers. ### Personal and Sensitive Information No dataset should contain personal or sensitive information. These were previously collected resources primarily collected from news sources, wikipedia, science communications, etc. and were not identified to have personally identifiable information. ## Considerations for Using the Data ### Social Impact of Dataset We hope this dataset will make a greatly positive social impact as text simplification is a task that serves children, second language learners, and people with reading/cognitive disabilities. By publicly releasing a dataset in 12 languages we hope to serve these global communities. One negative and unintended use case for this data would be reversing the labels to make a "text complification" model. We beleive the benefits of releasing this data outweigh the harms and hope that people use the dataset as intended. ### Discussion of Biases There may be biases of the annotators involved in writing the simplifications towards how they believe a simpler sentence should be written. Additionally annotators and editors have the choice of what information does not make the cut in the simpler sentence introducing information importance bias. ### Other Known Limitations Some of the included resources were automatically collected or machine translated. As such not every sentence is perfectly aligned. Users are recommended to use such individual resources with caution. ## Additional Information ### Dataset Curators **Michael Ryan**: [Scholar](https://scholar.google.com/citations?user=8APGEEkAAAAJ&hl=en) | [Twitter](http://twitter.com/michaelryan207) | [Github](https://github.com/XenonMolecule) | [LinkedIn](https://www.linkedin.com/in/michael-ryan-207/) | [Research Gate](https://www.researchgate.net/profile/Michael-Ryan-86) | [Personal Website](http://michaelryan.tech/) | [michaeljryan@stanford.edu](mailto://michaeljryan@stanford.edu) ### Licensing Information MIT License ### Citation Information Please cite the individual datasets that you use within the MultiSim benchmark as appropriate. Proper bibtex attributions for each of the datasets are included below. #### AdminIT ``` @inproceedings{miliani-etal-2022-neural, title = "Neural Readability Pairwise Ranking for Sentences in {I}talian Administrative Language", author = "Miliani, Martina and Auriemma, Serena and Alva-Manchego, Fernando and Lenci, Alessandro", booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing", month = nov, year = "2022", address = "Online only", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.aacl-main.63", pages = "849--866", abstract = "Automatic Readability Assessment aims at assigning a complexity level to a given text, which could help improve the accessibility to information in specific domains, such as the administrative one. In this paper, we investigate the behavior of a Neural Pairwise Ranking Model (NPRM) for sentence-level readability assessment of Italian administrative texts. To deal with data scarcity, we experiment with cross-lingual, cross- and in-domain approaches, and test our models on Admin-It, a new parallel corpus in the Italian administrative language, containing sentences simplified using three different rewriting strategies. We show that NPRMs are effective in zero-shot scenarios ({\textasciitilde}0.78 ranking accuracy), especially with ranking pairs containing simplifications produced by overall rewriting at the sentence-level, and that the best results are obtained by adding in-domain data (achieving perfect performance for such sentence pairs). Finally, we investigate where NPRMs failed, showing that the characteristics of the training data, rather than its size, have a bigger effect on a model{'}s performance.", } ``` #### ASSET ``` @inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Beno{\^\i}t and Specia, Lucia", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.424", pages = "4668--4679", } ``` #### CBST ``` @article{10.1007/s10579-017-9407-6, title={{The corpus of Basque simplified texts (CBST)}}, author={Gonzalez-Dios, Itziar and Aranzabe, Mar{\'\i}a Jes{\'u}s and D{\'\i}az de Ilarraza, Arantza}, journal={Language Resources and Evaluation}, volume={52}, number={1}, pages={217--247}, year={2018}, publisher={Springer} } ``` #### CLEAR ``` @inproceedings{grabar-cardon-2018-clear, title = "{CLEAR} {--} Simple Corpus for Medical {F}rench", author = "Grabar, Natalia and Cardon, R{\'e}mi", booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})", month = nov, year = "2018", address = "Tilburg, the Netherlands", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-7002", doi = "10.18653/v1/W18-7002", pages = "3--9", } ``` #### DSim ``` @inproceedings{klerke-sogaard-2012-dsim, title = "{DS}im, a {D}anish Parallel Corpus for Text Simplification", author = "Klerke, Sigrid and S{\o}gaard, Anders", booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)", month = may, year = "2012", address = "Istanbul, Turkey", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/270_Paper.pdf", pages = "4015--4018", abstract = "We present DSim, a new sentence aligned Danish monolingual parallel corpus extracted from 3701 pairs of news telegrams and corresponding professionally simplified short news articles. The corpus is intended for building automatic text simplification for adult readers. We compare DSim to different examples of monolingual parallel corpora, and we argue that this corpus is a promising basis for future development of automatic data-driven text simplification systems in Danish. The corpus contains both the collection of paired articles and a sentence aligned bitext, and we show that sentence alignment using simple tf*idf weighted cosine similarity scoring is on line with state―of―the―art when evaluated against a hand-aligned sample. The alignment results are compared to state of the art for English sentence alignment. We finally compare the source and simplified sides of the corpus in terms of lexical and syntactic characteristics and readability, and find that the one―to―many sentence aligned corpus is representative of the sentence simplifications observed in the unaligned collection of article pairs.", } ``` #### Easy Japanese ``` @inproceedings{maruyama-yamamoto-2018-simplified, title = "Simplified Corpus with Core Vocabulary", author = "Maruyama, Takumi and Yamamoto, Kazuhide", booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://aclanthology.org/L18-1185", } ``` #### Easy Japanese Extended ``` @inproceedings{katsuta-yamamoto-2018-crowdsourced, title = "Crowdsourced Corpus of Sentence Simplification with Core Vocabulary", author = "Katsuta, Akihiro and Yamamoto, Kazuhide", booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://aclanthology.org/L18-1072", } ``` #### GEOLino ``` @inproceedings{mallinson2020, title={Zero-Shot Crosslingual Sentence Simplification}, author={Mallinson, Jonathan and Sennrich, Rico and Lapata, Mirella}, year={2020}, booktitle={2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)} } ``` #### German News ``` @inproceedings{sauberli-etal-2020-benchmarking, title = "Benchmarking Data-driven Automatic Text Simplification for {G}erman", author = {S{\"a}uberli, Andreas and Ebling, Sarah and Volk, Martin}, booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.readi-1.7", pages = "41--48", abstract = "Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian. For German, no data-driven approach exists to this date, due to a lack of training data. In this paper, we present a parallel corpus of news items in German with corresponding simplifications on two complexity levels. The simplifications have been produced according to a well-documented set of guidelines. We then report on experiments in automatically simplifying the German news items using state-of-the-art neural machine translation techniques. We demonstrate that despite our small parallel corpus, our neural models were able to learn essential features of simplified language, such as lexical substitutions, deletion of less relevant words and phrases, and sentence shortening.", language = "English", ISBN = "979-10-95546-45-0", } ``` #### Newsela EN/ES ``` @article{xu-etal-2015-problems, title = "Problems in Current Text Simplification Research: New Data Can Help", author = "Xu, Wei and Callison-Burch, Chris and Napoles, Courtney", journal = "Transactions of the Association for Computational Linguistics", volume = "3", year = "2015", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q15-1021", doi = "10.1162/tacl_a_00139", pages = "283--297", abstract = "Simple Wikipedia has dominated simplification research in the past 5 years. In this opinion paper, we argue that focusing on Wikipedia limits simplification research. We back up our arguments with corpus analysis and by highlighting statements that other researchers have made in the simplification literature. We introduce a new simplification dataset that is a significant improvement over Simple Wikipedia, and present a novel quantitative-comparative approach to study the quality of simplification data resources.", } ``` #### PaCCSS-IT ``` @inproceedings{brunato-etal-2016-paccss, title = "{P}a{CCSS}-{IT}: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification", author = "Brunato, Dominique and Cimino, Andrea and Dell{'}Orletta, Felice and Venturi, Giulia", booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2016", address = "Austin, Texas", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D16-1034", doi = "10.18653/v1/D16-1034", pages = "351--361", } ``` #### PorSimples ``` @inproceedings{aluisio-gasperin-2010-fostering, title = "Fostering Digital Inclusion and Accessibility: The {P}or{S}imples project for Simplification of {P}ortuguese Texts", author = "Alu{\'\i}sio, Sandra and Gasperin, Caroline", booktitle = "Proceedings of the {NAACL} {HLT} 2010 Young Investigators Workshop on Computational Approaches to Languages of the {A}mericas", month = jun, year = "2010", address = "Los Angeles, California", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W10-1607", pages = "46--53", } ``` ``` @inproceedings{10.1007/978-3-642-16952-6_31, author="Scarton, Carolina and Gasperin, Caroline and Aluisio, Sandra", editor="Kuri-Morales, Angel and Simari, Guillermo R.", title="Revisiting the Readability Assessment of Texts in Portuguese", booktitle="Advances in Artificial Intelligence -- IBERAMIA 2010", year="2010", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="306--315", isbn="978-3-642-16952-6" } ``` #### RSSE ``` @inproceedings{sakhovskiy2021rusimplesenteval, title={{RuSimpleSentEval-2021 shared task:} evaluating sentence simplification for Russian}, author={Sakhovskiy, Andrey and Izhevskaya, Alexandra and Pestova, Alena and Tutubalina, Elena and Malykh, Valentin and Smurov, Ivana and Artemova, Ekaterina}, booktitle={Proceedings of the International Conference “Dialogue}, pages={607--617}, year={2021} } ``` #### RuAdapt ``` @inproceedings{Dmitrieva2021Quantitative, title={A quantitative study of simplification strategies in adapted texts for L2 learners of Russian}, author={Dmitrieva, Anna and Laposhina, Antonina and Lebedeva, Maria}, booktitle={Proceedings of the International Conference “Dialogue}, pages={191--203}, year={2021} } ``` ``` @inproceedings{dmitrieva-tiedemann-2021-creating, title = "Creating an Aligned {R}ussian Text Simplification Dataset from Language Learner Data", author = {Dmitrieva, Anna and Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.bsnlp-1.8", pages = "73--79", abstract = "Parallel language corpora where regular texts are aligned with their simplified versions can be used in both natural language processing and theoretical linguistic studies. They are essential for the task of automatic text simplification, but can also provide valuable insights into the characteristics that make texts more accessible and reveal strategies that human experts use to simplify texts. Today, there exist a few parallel datasets for English and Simple English, but many other languages lack such data. In this paper we describe our work on creating an aligned Russian-Simple Russian dataset composed of Russian literature texts adapted for learners of Russian as a foreign language. This will be the first parallel dataset in this domain, and one of the first Simple Russian datasets in general.", } ``` #### RuWikiLarge ``` @inproceedings{sakhovskiy2021rusimplesenteval, title={{RuSimpleSentEval-2021 shared task:} evaluating sentence simplification for Russian}, author={Sakhovskiy, Andrey and Izhevskaya, Alexandra and Pestova, Alena and Tutubalina, Elena and Malykh, Valentin and Smurov, Ivana and Artemova, Ekaterina}, booktitle={Proceedings of the International Conference “Dialogue}, pages={607--617}, year={2021} } ``` #### SIMPITIKI ``` @article{tonelli2016simpitiki, title={SIMPITIKI: a Simplification corpus for Italian}, author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca}, journal={Proceedings of CLiC-it}, year={2016} } ``` #### Simple German ``` @inproceedings{battisti-etal-2020-corpus, title = "A Corpus for Automatic Readability Assessment and Text Simplification of {G}erman", author = {Battisti, Alessia and Pf{\"u}tze, Dominik and S{\"a}uberli, Andreas and Kostrzewa, Marek and Ebling, Sarah}, booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.404", pages = "3302--3311", abstract = "In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.", language = "English", ISBN = "979-10-95546-34-4", } ``` #### Simplext ``` @article{10.1145/2738046, author = {Saggion, Horacio and \v{S}tajner, Sanja and Bott, Stefan and Mille, Simon and Rello, Luz and Drndarevic, Biljana}, title = {Making It Simplext: Implementation and Evaluation of a Text Simplification System for Spanish}, year = {2015}, issue_date = {June 2015}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {6}, number = {4}, issn = {1936-7228}, url = {https://doi.org/10.1145/2738046}, doi = {10.1145/2738046}, journal = {ACM Trans. Access. Comput.}, month = {may}, articleno = {14}, numpages = {36}, keywords = {Spanish, text simplification corpus, human evaluation, readability measures} } ``` #### SimplifyUR ``` @inproceedings{qasmi-etal-2020-simplifyur, title = "{S}implify{UR}: Unsupervised Lexical Text Simplification for {U}rdu", author = "Qasmi, Namoos Hayat and Zia, Haris Bin and Athar, Awais and Raza, Agha Ali", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.428", pages = "3484--3489", language = "English", ISBN = "979-10-95546-34-4", } ``` #### SloTS ``` @misc{gorenc2022slovene, title = {Slovene text simplification dataset {SloTS}}, author = {Gorenc, Sabina and Robnik-{\v S}ikonja, Marko}, url = {http://hdl.handle.net/11356/1682}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)}, issn = {2820-4042}, year = {2022} } ``` #### Terence and Teacher ``` @inproceedings{brunato-etal-2015-design, title = "Design and Annotation of the First {I}talian Corpus for Text Simplification", author = "Brunato, Dominique and Dell{'}Orletta, Felice and Venturi, Giulia and Montemagni, Simonetta", booktitle = "Proceedings of the 9th Linguistic Annotation Workshop", month = jun, year = "2015", address = "Denver, Colorado, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W15-1604", doi = "10.3115/v1/W15-1604", pages = "31--41", } ``` #### TextComplexityDE ``` @article{naderi2019subjective, title={Subjective Assessment of Text Complexity: A Dataset for German Language}, author={Naderi, Babak and Mohtaj, Salar and Ensikat, Kaspar and M{\"o}ller, Sebastian}, journal={arXiv preprint arXiv:1904.07733}, year={2019} } ``` #### WikiAuto ``` @inproceedings{acl/JiangMLZX20, author = {Chao Jiang and Mounica Maddela and Wuwei Lan and Yang Zhong and Wei Xu}, editor = {Dan Jurafsky and Joyce Chai and Natalie Schluter and Joel R. Tetreault}, title = {Neural {CRF} Model for Sentence Alignment in Text Simplification}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, {ACL} 2020, Online, July 5-10, 2020}, pages = {7943--7960}, publisher = {Association for Computational Linguistics}, year = {2020}, url = {https://www.aclweb.org/anthology/2020.acl-main.709/} } ``` #### WikiLargeFR ``` @inproceedings{cardon-grabar-2020-french, title = "{F}rench Biomedical Text Simplification: When Small and Precise Helps", author = "Cardon, R{\'e}mi and Grabar, Natalia", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.62", doi = "10.18653/v1/2020.coling-main.62", pages = "710--716", abstract = "We present experiments on biomedical text simplification in French. We use two kinds of corpora {--} parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French {--} and a lexicon that associates medical terms with paraphrases. Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences. We evaluate the results with BLEU, SARI and Kandel scores. The results point out that little specialized data helps significantly the simplification.", } ``` ## Data Availability ### Public Datasets Most of the public datasets are available as a part of this MultiSim Repo. A few are still pending availability. For all resources we provide alternative download links. | Dataset | Language | Availability in MultiSim Repo | Alternative Link | |---|---|---|---| | ASSET | English | Available | https://huggingface.co/datasets/asset | | WikiAuto | English | Available | https://huggingface.co/datasets/wiki_auto | | CLEAR | French | Available | http://natalia.grabar.free.fr/resources.php#remi | | WikiLargeFR | French | Available | http://natalia.grabar.free.fr/resources.php#remi | | GEOLino | German | Available | https://github.com/Jmallins/ZEST-data | | TextComplexityDE | German | Available | https://github.com/babaknaderi/TextComplexityDE | | AdminIT | Italian | Available | https://github.com/Unipisa/admin-It | | Simpitiki | Italian | Available | https://github.com/dhfbk/simpitiki# | | PaCCSS-IT | Italian | Available | http://www.italianlp.it/resources/paccss-it-parallel-corpus-of-complex-simple-sentences-for-italian/ | | Terence and Teacher | Italian | Available | http://www.italianlp.it/resources/terence-and-teacher/ | | Easy Japanese | Japanese | Available | https://www.jnlp.org/GengoHouse/snow/t15 | | Easy Japanese Extended | Japanese | Available | https://www.jnlp.org/GengoHouse/snow/t23 | | RuAdapt Encyclopedia | Russian | Available | https://github.com/Digital-Pushkin-Lab/RuAdapt | | RuAdapt Fairytales | Russian | Available | https://github.com/Digital-Pushkin-Lab/RuAdapt | | RuSimpleSentEval | Russian | Available | https://github.com/dialogue-evaluation/RuSimpleSentEval | | RuWikiLarge | Russian | Available | https://github.com/dialogue-evaluation/RuSimpleSentEval | | SloTS | Slovene | Available | https://github.com/sabina-skubic/text-simplification-slovene | | SimplifyUR | Urdu | Pending | https://github.com/harisbinzia/SimplifyUR | | PorSimples | Brazilian Portuguese | Available | [sandra@icmc.usp.br](mailto:sandra@icmc.usp.br) | ### On Request Datasets The authors of the original papers must be contacted for on request datasets. Contact information for the authors of each dataset is provided below. | Dataset | Language | Contact | |---|---|---| | CBST | Basque | http://www.ixa.eus/node/13007?language=en <br/> [itziar.gonzalezd@ehu.eus](mailto:itziar.gonzalezd@ehu.eus) | | DSim | Danish | [sk@eyejustread.com](mailto:sk@eyejustread.com) | | Newsela EN | English | [https://newsela.com/data/](https://newsela.com/data/) | | Newsela ES | Spanish | [https://newsela.com/data/](https://newsela.com/data/) | | German News | German | [ebling@cl.uzh.ch](mailto:ebling@cl.uzh.ch) | | Simple German | German | [ebling@cl.uzh.ch](mailto:ebling@cl.uzh.ch) | | Simplext | Spanish | [horacio.saggion@upf.edu](mailto:horacio.saggion@upf.edu) | | RuAdapt Literature | Russian | Partially Available: https://github.com/Digital-Pushkin-Lab/RuAdapt <br/> Full Dataset: [anna.dmitrieva@helsinki.fi](mailto:anna.dmitrieva@helsinki.fi) |
open-llm-leaderboard/details_flemmingmiguel__DareBeagle-7B
--- pretty_name: Evaluation run of flemmingmiguel/DareBeagle-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [flemmingmiguel/DareBeagle-7B](https://huggingface.co/flemmingmiguel/DareBeagle-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_flemmingmiguel__DareBeagle-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-16T20:16:03.594958](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__DareBeagle-7B/blob/main/results_2024-01-16T20-16-03.594958.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6572828321826875,\n\ \ \"acc_stderr\": 0.03195518858047768,\n \"acc_norm\": 0.6570740734999085,\n\ \ \"acc_norm_stderr\": 0.032615184025325115,\n \"mc1\": 0.5532435740514076,\n\ \ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6829983144235686,\n\ \ \"mc2_stderr\": 0.014999747071250642\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n\ \ \"acc_norm\": 0.7158703071672355,\n \"acc_norm_stderr\": 0.013179442447653886\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7044413463453495,\n\ \ \"acc_stderr\": 0.0045536094057472215,\n \"acc_norm\": 0.8798048197570205,\n\ \ \"acc_norm_stderr\": 0.0032452503945652944\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7320754716981132,\n \"acc_stderr\": 0.027257260322494845,\n\ \ \"acc_norm\": 0.7320754716981132,\n \"acc_norm_stderr\": 0.027257260322494845\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\ \ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n\ \ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n\ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411018,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411018\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\ : 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \ \ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \ \ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\ acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.7974683544303798,\n \"acc_stderr\": 0.02616056824660146,\n \"\ acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.02616056824660146\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179326\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4480446927374302,\n\ \ \"acc_stderr\": 0.016631976628930595,\n \"acc_norm\": 0.4480446927374302,\n\ \ \"acc_norm_stderr\": 0.016631976628930595\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \"\ acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5532435740514076,\n\ \ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6829983144235686,\n\ \ \"mc2_stderr\": 0.014999747071250642\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.819258089976322,\n \"acc_stderr\": 0.010814911009613983\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.711144806671721,\n \ \ \"acc_stderr\": 0.012484219800126666\n }\n}\n```" repo_url: https://huggingface.co/flemmingmiguel/DareBeagle-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|arc:challenge|25_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-16T20-16-03.594958.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|gsm8k|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hellaswag|10_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T20-16-03.594958.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T20-16-03.594958.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T20-16-03.594958.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_16T20_16_03.594958 path: - '**/details_harness|winogrande|5_2024-01-16T20-16-03.594958.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-16T20-16-03.594958.parquet' - config_name: results data_files: - split: 2024_01_16T20_16_03.594958 path: - results_2024-01-16T20-16-03.594958.parquet - split: latest path: - results_2024-01-16T20-16-03.594958.parquet --- # Dataset Card for Evaluation run of flemmingmiguel/DareBeagle-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [flemmingmiguel/DareBeagle-7B](https://huggingface.co/flemmingmiguel/DareBeagle-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_flemmingmiguel__DareBeagle-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-16T20:16:03.594958](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__DareBeagle-7B/blob/main/results_2024-01-16T20-16-03.594958.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6572828321826875, "acc_stderr": 0.03195518858047768, "acc_norm": 0.6570740734999085, "acc_norm_stderr": 0.032615184025325115, "mc1": 0.5532435740514076, "mc1_stderr": 0.017403977522557144, "mc2": 0.6829983144235686, "mc2_stderr": 0.014999747071250642 }, "harness|arc:challenge|25": { "acc": 0.6877133105802048, "acc_stderr": 0.013542598541688065, "acc_norm": 0.7158703071672355, "acc_norm_stderr": 0.013179442447653886 }, "harness|hellaswag|10": { "acc": 0.7044413463453495, "acc_stderr": 0.0045536094057472215, "acc_norm": 0.8798048197570205, "acc_norm_stderr": 0.0032452503945652944 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7320754716981132, "acc_stderr": 0.027257260322494845, "acc_norm": 0.7320754716981132, "acc_norm_stderr": 0.027257260322494845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7916666666666666, "acc_stderr": 0.033961162058453336, "acc_norm": 0.7916666666666666, "acc_norm_stderr": 0.033961162058453336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.049598599663841815, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.049598599663841815 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.04793724854411018, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411018 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660834, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660834 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.02616056824660146, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.02616056824660146 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179326, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179326 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4480446927374302, "acc_stderr": 0.016631976628930595, "acc_norm": 0.4480446927374302, "acc_norm_stderr": 0.016631976628930595 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.044612721759105085, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.044612721759105085 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness|truthfulqa:mc|0": { "mc1": 0.5532435740514076, "mc1_stderr": 0.017403977522557144, "mc2": 0.6829983144235686, "mc2_stderr": 0.014999747071250642 }, "harness|winogrande|5": { "acc": 0.819258089976322, "acc_stderr": 0.010814911009613983 }, "harness|gsm8k|5": { "acc": 0.711144806671721, "acc_stderr": 0.012484219800126666 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
sazirarrwth99/last_stage_dataset_kangoroo_SFT_final_dev
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 144936995 num_examples: 23730 download_size: 30808725 dataset_size: 144936995 configs: - config_name: default data_files: - split: train path: data/train-* ---
CrabfishAI/ptgm-chat
--- license: apache-2.0 task_categories: - text-generation - conversational language: - en size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name This dataset is perfect for fine-tuning models in conversational and text generation tasks, enhancing their ability to provide contextually relevant and coherent responses. It's invaluable for applications like chatbots and virtual assistants. This dataset card aims to be a base template for new datasets. It has been generated using [ChatGPT, and conversational data online.] ## Dataset Details Purpose: Fine-tuning models for conversational and text generation tasks. Content: Diverse text inputs and responses. Benefit: Enhances contextually relevant and coherent response generation. Application: Ideal for chatbots, virtual assistants, and more. Size- 100k+ rows ### Dataset Description This dataset, comprising a diverse range of text inputs and responses, can be used to fine-tune models for conversational and text generation tasks. By training smaller models with this dataset, you can adapt them to generate more contextually relevant and coherent responses in a conversational manner. The utility of such a dataset lies in its ability to help models understand the nuances of human language, context, and conversational flow. Through the training process, models can learn to generate text that not only mimics human conversations but can also provide meaningful, context-aware responses. This can be particularly beneficial for applications like chatbots, virtual assistants, or any system that aims to engage in natural language interactions. - **Created by:** [CrabfishAI] - **Language(s) (NLP):** [EN: english] - **License:** [aapache-2.0 ] ## Uses 1. Enhancing chatbots and virtual assistants. 2. Improving content generation models. 3. Advancing customer support systems. 4. Boosting language understanding. 5. Supporting conversational AI research. 6. Innovating in education and language learning. 7. Enabling better social media content moderation. 8. Providing enhanced user experiences. 9. Enhancing personal assistant capabilities. 10. Customizing AI for specific industries or domains. ### Direct Use 1. Text Generation: Generate creative content like stories, poems, or product descriptions. 2. Conversational AI: Create chatbots or virtual assistants that engage in natural and context-aware conversations. ### Out-of-Scope Use 1. Bias and Quality: The dataset's quality and bias can impact model responses. 2. Contextual Challenges: Models may struggle with long conversations or specialized topics. 3. Generalization Issues: Difficulty in handling novel conversational scenarios. 4. Inappropriate Content: Risk of containing offensive or inappropriate content. 5. Data Sparsity: Limited data in specific domains or topics. 6. Computation Intensity: Resource-intensive for model training. 7. Privacy and Ethics: Concerns about data privacy and consent. 8. Lack of Multimodal Data: Limited to text, excluding visual or audio elements. 9. Overfitting Risk: Models may overfit to dataset patterns. 9. Maintenance and Updates: Requires ongoing updates to stay current. ## Dataset Structure The data structure of a dataset used for conversational and text generation tasks typically includes the following components: Input Text: This is the text provided as an initial input, such as a user's message or query in a conversation. Response Text: The corresponding text generated by a model as a response to the input. Metadata: Additional information about each interaction, such as timestamps, user IDs, or context identifiers, which can help in maintaining the conversational flow. Conversations: A grouping or organization of interactions, where each conversation consists of a series of input and response pairs that maintain context. Labels: If the dataset includes labeled data for training purposes, these labels might indicate the desired or correct responses. Domain or Task Tags: Tags or labels that specify the domain or task associated with each interaction, helping models understand the context and purpose of the conversation. Source Information: Details about the source of the data, which could include where and how the conversations were collected. ## Dataset Creation Created by: CrabfishAI. ### Source Data 1. ChatGPT-3.5 2. Wikipedia 3. Other datsets 4. Google's BradAI
irds/mr-tydi_en_dev
--- pretty_name: '`mr-tydi/en/dev`' viewer: false source_datasets: ['irds/mr-tydi_en'] task_categories: - text-retrieval --- # Dataset Card for `mr-tydi/en/dev` The `mr-tydi/en/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/en/dev). # Data This dataset provides: - `queries` (i.e., topics); count=878 - `qrels`: (relevance assessments); count=878 - For `docs`, use [`irds/mr-tydi_en`](https://huggingface.co/datasets/irds/mr-tydi_en) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/mr-tydi_en_dev', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/mr-tydi_en_dev', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Zhang2021MrTyDi, title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, year={2021}, journal={arXiv:2108.08787}, } @article{Clark2020TyDiQa, title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}, year={2020}, journal={Transactions of the Association for Computational Linguistics} } ```
CyberHarem/minami_mother_lovelive
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of minami_mother (Love Live!) This is the dataset of minami_mother (Love Live!), containing 28 images and their tags. The core tags of this character are `long_hair, breasts, brown_hair, large_breasts, yellow_eyes, brown_eyes, bangs, blunt_bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 28 | 29.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minami_mother_lovelive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 28 | 20.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minami_mother_lovelive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 69 | 43.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minami_mother_lovelive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 28 | 27.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minami_mother_lovelive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 69 | 52.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/minami_mother_lovelive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/minami_mother_lovelive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | looking_at_viewer, blush, 1girl, solo, bra, open_clothes, shirt, smile, 2girls, black_panties, cleavage, cover_page, nipples, sitting, skirt_suit | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, looking_at_viewer, solo, smile, hair_bow, open_mouth, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | blush | 1girl | solo | bra | open_clothes | shirt | smile | 2girls | black_panties | cleavage | cover_page | nipples | sitting | skirt_suit | hair_bow | open_mouth | simple_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:--------|:-------|:------|:---------------|:--------|:--------|:---------|:----------------|:-----------|:-------------|:----------|:----------|:-------------|:-----------|:-------------|:--------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | X | | | | | | | | X | X | X |
sdmattpotter/sdcctest
--- dataset_info: features: - name: ITEMNO. dtype: string - name: O dtype: string - name: '00000' dtype: float64 - name: Motion/Second dtype: string - name: Recorder dtype: string - name: action dtype: string - name: drescpt dtype: string - name: Meeting Type dtype: string - name: Roll Call dtype: string - name: Attendance dtype: string - name: DateTimeDate dtype: timestamp[ns] - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 359956 num_examples: 1185 download_size: 137681 dataset_size: 359956 --- # Dataset Card for "sdcctest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/200_Vietnamese_3D_Living_Face_Anti_Spoofing_Data
--- license: cc-by-nc-nd-4.0 --- ## Description 200 Vietnamese– 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution includes under 18 years old, 18 to 45 years old, 46 to 60 years old, over 60 years old. The device includes iPhone X, iPhone XR, iPhone11, iPhone12, iPhone13. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing. For more details, please refer to the link: https://www.nexdata.ai/dataset/1259?source=Huggingface ## Data size 200 people, 186 images for each person ## Race distribution Asian ## Nationality distribution Vietnam ## Gender distribution 99 males, 101 females ## Age distribution 20 people under 18 years old, 100 people aged from 18 to 45 years old, 60 people aged from 46 to 60 years old, 20 people over 60 years old ## Collecting environment indoor scenes 140 people, outdoor scenes 60 people ## Data diversity multiple expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes ## Device iphone x、iphone xr、iphone 11、iphone 12、iphone 13.etc ## Data format .jpg, .xml, .json ## Data format label the person – ID, nationality, gender, age, facial action, collecting scene, light condition ## Annotation content label the person – ID, nationality, gender, age, facial action, collecting scene, light condition ## Accuracy based on the accuracy of the actions, the accuracy exceeds 97%; the accuracy of label annotation is not less than 97% # Licensing Information Commercial License
wkrmbm/regularisation_images
--- license: cc0-1.0 ---
projecte-aina/catalan_textual_corpus
--- annotations_creators: - no-annotation language_creators: - found language: - ca license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Catalan Textual Corpus size_categories: - 10M<n<100M source_datasets: - original - extended|opus_dogc - extended|cawac - extended|oscar - extended|open_subtitles - extended|wikipedia - extended|projecte-aina/catalan_general_crawling - extended|projecte-aina/catalan_government_crawling task_categories: - fill-mask task_ids: [] --- # Dataset Card for Catalan Textual Corpus ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://zenodo.org/record/4519349 - **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903) - **Point of Contact** langtech@bsc.es ### Dataset Summary The Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources. It consists of 1,758,388,896 tokens, 73,172,152 sentences, and 12,556,365 documents. Documents are separated by single new lines. These boundaries have been preserved as long as the license allowed it. This work is licensed under a [Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Supported Tasks and Leaderboards This corpus is mainly intended to pretrain language models and word representations. ### Languages The dataset is in Catalan (`ca-ES`). ## Dataset Structure ### Data Instances ``` {'text': "L'operatiu continuarà durant aquest divendres."} ``` ### Data Fields - `text` (str): Text. ### Data Splits The dataset contains a single split: `train`. ## Dataset Creation ### Curation Rationale We created this corpus to contribute to the development of language models in Catalan, a low-resource language. ### Source Data #### Initial Data Collection and Normalization The Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources: existing corpora such as DOGC, CaWac (non-dedup version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia, and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency. For preprocessing we used [Corpus-Cleaner](https://github.com/TeMU-BSC/corpus-cleaner-acl), a modular Python-based toolkit to clean raw text corpora through generator pipelines. #### Who are the source language producers? The original data comes from various sources: existing corpora and crawlings from public websites. ### Annotations The dataset is unannotated. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information No anonymisation process was performed. ## Considerations for Using the Data ### Social Impact of Dataset We hope this corpus contributes to the development of language models in Catalan, a low-resource language. ### Discussion of Biases We are aware that since the data comes from unreliable web pages and multilingual crawled corpora, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact. ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ### Licensing Information [Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", eprint={2107.07903}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
tangjian234/openassistant-guanaco-300
--- license: openrail ---
yzhuang/autotree_automl_pol_gosdt_l512_d3_sd1
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: int64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: int64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 13320800000 num_examples: 100000 - name: validation num_bytes: 1332080000 num_examples: 10000 download_size: 960146994 dataset_size: 14652880000 --- # Dataset Card for "autotree_automl_pol_gosdt_l512_d3_sd1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
msimbao/mark
--- license: mit ---
AdapterOcean/python3-standardized_cluster_5_std
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 12869866 num_examples: 15460 download_size: 0 dataset_size: 12869866 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "python3-standardized_cluster_5_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hkust-nlp/deita-quality-scorer-data
--- license: mit language: - en size_categories: - 1K<n<10K --- <img src="https://huggingface.co/datasets/hkust-nlp/deita-images/resolve/main/logo-final.png" alt="Deita banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Dataset Card for Deita Quality Scorer Training Data [GitHub](https://github.com/hkust-nlp/deita) | [Paper](https://arxiv.org/abs/2312.15685) Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs). This dataset includes data for training Deita Quality Scorer. **Model Family**: Other models and the dataset are found in the [Deita Collection](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4) ## Performance | Model | Align | Data Size | MT-Bench | AlpacaEval(%) | OpenLLM (Avg.) | |------------------------------------------------|-----------|------------|----------|---------------|----------------| | **Proprietary Models** | | | | | | | GPT-4-Turbo | ? | -- | 9.32 | 97.70 | -- | | GPT-4 | SFT + PPO | -- | 8.99 | 95.03 | -- | | Claude-2 | SFT + PPO | -- | 8.06 | 91.36 | -- | | GPT-3.5-turbo | SFT + PPO | -- | 7.94 | 89.37 | -- | | **Open-sourced Models based on LLaMA-1-13B** | | | | | | | LIMA | SFT | 1K SFT | 4.29 | 41.98 | 59.82 | | WizardLM-13B | SFT | 70K SFT | 6.35 | 75.31 | 58.96 | | Vicuna-13B-v1.3 | SFT | 125K SFT | 6.39 | 82.11 | 60.01 | | Random | SFT | 10K SFT | 6.03 | 71.52 | 60.14 | | DEITA-LLaMA1-13B-v1.0-sft | SFT | 10K SFT | 6.60 | 78.01 | 64.27 | | **Open-sourced Models based on LLaMA-2-13B** | | | | | | | Tulu-2-13B | SFT | 326K SFT | 6.70 | 78.90 | -- | | Tulu-2-13B+DPO | SFT + DPO | 326K SFT + 60K DPO | 7.00 | 89.50 | -- | | LLaMA2-13B-Chat | SFT + PPO | -- | 6.65 | 81.09 | -- | | WizardLM-13B-v1.2 | SFT | >70K SFT | 7.09 | 89.17 | -- | | Vicuna-13B-v1.5 | SFT | 125K SFT | 6.57 | 78.80 | 61.63 | | Random | SFT | 10K SFT | 5.78 | 65.19 | 61.32 | | DEITA-LLaMA2-13B-v1.0-sft | SFT | 10K SFT | 6.79 | 81.09 | 62.71 | | **Open-sourced Models based on Mistral-7B** | | | | | | | Mistral-7B-Instruct-v0.1 | -- | -- | 6.84 | 69.65 | 60.45 | | Zephyr-7B-sft | SFT | 200K SFT | 5.32 | 75.12 | 60.93 | | $\text{Zephyr-7B-}\beta$ | SFT + DPO | 200K SFT + 60K DPO | 7.34 | 90.60 | 66.36 | | OpenChat-3.5 | C-RLFT | >> 70K C-RLFT | 7.81 | 88.51 | -- | | Starling-7B | C-RLFT + APA | >>70K C-RLFT + 183K APA | 8.09 | 91.99 | -- | | Random | SFT | 10K SFT | 5.89 | 56.90 | 61.72 | | DEITA-7B-v1.0-sft (6K) | SFT | 6K SFT | 7.22 | 80.78 | 64.94 | | DEITA-7B-v1.0-sft (10K) | SFT | 10K SFT | 7.32 | 81.67 | 64.00 | | DEITA-7B-v1.0 | SFT + DPO | 6K SFT + 10K DPO | 7.55 | 90.06 | 69.86 | ## Citation If you find the content of this project helpful, please cite our paper as follows: ``` @misc{liu2023what, title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning}, author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He}, year={2023}, eprint={2312.15685}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
joelr23/animation_eval
--- license: apache-2.0 ---
FINNUMBER/FINCH_TRAIN_NQA_EXT_100_NEWFORMAT
--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 397367 num_examples: 100 download_size: 253747 dataset_size: 397367 configs: - config_name: default data_files: - split: train path: data/train-* ---
CVasNLPExperiments/TinyImagenet_200_validation_text_davinci_003_mode_T_SPECIFIC_A_rices_ns_20
--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 57087 num_examples: 20 download_size: 26899 dataset_size: 57087 --- # Dataset Card for "TinyImagenet_200_validation_text_davinci_003_mode_T_SPECIFIC_A_rices_ns_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Andyrasika/Ecommerce_FAQ
--- license: creativeml-openrail-m --- Ecommerce FAQ Chatbot Dataset Overview The Ecommerce FAQ Chatbot Dataset is a valuable collection of questions and corresponding answers, meticulously curated for training and evaluating chatbot models in the context of an Ecommerce environment. This dataset is designed to assist developers, researchers, and data scientists in building effective chatbots that can handle customer inquiries related to an Ecommerce platform. Contents The dataset comprises a total of 79 question-answer pairs, where each item consists of: Question: The user's query related to the Ecommerce platform. Answer: The appropriate response or solution provided by the Ecommerce chatbot. The questions cover a wide range of common Ecommerce-related topics, including account management, product inquiries, order processing, payment methods, shipping details, and general platform usage. Use Cases Chatbot Development: This dataset can be used to train and fine-tune chatbot models for an Ecommerce chatbot capable of handling various customer queries and providing relevant responses. Natural Language Processing (NLP) Research: Researchers can utilize this dataset to study language understanding, response generation, and conversation flow in the context of Ecommerce interactions. Customer Support Automation: Ecommerce businesses can explore the possibility of implementing a chatbot-based customer support system to enhance customer satisfaction and reduce response times. Data Format The dataset is provided in a JSON format, where each item contains a "question" field and an "answer" field. The data is easily accessible and can be integrated into various machine learning frameworks for training purposes. Dataset Citation If you use this dataset in your research or project, kindly cite it as follows: ``` @dataset{saadmakhdoom/ecommerce-faq-chatbot-dataset, title = {Ecommerce FAQ Chatbot Dataset}, author = {Saad Makhdoom}, year = {Year of Dataset Creation}, publisher = {Kaggle}, url = {https://www.kaggle.com/datasets/saadmakhdoom/ecommerce-faq-chatbot-dataset} } ``` Acknowledgments We would like to express our gratitude to Saad Makhdoom for creating and sharing this valuable dataset on Kaggle. Their efforts in curating and providing the data have contributed significantly to the advancement of chatbot research and development.
cointegrated/taiga_stripped_stihi
--- dataset_info: features: - name: text dtype: string - name: file dtype: string splits: - name: train num_bytes: 14185482821 num_examples: 9157973 download_size: 7745419481 dataset_size: 14185482821 license: cc-by-sa-3.0 language: - ru tags: - taiga - tayga size_categories: - 1M<n<10M task_categories: - text-generation - fill-mask --- # Dataset Card for "taiga_stripped_stihi" This is a subset of the Taiga corpus (https://tatianashavrina.github.io/taiga_site), derived from the `stihi` source (a.k.a. "Poetry"). The dataset consists of plain texts, without morphological and syntactic annotation or metainformation. Apart from stripping the annotations, the texts were not modified. For more details and analysis, and for the texts with annotation or metadata, please refer to website of the corpus. Other subsets of Taiga: [proza](https://huggingface.co/datasets/cointegrated/taiga_stripped_proza) (fiction) and [other sources](https://huggingface.co/datasets/cointegrated/taiga_stripped_rest) (news, subtitles, and social media). License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
sudarsa/aud_tts
--- license: apache-2.0 dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: float64 splits: - name: train num_bytes: 574161164.3 num_examples: 1174 download_size: 360393023 dataset_size: 574161164.3 configs: - config_name: default data_files: - split: train path: data/train-* ---
vwxyzjn/openhermes-dev-2048-new-tokens__mistralai_Mixtral-8x7B-Instruct-v0.1__1707789379
--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidate0_policy dtype: string - name: candidate0 list: - name: content dtype: string - name: role dtype: string - name: candidate1 list: - name: content dtype: string - name: role dtype: string - name: candidate1_policy dtype: string splits: - name: train num_bytes: 38790943.0 num_examples: 10000 download_size: 21802995 dataset_size: 38790943.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Saugatkafley/alpaca-nepali-sft
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 47829648 num_examples: 52005 download_size: 18949833 dataset_size: 47829648 configs: - config_name: default data_files: - split: train path: data/train-* ---
Tarun1986/bigbrewskiV2
--- dataset_info: features: - name: number dtype: int64 - name: messages sequence: string splits: - name: train num_bytes: 285494 num_examples: 1355 - name: test num_bytes: 42077 num_examples: 194 download_size: 125179 dataset_size: 327571 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
TrainingDataPro/ocr-text-detection-in-the-documents
--- license: cc-by-nc-nd-4.0 task_categories: - image-to-text - object-detection language: - en tags: - code - legal - finance --- # OCR Text Detection in the Documents Dataset The dataset is a collection of images that have been annotated with the location of text in the document. The dataset is specifically curated for text detection and recognition tasks in documents such as scanned papers, forms, invoices, and handwritten notes. The dataset contains a variety of document types, including different *layouts, font sizes, and styles*. The images come from diverse sources, ensuring a representative collection of document styles and quality. Each image in the dataset is accompanied by bounding box annotations that outline the exact location of the text within the document. The Text Detection in the Documents dataset provides an invaluable resource for developing and testing algorithms for text extraction, recognition, and analysis. It enables researchers to explore and innovate in various applications, including *optical character recognition (OCR), information extraction, and document understanding*. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6986071a88d8a9829fee98d5b49d9ff8%2FMacBook%20Air%20-%201%20(1).png?generation=1691059158337136&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-text-detection-in-the-documents) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - **images** - contains of original images of documents - **boxes** - includes bounding box labeling for the original images - **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for text detection. For each point, the x and y coordinates are provided. ### Labels for the text: - **"Text Title"** - corresponds to titles, the box is **red** - **"Text Paragraph"** - corresponds to paragraphs of text, the box is **blue** - **"Table"** - corresponds to the table, the box is **green** - **"Handwritten"** - corresponds to handwritten text, the box is **purple** # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F38e02db515561a30e29faca9f5b176b0%2Fcarbon.png?generation=1691058761924879&alt=media) # Text Detection in the Documents might be made in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-text-detection-in-the-documents) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
BeIR/nfcorpus
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
ugurcanvurgun/ASD_CHILDES
--- license: mit ---
huggingartists/agata-christie
--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/agata-christie" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.143508 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/61b6b0a0b7f6587d1b33542d5c18ad3c.489x489x1.jpg&#39;)"> </div> </div> <a href="https://huggingface.co/huggingartists/agata-christie"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Агата Кристи (Agata Christie)</div> <a href="https://genius.com/artists/agata-christie"> <div style="text-align: center; font-size: 14px;">@agata-christie</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/agata-christie). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/agata-christie") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |78| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/agata-christie") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
TrainingDataPro/low_quality_webcam_video_attacks
--- license: cc-by-nc-nd-4.0 task_categories: - video-classification language: - en tags: - finance - legal - code --- # Low Quality Live Attacks The dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **low-quality** webcams with resolutions like QVGA, QQVGA and QCIF. # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks) to discuss your requirements, learn about the price and buy the dataset. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F43bc66b1f16995fb42f10075db8f9ba5%2F4.png?generation=1684704084546644&alt=media) # Webcam Resolution The collection of different video resolutions is provided, like: - QVGA (320p x 240p), - QQVGA (120p x 160p), - QCIF (176p x 144p) and others. # Metadata Each attack instance is accompanied by the following details: - Unique attack identifier - Identifier of the user recording the attack - User's age - User's gender - User's country of origin - Attack resolution Additionally, the model of the webcam is also specified. Metadata is represented in the `file_info.csv`. ## [**TrainingData**](https://trainingdata.pro/data-market/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
jordyvl/arxiv_dataset_prep
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Physics;Other Condensed Matter '350': Applied Physics;Other Condensed Matter;Optics '351': Applied Physics;Plasma Physics '352': Applied Physics;Quantum Physics '353': Applied Physics;Signal Processing '354': Applied Physics;Signal Processing;Optics '355': Applied Physics;Soft Condensed Matter '356': Applied Physics;Soft Condensed Matter;Fluid Dynamics '357': Applied Physics;Strongly Correlated Electrons '358': Applied Physics;Superconductivity '359': Applied Physics;Superconductivity;Quantum Physics '360': Applied Physics;Systems and Control;Systems and Control '361': Artificial Intelligence '362': Artificial Intelligence;Applications '363': Artificial Intelligence;Computation and Language '364': Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition '365': Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition;Machine Learning '366': Artificial Intelligence;Computation and Language;Computers and Society '367': Artificial Intelligence;Computation and Language;Databases '368': Artificial Intelligence;Computation and Language;Human-Computer Interaction '369': Artificial Intelligence;Computation and Language;Information Retrieval '370': Artificial Intelligence;Computation and Language;Logic in Computer Science '371': Artificial Intelligence;Computation and Language;Machine Learning '372': Artificial Intelligence;Computation and Language;Machine Learning;Multiagent Systems '373': Artificial Intelligence;Computation and Language;Robotics '374': Artificial Intelligence;Computational Complexity '375': Artificial Intelligence;Computational Complexity;Logic in Computer Science '376': Artificial Intelligence;Computational Complexity;Machine Learning '377': Artificial Intelligence;Computational Engineering, Finance, and Science '378': Artificial Intelligence;Computer Science and Game Theory '379': Artificial Intelligence;Computer Science and Game Theory;Logic in Computer Science '380': Artificial Intelligence;Computer Science and Game Theory;Machine Learning '381': Artificial Intelligence;Computer Science and Game Theory;Multiagent Systems '382': Artificial Intelligence;Computer Vision and Pattern Recognition '383': Artificial Intelligence;Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning '384': Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '385': Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Robotics '386': Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics '387': Artificial Intelligence;Computers and Society '388': Artificial Intelligence;Computers and Society;Human-Computer Interaction '389': Artificial Intelligence;Computers and Society;Human-Computer Interaction;Machine Learning '390': Artificial Intelligence;Computers and Society;Machine Learning '391': Artificial Intelligence;Computers and Society;Social and Information Networks '392': Artificial Intelligence;Cryptography and Security '393': Artificial Intelligence;Cryptography and Security;Machine Learning '394': Artificial Intelligence;Cryptography and Security;Neural and Evolutionary Computing '395': Artificial Intelligence;Data Structures and Algorithms '396': Artificial Intelligence;Databases '397': Artificial Intelligence;Databases;Information Retrieval '398': Artificial Intelligence;Databases;Logic in Computer Science '399': Artificial Intelligence;Databases;Machine Learning '400': Artificial Intelligence;Digital Libraries '401': Artificial Intelligence;Discrete Mathematics '402': Artificial Intelligence;Discrete Mathematics;Machine Learning '403': Artificial Intelligence;Distributed, Parallel, and Cluster Computing '404': Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Machine Learning '405': Artificial Intelligence;Formal Languages and Automata Theory '406': Artificial Intelligence;General Economics;Economics '407': Artificial Intelligence;Graphics '408': Artificial Intelligence;Human-Computer Interaction '409': Artificial Intelligence;Human-Computer Interaction;Information Retrieval '410': Artificial Intelligence;Human-Computer Interaction;Machine Learning '411': Artificial Intelligence;Human-Computer Interaction;Multiagent Systems '412': Artificial Intelligence;Human-Computer Interaction;Robotics '413': Artificial Intelligence;Information Retrieval '414': Artificial Intelligence;Information Retrieval;Machine Learning '415': Artificial Intelligence;Logic '416': Artificial Intelligence;Logic in Computer Science '417': Artificial Intelligence;Logic in Computer Science;Logic '418': Artificial Intelligence;Logic in Computer Science;Multiagent Systems '419': Artificial Intelligence;Logic in Computer Science;Programming Languages '420': Artificial Intelligence;Logic in Computer Science;Robotics '421': Artificial Intelligence;Machine Learning '422': Artificial Intelligence;Machine Learning;Logic in Computer Science '423': Artificial Intelligence;Machine Learning;Machine Learning '424': Artificial Intelligence;Machine Learning;Multiagent Systems '425': Artificial Intelligence;Machine Learning;Multiagent Systems;Robotics '426': Artificial Intelligence;Machine Learning;Networking and Internet Architecture '427': Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '428': Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing;Machine Learning '429': Artificial Intelligence;Machine Learning;Neurons and Cognition '430': Artificial Intelligence;Machine Learning;Optimization and Control '431': Artificial Intelligence;Machine Learning;Programming Languages '432': Artificial Intelligence;Machine Learning;Robotics '433': Artificial Intelligence;Machine Learning;Signal Processing '434': Artificial Intelligence;Machine Learning;Social and Information Networks '435': Artificial Intelligence;Machine Learning;Software Engineering '436': Artificial Intelligence;Machine Learning;Symbolic Computation '437': Artificial Intelligence;Machine Learning;Systems and Control;Systems and Control '438': Artificial Intelligence;Methodology '439': Artificial Intelligence;Multiagent Systems '440': Artificial Intelligence;Multiagent Systems;Robotics '441': Artificial Intelligence;Multimedia '442': Artificial Intelligence;Networking and Internet Architecture '443': Artificial Intelligence;Neural and Evolutionary Computing '444': Artificial Intelligence;Neural and Evolutionary Computing;Optimization and Control '445': Artificial Intelligence;Neural and Evolutionary Computing;Robotics '446': Artificial Intelligence;Neurons and Cognition '447': Artificial Intelligence;Optimization and Control '448': Artificial Intelligence;Physics and Society '449': Artificial Intelligence;Probability '450': Artificial Intelligence;Programming Languages '451': Artificial Intelligence;Quantitative Methods '452': Artificial Intelligence;Quantum Physics '453': Artificial Intelligence;Robotics '454': Artificial Intelligence;Robotics;Systems and Control '455': Artificial Intelligence;Robotics;Systems and Control;Systems and Control '456': Artificial Intelligence;Signal Processing '457': Artificial Intelligence;Social and Information Networks '458': Artificial Intelligence;Software Engineering '459': Artificial Intelligence;Sound '460': Artificial Intelligence;Symbolic Computation '461': Artificial Intelligence;Systems and Control '462': Artificial Intelligence;Systems and Control;Systems and Control '463': Astrophysics '464': Astrophysics of Galaxies '465': Astrophysics of Galaxies;Applications '466': Astrophysics of Galaxies;Atomic Physics '467': Astrophysics of Galaxies;Chaotic Dynamics '468': Astrophysics of Galaxies;Chemical Physics '469': Astrophysics of Galaxies;Computational Physics '470': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics '471': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '472': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '473': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '474': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '475': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '476': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '477': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '478': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '479': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics '480': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '481': Astrophysics of Galaxies;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '482': Astrophysics of Galaxies;Earth and Planetary Astrophysics '483': Astrophysics of Galaxies;Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '484': Astrophysics of Galaxies;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '485': Astrophysics of Galaxies;Fluid Dynamics '486': Astrophysics of Galaxies;Fluid Dynamics;Plasma Physics '487': Astrophysics of Galaxies;General Relativity and Quantum Cosmology '488': Astrophysics of Galaxies;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '489': Astrophysics of Galaxies;High Energy Astrophysical Phenomena '490': Astrophysics of Galaxies;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '491': Astrophysics of Galaxies;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '492': Astrophysics of Galaxies;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '493': Astrophysics of Galaxies;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '494': Astrophysics of Galaxies;High Energy Physics - Phenomenology '495': Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '496': Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;Chemical Physics '497': Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology '498': Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;Machine Learning '499': Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '500': Astrophysics of Galaxies;Machine Learning '501': Astrophysics of Galaxies;Plasma Physics '502': Astrophysics of Galaxies;Solar and Stellar Astrophysics '503': Astrophysics of Galaxies;Solar and Stellar Astrophysics;Chemical Physics '504': Astrophysics of Galaxies;Solar and Stellar Astrophysics;Fluid Dynamics '505': Astrophysics of Galaxies;Space Physics '506': Astrophysics of Galaxies;Statistical Mechanics '507': Astrophysics;Atmospheric and Oceanic Physics '508': Astrophysics;Atomic Physics '509': Astrophysics;Chaotic Dynamics '510': Astrophysics;Chaotic Dynamics;Chaotic Dynamics '511': Astrophysics;Computational Physics '512': Astrophysics;Condensed Matter '513': Astrophysics;Cosmology and Nongalactic Astrophysics '514': Astrophysics;Data Analysis, Statistics and Probability '515': Astrophysics;Fluid Dynamics '516': Astrophysics;Fluid Dynamics;Plasma Physics '517': Astrophysics;General Relativity and Quantum Cosmology '518': Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '519': Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '520': Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Nuclear Theory '521': Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '522': Astrophysics;General Relativity and Quantum Cosmology;Nuclear Theory '523': Astrophysics;General Relativity and Quantum Cosmology;Space Physics '524': Astrophysics;Geophysics '525': Astrophysics;High Energy Physics - Experiment '526': Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '527': Astrophysics;High Energy Physics - Experiment;Nuclear Experiment '528': Astrophysics;High Energy Physics - Phenomenology '529': Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory '530': Astrophysics;High Energy Physics - Phenomenology;Nuclear Experiment '531': Astrophysics;High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '532': Astrophysics;High Energy Physics - Phenomenology;Nuclear Theory '533': Astrophysics;High Energy Physics - Phenomenology;Space Physics '534': Astrophysics;High Energy Physics - Theory '535': Astrophysics;Instrumentation and Detectors '536': Astrophysics;Nuclear Experiment '537': Astrophysics;Nuclear Experiment;Nuclear Theory '538': Astrophysics;Nuclear Theory '539': Astrophysics;Plasma Physics '540': Astrophysics;Plasma Physics;Space Physics '541': Astrophysics;Space Physics '542': Astrophysics;Statistical Mechanics '543': Atmospheric and Oceanic Physics '544': Atmospheric and Oceanic Physics;Applications '545': Atmospheric and Oceanic Physics;Artificial Intelligence;Machine Learning '546': Atmospheric and Oceanic Physics;Chaotic Dynamics '547': Atmospheric and Oceanic Physics;Chaotic Dynamics;Fluid Dynamics '548': Atmospheric and Oceanic Physics;Chemical Physics '549': Atmospheric and Oceanic Physics;Computational Engineering, Finance, and Science '550': Atmospheric and Oceanic Physics;Computational Physics '551': Atmospheric and Oceanic Physics;Computational Physics;Fluid Dynamics '552': Atmospheric and Oceanic Physics;Computer Vision and Pattern Recognition '553': Atmospheric and Oceanic Physics;Data Analysis, Statistics and Probability '554': Atmospheric and Oceanic Physics;Data Analysis, Statistics and Probability;Geophysics '555': Atmospheric and Oceanic Physics;Dynamical Systems '556': Atmospheric and Oceanic Physics;Earth and Planetary Astrophysics '557': Atmospheric and Oceanic Physics;Earth and Planetary Astrophysics;Geophysics '558': Atmospheric and Oceanic Physics;Fluid Dynamics '559': Atmospheric and Oceanic Physics;Fluid Dynamics;Geophysics '560': Atmospheric and Oceanic Physics;Geophysics '561': Atmospheric and Oceanic Physics;Instrumentation and Detectors '562': Atmospheric and Oceanic Physics;Instrumentation and Methods for Astrophysics '563': Atmospheric and Oceanic Physics;Machine Learning '564': Atmospheric and Oceanic Physics;Machine Learning;Machine Learning '565': Atmospheric and Oceanic Physics;Optics '566': Atmospheric and Oceanic Physics;Physics and Society '567': Atmospheric and Oceanic Physics;Plasma Physics '568': Atmospheric and Oceanic Physics;Space Physics '569': Atomic Physics '570': Atomic Physics;Accelerator Physics '571': Atomic Physics;Applied Physics '572': Atomic Physics;Applied Physics;Quantum Physics '573': Atomic Physics;Astrophysics '574': Atomic Physics;Atomic Physics '575': Atomic Physics;Atomic and Molecular Clusters '576': Atomic Physics;Atomic and Molecular Clusters;Chemical Physics '577': Atomic Physics;Atomic and Molecular Clusters;Quantum Physics '578': Atomic Physics;Chaotic Dynamics '579': Atomic Physics;Chemical Physics '580': Atomic Physics;Chemical Physics;Computational Physics '581': Atomic Physics;Chemical Physics;Computational Physics;Quantum Physics '582': Atomic Physics;Chemical Physics;Optics '583': Atomic Physics;Chemical Physics;Optics;Quantum Physics '584': Atomic Physics;Chemical Physics;Quantum Physics '585': Atomic Physics;Classical Physics '586': Atomic Physics;Computational Physics '587': Atomic Physics;Computational Physics;Quantum Physics '588': Atomic Physics;Cosmology and Nongalactic Astrophysics '589': Atomic Physics;General Physics '590': Atomic Physics;General Relativity and Quantum Cosmology '591': Atomic Physics;General Relativity and Quantum Cosmology;Quantum Physics '592': Atomic Physics;Geophysics '593': Atomic Physics;High Energy Physics - Experiment '594': Atomic Physics;High Energy Physics - Experiment;Nuclear Experiment '595': Atomic Physics;High Energy Physics - Phenomenology '596': Atomic Physics;High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '597': Atomic Physics;High Energy Physics - Phenomenology;Nuclear Theory '598': Atomic Physics;High Energy Physics - Phenomenology;Quantum Physics '599': Atomic Physics;Instrumentation and Detectors '600': Atomic Physics;Instrumentation and Detectors;Optics '601': Atomic Physics;Instrumentation and Detectors;Quantum Physics '602': Atomic Physics;Instrumentation and Methods for Astrophysics '603': Atomic Physics;Materials Science '604': Atomic Physics;Mesoscale and Nanoscale Physics '605': Atomic Physics;Mesoscale and Nanoscale Physics;Optics '606': Atomic Physics;Nuclear Experiment '607': Atomic Physics;Nuclear Experiment;Nuclear Theory '608': Atomic Physics;Nuclear Theory '609': Atomic Physics;Optics '610': Atomic Physics;Optics;Quantum Physics '611': Atomic Physics;Other Condensed Matter '612': Atomic Physics;Other Condensed Matter;Quantum Physics '613': Atomic Physics;Plasma Physics '614': Atomic Physics;Quantum Gases '615': Atomic Physics;Quantum Gases;Chemical Physics '616': Atomic Physics;Quantum Gases;Chemical Physics;Quantum Physics '617': Atomic Physics;Quantum Gases;Nuclear Theory '618': Atomic Physics;Quantum Gases;Optics '619': Atomic Physics;Quantum Gases;Optics;Quantum Physics '620': Atomic Physics;Quantum Gases;Quantum Physics '621': Atomic Physics;Quantum Physics '622': Atomic Physics;Solar and Stellar Astrophysics '623': Atomic Physics;Statistical Mechanics '624': Atomic and Molecular Clusters '625': Atomic and Molecular Clusters;Atomic Physics '626': Atomic and Molecular Clusters;Atomic Physics;Chemical Physics '627': Atomic and Molecular Clusters;Chemical Physics '628': Atomic and Molecular Clusters;Computational Physics '629': Atomic and Molecular Clusters;Condensed Matter '630': Atomic and Molecular Clusters;Materials Science '631': Atomic and Molecular Clusters;Materials Science;Chemical Physics '632': Atomic and Molecular Clusters;Mesoscale and Nanoscale Physics '633': Atomic and Molecular Clusters;Nuclear Theory '634': Atomic and Molecular Clusters;Optics '635': Atomic and Molecular Clusters;Other Condensed Matter '636': Atomic and Molecular Clusters;Plasma Physics '637': Atomic and Molecular Clusters;Quantum Physics '638': Audio and Speech Processing '639': Audio and Speech Processing;Artificial Intelligence '640': Audio and Speech Processing;Artificial Intelligence;Computation and Language '641': Audio and Speech Processing;Artificial Intelligence;Computation and Language;Machine Learning;Sound '642': Audio and Speech Processing;Artificial Intelligence;Computation and Language;Sound '643': Audio and Speech Processing;Artificial Intelligence;Machine Learning '644': Audio and Speech Processing;Artificial Intelligence;Machine Learning;Sound '645': Audio and Speech Processing;Artificial Intelligence;Machine Learning;Sound;Signal Processing '646': Audio and Speech Processing;Artificial Intelligence;Sound '647': Audio and Speech Processing;Artificial Intelligence;Sound;Signal Processing '648': Audio and Speech Processing;Computation and Language '649': Audio and Speech Processing;Computation and Language;Machine Learning '650': Audio and Speech Processing;Computation and Language;Machine Learning;Sound '651': Audio and Speech Processing;Computation and Language;Machine Learning;Sound;Machine Learning '652': Audio and Speech Processing;Computation and Language;Sound '653': Audio and Speech Processing;Computation and Language;Sound;Machine Learning '654': Audio and Speech Processing;Computer Vision and Pattern Recognition;Machine Learning;Sound '655': Audio and Speech Processing;Computer Vision and Pattern Recognition;Sound '656': Audio and Speech Processing;Cryptography and Security;Machine Learning;Sound '657': Audio and Speech Processing;Cryptography and Security;Sound '658': Audio and Speech Processing;Human-Computer Interaction;Machine Learning;Sound '659': Audio and Speech Processing;Machine Learning '660': Audio and Speech Processing;Machine Learning;Signal Processing '661': Audio and Speech Processing;Machine Learning;Sound '662': Audio and Speech Processing;Machine Learning;Sound;Machine Learning '663': Audio and Speech Processing;Machine Learning;Sound;Signal Processing '664': Audio and Speech Processing;Machine Learning;Sound;Signal Processing;Machine Learning '665': Audio and Speech Processing;Multimedia;Sound '666': Audio and Speech Processing;Signal Processing '667': Audio and Speech Processing;Sound '668': Audio and Speech Processing;Sound;Image and Video Processing '669': Audio and Speech Processing;Sound;Machine Learning '670': Audio and Speech Processing;Sound;Quantitative Methods '671': Audio and Speech Processing;Sound;Signal Processing '672': Biological Physics '673': Biological Physics;Adaptation and Self-Organizing Systems '674': Biological Physics;Applied Physics '675': Biological Physics;Biomolecules '676': Biological Physics;Biomolecules;Quantum Physics '677': Biological Physics;Cell Behavior '678': Biological Physics;Chaotic Dynamics '679': Biological Physics;Chemical Physics '680': Biological Physics;Chemical Physics;Biomolecules '681': Biological Physics;Chemical Physics;Computational Physics '682': Biological Physics;Chemical Physics;Quantum Physics '683': Biological Physics;Classical Physics '684': Biological Physics;Computational Physics '685': Biological Physics;Computational Physics;Biomolecules '686': Biological Physics;Data Analysis, Statistics and Probability '687': Biological Physics;Data Analysis, Statistics and Probability;Neurons and Cognition '688': Biological Physics;Data Analysis, Statistics and Probability;Quantitative Methods '689': Biological Physics;Disordered Systems and Neural Networks '690': Biological Physics;Disordered Systems and Neural Networks;Neurons and Cognition '691': Biological Physics;Fluid Dynamics '692': Biological Physics;Fluid Dynamics;Cell Behavior '693': Biological Physics;General Physics '694': Biological Physics;Genomics '695': Biological Physics;Instrumentation and Detectors '696': Biological Physics;Materials Science '697': Biological Physics;Materials Science;Biomolecules '698': Biological Physics;Medical Physics '699': Biological Physics;Medical Physics;Optics '700': Biological Physics;Medical Physics;Tissues and Organs '701': Biological Physics;Mesoscale and Nanoscale Physics '702': Biological Physics;Molecular Networks '703': Biological Physics;Neurons and Cognition '704': Biological Physics;Optics '705': Biological Physics;Other Quantitative Biology '706': Biological Physics;Pattern Formation and Solitons '707': Biological Physics;Physics and Society '708': Biological Physics;Physics and Society;Populations and Evolution '709': Biological Physics;Populations and Evolution '710': Biological Physics;Quantitative Methods '711': Biological Physics;Quantum Physics '712': Biological Physics;Soft Condensed Matter '713': Biological Physics;Soft Condensed Matter;Biomolecules '714': Biological Physics;Soft Condensed Matter;Cell Behavior '715': Biological Physics;Soft Condensed Matter;Chemical Physics '716': Biological Physics;Soft Condensed Matter;Chemical Physics;Biomolecules '717': Biological Physics;Soft Condensed Matter;Computational Physics '718': Biological Physics;Soft Condensed Matter;Fluid Dynamics '719': Biological Physics;Soft Condensed Matter;Statistical Mechanics '720': Biological Physics;Soft Condensed Matter;Statistical Mechanics;Biomolecules '721': Biological Physics;Soft Condensed Matter;Subcellular Processes '722': Biological Physics;Soft Condensed Matter;Tissues and Organs '723': Biological Physics;Statistical Mechanics '724': Biological Physics;Statistical Mechanics;Adaptation and Self-Organizing Systems '725': Biological Physics;Statistical Mechanics;Biomolecules '726': Biological Physics;Statistical Mechanics;Cell Behavior '727': Biological Physics;Statistical Mechanics;Molecular Networks '728': Biological Physics;Statistical Mechanics;Populations and Evolution '729': Biological Physics;Statistical Mechanics;Quantitative Methods '730': Biological Physics;Statistical Mechanics;Subcellular Processes '731': Biological Physics;Subcellular Processes '732': Biological Physics;Tissues and Organs '733': Biomolecules '734': Biomolecules;Artificial Intelligence;Machine Learning '735': Biomolecules;Biological Physics '736': Biomolecules;Biological Physics;Chemical Physics '737': Biomolecules;Biological Physics;Chemical Physics;Computational Physics '738': Biomolecules;Biological Physics;Quantitative Methods '739': Biomolecules;Cell Behavior '740': Biomolecules;Chemical Physics '741': Biomolecules;Genomics '742': Biomolecules;Machine Learning '743': Biomolecules;Machine Learning;Machine Learning '744': Biomolecules;Machine Learning;Quantitative Methods '745': Biomolecules;Molecular Networks '746': Biomolecules;Populations and Evolution '747': Biomolecules;Quantitative Methods '748': Biomolecules;Soft Condensed Matter '749': Biomolecules;Soft Condensed Matter;Biological Physics '750': Biomolecules;Soft Condensed Matter;Biological Physics;Chemical Physics '751': Biomolecules;Soft Condensed Matter;Statistical Mechanics '752': Biomolecules;Soft Condensed Matter;Statistical Mechanics;Biological Physics '753': Biomolecules;Statistical Mechanics '754': Biomolecules;Statistical Mechanics;Biological Physics '755': Biomolecules;Subcellular Processes '756': Biomolecules;Tissues and Organs '757': Category Theory '758': Category Theory;Algebraic Geometry '759': Category Theory;Algebraic Geometry;Algebraic Topology '760': Category Theory;Algebraic Geometry;Algebraic Topology;Representation Theory '761': Category Theory;Algebraic Geometry;K-Theory and Homology '762': Category Theory;Algebraic Geometry;Representation Theory '763': Category Theory;Algebraic Topology '764': Category Theory;Algebraic Topology;Combinatorics '765': Category Theory;Algebraic Topology;K-Theory and Homology '766': Category Theory;Algebraic Topology;Logic '767': Category Theory;Algebraic Topology;Quantum Algebra '768': Category Theory;Algebraic Topology;Representation Theory '769': Category Theory;Combinatorics '770': Category Theory;Commutative Algebra '771': Category Theory;Commutative Algebra;Rings and Algebras '772': Category Theory;Differential Geometry '773': Category Theory;Dynamical Systems '774': Category Theory;Functional Analysis '775': Category Theory;General Topology '776': Category Theory;Group Theory '777': Category Theory;K-Theory and Homology '778': Category Theory;Logic '779': Category Theory;Logic in Computer Science '780': Category Theory;Logic in Computer Science;Logic '781': Category Theory;Operator Algebras '782': Category Theory;Probability '783': Category Theory;Programming Languages '784': Category Theory;Quantum Algebra '785': Category Theory;Quantum Algebra;Rings and Algebras '786': Category Theory;Quantum Physics '787': Category Theory;Representation Theory '788': Category Theory;Rings and Algebras '789': Category Theory;Rings and Algebras;Representation Theory '790': Cell Behavior '791': Cell Behavior;Adaptation and Self-Organizing Systems '792': Cell Behavior;Biological Physics '793': Cell Behavior;Biological Physics;Tissues and Organs '794': Cell Behavior;Biomolecules '795': Cell Behavior;Dynamical Systems '796': Cell Behavior;Molecular Networks '797': Cell Behavior;Populations and Evolution '798': Cell Behavior;Quantitative Methods '799': Cell Behavior;Soft Condensed Matter '800': Cell Behavior;Soft Condensed Matter;Biological Physics '801': Cell Behavior;Statistical Mechanics '802': Cell Behavior;Statistical Mechanics;Biological Physics '803': Cell Behavior;Subcellular Processes '804': Cell Behavior;Tissues and Organs '805': Cellular Automata and Lattice Gases '806': Cellular Automata and Lattice Gases;Adaptation and Self-Organizing Systems '807': Cellular Automata and Lattice Gases;Cellular Automata and Lattice Gases '808': Cellular Automata and Lattice Gases;Pattern Formation and Solitons '809': Cellular Automata and Lattice Gases;Statistical Mechanics '810': Chaotic Dynamics '811': Chaotic Dynamics;Adaptation and Self-Organizing Systems '812': Chaotic Dynamics;Adaptation, Noise, and Self-Organizing Systems;Adaptation and Self-Organizing Systems;Chaotic Dynamics '813': Chaotic Dynamics;Astrophysics '814': Chaotic Dynamics;Astrophysics of Galaxies '815': Chaotic Dynamics;Astrophysics;Chaotic Dynamics '816': Chaotic Dynamics;Atmospheric and Oceanic Physics '817': Chaotic Dynamics;Atmospheric and Oceanic Physics;Fluid Dynamics '818': Chaotic Dynamics;Atomic Physics '819': Chaotic Dynamics;Biological Physics '820': Chaotic Dynamics;Cellular Automata and Lattice Gases;Chaotic Dynamics;Cellular Automata and Lattice Gases '821': Chaotic Dynamics;Chaotic Dynamics '822': Chaotic Dynamics;Chaotic Dynamics;Pattern Formation and Solitons;Pattern Formation and Solitons '823': Chaotic Dynamics;Chaotic Dynamics;Quantum Physics '824': Chaotic Dynamics;Chemical Physics '825': Chaotic Dynamics;Classical Physics '826': Chaotic Dynamics;Computational Physics '827': Chaotic Dynamics;Condensed Matter '828': Chaotic Dynamics;Condensed Matter;Chaotic Dynamics '829': Chaotic Dynamics;Condensed Matter;Chaotic Dynamics;Quantum Physics '830': Chaotic Dynamics;Condensed Matter;High Energy Physics - Theory;Chaotic Dynamics '831': Chaotic Dynamics;Cryptography and Security '832': Chaotic Dynamics;Data Analysis, Statistics and Probability '833': Chaotic Dynamics;Disordered Systems and Neural Networks '834': Chaotic Dynamics;Disordered Systems and Neural Networks;Statistical Mechanics '835': Chaotic Dynamics;Dynamical Systems '836': Chaotic Dynamics;Dynamical Systems;Chemical Physics '837': Chaotic Dynamics;Earth and Planetary Astrophysics '838': Chaotic Dynamics;Exactly Solvable and Integrable Systems '839': Chaotic Dynamics;Fluid Dynamics '840': Chaotic Dynamics;High Energy Physics - Theory '841': Chaotic Dynamics;High Energy Physics - Theory;Chaotic Dynamics '842': Chaotic Dynamics;Mesoscale and Nanoscale Physics '843': Chaotic Dynamics;Mesoscale and Nanoscale Physics;Chaotic Dynamics '844': Chaotic Dynamics;Mesoscale and Nanoscale Physics;Quantum Physics '845': Chaotic Dynamics;Neurons and Cognition '846': Chaotic Dynamics;Optics '847': Chaotic Dynamics;Other Condensed Matter '848': Chaotic Dynamics;Other Condensed Matter;Quantum Physics '849': Chaotic Dynamics;Pattern Formation and Solitons '850': Chaotic Dynamics;Physics and Society '851': Chaotic Dynamics;Plasma Physics '852': Chaotic Dynamics;Quantum Physics '853': Chaotic Dynamics;Soft Condensed Matter '854': Chaotic Dynamics;Statistical Mechanics '855': Chaotic Dynamics;Statistical Mechanics;Chaotic Dynamics '856': Chaotic Dynamics;Statistical Mechanics;Dynamical Systems '857': Chaotic Dynamics;Statistical Mechanics;Fluid Dynamics '858': Chaotic Dynamics;Statistical Mechanics;Quantum Physics '859': Chemical Physics '860': Chemical Physics;Applied Physics '861': Chemical Physics;Artificial Intelligence;Machine Learning '862': Chemical Physics;Astrophysics of Galaxies '863': Chemical Physics;Atmospheric and Oceanic Physics '864': Chemical Physics;Atomic Physics '865': Chemical Physics;Atomic Physics;Computational Physics '866': Chemical Physics;Atomic Physics;Optics '867': Chemical Physics;Atomic Physics;Quantum Physics '868': Chemical Physics;Atomic and Molecular Clusters '869': Chemical Physics;Atomic and Molecular Clusters;Atomic Physics '870': Chemical Physics;Atomic and Molecular Clusters;Computational Physics '871': Chemical Physics;Atomic and Molecular Clusters;Quantum Physics '872': Chemical Physics;Biological Physics '873': Chemical Physics;Biological Physics;Biomolecules '874': Chemical Physics;Biological Physics;Computational Physics '875': Chemical Physics;Biological Physics;Quantum Physics '876': Chemical Physics;Biomolecules '877': Chemical Physics;Chaotic Dynamics '878': Chemical Physics;Chemical Physics '879': Chemical Physics;Classical Physics '880': Chemical Physics;Computational Physics '881': Chemical Physics;Computational Physics;Quantum Physics '882': Chemical Physics;Condensed Matter '883': Chemical Physics;Condensed Matter;Chemical Physics '884': Chemical Physics;Data Analysis, Statistics and Probability '885': Chemical Physics;Earth and Planetary Astrophysics '886': Chemical Physics;Fluid Dynamics '887': Chemical Physics;General Physics '888': Chemical Physics;Geophysics '889': Chemical Physics;Instrumentation and Detectors '890': Chemical Physics;Machine Learning '891': Chemical Physics;Machine Learning;Biomolecules '892': Chemical Physics;Machine Learning;Computational Physics '893': Chemical Physics;Machine Learning;Machine Learning '894': Chemical Physics;Materials Science '895': Chemical Physics;Materials Science;Applied Physics '896': Chemical Physics;Materials Science;Atomic Physics '897': Chemical Physics;Materials Science;Atomic and Molecular Clusters '898': Chemical Physics;Materials Science;Computational Physics '899': Chemical Physics;Materials Science;Computational Physics;Quantum Physics '900': Chemical Physics;Materials Science;Machine Learning '901': Chemical Physics;Materials Science;Optics '902': Chemical Physics;Materials Science;Other Condensed Matter '903': Chemical Physics;Materials Science;Quantum Physics '904': Chemical Physics;Materials Science;Soft Condensed Matter '905': Chemical Physics;Materials Science;Statistical Mechanics '906': Chemical Physics;Materials Science;Strongly Correlated Electrons '907': Chemical Physics;Materials Science;Strongly Correlated Electrons;Computational Physics '908': Chemical Physics;Mesoscale and Nanoscale Physics '909': Chemical Physics;Mesoscale and Nanoscale Physics;Materials Science '910': Chemical Physics;Mesoscale and Nanoscale Physics;Quantum Physics '911': Chemical Physics;Optics '912': Chemical Physics;Optics;Quantum Physics '913': Chemical Physics;Other Condensed Matter '914': Chemical Physics;Other Condensed Matter;Computational Physics '915': Chemical Physics;Other Condensed Matter;Quantum Physics '916': Chemical Physics;Other Condensed Matter;Strongly Correlated Electrons;Computational Physics '917': Chemical Physics;Plasma Physics '918': Chemical Physics;Quantitative Methods '919': Chemical Physics;Quantum Physics '920': Chemical Physics;Soft Condensed Matter '921': Chemical Physics;Soft Condensed Matter;Biological Physics '922': Chemical Physics;Soft Condensed Matter;Computational Physics '923': Chemical Physics;Soft Condensed Matter;Fluid Dynamics '924': Chemical Physics;Soft Condensed Matter;Statistical Mechanics '925': Chemical Physics;Soft Condensed Matter;Statistical Mechanics;Computational Physics '926': Chemical Physics;Statistical Mechanics '927': Chemical Physics;Statistical Mechanics;Biological Physics '928': Chemical Physics;Statistical Mechanics;Computational Physics '929': Chemical Physics;Statistical Mechanics;Quantum Physics '930': Chemical Physics;Strongly Correlated Electrons '931': Chemical Physics;Strongly Correlated Electrons;Computational Physics '932': Chemical Physics;Strongly Correlated Electrons;Computational Physics;Quantum Physics '933': Chemical Physics;Strongly Correlated Electrons;Quantum Physics '934': Classical Analysis and ODEs '935': Classical Analysis and ODEs;Algebraic Geometry '936': Classical Analysis and ODEs;Analysis of PDEs '937': Classical Analysis and ODEs;Analysis of PDEs;Complex Variables '938': Classical Analysis and ODEs;Analysis of PDEs;Differential Geometry '939': Classical Analysis and ODEs;Analysis of PDEs;Functional Analysis '940': Classical Analysis and ODEs;Analysis of PDEs;Metric Geometry '941': Classical Analysis and ODEs;Analysis of PDEs;Probability '942': Classical Analysis and ODEs;Combinatorics '943': Classical Analysis and ODEs;Combinatorics;Metric Geometry '944': Classical Analysis and ODEs;Combinatorics;Number Theory '945': Classical Analysis and ODEs;Combinatorics;Quantum Algebra '946': Classical Analysis and ODEs;Commutative Algebra '947': Classical Analysis and ODEs;Complex Variables '948': Classical Analysis and ODEs;Complex Variables;Dynamical Systems '949': Classical Analysis and ODEs;Complex Variables;Functional Analysis '950': Classical Analysis and ODEs;Complex Variables;Number Theory '951': Classical Analysis and ODEs;Complex Variables;Spectral Theory '952': Classical Analysis and ODEs;Differential Geometry '953': Classical Analysis and ODEs;Dynamical Systems '954': Classical Analysis and ODEs;Dynamical Systems;Metric Geometry '955': Classical Analysis and ODEs;Dynamical Systems;Number Theory '956': Classical Analysis and ODEs;Dynamical Systems;Probability '957': Classical Analysis and ODEs;Exactly Solvable and Integrable Systems '958': Classical Analysis and ODEs;Functional Analysis '959': Classical Analysis and ODEs;Functional Analysis;Metric Geometry '960': Classical Analysis and ODEs;Functional Analysis;Number Theory '961': Classical Analysis and ODEs;Functional Analysis;Operator Algebras '962': Classical Analysis and ODEs;Functional Analysis;Probability '963': Classical Analysis and ODEs;Functional Analysis;Spectral Theory '964': Classical Analysis and ODEs;General Mathematics '965': Classical Analysis and ODEs;General Topology '966': Classical Analysis and ODEs;Group Theory '967': Classical Analysis and ODEs;History and Overview '968': Classical Analysis and ODEs;Logic '969': Classical Analysis and ODEs;Metric Geometry '970': Classical Analysis and ODEs;Number Theory '971': Classical Analysis and ODEs;Numerical Analysis '972': Classical Analysis and ODEs;Numerical Analysis;Numerical Analysis '973': Classical Analysis and ODEs;Operator Algebras '974': Classical Analysis and ODEs;Optimization and Control '975': Classical Analysis and ODEs;Populations and Evolution '976': Classical Analysis and ODEs;Probability '977': Classical Analysis and ODEs;Quantum Algebra '978': Classical Analysis and ODEs;Representation Theory '979': Classical Analysis and ODEs;Rings and Algebras '980': Classical Analysis and ODEs;Spectral Theory '981': Classical Physics '982': Classical Physics;Accelerator Physics '983': Classical Physics;Analysis of PDEs '984': Classical Physics;Applied Physics '985': Classical Physics;Applied Physics;Optics '986': Classical Physics;Atomic Physics '987': Classical Physics;Biological Physics '988': Classical Physics;Chaotic Dynamics '989': Classical Physics;Computational Physics '990': Classical Physics;Dynamical Systems '991': Classical Physics;Exactly Solvable and Integrable Systems '992': Classical Physics;Fluid Dynamics '993': Classical Physics;General Physics '994': Classical Physics;General Relativity and Quantum Cosmology '995': Classical Physics;General Relativity and Quantum Cosmology;General Physics '996': Classical Physics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '997': Classical Physics;Geophysics '998': Classical Physics;High Energy Physics - Theory '999': Classical Physics;History and Philosophy of Physics '1000': Classical Physics;Instrumentation and Detectors '1001': Classical Physics;Materials Science '1002': Classical Physics;Mesoscale and Nanoscale Physics '1003': Classical Physics;Numerical Analysis '1004': Classical Physics;Optics '1005': Classical Physics;Optics;Quantum Physics '1006': Classical Physics;Other Condensed Matter '1007': Classical Physics;Pattern Formation and Solitons '1008': Classical Physics;Physics Education '1009': Classical Physics;Plasma Physics '1010': Classical Physics;Popular Physics '1011': Classical Physics;Quantum Physics '1012': Classical Physics;Soft Condensed Matter '1013': Classical Physics;Statistical Mechanics '1014': Combinatorics '1015': Combinatorics;Algebraic Geometry '1016': Combinatorics;Algebraic Geometry;Algebraic Topology '1017': Combinatorics;Algebraic Geometry;Group Theory '1018': Combinatorics;Algebraic Geometry;Metric Geometry '1019': Combinatorics;Algebraic Geometry;Number Theory '1020': Combinatorics;Algebraic Geometry;Representation Theory '1021': Combinatorics;Algebraic Geometry;Rings and Algebras '1022': Combinatorics;Algebraic Topology '1023': Combinatorics;Algebraic Topology;Category Theory '1024': Combinatorics;Algebraic Topology;Geometric Topology '1025': Combinatorics;Algebraic Topology;Metric Geometry '1026': Combinatorics;Algebraic Topology;Probability '1027': Combinatorics;Algebraic Topology;Representation Theory '1028': Combinatorics;Biomolecules '1029': Combinatorics;Category Theory '1030': Combinatorics;Classical Analysis and ODEs '1031': Combinatorics;Classical Analysis and ODEs;Number Theory '1032': Combinatorics;Commutative Algebra '1033': Combinatorics;Commutative Algebra;Algebraic Geometry '1034': Combinatorics;Commutative Algebra;Number Theory '1035': Combinatorics;Commutative Algebra;Representation Theory '1036': Combinatorics;Commutative Algebra;Rings and Algebras '1037': Combinatorics;Complex Variables '1038': Combinatorics;Computational Complexity '1039': Combinatorics;Computational Complexity;Discrete Mathematics '1040': Combinatorics;Computational Complexity;Discrete Mathematics;Data Structures and Algorithms '1041': Combinatorics;Computational Geometry '1042': Combinatorics;Computational Geometry;Discrete Mathematics '1043': Combinatorics;Computational Geometry;Metric Geometry '1044': Combinatorics;Computer Science and Game Theory '1045': Combinatorics;Cryptography and Security '1046': Combinatorics;Data Structures and Algorithms '1047': Combinatorics;Data Structures and Algorithms;Probability '1048': Combinatorics;Differential Geometry '1049': Combinatorics;Discrete Mathematics '1050': Combinatorics;Discrete Mathematics;Computer Science and Game Theory '1051': Combinatorics;Discrete Mathematics;Data Structures and Algorithms '1052': Combinatorics;Discrete Mathematics;Data Structures and Algorithms;Optimization and Control '1053': Combinatorics;Discrete Mathematics;Dynamical Systems '1054': Combinatorics;Discrete Mathematics;Formal Languages and Automata Theory '1055': Combinatorics;Discrete Mathematics;Group Theory '1056': Combinatorics;Discrete Mathematics;Logic '1057': Combinatorics;Discrete Mathematics;Metric Geometry '1058': Combinatorics;Discrete Mathematics;Number Theory '1059': Combinatorics;Discrete Mathematics;Optimization and Control '1060': Combinatorics;Discrete Mathematics;Populations and Evolution '1061': Combinatorics;Discrete Mathematics;Probability '1062': Combinatorics;Discrete Mathematics;Spectral Theory '1063': Combinatorics;Dynamical Systems '1064': Combinatorics;Dynamical Systems;Number Theory '1065': Combinatorics;Formal Languages and Automata Theory '1066': Combinatorics;Functional Analysis '1067': Combinatorics;Functional Analysis;Probability '1068': Combinatorics;General Mathematics '1069': Combinatorics;General Topology '1070': Combinatorics;Geometric Topology '1071': Combinatorics;Group Theory '1072': Combinatorics;Group Theory;Geometric Topology '1073': Combinatorics;Group Theory;Number Theory '1074': Combinatorics;Group Theory;Probability '1075': Combinatorics;Group Theory;Representation Theory '1076': Combinatorics;Group Theory;Rings and Algebras '1077': Combinatorics;High Energy Physics - Theory '1078': Combinatorics;High Energy Physics - Theory;Algebraic Geometry '1079': Combinatorics;History and Overview '1080': Combinatorics;K-Theory and Homology '1081': Combinatorics;Logic '1082': Combinatorics;Logic in Computer Science '1083': Combinatorics;Metric Geometry '1084': Combinatorics;Metric Geometry;Number Theory '1085': Combinatorics;Metric Geometry;Probability '1086': Combinatorics;Neurons and Cognition '1087': Combinatorics;Number Theory '1088': Combinatorics;Number Theory;Probability '1089': Combinatorics;Number Theory;Quantum Algebra '1090': Combinatorics;Number Theory;Representation Theory '1091': Combinatorics;Operator Algebras '1092': Combinatorics;Optimization and Control '1093': Combinatorics;Populations and Evolution '1094': Combinatorics;Probability '1095': Combinatorics;Probability;Representation Theory '1096': Combinatorics;Quantum Algebra '1097': Combinatorics;Quantum Algebra;Representation Theory '1098': Combinatorics;Quantum Algebra;Rings and Algebras '1099': Combinatorics;Quantum Physics '1100': Combinatorics;Representation Theory '1101': Combinatorics;Rings and Algebras '1102': Combinatorics;Rings and Algebras;Representation Theory '1103': Combinatorics;Spectral Theory '1104': Combinatorics;Statistical Mechanics '1105': Combinatorics;Symbolic Computation '1106': Combinatorics;Symplectic Geometry '1107': Commutative Algebra '1108': Commutative Algebra;Algebraic Geometry '1109': Commutative Algebra;Algebraic Geometry;Combinatorics '1110': Commutative Algebra;Algebraic Geometry;Complex Variables '1111': Commutative Algebra;Algebraic Geometry;K-Theory and Homology '1112': Commutative Algebra;Algebraic Geometry;Number Theory '1113': Commutative Algebra;Algebraic Geometry;Representation Theory '1114': Commutative Algebra;Algebraic Geometry;Rings and Algebras '1115': Commutative Algebra;Algebraic Topology '1116': Commutative Algebra;Algebraic Topology;Combinatorics '1117': Commutative Algebra;Category Theory '1118': Commutative Algebra;Combinatorics '1119': Commutative Algebra;Combinatorics;Number Theory '1120': Commutative Algebra;Combinatorics;Representation Theory '1121': Commutative Algebra;Combinatorics;Rings and Algebras '1122': Commutative Algebra;Complex Variables '1123': Commutative Algebra;General Topology '1124': Commutative Algebra;Group Theory '1125': Commutative Algebra;K-Theory and Homology '1126': Commutative Algebra;K-Theory and Homology;Rings and Algebras '1127': Commutative Algebra;Logic '1128': Commutative Algebra;Number Theory '1129': Commutative Algebra;Numerical Analysis '1130': Commutative Algebra;Representation Theory '1131': Commutative Algebra;Rings and Algebras '1132': Commutative Algebra;Rings and Algebras;Representation Theory '1133': Commutative Algebra;Symbolic Computation '1134': Commutative Algebra;Symbolic Computation;Algebraic Geometry '1135': Complex Variables '1136': Complex Variables;Algebraic Geometry '1137': Complex Variables;Algebraic Geometry;Differential Geometry '1138': Complex Variables;Algebraic Geometry;Dynamical Systems '1139': Complex Variables;Algebraic Geometry;Probability '1140': Complex Variables;Algebraic Topology '1141': Complex Variables;Analysis of PDEs '1142': Complex Variables;Analysis of PDEs;Classical Analysis and ODEs '1143': Complex Variables;Analysis of PDEs;Differential Geometry '1144': Complex Variables;Analysis of PDEs;Functional Analysis '1145': Complex Variables;Classical Analysis and ODEs '1146': Complex Variables;Classical Analysis and ODEs;Dynamical Systems '1147': Complex Variables;Classical Analysis and ODEs;Functional Analysis '1148': Complex Variables;Combinatorics '1149': Complex Variables;Commutative Algebra '1150': Complex Variables;Commutative Algebra;Algebraic Geometry '1151': Complex Variables;Differential Geometry '1152': Complex Variables;Differential Geometry;Symplectic Geometry '1153': Complex Variables;Dynamical Systems '1154': Complex Variables;Functional Analysis '1155': Complex Variables;Geometric Topology '1156': Complex Variables;Group Theory '1157': Complex Variables;Metric Geometry '1158': Complex Variables;Number Theory '1159': Complex Variables;Numerical Analysis '1160': Complex Variables;Operator Algebras '1161': Complex Variables;Optimization and Control '1162': Complex Variables;Probability '1163': Complex Variables;Representation Theory '1164': Complex Variables;Rings and Algebras '1165': Complex Variables;Spectral Theory '1166': Complex Variables;Symplectic Geometry '1167': Computation '1168': Computation and Language '1169': Computation and Language;Applications '1170': Computation and Language;Artificial Intelligence '1171': Computation and Language;Artificial Intelligence;Audio and Speech Processing '1172': Computation and Language;Artificial Intelligence;Computer Vision and Pattern Recognition '1173': Computation and Language;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '1174': Computation and Language;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Multimedia '1175': Computation and Language;Artificial Intelligence;Computers and Society '1176': Computation and Language;Artificial Intelligence;Computers and Society;Human-Computer Interaction '1177': Computation and Language;Artificial Intelligence;Computers and Society;Machine Learning '1178': Computation and Language;Artificial Intelligence;Computers and Society;Machine Learning;Social and Information Networks '1179': Computation and Language;Artificial Intelligence;Cryptography and Security '1180': Computation and Language;Artificial Intelligence;Cryptography and Security;Machine Learning '1181': Computation and Language;Artificial Intelligence;Databases '1182': Computation and Language;Artificial Intelligence;Databases;Information Retrieval;Machine Learning '1183': Computation and Language;Artificial Intelligence;Databases;Machine Learning '1184': Computation and Language;Artificial Intelligence;Digital Libraries '1185': Computation and Language;Artificial Intelligence;Human-Computer Interaction '1186': Computation and Language;Artificial Intelligence;Human-Computer Interaction;Machine Learning '1187': Computation and Language;Artificial Intelligence;Information Retrieval '1188': Computation and Language;Artificial Intelligence;Information Retrieval;Machine Learning '1189': Computation and Language;Artificial Intelligence;Logic in Computer Science '1190': Computation and Language;Artificial Intelligence;Machine Learning '1191': Computation and Language;Artificial Intelligence;Machine Learning;Audio and Speech Processing '1192': Computation and Language;Artificial Intelligence;Machine Learning;Machine Learning '1193': Computation and Language;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '1194': Computation and Language;Artificial Intelligence;Machine Learning;Robotics '1195': Computation and Language;Artificial Intelligence;Machine Learning;Social and Information Networks '1196': Computation and Language;Artificial Intelligence;Machine Learning;Sound;Audio and Speech Processing '1197': Computation and Language;Artificial Intelligence;Multiagent Systems '1198': Computation and Language;Artificial Intelligence;Multimedia '1199': Computation and Language;Artificial Intelligence;Neural and Evolutionary Computing '1200': Computation and Language;Artificial Intelligence;Neural and Evolutionary Computing;Machine Learning '1201': Computation and Language;Artificial Intelligence;Robotics '1202': Computation and Language;Artificial Intelligence;Social and Information Networks '1203': Computation and Language;Artificial Intelligence;Software Engineering '1204': Computation and Language;Artificial Intelligence;Sound;Audio and Speech Processing '1205': Computation and Language;Audio and Speech Processing '1206': Computation and Language;Category Theory '1207': Computation and Language;Computation and Language '1208': Computation and Language;Computer Vision and Pattern Recognition '1209': Computation and Language;Computer Vision and Pattern Recognition;Information Retrieval '1210': Computation and Language;Computer Vision and Pattern Recognition;Machine Learning '1211': Computation and Language;Computer Vision and Pattern Recognition;Multimedia '1212': Computation and Language;Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing '1213': Computation and Language;Computers and Society '1214': Computation and Language;Computers and Society;Human-Computer Interaction '1215': Computation and Language;Computers and Society;Information Retrieval '1216': Computation and Language;Computers and Society;Machine Learning '1217': Computation and Language;Computers and Society;Social and Information Networks '1218': Computation and Language;Cryptography and Security '1219': Computation and Language;Cryptography and Security;Machine Learning '1220': Computation and Language;Data Analysis, Statistics and Probability '1221': Computation and Language;Data Structures and Algorithms '1222': Computation and Language;Databases '1223': Computation and Language;Databases;Machine Learning '1224': Computation and Language;Digital Libraries '1225': Computation and Language;Digital Libraries;Information Retrieval '1226': Computation and Language;Digital Libraries;Machine Learning '1227': Computation and Language;Formal Languages and Automata Theory '1228': Computation and Language;Human-Computer Interaction '1229': Computation and Language;Human-Computer Interaction;Information Retrieval '1230': Computation and Language;Human-Computer Interaction;Machine Learning '1231': Computation and Language;Information Retrieval '1232': Computation and Language;Information Retrieval;Machine Learning '1233': Computation and Language;Information Retrieval;Machine Learning;Machine Learning '1234': Computation and Language;Information Retrieval;Machine Learning;Social and Information Networks '1235': Computation and Language;Information Retrieval;Social and Information Networks '1236': Computation and Language;Logic in Computer Science '1237': Computation and Language;Machine Learning '1238': Computation and Language;Machine Learning;Audio and Speech Processing '1239': Computation and Language;Machine Learning;Machine Learning '1240': Computation and Language;Machine Learning;Neural and Evolutionary Computing '1241': Computation and Language;Machine Learning;Neural and Evolutionary Computing;Machine Learning '1242': Computation and Language;Machine Learning;Social and Information Networks '1243': Computation and Language;Machine Learning;Software Engineering '1244': Computation and Language;Machine Learning;Sound '1245': Computation and Language;Machine Learning;Sound;Audio and Speech Processing '1246': Computation and Language;Materials Science '1247': Computation and Language;Multimedia '1248': Computation and Language;Neural and Evolutionary Computing '1249': Computation and Language;Neural and Evolutionary Computing;Machine Learning '1250': Computation and Language;Neurons and Cognition '1251': Computation and Language;Physics and Society '1252': Computation and Language;Programming Languages '1253': Computation and Language;Quantum Physics '1254': Computation and Language;Robotics '1255': Computation and Language;Social and Information Networks '1256': Computation and Language;Social and Information Networks;Physics and Society '1257': Computation and Language;Software Engineering '1258': Computation and Language;Sound '1259': Computation and Language;Sound;Audio and Speech Processing '1260': Computation and Language;Sound;Audio and Speech Processing;Machine Learning '1261': Computation;Applications '1262': Computation;Applications;Methodology '1263': Computation;Data Structures and Algorithms '1264': Computation;Distributed, Parallel, and Cluster Computing '1265': Computation;Machine Learning '1266': Computation;Machine Learning;Machine Learning '1267': Computation;Machine Learning;Methodology;Machine Learning '1268': Computation;Mathematical Software '1269': Computation;Methodology '1270': Computation;Methodology;Machine Learning '1271': Computation;Numerical Analysis '1272': Computation;Numerical Analysis;Numerical Analysis '1273': Computation;Numerical Analysis;Numerical Analysis;Methodology '1274': Computation;Numerical Analysis;Numerical Analysis;Probability '1275': Computation;Optimization and Control '1276': Computation;Other Statistics '1277': Computation;Probability '1278': Computation;Probability;Methodology '1279': Computation;Quantitative Methods '1280': Computational Complexity '1281': Computational Complexity;Algebraic Geometry '1282': Computational Complexity;Artificial Intelligence '1283': Computational Complexity;Combinatorics '1284': Computational Complexity;Computational Geometry '1285': Computational Complexity;Computational Geometry;Data Structures and Algorithms '1286': Computational Complexity;Computer Science and Game Theory '1287': Computational Complexity;Cryptography and Security '1288': Computational Complexity;Data Structures and Algorithms '1289': Computational Complexity;Data Structures and Algorithms;Combinatorics '1290': Computational Complexity;Data Structures and Algorithms;Logic in Computer Science '1291': Computational Complexity;Data Structures and Algorithms;Machine Learning '1292': Computational Complexity;Discrete Mathematics '1293': Computational Complexity;Discrete Mathematics;Combinatorics '1294': Computational Complexity;Discrete Mathematics;Data Structures and Algorithms '1295': Computational Complexity;Discrete Mathematics;Data Structures and Algorithms;Combinatorics '1296': Computational Complexity;Discrete Mathematics;Logic in Computer Science '1297': Computational Complexity;Distributed, Parallel, and Cluster Computing '1298': Computational Complexity;Formal Languages and Automata Theory '1299': Computational Complexity;Formal Languages and Automata Theory;Quantum Physics '1300': Computational Complexity;Group Theory '1301': Computational Complexity;Logic '1302': Computational Complexity;Logic in Computer Science '1303': Computational Complexity;Logic in Computer Science;Logic '1304': Computational Complexity;Machine Learning '1305': Computational Complexity;Optimization and Control '1306': Computational Complexity;Probability '1307': Computational Complexity;Quantum Physics '1308': Computational Complexity;Representation Theory '1309': Computational Complexity;Statistical Mechanics '1310': Computational Complexity;Symbolic Computation '1311': Computational Engineering, Finance, and Science '1312': Computational Engineering, Finance, and Science;Applications '1313': Computational Engineering, Finance, and Science;Applied Physics '1314': Computational Engineering, Finance, and Science;Artificial Intelligence '1315': Computational Engineering, Finance, and Science;Artificial Intelligence;Machine Learning '1316': Computational Engineering, Finance, and Science;Biomolecules '1317': Computational Engineering, Finance, and Science;Classical Physics '1318': Computational Engineering, Finance, and Science;Computational Geometry '1319': Computational Engineering, Finance, and Science;Computational Physics '1320': Computational Engineering, Finance, and Science;Computational Physics;Fluid Dynamics '1321': Computational Engineering, Finance, and Science;Computers and Society '1322': Computational Engineering, Finance, and Science;Data Structures and Algorithms '1323': Computational Engineering, Finance, and Science;Distributed, Parallel, and Cluster Computing '1324': Computational Engineering, Finance, and Science;Fluid Dynamics '1325': Computational Engineering, Finance, and Science;Genomics '1326': Computational Engineering, Finance, and Science;Geophysics '1327': Computational Engineering, Finance, and Science;Logic in Computer Science '1328': Computational Engineering, Finance, and Science;Machine Learning '1329': Computational Engineering, Finance, and Science;Machine Learning;Machine Learning '1330': Computational Engineering, Finance, and Science;Machine Learning;Numerical Analysis;Numerical Analysis '1331': Computational Engineering, Finance, and Science;Materials Science '1332': Computational Engineering, Finance, and Science;Mathematical Software '1333': Computational Engineering, Finance, and Science;Medical Physics '1334': Computational Engineering, Finance, and Science;Molecular Networks '1335': Computational Engineering, Finance, and Science;Neural and Evolutionary Computing '1336': Computational Engineering, Finance, and Science;Numerical Analysis '1337': Computational Engineering, Finance, and Science;Numerical Analysis;Numerical Analysis '1338': Computational Engineering, Finance, and Science;Optimization and Control '1339': Computational Engineering, Finance, and Science;Quantitative Methods '1340': Computational Engineering, Finance, and Science;Signal Processing '1341': Computational Engineering, Finance, and Science;Systems and Control;Systems and Control '1342': Computational Finance '1343': Computational Finance;Computational Engineering, Finance, and Science '1344': Computational Finance;General Finance '1345': Computational Finance;Machine Learning '1346': Computational Finance;Mathematical Finance '1347': Computational Finance;Mathematical Finance;Pricing of Securities '1348': Computational Finance;Numerical Analysis '1349': Computational Finance;Numerical Analysis;Numerical Analysis '1350': Computational Finance;Pricing of Securities '1351': Computational Finance;Probability '1352': Computational Finance;Probability;Pricing of Securities '1353': Computational Finance;Risk Management '1354': Computational Finance;Statistical Finance '1355': Computational Finance;Trading and Market Microstructure '1356': Computational Geometry '1357': Computational Geometry;Algebraic Topology '1358': Computational Geometry;Artificial Intelligence '1359': Computational Geometry;Combinatorics '1360': Computational Geometry;Combinatorics;Metric Geometry '1361': Computational Geometry;Computational Complexity '1362': Computational Geometry;Computational Complexity;Data Structures and Algorithms '1363': Computational Geometry;Computer Vision and Pattern Recognition '1364': Computational Geometry;Data Structures and Algorithms '1365': Computational Geometry;Data Structures and Algorithms;Combinatorics '1366': Computational Geometry;Data Structures and Algorithms;Machine Learning '1367': Computational Geometry;Differential Geometry '1368': Computational Geometry;Discrete Mathematics '1369': Computational Geometry;Discrete Mathematics;Combinatorics '1370': Computational Geometry;Discrete Mathematics;Data Structures and Algorithms '1371': Computational Geometry;Geometric Topology '1372': Computational Geometry;Graphics '1373': Computational Geometry;Machine Learning '1374': Computational Geometry;Mathematical Software '1375': Computational Geometry;Metric Geometry '1376': Computational Geometry;Networking and Internet Architecture '1377': Computational Geometry;Numerical Analysis '1378': Computational Geometry;Numerical Analysis;Numerical Analysis '1379': Computational Geometry;Optimization and Control '1380': Computational Geometry;Robotics '1381': Computational Physics '1382': Computational Physics;Accelerator Physics '1383': Computational Physics;Applied Physics '1384': Computational Physics;Atmospheric and Oceanic Physics '1385': Computational Physics;Atomic Physics '1386': Computational Physics;Atomic Physics;Chemical Physics '1387': Computational Physics;Atomic Physics;Quantum Physics '1388': Computational Physics;Atomic and Molecular Clusters '1389': Computational Physics;Biological Physics '1390': Computational Physics;Chaotic Dynamics '1391': Computational Physics;Chemical Physics '1392': Computational Physics;Chemical Physics;Quantum Physics '1393': Computational Physics;Classical Physics '1394': Computational Physics;Computational Engineering, Finance, and Science '1395': Computational Physics;Computational Engineering, Finance, and Science;Fluid Dynamics '1396': Computational Physics;Data Analysis, Statistics and Probability '1397': Computational Physics;Disordered Systems and Neural Networks '1398': Computational Physics;Disordered Systems and Neural Networks;Materials Science '1399': Computational Physics;Distributed, Parallel, and Cluster Computing '1400': Computational Physics;Fluid Dynamics '1401': Computational Physics;Fluid Dynamics;Plasma Physics '1402': Computational Physics;General Physics '1403': Computational Physics;General Relativity and Quantum Cosmology '1404': Computational Physics;Geophysics '1405': Computational Physics;High Energy Physics - Experiment '1406': Computational Physics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '1407': Computational Physics;High Energy Physics - Phenomenology '1408': Computational Physics;Instrumentation and Detectors '1409': Computational Physics;Instrumentation and Methods for Astrophysics '1410': Computational Physics;Machine Learning '1411': Computational Physics;Machine Learning;Chemical Physics '1412': Computational Physics;Machine Learning;Chemical Physics;Machine Learning '1413': Computational Physics;Machine Learning;Fluid Dynamics '1414': Computational Physics;Machine Learning;Machine Learning '1415': Computational Physics;Machine Learning;Numerical Analysis;Numerical Analysis '1416': Computational Physics;Materials Science '1417': Computational Physics;Materials Science;Chemical Physics '1418': Computational Physics;Materials Science;Data Analysis, Statistics and Probability '1419': Computational Physics;Materials Science;Machine Learning '1420': Computational Physics;Materials Science;Numerical Analysis '1421': Computational Physics;Materials Science;Optics '1422': Computational Physics;Materials Science;Quantum Physics '1423': Computational Physics;Mathematical Software '1424': Computational Physics;Medical Physics '1425': Computational Physics;Mesoscale and Nanoscale Physics '1426': Computational Physics;Mesoscale and Nanoscale Physics;Materials Science '1427': Computational Physics;Mesoscale and Nanoscale Physics;Optics '1428': Computational Physics;Nuclear Experiment '1429': Computational Physics;Nuclear Theory '1430': Computational Physics;Numerical Analysis '1431': Computational Physics;Numerical Analysis;Fluid Dynamics '1432': Computational Physics;Numerical Analysis;Numerical Analysis '1433': Computational Physics;Numerical Analysis;Numerical Analysis;Fluid Dynamics '1434': Computational Physics;Numerical Analysis;Plasma Physics '1435': Computational Physics;Optics '1436': Computational Physics;Other Condensed Matter '1437': Computational Physics;Physics and Society '1438': Computational Physics;Plasma Physics '1439': Computational Physics;Quantum Gases '1440': Computational Physics;Quantum Physics '1441': Computational Physics;Soft Condensed Matter '1442': Computational Physics;Soft Condensed Matter;Chemical Physics '1443': Computational Physics;Soft Condensed Matter;Fluid Dynamics '1444': Computational Physics;Solar and Stellar Astrophysics '1445': Computational Physics;Statistical Mechanics '1446': Computational Physics;Statistical Mechanics;Chemical Physics '1447': Computational Physics;Strongly Correlated Electrons '1448': Computational Physics;Strongly Correlated Electrons;Chemical Physics '1449': Computer Science and Game Theory '1450': Computer Science and Game Theory;Artificial Intelligence '1451': Computer Science and Game Theory;Artificial Intelligence;Data Structures and Algorithms '1452': Computer Science and Game Theory;Artificial Intelligence;Machine Learning '1453': Computer Science and Game Theory;Artificial Intelligence;Machine Learning;Multiagent Systems '1454': Computer Science and Game Theory;Artificial Intelligence;Multiagent Systems '1455': Computer Science and Game Theory;Artificial Intelligence;Multiagent Systems;Theoretical Economics '1456': Computer Science and Game Theory;Artificial Intelligence;Theoretical Economics '1457': Computer Science and Game Theory;Combinatorics '1458': Computer Science and Game Theory;Computational Complexity '1459': Computer Science and Game Theory;Computational Complexity;Data Structures and Algorithms '1460': Computer Science and Game Theory;Computational Complexity;Multiagent Systems '1461': Computer Science and Game Theory;Computational Engineering, Finance, and Science '1462': Computer Science and Game Theory;Computers and Society '1463': Computer Science and Game Theory;Cryptography and Security '1464': Computer Science and Game Theory;Data Structures and Algorithms '1465': Computer Science and Game Theory;Data Structures and Algorithms;Machine Learning '1466': Computer Science and Game Theory;Data Structures and Algorithms;Multiagent Systems '1467': Computer Science and Game Theory;Data Structures and Algorithms;Theoretical Economics '1468': Computer Science and Game Theory;Discrete Mathematics '1469': Computer Science and Game Theory;Discrete Mathematics;Combinatorics '1470': Computer Science and Game Theory;Discrete Mathematics;Data Structures and Algorithms '1471': Computer Science and Game Theory;Distributed, Parallel, and Cluster Computing '1472': Computer Science and Game Theory;Dynamical Systems '1473': Computer Science and Game Theory;Formal Languages and Automata Theory '1474': Computer Science and Game Theory;Formal Languages and Automata Theory;Logic in Computer Science '1475': Computer Science and Game Theory;General Economics;Economics '1476': Computer Science and Game Theory;Logic in Computer Science '1477': Computer Science and Game Theory;Machine Learning '1478': Computer Science and Game Theory;Machine Learning;Machine Learning '1479': Computer Science and Game Theory;Machine Learning;Multiagent Systems '1480': Computer Science and Game Theory;Machine Learning;Optimization and Control '1481': Computer Science and Game Theory;Machine Learning;Theoretical Economics '1482': Computer Science and Game Theory;Multiagent Systems '1483': Computer Science and Game Theory;Multiagent Systems;Optimization and Control '1484': Computer Science and Game Theory;Networking and Internet Architecture '1485': Computer Science and Game Theory;Optimization and Control '1486': Computer Science and Game Theory;Physics and Society '1487': Computer Science and Game Theory;Physics and Society;Populations and Evolution '1488': Computer Science and Game Theory;Populations and Evolution '1489': Computer Science and Game Theory;Probability '1490': Computer Science and Game Theory;Quantum Physics '1491': Computer Science and Game Theory;Social and Information Networks '1492': Computer Science and Game Theory;Social and Information Networks;Physics and Society '1493': Computer Science and Game Theory;Systems and Control '1494': Computer Science and Game Theory;Systems and Control;Optimization and Control '1495': Computer Science and Game Theory;Systems and Control;Systems and Control '1496': Computer Science and Game Theory;Systems and Control;Systems and Control;Optimization and Control '1497': Computer Science and Game Theory;Theoretical Economics '1498': Computer Vision and Pattern Recognition '1499': Computer Vision and Pattern Recognition;Algebraic Topology '1500': Computer Vision and Pattern Recognition;Applications '1501': Computer Vision and Pattern Recognition;Applications;Machine Learning '1502': Computer Vision and Pattern Recognition;Artificial Intelligence '1503': Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language '1504': Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language;Machine Learning '1505': Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language;Machine Learning;Multimedia '1506': Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language;Multimedia '1507': Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language;Robotics '1508': Computer Vision and Pattern Recognition;Artificial Intelligence;Computers and Society '1509': Computer Vision and Pattern Recognition;Artificial Intelligence;Computers and Society;Machine Learning '1510': Computer Vision and Pattern Recognition;Artificial Intelligence;Cryptography and Security '1511': Computer Vision and Pattern Recognition;Artificial Intelligence;Cryptography and Security;Machine Learning '1512': Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics '1513': Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics;Machine Learning '1514': Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics;Machine Learning;Robotics '1515': Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics;Robotics '1516': Computer Vision and Pattern Recognition;Artificial Intelligence;Human-Computer Interaction '1517': Computer Vision and Pattern Recognition;Artificial Intelligence;Human-Computer Interaction;Machine Learning '1518': Computer Vision and Pattern Recognition;Artificial Intelligence;Image and Video Processing '1519': Computer Vision and Pattern Recognition;Artificial Intelligence;Information Retrieval '1520': Computer Vision and Pattern Recognition;Artificial Intelligence;Information Retrieval;Machine Learning '1521': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning '1522': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Image and Video Processing '1523': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Image and Video Processing;Signal Processing '1524': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Machine Learning '1525': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Multimedia '1526': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '1527': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing;Machine Learning '1528': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Neurons and Cognition '1529': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics '1530': Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics;Machine Learning '1531': Computer Vision and Pattern Recognition;Artificial Intelligence;Multimedia '1532': Computer Vision and Pattern Recognition;Artificial Intelligence;Neural and Evolutionary Computing '1533': Computer Vision and Pattern Recognition;Artificial Intelligence;Neurons and Cognition '1534': Computer Vision and Pattern Recognition;Artificial Intelligence;Robotics '1535': Computer Vision and Pattern Recognition;Artificial Intelligence;Signal Processing '1536': Computer Vision and Pattern Recognition;Audio and Speech Processing '1537': Computer Vision and Pattern Recognition;Computation and Language '1538': Computer Vision and Pattern Recognition;Computation and Language;Graphics;Machine Learning '1539': Computer Vision and Pattern Recognition;Computation and Language;Information Retrieval '1540': Computer Vision and Pattern Recognition;Computation and Language;Machine Learning '1541': Computer Vision and Pattern Recognition;Computation and Language;Machine Learning;Image and Video Processing '1542': Computer Vision and Pattern Recognition;Computation and Language;Machine Learning;Machine Learning '1543': Computer Vision and Pattern Recognition;Computation and Language;Machine Learning;Multimedia '1544': Computer Vision and Pattern Recognition;Computation and Language;Multimedia '1545': Computer Vision and Pattern Recognition;Computation and Language;Robotics '1546': Computer Vision and Pattern Recognition;Computation and Language;Sound;Audio and Speech Processing '1547': Computer Vision and Pattern Recognition;Computational Engineering, Finance, and Science '1548': Computer Vision and Pattern Recognition;Computational Geometry '1549': Computer Vision and Pattern Recognition;Computational Geometry;Graphics '1550': Computer Vision and Pattern Recognition;Computational Geometry;Machine Learning '1551': Computer Vision and Pattern Recognition;Computer Science and Game Theory '1552': Computer Vision and Pattern Recognition;Computers and Society '1553': Computer Vision and Pattern Recognition;Computers and Society;Machine Learning '1554': Computer Vision and Pattern Recognition;Cryptography and Security '1555': Computer Vision and Pattern Recognition;Cryptography and Security;Image and Video Processing '1556': Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning '1557': Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning;Image and Video Processing '1558': Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning;Machine Learning '1559': Computer Vision and Pattern Recognition;Data Analysis, Statistics and Probability '1560': Computer Vision and Pattern Recognition;Data Structures and Algorithms '1561': Computer Vision and Pattern Recognition;Databases '1562': Computer Vision and Pattern Recognition;Differential Geometry '1563': Computer Vision and Pattern Recognition;Digital Libraries '1564': Computer Vision and Pattern Recognition;Discrete Mathematics '1565': Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing '1566': Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing;Machine Learning '1567': Computer Vision and Pattern Recognition;Geophysics '1568': Computer Vision and Pattern Recognition;Graphics '1569': Computer Vision and Pattern Recognition;Graphics;Human-Computer Interaction '1570': Computer Vision and Pattern Recognition;Graphics;Image and Video Processing '1571': Computer Vision and Pattern Recognition;Graphics;Machine Learning '1572': Computer Vision and Pattern Recognition;Graphics;Machine Learning;Image and Video Processing '1573': Computer Vision and Pattern Recognition;Graphics;Machine Learning;Machine Learning '1574': Computer Vision and Pattern Recognition;Graphics;Machine Learning;Robotics '1575': Computer Vision and Pattern Recognition;Graphics;Multimedia '1576': Computer Vision and Pattern Recognition;Graphics;Robotics '1577': Computer Vision and Pattern Recognition;Hardware Architecture '1578': Computer Vision and Pattern Recognition;Hardware Architecture;Machine Learning '1579': Computer Vision and Pattern Recognition;Human-Computer Interaction '1580': Computer Vision and Pattern Recognition;Human-Computer Interaction;Image and Video Processing '1581': Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning '1582': Computer Vision and Pattern Recognition;Human-Computer Interaction;Multimedia '1583': Computer Vision and Pattern Recognition;Human-Computer Interaction;Robotics '1584': Computer Vision and Pattern Recognition;Image and Video Processing '1585': Computer Vision and Pattern Recognition;Image and Video Processing;Machine Learning '1586': Computer Vision and Pattern Recognition;Image and Video Processing;Quantitative Methods '1587': Computer Vision and Pattern Recognition;Image and Video Processing;Signal Processing '1588': Computer Vision and Pattern Recognition;Information Retrieval '1589': Computer Vision and Pattern Recognition;Information Retrieval;Machine Learning '1590': Computer Vision and Pattern Recognition;Information Retrieval;Machine Learning;Multimedia '1591': Computer Vision and Pattern Recognition;Information Retrieval;Multimedia '1592': Computer Vision and Pattern Recognition;Instrumentation and Methods for Astrophysics '1593': Computer Vision and Pattern Recognition;Machine Learning '1594': Computer Vision and Pattern Recognition;Machine Learning;Applications '1595': Computer Vision and Pattern Recognition;Machine Learning;Audio and Speech Processing '1596': Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing '1597': Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Machine Learning '1598': Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Neurons and Cognition '1599': Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Quantitative Methods '1600': Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Signal Processing '1601': Computer Vision and Pattern Recognition;Machine Learning;Machine Learning '1602': Computer Vision and Pattern Recognition;Machine Learning;Multimedia '1603': Computer Vision and Pattern Recognition;Machine Learning;Multimedia;Image and Video Processing '1604': Computer Vision and Pattern Recognition;Machine Learning;Multimedia;Sound;Audio and Speech Processing '1605': Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing '1606': Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing;Image and Video Processing '1607': Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing;Machine Learning '1608': Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing;Robotics '1609': Computer Vision and Pattern Recognition;Machine Learning;Neurons and Cognition '1610': Computer Vision and Pattern Recognition;Machine Learning;Numerical Analysis;Numerical Analysis '1611': Computer Vision and Pattern Recognition;Machine Learning;Optimization and Control '1612': Computer Vision and Pattern Recognition;Machine Learning;Quantitative Methods '1613': Computer Vision and Pattern Recognition;Machine Learning;Robotics '1614': Computer Vision and Pattern Recognition;Machine Learning;Robotics;Image and Video Processing '1615': Computer Vision and Pattern Recognition;Machine Learning;Robotics;Machine Learning '1616': Computer Vision and Pattern Recognition;Machine Learning;Signal Processing '1617': Computer Vision and Pattern Recognition;Machine Learning;Sound;Audio and Speech Processing '1618': Computer Vision and Pattern Recognition;Materials Science '1619': Computer Vision and Pattern Recognition;Medical Physics '1620': Computer Vision and Pattern Recognition;Methodology '1621': Computer Vision and Pattern Recognition;Multiagent Systems '1622': Computer Vision and Pattern Recognition;Multiagent Systems;Robotics '1623': Computer Vision and Pattern Recognition;Multimedia '1624': Computer Vision and Pattern Recognition;Multimedia;Image and Video Processing '1625': Computer Vision and Pattern Recognition;Multimedia;Sound;Audio and Speech Processing '1626': Computer Vision and Pattern Recognition;Networking and Internet Architecture '1627': Computer Vision and Pattern Recognition;Neural and Evolutionary Computing '1628': Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Image and Video Processing '1629': Computer Vision and Pattern Recognition;Neurons and Cognition '1630': Computer Vision and Pattern Recognition;Numerical Analysis '1631': Computer Vision and Pattern Recognition;Numerical Analysis;Numerical Analysis '1632': Computer Vision and Pattern Recognition;Optics '1633': Computer Vision and Pattern Recognition;Optimization and Control '1634': Computer Vision and Pattern Recognition;Performance '1635': Computer Vision and Pattern Recognition;Quantitative Methods '1636': Computer Vision and Pattern Recognition;Robotics '1637': Computer Vision and Pattern Recognition;Robotics;Image and Video Processing '1638': Computer Vision and Pattern Recognition;Signal Processing '1639': Computer Vision and Pattern Recognition;Social and Information Networks '1640': Computer Vision and Pattern Recognition;Software Engineering '1641': Computer Vision and Pattern Recognition;Sound '1642': Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing '1643': Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing;Image and Video Processing '1644': Computer Vision and Pattern Recognition;Systems and Control;Systems and Control '1645': Computer Vision and Pattern Recognition;Tissues and Organs '1646': Computers and Society '1647': Computers and Society;Applications '1648': Computers and Society;Artificial Intelligence '1649': Computers and Society;Artificial Intelligence;Computation and Language '1650': Computers and Society;Artificial Intelligence;Computation and Language;Machine Learning '1651': Computers and Society;Artificial Intelligence;Human-Computer Interaction '1652': Computers and Society;Artificial Intelligence;Human-Computer Interaction;Machine Learning '1653': Computers and Society;Artificial Intelligence;Machine Learning '1654': Computers and Society;Artificial Intelligence;Machine Learning;Machine Learning '1655': Computers and Society;Artificial Intelligence;Social and Information Networks '1656': Computers and Society;Computation and Language '1657': Computers and Society;Computation and Language;Machine Learning '1658': Computers and Society;Computation and Language;Social and Information Networks '1659': Computers and Society;Computer Science and Game Theory '1660': Computers and Society;Computer Vision and Pattern Recognition '1661': Computers and Society;Computer Vision and Pattern Recognition;Machine Learning '1662': Computers and Society;Cryptography and Security '1663': Computers and Society;Cryptography and Security;Human-Computer Interaction '1664': Computers and Society;Cryptography and Security;Machine Learning '1665': Computers and Society;Cryptography and Security;Networking and Internet Architecture '1666': Computers and Society;Cryptography and Security;Social and Information Networks '1667': Computers and Society;Databases '1668': Computers and Society;Digital Libraries '1669': Computers and Society;Distributed, Parallel, and Cluster Computing '1670': Computers and Society;General Economics;Economics '1671': Computers and Society;General Literature '1672': Computers and Society;Human-Computer Interaction '1673': Computers and Society;Human-Computer Interaction;Information Retrieval '1674': Computers and Society;Human-Computer Interaction;Machine Learning '1675': Computers and Society;Human-Computer Interaction;Social and Information Networks '1676': Computers and Society;Information Retrieval '1677': Computers and Society;Information Retrieval;Machine Learning '1678': Computers and Society;Logic in Computer Science '1679': Computers and Society;Machine Learning '1680': Computers and Society;Machine Learning;Applications '1681': Computers and Society;Machine Learning;Machine Learning '1682': Computers and Society;Machine Learning;Social and Information Networks '1683': Computers and Society;Multiagent Systems '1684': Computers and Society;Multimedia '1685': Computers and Society;Networking and Internet Architecture '1686': Computers and Society;Optimization and Control '1687': Computers and Society;Physics Education '1688': Computers and Society;Physics and Society '1689': Computers and Society;Programming Languages '1690': Computers and Society;Robotics '1691': Computers and Society;Signal Processing '1692': Computers and Society;Social and Information Networks '1693': Computers and Society;Social and Information Networks;Applications '1694': Computers and Society;Social and Information Networks;Physics and Society '1695': Computers and Society;Software Engineering '1696': Computers and Society;Systems and Control;Systems and Control '1697': Condensed Matter '1698': Condensed Matter;Adaptation and Self-Organizing Systems '1699': Condensed Matter;Adaptation, Noise, and Self-Organizing Systems;Adaptation and Self-Organizing Systems '1700': Condensed Matter;Atomic Physics '1701': Condensed Matter;Atomic Physics;Quantum Physics '1702': Condensed Matter;Atomic and Molecular Clusters '1703': Condensed Matter;Chaotic Dynamics '1704': Condensed Matter;Chaotic Dynamics;Chaotic Dynamics '1705': Condensed Matter;Chaotic Dynamics;Chaotic Dynamics;Quantum Physics '1706': Condensed Matter;Chemical Physics '1707': Condensed Matter;Chemical Physics;Materials Science '1708': Condensed Matter;General Relativity and Quantum Cosmology '1709': Condensed Matter;High Energy Physics - Lattice '1710': Condensed Matter;High Energy Physics - Lattice;High Energy Physics - Theory '1711': Condensed Matter;High Energy Physics - Phenomenology '1712': Condensed Matter;High Energy Physics - Phenomenology;High Energy Physics - Theory '1713': Condensed Matter;High Energy Physics - Phenomenology;Nuclear Theory '1714': Condensed Matter;High Energy Physics - Theory '1715': Condensed Matter;High Energy Physics - Theory;Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '1716': Condensed Matter;High Energy Physics - Theory;Quantum Physics '1717': Condensed Matter;Materials Science '1718': Condensed Matter;Nuclear Theory '1719': Condensed Matter;Optics '1720': Condensed Matter;Pattern Formation and Solitons '1721': Condensed Matter;Pattern Formation and Solitons;Pattern Formation and Solitons '1722': Condensed Matter;Populations and Evolution '1723': Condensed Matter;Quantum Physics '1724': Condensed Matter;Superconductivity '1725': Cosmology and Nongalactic Astrophysics '1726': Cosmology and Nongalactic Astrophysics;Applications '1727': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '1728': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology '1729': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '1730': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '1731': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology;High Energy Physics - Theory '1732': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '1733': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '1734': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '1735': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Physics - Phenomenology '1736': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Physics - Phenomenology;High Energy Physics - Theory '1737': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Physics - Theory '1738': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '1739': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '1740': Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;Statistical Mechanics '1741': Cosmology and Nongalactic Astrophysics;Atomic Physics '1742': Cosmology and Nongalactic Astrophysics;Computational Physics '1743': Cosmology and Nongalactic Astrophysics;Data Analysis, Statistics and Probability '1744': Cosmology and Nongalactic Astrophysics;Earth and Planetary Astrophysics '1745': Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '1746': Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '1747': Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '1748': Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '1749': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '1750': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '1751': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '1752': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '1753': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Theory '1754': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '1755': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;High Energy Physics - Theory '1756': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '1757': Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '1758': Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment '1759': Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '1760': Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Instrumentation and Detectors '1761': Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;Instrumentation and Detectors '1762': Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '1763': Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory '1764': Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology;Nuclear Theory '1765': Cosmology and Nongalactic Astrophysics;High Energy Physics - Theory '1766': Cosmology and Nongalactic Astrophysics;Instrumentation and Detectors '1767': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics '1768': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Applications '1769': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Data Analysis, Statistics and Probability '1770': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology '1771': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment '1772': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment;Instrumentation and Detectors '1773': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;High Energy Physics - Phenomenology '1774': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '1775': Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;Machine Learning '1776': Cosmology and Nongalactic Astrophysics;Machine Learning '1777': Cosmology and Nongalactic Astrophysics;Nuclear Theory '1778': Cosmology and Nongalactic Astrophysics;Plasma Physics '1779': Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '1780': Cosmology and Nongalactic Astrophysics;Space Physics '1781': Cosmology and Nongalactic Astrophysics;Statistical Mechanics '1782': Cryptography and Security '1783': Cryptography and Security;Algebraic Geometry '1784': Cryptography and Security;Applications '1785': Cryptography and Security;Artificial Intelligence '1786': Cryptography and Security;Artificial Intelligence;Computation and Language '1787': Cryptography and Security;Artificial Intelligence;Computation and Language;Machine Learning '1788': Cryptography and Security;Artificial Intelligence;Computer Vision and Pattern Recognition '1789': Cryptography and Security;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '1790': Cryptography and Security;Artificial Intelligence;Computers and Society '1791': Cryptography and Security;Artificial Intelligence;Distributed, Parallel, and Cluster Computing '1792': Cryptography and Security;Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Machine Learning '1793': Cryptography and Security;Artificial Intelligence;Machine Learning '1794': Cryptography and Security;Artificial Intelligence;Machine Learning;Machine Learning '1795': Cryptography and Security;Artificial Intelligence;Machine Learning;Networking and Internet Architecture '1796': Cryptography and Security;Artificial Intelligence;Networking and Internet Architecture '1797': Cryptography and Security;Artificial Intelligence;Software Engineering '1798': Cryptography and Security;Chaotic Dynamics '1799': Cryptography and Security;Combinatorics '1800': Cryptography and Security;Computation and Language '1801': Cryptography and Security;Computation and Language;Machine Learning '1802': Cryptography and Security;Computational Complexity '1803': Cryptography and Security;Computational Complexity;Quantum Physics '1804': Cryptography and Security;Computational Engineering, Finance, and Science '1805': Cryptography and Security;Computer Science and Game Theory '1806': Cryptography and Security;Computer Vision and Pattern Recognition '1807': Cryptography and Security;Computer Vision and Pattern Recognition;Image and Video Processing '1808': Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning '1809': Cryptography and Security;Computers and Society '1810': Cryptography and Security;Computers and Society;Distributed, Parallel, and Cluster Computing '1811': Cryptography and Security;Computers and Society;Human-Computer Interaction '1812': Cryptography and Security;Computers and Society;Machine Learning '1813': Cryptography and Security;Computers and Society;Networking and Internet Architecture '1814': Cryptography and Security;Computers and Society;Social and Information Networks '1815': Cryptography and Security;Computers and Society;Software Engineering '1816': Cryptography and Security;Data Structures and Algorithms '1817': Cryptography and Security;Data Structures and Algorithms;Machine Learning '1818': Cryptography and Security;Data Structures and Algorithms;Machine Learning;Machine Learning '1819': Cryptography and Security;Databases '1820': Cryptography and Security;Databases;Machine Learning '1821': Cryptography and Security;Discrete Mathematics '1822': Cryptography and Security;Distributed, Parallel, and Cluster Computing '1823': Cryptography and Security;Distributed, Parallel, and Cluster Computing;Computer Science and Game Theory '1824': Cryptography and Security;Distributed, Parallel, and Cluster Computing;Machine Learning '1825': Cryptography and Security;Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture '1826': Cryptography and Security;Emerging Technologies '1827': Cryptography and Security;Formal Languages and Automata Theory '1828': Cryptography and Security;Group Theory '1829': Cryptography and Security;Hardware Architecture '1830': Cryptography and Security;Hardware Architecture;Machine Learning '1831': Cryptography and Security;Human-Computer Interaction '1832': Cryptography and Security;Information Retrieval '1833': Cryptography and Security;Logic in Computer Science '1834': Cryptography and Security;Logic in Computer Science;Programming Languages '1835': Cryptography and Security;Machine Learning '1836': Cryptography and Security;Machine Learning;Machine Learning '1837': Cryptography and Security;Machine Learning;Multimedia '1838': Cryptography and Security;Machine Learning;Networking and Internet Architecture '1839': Cryptography and Security;Machine Learning;Neural and Evolutionary Computing '1840': Cryptography and Security;Machine Learning;Neural and Evolutionary Computing;Machine Learning '1841': Cryptography and Security;Machine Learning;Signal Processing '1842': Cryptography and Security;Machine Learning;Social and Information Networks '1843': Cryptography and Security;Machine Learning;Software Engineering '1844': Cryptography and Security;Machine Learning;Sound;Audio and Speech Processing '1845': Cryptography and Security;Machine Learning;Systems and Control;Systems and Control '1846': Cryptography and Security;Methodology '1847': Cryptography and Security;Multiagent Systems '1848': Cryptography and Security;Multimedia '1849': Cryptography and Security;Networking and Internet Architecture '1850': Cryptography and Security;Networking and Internet Architecture;Systems and Control;Systems and Control '1851': Cryptography and Security;Neural and Evolutionary Computing '1852': Cryptography and Security;Number Theory '1853': Cryptography and Security;Operating Systems '1854': Cryptography and Security;Optimization and Control '1855': Cryptography and Security;Performance '1856': Cryptography and Security;Probability '1857': Cryptography and Security;Programming Languages '1858': Cryptography and Security;Programming Languages;Software Engineering '1859': Cryptography and Security;Quantum Physics '1860': Cryptography and Security;Rings and Algebras '1861': Cryptography and Security;Robotics '1862': Cryptography and Security;Signal Processing '1863': Cryptography and Security;Social and Information Networks '1864': Cryptography and Security;Software Engineering '1865': Cryptography and Security;Sound;Audio and Speech Processing '1866': Cryptography and Security;Symbolic Computation '1867': Cryptography and Security;Systems and Control '1868': Cryptography and Security;Systems and Control;Systems and Control '1869': Data Analysis, Statistics and Probability '1870': Data Analysis, Statistics and Probability;Adaptation and Self-Organizing Systems '1871': Data Analysis, Statistics and Probability;Applications '1872': Data Analysis, Statistics and Probability;Astrophysics '1873': Data Analysis, Statistics and Probability;Atmospheric and Oceanic Physics '1874': Data Analysis, Statistics and Probability;Atmospheric and Oceanic Physics;Geophysics '1875': Data Analysis, Statistics and Probability;Biological Physics '1876': Data Analysis, Statistics and Probability;Chaotic Dynamics '1877': Data Analysis, Statistics and Probability;Chemical Physics '1878': Data Analysis, Statistics and Probability;Computational Physics '1879': Data Analysis, Statistics and Probability;Computer Vision and Pattern Recognition '1880': Data Analysis, Statistics and Probability;Disordered Systems and Neural Networks '1881': Data Analysis, Statistics and Probability;Fluid Dynamics '1882': Data Analysis, Statistics and Probability;General Physics '1883': Data Analysis, Statistics and Probability;Geophysics '1884': Data Analysis, Statistics and Probability;High Energy Physics - Experiment '1885': Data Analysis, Statistics and Probability;High Energy Physics - Experiment;High Energy Physics - Phenomenology '1886': Data Analysis, Statistics and Probability;High Energy Physics - Experiment;Nuclear Experiment '1887': Data Analysis, Statistics and Probability;High Energy Physics - Phenomenology '1888': Data Analysis, Statistics and Probability;Instrumentation and Detectors '1889': Data Analysis, Statistics and Probability;Instrumentation and Methods for Astrophysics '1890': Data Analysis, Statistics and Probability;Machine Learning '1891': Data Analysis, Statistics and Probability;Machine Learning;High Energy Physics - Experiment '1892': Data Analysis, Statistics and Probability;Materials Science '1893': Data Analysis, Statistics and Probability;Medical Physics '1894': Data Analysis, Statistics and Probability;Methodology '1895': Data Analysis, Statistics and Probability;Neurons and Cognition '1896': Data Analysis, Statistics and Probability;Nuclear Experiment '1897': Data Analysis, Statistics and Probability;Optics '1898': Data Analysis, Statistics and Probability;Physics and Society '1899': Data Analysis, Statistics and Probability;Physics and Society;Statistical Finance '1900': Data Analysis, Statistics and Probability;Plasma Physics '1901': Data Analysis, Statistics and Probability;Probability '1902': Data Analysis, Statistics and Probability;Quantum Physics '1903': Data Analysis, Statistics and Probability;Social and Information Networks;Physics and Society '1904': Data Analysis, Statistics and Probability;Space Physics '1905': Data Analysis, Statistics and Probability;Statistical Finance '1906': Data Analysis, Statistics and Probability;Statistical Mechanics '1907': Data Analysis, Statistics and Probability;Statistical Mechanics;Physics and Society '1908': Data Structures and Algorithms '1909': Data Structures and Algorithms;Artificial Intelligence '1910': Data Structures and Algorithms;Artificial Intelligence;Discrete Mathematics '1911': Data Structures and Algorithms;Artificial Intelligence;Machine Learning '1912': Data Structures and Algorithms;Combinatorics '1913': Data Structures and Algorithms;Combinatorics;Optimization and Control '1914': Data Structures and Algorithms;Combinatorics;Probability '1915': Data Structures and Algorithms;Computation '1916': Data Structures and Algorithms;Computational Complexity '1917': Data Structures and Algorithms;Computational Complexity;Combinatorics '1918': Data Structures and Algorithms;Computational Complexity;Computational Geometry '1919': Data Structures and Algorithms;Computational Complexity;Discrete Mathematics '1920': Data Structures and Algorithms;Computational Complexity;Discrete Mathematics;Combinatorics '1921': Data Structures and Algorithms;Computational Complexity;Distributed, Parallel, and Cluster Computing '1922': Data Structures and Algorithms;Computational Complexity;Logic in Computer Science '1923': Data Structures and Algorithms;Computational Complexity;Machine Learning '1924': Data Structures and Algorithms;Computational Complexity;Optimization and Control '1925': Data Structures and Algorithms;Computational Engineering, Finance, and Science '1926': Data Structures and Algorithms;Computational Geometry '1927': Data Structures and Algorithms;Computational Geometry;Combinatorics '1928': Data Structures and Algorithms;Computational Geometry;Discrete Mathematics '1929': Data Structures and Algorithms;Computational Geometry;Machine Learning '1930': Data Structures and Algorithms;Computer Science and Game Theory '1931': Data Structures and Algorithms;Computer Science and Game Theory;Machine Learning '1932': Data Structures and Algorithms;Computer Vision and Pattern Recognition '1933': Data Structures and Algorithms;Cryptography and Security '1934': Data Structures and Algorithms;Cryptography and Security;Machine Learning '1935': Data Structures and Algorithms;Databases '1936': Data Structures and Algorithms;Databases;Distributed, Parallel, and Cluster Computing '1937': Data Structures and Algorithms;Databases;Machine Learning '1938': Data Structures and Algorithms;Discrete Mathematics '1939': Data Structures and Algorithms;Discrete Mathematics;Combinatorics '1940': Data Structures and Algorithms;Discrete Mathematics;Combinatorics;Optimization and Control '1941': Data Structures and Algorithms;Discrete Mathematics;Combinatorics;Probability '1942': Data Structures and Algorithms;Discrete Mathematics;Computer Science and Game Theory '1943': Data Structures and Algorithms;Discrete Mathematics;Logic in Computer Science '1944': Data Structures and Algorithms;Discrete Mathematics;Machine Learning '1945': Data Structures and Algorithms;Discrete Mathematics;Optimization and Control '1946': Data Structures and Algorithms;Discrete Mathematics;Probability '1947': Data Structures and Algorithms;Discrete Mathematics;Social and Information Networks '1948': Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing '1949': Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing;Combinatorics '1950': Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing;Discrete Mathematics '1951': Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing;Machine Learning '1952': Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture '1953': Data Structures and Algorithms;Formal Languages and Automata Theory '1954': Data Structures and Algorithms;Genomics '1955': Data Structures and Algorithms;Information Retrieval '1956': Data Structures and Algorithms;Logic in Computer Science '1957': Data Structures and Algorithms;Machine Learning '1958': Data Structures and Algorithms;Machine Learning;Machine Learning '1959': Data Structures and Algorithms;Machine Learning;Optimization and Control '1960': Data Structures and Algorithms;Mathematical Software '1961': Data Structures and Algorithms;Metric Geometry '1962': Data Structures and Algorithms;Networking and Internet Architecture '1963': Data Structures and Algorithms;Neural and Evolutionary Computing '1964': Data Structures and Algorithms;Numerical Analysis '1965': Data Structures and Algorithms;Numerical Analysis;Numerical Analysis '1966': Data Structures and Algorithms;Optimization and Control '1967': Data Structures and Algorithms;Performance '1968': Data Structures and Algorithms;Populations and Evolution '1969': Data Structures and Algorithms;Probability '1970': Data Structures and Algorithms;Programming Languages '1971': Data Structures and Algorithms;Quantitative Methods '1972': Data Structures and Algorithms;Quantum Physics '1973': Data Structures and Algorithms;Robotics '1974': Data Structures and Algorithms;Social and Information Networks '1975': Data Structures and Algorithms;Social and Information Networks;Physics and Society '1976': Data Structures and Algorithms;Symbolic Computation '1977': Databases '1978': Databases;Artificial Intelligence '1979': Databases;Artificial Intelligence;Information Retrieval '1980': Databases;Artificial Intelligence;Logic in Computer Science '1981': Databases;Artificial Intelligence;Machine Learning '1982': Databases;Computation and Language '1983': Databases;Computation and Language;Machine Learning '1984': Databases;Computational Complexity '1985': Databases;Computational Engineering, Finance, and Science '1986': Databases;Computer Vision and Pattern Recognition '1987': Databases;Computers and Society '1988': Databases;Cryptography and Security '1989': Databases;Cryptography and Security;Distributed, Parallel, and Cluster Computing '1990': Databases;Data Structures and Algorithms '1991': Databases;Data Structures and Algorithms;Machine Learning '1992': Databases;Digital Libraries '1993': Databases;Distributed, Parallel, and Cluster Computing '1994': Databases;Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms '1995': Databases;Distributed, Parallel, and Cluster Computing;Machine Learning '1996': Databases;Distributed, Parallel, and Cluster Computing;Performance '1997': Databases;Formal Languages and Automata Theory '1998': Databases;Hardware Architecture '1999': Databases;Human-Computer Interaction '2000': Databases;Information Retrieval '2001': Databases;Information Retrieval;Machine Learning '2002': Databases;Logic in Computer Science '2003': Databases;Machine Learning '2004': Databases;Machine Learning;Machine Learning '2005': Databases;Networking and Internet Architecture '2006': Databases;Performance '2007': Databases;Programming Languages '2008': Databases;Social and Information Networks '2009': Databases;Software Engineering '2010': Differential Geometry '2011': Differential Geometry;Algebraic Geometry '2012': Differential Geometry;Algebraic Geometry;Algebraic Geometry;Differential Geometry '2013': Differential Geometry;Algebraic Geometry;Algebraic Topology '2014': Differential Geometry;Algebraic Geometry;Analysis of PDEs '2015': Differential Geometry;Algebraic Geometry;Complex Variables '2016': Differential Geometry;Algebraic Geometry;Geometric Topology '2017': Differential Geometry;Algebraic Geometry;Metric Geometry '2018': Differential Geometry;Algebraic Geometry;Symplectic Geometry '2019': Differential Geometry;Algebraic Topology '2020': Differential Geometry;Algebraic Topology;Geometric Topology '2021': Differential Geometry;Algebraic Topology;Symplectic Geometry '2022': Differential Geometry;Analysis of PDEs '2023': Differential Geometry;Analysis of PDEs;Classical Analysis and ODEs '2024': Differential Geometry;Analysis of PDEs;Complex Variables '2025': Differential Geometry;Analysis of PDEs;Dynamical Systems '2026': Differential Geometry;Analysis of PDEs;Functional Analysis '2027': Differential Geometry;Analysis of PDEs;Geometric Topology '2028': Differential Geometry;Analysis of PDEs;Metric Geometry '2029': Differential Geometry;Analysis of PDEs;Optimization and Control '2030': Differential Geometry;Analysis of PDEs;Probability '2031': Differential Geometry;Analysis of PDEs;Spectral Theory '2032': Differential Geometry;Analysis of PDEs;Symplectic Geometry '2033': Differential Geometry;Category Theory '2034': Differential Geometry;Classical Analysis and ODEs '2035': Differential Geometry;Combinatorics '2036': Differential Geometry;Complex Variables '2037': Differential Geometry;Complex Variables;Geometric Topology '2038': Differential Geometry;Complex Variables;Metric Geometry '2039': Differential Geometry;Complex Variables;Symplectic Geometry '2040': Differential Geometry;Computational Geometry '2041': Differential Geometry;Differential Geometry '2042': Differential Geometry;Dynamical Systems '2043': Differential Geometry;Dynamical Systems;Geometric Topology '2044': Differential Geometry;Dynamical Systems;Symplectic Geometry '2045': Differential Geometry;Exactly Solvable and Integrable Systems '2046': Differential Geometry;Functional Analysis '2047': Differential Geometry;Functional Analysis;Metric Geometry '2048': Differential Geometry;General Relativity and Quantum Cosmology '2049': Differential Geometry;General Relativity and Quantum Cosmology;Analysis of PDEs '2050': Differential Geometry;General Relativity and Quantum Cosmology;High Energy Physics - Theory '2051': Differential Geometry;General Topology '2052': Differential Geometry;Geometric Topology '2053': Differential Geometry;Geometric Topology;Metric Geometry '2054': Differential Geometry;Geometric Topology;Spectral Theory '2055': Differential Geometry;Geometric Topology;Symplectic Geometry '2056': Differential Geometry;Group Theory '2057': Differential Geometry;Group Theory;Geometric Topology '2058': Differential Geometry;Group Theory;Metric Geometry '2059': Differential Geometry;High Energy Physics - Theory '2060': Differential Geometry;High Energy Physics - Theory;Algebraic Geometry '2061': Differential Geometry;High Energy Physics - Theory;Algebraic Geometry;Symplectic Geometry '2062': Differential Geometry;High Energy Physics - Theory;Differential Geometry '2063': Differential Geometry;High Energy Physics - Theory;Symplectic Geometry '2064': Differential Geometry;History and Overview '2065': Differential Geometry;K-Theory and Homology '2066': Differential Geometry;K-Theory and Homology;Operator Algebras '2067': Differential Geometry;Metric Geometry '2068': Differential Geometry;Metric Geometry;Optimization and Control '2069': Differential Geometry;Number Theory '2070': Differential Geometry;Numerical Analysis '2071': Differential Geometry;Operator Algebras '2072': Differential Geometry;Optimization and Control '2073': Differential Geometry;Probability '2074': Differential Geometry;Quantum Algebra '2075': Differential Geometry;Representation Theory '2076': Differential Geometry;Representation Theory;Spectral Theory '2077': Differential Geometry;Representation Theory;Symplectic Geometry '2078': Differential Geometry;Rings and Algebras '2079': Differential Geometry;Spectral Theory '2080': Differential Geometry;Symplectic Geometry '2081': Digital Libraries '2082': Digital Libraries;Applications '2083': Digital Libraries;Artificial Intelligence '2084': Digital Libraries;Computation and Language '2085': Digital Libraries;Computation and Language;Information Retrieval '2086': Digital Libraries;Computers and Society '2087': Digital Libraries;Computers and Society;Social and Information Networks '2088': Digital Libraries;Data Analysis, Statistics and Probability;Physics and Society '2089': Digital Libraries;Databases '2090': Digital Libraries;Human-Computer Interaction '2091': Digital Libraries;Information Retrieval '2092': Digital Libraries;Information Retrieval;Physics and Society '2093': Digital Libraries;Information Retrieval;Social and Information Networks '2094': Digital Libraries;Instrumentation and Methods for Astrophysics '2095': Digital Libraries;Machine Learning '2096': Digital Libraries;Physics and Society '2097': Digital Libraries;Physics and Society;Applications '2098': Digital Libraries;Social and Information Networks '2099': Digital Libraries;Social and Information Networks;Physics and Society '2100': Digital Libraries;Software Engineering '2101': Discrete Mathematics '2102': Discrete Mathematics;Artificial Intelligence '2103': Discrete Mathematics;Cellular Automata and Lattice Gases '2104': Discrete Mathematics;Combinatorics '2105': Discrete Mathematics;Combinatorics;Optimization and Control '2106': Discrete Mathematics;Combinatorics;Probability '2107': Discrete Mathematics;Computational Complexity '2108': Discrete Mathematics;Computational Complexity;Combinatorics '2109': Discrete Mathematics;Computational Complexity;Data Structures and Algorithms '2110': Discrete Mathematics;Computational Complexity;Data Structures and Algorithms;Combinatorics '2111': Discrete Mathematics;Computational Geometry '2112': Discrete Mathematics;Computational Geometry;Combinatorics '2113': Discrete Mathematics;Computer Science and Game Theory '2114': Discrete Mathematics;Cryptography and Security '2115': Discrete Mathematics;Data Structures and Algorithms '2116': Discrete Mathematics;Data Structures and Algorithms;Combinatorics '2117': Discrete Mathematics;Data Structures and Algorithms;Combinatorics;Probability '2118': Discrete Mathematics;Distributed, Parallel, and Cluster Computing '2119': Discrete Mathematics;Dynamical Systems '2120': Discrete Mathematics;Formal Languages and Automata Theory '2121': Discrete Mathematics;Formal Languages and Automata Theory;Combinatorics '2122': Discrete Mathematics;Logic in Computer Science '2123': Discrete Mathematics;Machine Learning '2124': Discrete Mathematics;Networking and Internet Architecture '2125': Discrete Mathematics;Number Theory '2126': Discrete Mathematics;Optimization and Control '2127': Discrete Mathematics;Probability '2128': Discrete Mathematics;Social and Information Networks '2129': Disordered Systems and Neural Networks '2130': Disordered Systems and Neural Networks;Adaptation and Self-Organizing Systems '2131': Disordered Systems and Neural Networks;Adaptation and Self-Organizing Systems;Physics and Society '2132': Disordered Systems and Neural Networks;Biological Physics;Neurons and Cognition '2133': Disordered Systems and Neural Networks;Chaotic Dynamics '2134': Disordered Systems and Neural Networks;Chaotic Dynamics;Neurons and Cognition '2135': Disordered Systems and Neural Networks;Chaotic Dynamics;Quantum Physics '2136': Disordered Systems and Neural Networks;Chemical Physics '2137': Disordered Systems and Neural Networks;Computational Physics '2138': Disordered Systems and Neural Networks;Data Analysis, Statistics and Probability '2139': Disordered Systems and Neural Networks;High Energy Physics - Lattice '2140': Disordered Systems and Neural Networks;High Energy Physics - Theory '2141': Disordered Systems and Neural Networks;Machine Learning '2142': Disordered Systems and Neural Networks;Machine Learning;Machine Learning '2143': Disordered Systems and Neural Networks;Materials Science '2144': Disordered Systems and Neural Networks;Materials Science;Soft Condensed Matter '2145': Disordered Systems and Neural Networks;Materials Science;Soft Condensed Matter;Statistical Mechanics '2146': Disordered Systems and Neural Networks;Materials Science;Statistical Mechanics '2147': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '2148': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Chaotic Dynamics '2149': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Materials Science '2150': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Optics '2151': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Quantum Physics '2152': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Statistical Mechanics '2153': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Statistical Mechanics;Quantum Physics '2154': Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '2155': Disordered Systems and Neural Networks;Neurons and Cognition '2156': Disordered Systems and Neural Networks;Optics '2157': Disordered Systems and Neural Networks;Optics;Quantum Physics '2158': Disordered Systems and Neural Networks;Other Condensed Matter '2159': Disordered Systems and Neural Networks;Physics and Society '2160': Disordered Systems and Neural Networks;Populations and Evolution '2161': Disordered Systems and Neural Networks;Probability '2162': Disordered Systems and Neural Networks;Quantum Gases '2163': Disordered Systems and Neural Networks;Quantum Gases;Quantum Physics '2164': Disordered Systems and Neural Networks;Quantum Gases;Statistical Mechanics '2165': Disordered Systems and Neural Networks;Quantum Gases;Statistical Mechanics;Quantum Physics '2166': Disordered Systems and Neural Networks;Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons '2167': Disordered Systems and Neural Networks;Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '2168': Disordered Systems and Neural Networks;Quantum Gases;Strongly Correlated Electrons '2169': Disordered Systems and Neural Networks;Quantum Physics '2170': Disordered Systems and Neural Networks;Social and Information Networks;Physics and Society '2171': Disordered Systems and Neural Networks;Soft Condensed Matter '2172': Disordered Systems and Neural Networks;Soft Condensed Matter;Statistical Mechanics '2173': Disordered Systems and Neural Networks;Statistical Finance '2174': Disordered Systems and Neural Networks;Statistical Mechanics '2175': Disordered Systems and Neural Networks;Statistical Mechanics;Adaptation and Self-Organizing Systems '2176': Disordered Systems and Neural Networks;Statistical Mechanics;Chaotic Dynamics '2177': Disordered Systems and Neural Networks;Statistical Mechanics;Computational Complexity '2178': Disordered Systems and Neural Networks;Statistical Mechanics;Computational Physics '2179': Disordered Systems and Neural Networks;Statistical Mechanics;High Energy Physics - Lattice '2180': Disordered Systems and Neural Networks;Statistical Mechanics;High Energy Physics - Theory '2181': Disordered Systems and Neural Networks;Statistical Mechanics;Machine Learning '2182': Disordered Systems and Neural Networks;Statistical Mechanics;Neurons and Cognition '2183': Disordered Systems and Neural Networks;Statistical Mechanics;Optics '2184': Disordered Systems and Neural Networks;Statistical Mechanics;Physics and Society '2185': Disordered Systems and Neural Networks;Statistical Mechanics;Probability '2186': Disordered Systems and Neural Networks;Statistical Mechanics;Quantum Physics '2187': Disordered Systems and Neural Networks;Statistical Mechanics;Strongly Correlated Electrons '2188': Disordered Systems and Neural Networks;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '2189': Disordered Systems and Neural Networks;Statistical Mechanics;Superconductivity '2190': Disordered Systems and Neural Networks;Strongly Correlated Electrons '2191': Disordered Systems and Neural Networks;Strongly Correlated Electrons;Quantum Physics '2192': Disordered Systems and Neural Networks;Superconductivity '2193': Disordered Systems and Neural Networks;Trading and Market Microstructure '2194': Distributed, Parallel, and Cluster Computing '2195': Distributed, Parallel, and Cluster Computing;Artificial Intelligence '2196': Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Machine Learning '2197': Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Machine Learning;Performance '2198': Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Networking and Internet Architecture '2199': Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Performance '2200': Distributed, Parallel, and Cluster Computing;Computation and Language '2201': Distributed, Parallel, and Cluster Computing;Computational Complexity '2202': Distributed, Parallel, and Cluster Computing;Computational Complexity;Data Structures and Algorithms '2203': Distributed, Parallel, and Cluster Computing;Computational Engineering, Finance, and Science '2204': Distributed, Parallel, and Cluster Computing;Computational Physics '2205': Distributed, Parallel, and Cluster Computing;Computer Science and Game Theory '2206': Distributed, Parallel, and Cluster Computing;Computer Vision and Pattern Recognition '2207': Distributed, Parallel, and Cluster Computing;Computer Vision and Pattern Recognition;Machine Learning '2208': Distributed, Parallel, and Cluster Computing;Computers and Society '2209': Distributed, Parallel, and Cluster Computing;Cryptography and Security '2210': Distributed, Parallel, and Cluster Computing;Cryptography and Security;Computers and Society '2211': Distributed, Parallel, and Cluster Computing;Cryptography and Security;Machine Learning '2212': Distributed, Parallel, and Cluster Computing;Cryptography and Security;Networking and Internet Architecture '2213': Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms '2214': Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms;Networking and Internet Architecture '2215': Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms;Performance '2216': Distributed, Parallel, and Cluster Computing;Databases '2217': Distributed, Parallel, and Cluster Computing;Databases;Data Structures and Algorithms '2218': Distributed, Parallel, and Cluster Computing;Databases;Performance '2219': Distributed, Parallel, and Cluster Computing;Discrete Mathematics '2220': Distributed, Parallel, and Cluster Computing;Discrete Mathematics;Data Structures and Algorithms '2221': Distributed, Parallel, and Cluster Computing;Emerging Technologies '2222': Distributed, Parallel, and Cluster Computing;Formal Languages and Automata Theory '2223': Distributed, Parallel, and Cluster Computing;Genomics '2224': Distributed, Parallel, and Cluster Computing;Graphics '2225': Distributed, Parallel, and Cluster Computing;Hardware Architecture '2226': Distributed, Parallel, and Cluster Computing;Hardware Architecture;Machine Learning '2227': Distributed, Parallel, and Cluster Computing;Hardware Architecture;Performance '2228': Distributed, Parallel, and Cluster Computing;High Energy Physics - Experiment '2229': Distributed, Parallel, and Cluster Computing;High Energy Physics - Lattice '2230': Distributed, Parallel, and Cluster Computing;Human-Computer Interaction '2231': Distributed, Parallel, and Cluster Computing;Information Retrieval '2232': Distributed, Parallel, and Cluster Computing;Instrumentation and Methods for Astrophysics '2233': Distributed, Parallel, and Cluster Computing;Logic in Computer Science '2234': Distributed, Parallel, and Cluster Computing;Machine Learning '2235': Distributed, Parallel, and Cluster Computing;Machine Learning;Machine Learning '2236': Distributed, Parallel, and Cluster Computing;Machine Learning;Networking and Internet Architecture '2237': Distributed, Parallel, and Cluster Computing;Machine Learning;Neural and Evolutionary Computing '2238': Distributed, Parallel, and Cluster Computing;Machine Learning;Optimization and Control '2239': Distributed, Parallel, and Cluster Computing;Machine Learning;Performance '2240': Distributed, Parallel, and Cluster Computing;Mathematical Software '2241': Distributed, Parallel, and Cluster Computing;Mathematical Software;Numerical Analysis '2242': Distributed, Parallel, and Cluster Computing;Mathematical Software;Performance '2243': Distributed, Parallel, and Cluster Computing;Multiagent Systems '2244': Distributed, Parallel, and Cluster Computing;Multimedia '2245': Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture '2246': Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture;Performance '2247': Distributed, Parallel, and Cluster Computing;Neural and Evolutionary Computing '2248': Distributed, Parallel, and Cluster Computing;Numerical Analysis '2249': Distributed, Parallel, and Cluster Computing;Numerical Analysis;Numerical Analysis '2250': Distributed, Parallel, and Cluster Computing;Operating Systems '2251': Distributed, Parallel, and Cluster Computing;Operating Systems;Performance '2252': Distributed, Parallel, and Cluster Computing;Optimization and Control '2253': Distributed, Parallel, and Cluster Computing;Performance '2254': Distributed, Parallel, and Cluster Computing;Performance;Programming Languages '2255': Distributed, Parallel, and Cluster Computing;Performance;Software Engineering '2256': Distributed, Parallel, and Cluster Computing;Probability '2257': Distributed, Parallel, and Cluster Computing;Programming Languages '2258': Distributed, Parallel, and Cluster Computing;Programming Languages;Software Engineering '2259': Distributed, Parallel, and Cluster Computing;Robotics '2260': Distributed, Parallel, and Cluster Computing;Signal Processing '2261': Distributed, Parallel, and Cluster Computing;Social and Information Networks '2262': Distributed, Parallel, and Cluster Computing;Software Engineering '2263': Distributed, Parallel, and Cluster Computing;Systems and Control '2264': Distributed, Parallel, and Cluster Computing;Systems and Control;Systems and Control '2265': Dynamical Systems '2266': Dynamical Systems;Adaptation and Self-Organizing Systems '2267': Dynamical Systems;Algebraic Geometry '2268': Dynamical Systems;Algebraic Geometry;Complex Variables '2269': Dynamical Systems;Algebraic Geometry;Geometric Topology '2270': Dynamical Systems;Algebraic Geometry;Number Theory '2271': Dynamical Systems;Algebraic Topology '2272': Dynamical Systems;Analysis of PDEs '2273': Dynamical Systems;Analysis of PDEs;Classical Analysis and ODEs '2274': Dynamical Systems;Analysis of PDEs;Functional Analysis '2275': Dynamical Systems;Analysis of PDEs;Optimization and Control '2276': Dynamical Systems;Analysis of PDEs;Probability '2277': Dynamical Systems;Atmospheric and Oceanic Physics '2278': Dynamical Systems;Category Theory '2279': Dynamical Systems;Cellular Automata and Lattice Gases '2280': Dynamical Systems;Chaotic Dynamics '2281': Dynamical Systems;Classical Analysis and ODEs '2282': Dynamical Systems;Classical Analysis and ODEs;Metric Geometry '2283': Dynamical Systems;Classical Analysis and ODEs;Number Theory '2284': Dynamical Systems;Classical Analysis and ODEs;Probability '2285': Dynamical Systems;Classical Physics '2286': Dynamical Systems;Combinatorics '2287': Dynamical Systems;Combinatorics;Group Theory '2288': Dynamical Systems;Combinatorics;Number Theory '2289': Dynamical Systems;Combinatorics;Probability '2290': Dynamical Systems;Commutative Algebra '2291': Dynamical Systems;Complex Variables '2292': Dynamical Systems;Complex Variables;Differential Geometry '2293': Dynamical Systems;Complex Variables;General Topology '2294': Dynamical Systems;Complex Variables;Geometric Topology '2295': Dynamical Systems;Complex Variables;Number Theory '2296': Dynamical Systems;Complex Variables;Probability '2297': Dynamical Systems;Computational Complexity '2298': Dynamical Systems;Data Analysis, Statistics and Probability '2299': Dynamical Systems;Differential Geometry '2300': Dynamical Systems;Differential Geometry;Geometric Topology '2301': Dynamical Systems;Differential Geometry;Symplectic Geometry '2302': Dynamical Systems;Discrete Mathematics '2303': Dynamical Systems;Earth and Planetary Astrophysics '2304': Dynamical Systems;Exactly Solvable and Integrable Systems '2305': Dynamical Systems;Fluid Dynamics '2306': Dynamical Systems;Formal Languages and Automata Theory '2307': Dynamical Systems;Functional Analysis '2308': Dynamical Systems;Functional Analysis;Group Theory '2309': Dynamical Systems;Functional Analysis;Operator Algebras '2310': Dynamical Systems;Functional Analysis;Probability '2311': Dynamical Systems;General Topology '2312': Dynamical Systems;Geometric Topology '2313': Dynamical Systems;Geometric Topology;Number Theory '2314': Dynamical Systems;Group Theory '2315': Dynamical Systems;Group Theory;Geometric Topology '2316': Dynamical Systems;Group Theory;Logic '2317': Dynamical Systems;Group Theory;Number Theory '2318': Dynamical Systems;Group Theory;Operator Algebras '2319': Dynamical Systems;Group Theory;Probability '2320': Dynamical Systems;Logic '2321': Dynamical Systems;Machine Learning '2322': Dynamical Systems;Metric Geometry '2323': Dynamical Systems;Metric Geometry;Number Theory '2324': Dynamical Systems;Molecular Networks '2325': Dynamical Systems;Neurons and Cognition '2326': Dynamical Systems;Number Theory '2327': Dynamical Systems;Number Theory;Probability '2328': Dynamical Systems;Numerical Analysis '2329': Dynamical Systems;Numerical Analysis;Numerical Analysis '2330': Dynamical Systems;Operator Algebras '2331': Dynamical Systems;Optimization and Control '2332': Dynamical Systems;Optimization and Control;Chaotic Dynamics '2333': Dynamical Systems;Pattern Formation and Solitons '2334': Dynamical Systems;Physics and Society '2335': Dynamical Systems;Physics and Society;Populations and Evolution '2336': Dynamical Systems;Populations and Evolution '2337': Dynamical Systems;Probability '2338': Dynamical Systems;Probability;Chaotic Dynamics '2339': Dynamical Systems;Quantitative Methods '2340': Dynamical Systems;Representation Theory '2341': Dynamical Systems;Rings and Algebras '2342': Dynamical Systems;Spectral Theory '2343': Dynamical Systems;Statistical Mechanics '2344': Dynamical Systems;Symplectic Geometry '2345': Dynamical Systems;Symplectic Geometry;Exactly Solvable and Integrable Systems '2346': Dynamical Systems;Systems and Control '2347': Dynamical Systems;Systems and Control;Optimization and Control '2348': Dynamical Systems;Systems and Control;Systems and Control '2349': Dynamical Systems;Systems and Control;Systems and Control;Optimization and Control '2350': Dynamical Systems;Tissues and Organs '2351': Earth and Planetary Astrophysics '2352': Earth and Planetary Astrophysics;Astrophysics of Galaxies '2353': Earth and Planetary Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '2354': Earth and Planetary Astrophysics;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '2355': Earth and Planetary Astrophysics;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '2356': Earth and Planetary Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '2357': Earth and Planetary Astrophysics;Atmospheric and Oceanic Physics '2358': Earth and Planetary Astrophysics;Atmospheric and Oceanic Physics;Fluid Dynamics '2359': Earth and Planetary Astrophysics;Atmospheric and Oceanic Physics;Geophysics '2360': Earth and Planetary Astrophysics;Atomic Physics '2361': Earth and Planetary Astrophysics;Biological Physics '2362': Earth and Planetary Astrophysics;Chaotic Dynamics '2363': Earth and Planetary Astrophysics;Chemical Physics '2364': Earth and Planetary Astrophysics;Classical Physics '2365': Earth and Planetary Astrophysics;Computational Physics '2366': Earth and Planetary Astrophysics;Cosmology and Nongalactic Astrophysics '2367': Earth and Planetary Astrophysics;Dynamical Systems '2368': Earth and Planetary Astrophysics;Dynamical Systems;Chaotic Dynamics '2369': Earth and Planetary Astrophysics;Fluid Dynamics '2370': Earth and Planetary Astrophysics;Fluid Dynamics;Geophysics '2371': Earth and Planetary Astrophysics;General Relativity and Quantum Cosmology '2372': Earth and Planetary Astrophysics;Geophysics '2373': Earth and Planetary Astrophysics;Geophysics;Space Physics '2374': Earth and Planetary Astrophysics;High Energy Astrophysical Phenomena '2375': Earth and Planetary Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '2376': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics '2377': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Applied Physics '2378': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Computational Physics '2379': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Geophysics '2380': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Machine Learning '2381': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Popular Physics '2382': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '2383': Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Space Physics '2384': Earth and Planetary Astrophysics;Machine Learning '2385': Earth and Planetary Astrophysics;Materials Science '2386': Earth and Planetary Astrophysics;Plasma Physics '2387': Earth and Planetary Astrophysics;Plasma Physics;Space Physics '2388': Earth and Planetary Astrophysics;Popular Physics '2389': Earth and Planetary Astrophysics;Soft Condensed Matter '2390': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '2391': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Atmospheric and Oceanic Physics '2392': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Chemical Physics '2393': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Fluid Dynamics '2394': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Geophysics '2395': Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Space Physics '2396': Earth and Planetary Astrophysics;Space Physics '2397': Econometrics '2398': Econometrics;Applications '2399': Econometrics;Applications;Machine Learning '2400': Econometrics;Applications;Methodology '2401': Econometrics;Computation '2402': Econometrics;General Economics;Economics '2403': Econometrics;General Finance '2404': Econometrics;Machine Learning '2405': Econometrics;Machine Learning;Machine Learning '2406': Econometrics;Methodology '2407': Econometrics;Methodology;Machine Learning '2408': Econometrics;Statistical Finance '2409': Econometrics;Theoretical Economics '2410': Economics '2411': Economics;Computer Science and Game Theory '2412': Economics;General Finance '2413': Economics;Physics and Society '2414': Emerging Technologies '2415': Emerging Technologies;Applied Physics '2416': Emerging Technologies;Artificial Intelligence '2417': Emerging Technologies;Cryptography and Security '2418': Emerging Technologies;Disordered Systems and Neural Networks '2419': Emerging Technologies;Hardware Architecture '2420': Emerging Technologies;Machine Learning '2421': Emerging Technologies;Machine Learning;Neural and Evolutionary Computing '2422': Emerging Technologies;Machine Learning;Optics '2423': Emerging Technologies;Mesoscale and Nanoscale Physics '2424': Emerging Technologies;Networking and Internet Architecture '2425': Emerging Technologies;Neural and Evolutionary Computing '2426': Emerging Technologies;Optics '2427': Emerging Technologies;Quantum Physics '2428': Emerging Technologies;Signal Processing '2429': Emerging Technologies;Systems and Control;Systems and Control '2430': Exactly Solvable and Integrable Systems '2431': Exactly Solvable and Integrable Systems;Algebraic Geometry '2432': Exactly Solvable and Integrable Systems;Analysis of PDEs '2433': Exactly Solvable and Integrable Systems;Cellular Automata and Lattice Gases '2434': Exactly Solvable and Integrable Systems;Chaotic Dynamics '2435': Exactly Solvable and Integrable Systems;Classical Analysis and ODEs '2436': Exactly Solvable and Integrable Systems;Combinatorics '2437': Exactly Solvable and Integrable Systems;Complex Variables '2438': Exactly Solvable and Integrable Systems;Condensed Matter;High Energy Physics - Theory;Exactly Solvable and Integrable Systems '2439': Exactly Solvable and Integrable Systems;Differential Geometry '2440': Exactly Solvable and Integrable Systems;Dynamical Systems '2441': Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '2442': Exactly Solvable and Integrable Systems;Fluid Dynamics '2443': Exactly Solvable and Integrable Systems;High Energy Physics - Theory '2444': Exactly Solvable and Integrable Systems;High Energy Physics - Theory;Exactly Solvable and Integrable Systems '2445': Exactly Solvable and Integrable Systems;High Energy Physics - Theory;Quantum Algebra '2446': Exactly Solvable and Integrable Systems;High Energy Physics - Theory;Quantum Algebra;Exactly Solvable and Integrable Systems;Quantum Algebra '2447': Exactly Solvable and Integrable Systems;Optics '2448': Exactly Solvable and Integrable Systems;Pattern Formation and Solitons '2449': Exactly Solvable and Integrable Systems;Pattern Formation and Solitons;Exactly Solvable and Integrable Systems;Pattern Formation and Solitons '2450': Exactly Solvable and Integrable Systems;Quantum Algebra '2451': Exactly Solvable and Integrable Systems;Quantum Physics '2452': Fluid Dynamics '2453': Fluid Dynamics;Adaptation and Self-Organizing Systems '2454': Fluid Dynamics;Analysis of PDEs '2455': Fluid Dynamics;Applied Physics '2456': Fluid Dynamics;Astrophysics '2457': Fluid Dynamics;Astrophysics of Galaxies '2458': Fluid Dynamics;Atmospheric and Oceanic Physics '2459': Fluid Dynamics;Atmospheric and Oceanic Physics;Computational Physics '2460': Fluid Dynamics;Atmospheric and Oceanic Physics;Geophysics '2461': Fluid Dynamics;Biological Physics '2462': Fluid Dynamics;Biological Physics;Computational Physics '2463': Fluid Dynamics;Chaotic Dynamics '2464': Fluid Dynamics;Chaotic Dynamics;Atmospheric and Oceanic Physics '2465': Fluid Dynamics;Chaotic Dynamics;Classical Physics '2466': Fluid Dynamics;Chaotic Dynamics;Computational Physics '2467': Fluid Dynamics;Chemical Physics '2468': Fluid Dynamics;Classical Physics '2469': Fluid Dynamics;Computational Engineering, Finance, and Science '2470': Fluid Dynamics;Computational Physics '2471': Fluid Dynamics;Computational Physics;Data Analysis, Statistics and Probability '2472': Fluid Dynamics;Computational Physics;Geophysics '2473': Fluid Dynamics;Data Analysis, Statistics and Probability '2474': Fluid Dynamics;Dynamical Systems '2475': Fluid Dynamics;Dynamical Systems;Chaotic Dynamics '2476': Fluid Dynamics;Earth and Planetary Astrophysics '2477': Fluid Dynamics;Earth and Planetary Astrophysics;Geophysics '2478': Fluid Dynamics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '2479': Fluid Dynamics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Geophysics '2480': Fluid Dynamics;Exactly Solvable and Integrable Systems '2481': Fluid Dynamics;General Physics '2482': Fluid Dynamics;General Relativity and Quantum Cosmology '2483': Fluid Dynamics;Geophysics '2484': Fluid Dynamics;High Energy Astrophysical Phenomena '2485': Fluid Dynamics;Instrumentation and Detectors '2486': Fluid Dynamics;Instrumentation and Methods for Astrophysics '2487': Fluid Dynamics;Machine Learning '2488': Fluid Dynamics;Machine Learning;Chaotic Dynamics '2489': Fluid Dynamics;Machine Learning;Computational Physics '2490': Fluid Dynamics;Materials Science '2491': Fluid Dynamics;Materials Science;Soft Condensed Matter '2492': Fluid Dynamics;Medical Physics '2493': Fluid Dynamics;Mesoscale and Nanoscale Physics '2494': Fluid Dynamics;Numerical Analysis '2495': Fluid Dynamics;Numerical Analysis;Computational Physics '2496': Fluid Dynamics;Numerical Analysis;Numerical Analysis '2497': Fluid Dynamics;Numerical Analysis;Numerical Analysis;Computational Physics '2498': Fluid Dynamics;Optics '2499': Fluid Dynamics;Optimization and Control '2500': Fluid Dynamics;Other Condensed Matter '2501': Fluid Dynamics;Other Quantitative Biology '2502': Fluid Dynamics;Pattern Formation and Solitons '2503': Fluid Dynamics;Plasma Physics '2504': Fluid Dynamics;Quantitative Methods '2505': Fluid Dynamics;Quantum Gases '2506': Fluid Dynamics;Quantum Physics '2507': Fluid Dynamics;Soft Condensed Matter '2508': Fluid Dynamics;Soft Condensed Matter;Biological Physics '2509': Fluid Dynamics;Soft Condensed Matter;Chaotic Dynamics '2510': Fluid Dynamics;Soft Condensed Matter;Chemical Physics '2511': Fluid Dynamics;Soft Condensed Matter;Classical Physics '2512': Fluid Dynamics;Soft Condensed Matter;Computational Physics '2513': Fluid Dynamics;Soft Condensed Matter;Geophysics '2514': Fluid Dynamics;Soft Condensed Matter;Pattern Formation and Solitons '2515': Fluid Dynamics;Soft Condensed Matter;Statistical Mechanics '2516': Fluid Dynamics;Solar and Stellar Astrophysics '2517': Fluid Dynamics;Solar and Stellar Astrophysics;Plasma Physics '2518': Fluid Dynamics;Space Physics '2519': Fluid Dynamics;Statistical Mechanics '2520': Fluid Dynamics;Statistical Mechanics;Atmospheric and Oceanic Physics '2521': Fluid Dynamics;Statistical Mechanics;Chaotic Dynamics '2522': Fluid Dynamics;Statistical Mechanics;Computational Physics '2523': Fluid Dynamics;Statistical Mechanics;High Energy Physics - Theory '2524': Fluid Dynamics;Systems and Control;Systems and Control '2525': Formal Languages and Automata Theory '2526': Formal Languages and Automata Theory;Artificial Intelligence '2527': Formal Languages and Automata Theory;Category Theory '2528': Formal Languages and Automata Theory;Cellular Automata and Lattice Gases '2529': Formal Languages and Automata Theory;Combinatorics '2530': Formal Languages and Automata Theory;Computation and Language '2531': Formal Languages and Automata Theory;Computational Complexity '2532': Formal Languages and Automata Theory;Computational Complexity;Discrete Mathematics;Cellular Automata and Lattice Gases '2533': Formal Languages and Automata Theory;Computational Complexity;Quantum Physics '2534': Formal Languages and Automata Theory;Computer Science and Game Theory '2535': Formal Languages and Automata Theory;Data Structures and Algorithms '2536': Formal Languages and Automata Theory;Discrete Mathematics '2537': Formal Languages and Automata Theory;Discrete Mathematics;Combinatorics '2538': Formal Languages and Automata Theory;Distributed, Parallel, and Cluster Computing '2539': Formal Languages and Automata Theory;Group Theory '2540': Formal Languages and Automata Theory;Logic '2541': Formal Languages and Automata Theory;Logic in Computer Science '2542': Formal Languages and Automata Theory;Logic in Computer Science;Programming Languages '2543': Formal Languages and Automata Theory;Machine Learning '2544': Formal Languages and Automata Theory;Number Theory '2545': Formal Languages and Automata Theory;Programming Languages '2546': Formal Languages and Automata Theory;Software Engineering '2547': Formal Languages and Automata Theory;Systems and Control '2548': Formal Languages and Automata Theory;Systems and Control;Systems and Control '2549': Functional Analysis '2550': Functional Analysis;Algebraic Geometry '2551': Functional Analysis;Analysis of PDEs '2552': Functional Analysis;Analysis of PDEs;Classical Analysis and ODEs '2553': Functional Analysis;Analysis of PDEs;Differential Geometry '2554': Functional Analysis;Analysis of PDEs;Dynamical Systems '2555': Functional Analysis;Analysis of PDEs;Metric Geometry '2556': Functional Analysis;Analysis of PDEs;Probability '2557': Functional Analysis;Analysis of PDEs;Representation Theory '2558': Functional Analysis;Analysis of PDEs;Spectral Theory '2559': Functional Analysis;Category Theory '2560': Functional Analysis;Classical Analysis and ODEs '2561': Functional Analysis;Classical Analysis and ODEs;Complex Variables '2562': Functional Analysis;Classical Analysis and ODEs;Complex Variables;Spectral Theory '2563': Functional Analysis;Classical Analysis and ODEs;Metric Geometry '2564': Functional Analysis;Classical Analysis and ODEs;Operator Algebras '2565': Functional Analysis;Classical Analysis and ODEs;Probability '2566': Functional Analysis;Classical Analysis and ODEs;Spectral Theory '2567': Functional Analysis;Combinatorics '2568': Functional Analysis;Combinatorics;Metric Geometry '2569': Functional Analysis;Complex Variables '2570': Functional Analysis;Complex Variables;Operator Algebras '2571': Functional Analysis;Complex Variables;Spectral Theory '2572': Functional Analysis;Differential Geometry '2573': Functional Analysis;Dynamical Systems '2574': Functional Analysis;Functional Analysis '2575': Functional Analysis;General Mathematics '2576': Functional Analysis;General Topology '2577': Functional Analysis;General Topology;Logic '2578': Functional Analysis;Geometric Topology '2579': Functional Analysis;Group Theory '2580': Functional Analysis;Group Theory;Operator Algebras '2581': Functional Analysis;High Energy Physics - Theory;Operator Algebras '2582': Functional Analysis;K-Theory and Homology '2583': Functional Analysis;Logic '2584': Functional Analysis;Machine Learning '2585': Functional Analysis;Machine Learning;Machine Learning '2586': Functional Analysis;Metric Geometry '2587': Functional Analysis;Metric Geometry;Probability '2588': Functional Analysis;Number Theory '2589': Functional Analysis;Numerical Analysis '2590': Functional Analysis;Numerical Analysis;Numerical Analysis '2591': Functional Analysis;Operator Algebras '2592': Functional Analysis;Operator Algebras;Quantum Algebra;Quantum Algebra '2593': Functional Analysis;Operator Algebras;Quantum Physics '2594': Functional Analysis;Operator Algebras;Representation Theory '2595': Functional Analysis;Operator Algebras;Rings and Algebras '2596': Functional Analysis;Operator Algebras;Spectral Theory '2597': Functional Analysis;Optimization and Control '2598': Functional Analysis;Optimization and Control;Probability '2599': Functional Analysis;Probability '2600': Functional Analysis;Probability;Spectral Theory '2601': Functional Analysis;Quantum Physics '2602': Functional Analysis;Representation Theory '2603': Functional Analysis;Rings and Algebras '2604': Functional Analysis;Spectral Theory '2605': General Economics;Artificial Intelligence;Economics '2606': General Economics;Computers and Society;Economics '2607': General Economics;Econometrics;Economics '2608': General Economics;Economics '2609': General Economics;Economics;Applications '2610': General Economics;Economics;General Finance '2611': General Economics;Economics;Methodology '2612': General Economics;Machine Learning;Economics '2613': General Economics;Optimization and Control;Economics '2614': General Economics;Physics and Society;Economics '2615': General Economics;Social and Information Networks;Economics '2616': General Economics;Systems and Control;Systems and Control;Economics '2617': General Economics;Theoretical Economics;Economics '2618': General Finance '2619': General Finance;Adaptation and Self-Organizing Systems '2620': General Finance;Applications '2621': General Finance;Computational Finance '2622': General Finance;Cryptography and Security '2623': General Finance;Data Analysis, Statistics and Probability '2624': General Finance;Data Analysis, Statistics and Probability;Physics and Society '2625': General Finance;Economics '2626': General Finance;General Economics;Economics '2627': General Finance;Machine Learning '2628': General Finance;Optimization and Control '2629': General Finance;Physics and Society '2630': General Finance;Physics and Society;Statistical Finance '2631': General Finance;Portfolio Management '2632': General Finance;Pricing of Securities '2633': General Finance;Probability '2634': General Finance;Quantum Physics '2635': General Finance;Risk Management '2636': General Finance;Statistical Finance '2637': General Finance;Trading and Market Microstructure '2638': General Literature '2639': General Mathematics '2640': General Mathematics;Category Theory '2641': General Mathematics;Combinatorics '2642': General Mathematics;Functional Analysis '2643': General Mathematics;Logic '2644': General Mathematics;Number Theory '2645': General Physics '2646': General Physics;Astrophysics of Galaxies '2647': General Physics;Atmospheric and Oceanic Physics;Physics and Society '2648': General Physics;Atomic Physics '2649': General Physics;Biological Physics '2650': General Physics;Chemical Physics '2651': General Physics;Classical Physics '2652': General Physics;Cosmology and Nongalactic Astrophysics '2653': General Physics;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '2654': General Physics;Data Analysis, Statistics and Probability '2655': General Physics;Fluid Dynamics '2656': General Physics;General Relativity and Quantum Cosmology '2657': General Physics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '2658': General Physics;Geophysics '2659': General Physics;High Energy Physics - Phenomenology '2660': General Physics;High Energy Physics - Phenomenology;High Energy Physics - Theory '2661': General Physics;High Energy Physics - Theory '2662': General Physics;High Energy Physics - Theory;Quantum Physics '2663': General Physics;History and Philosophy of Physics '2664': General Physics;Instrumentation and Detectors '2665': General Physics;Nuclear Theory '2666': General Physics;Optics '2667': General Physics;Physics Education '2668': General Physics;Physics and Society '2669': General Physics;Plasma Physics '2670': General Physics;Popular Physics '2671': General Physics;Quantum Physics '2672': General Physics;Space Physics '2673': General Physics;Statistical Mechanics '2674': General Relativity and Quantum Cosmology '2675': General Relativity and Quantum Cosmology;Analysis of PDEs '2676': General Relativity and Quantum Cosmology;Analysis of PDEs;Differential Geometry '2677': General Relativity and Quantum Cosmology;Astrophysics '2678': General Relativity and Quantum Cosmology;Astrophysics of Galaxies '2679': General Relativity and Quantum Cosmology;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '2680': General Relativity and Quantum Cosmology;Astrophysics of Galaxies;High Energy Physics - Phenomenology '2681': General Relativity and Quantum Cosmology;Astrophysics of Galaxies;High Energy Physics - Phenomenology;High Energy Physics - Theory '2682': General Relativity and Quantum Cosmology;Astrophysics of Galaxies;High Energy Physics - Theory '2683': General Relativity and Quantum Cosmology;Astrophysics;Geophysics;Space Physics '2684': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Phenomenology '2685': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory '2686': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory;Quantum Physics '2687': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Phenomenology;Space Physics '2688': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Theory '2689': General Relativity and Quantum Cosmology;Astrophysics;High Energy Physics - Theory;Quantum Physics '2690': General Relativity and Quantum Cosmology;Astrophysics;Quantum Physics '2691': General Relativity and Quantum Cosmology;Astrophysics;Space Physics '2692': General Relativity and Quantum Cosmology;Atomic Physics '2693': General Relativity and Quantum Cosmology;Atomic Physics;Quantum Physics '2694': General Relativity and Quantum Cosmology;Chaotic Dynamics '2695': General Relativity and Quantum Cosmology;Chaotic Dynamics;Chaotic Dynamics '2696': General Relativity and Quantum Cosmology;Classical Physics '2697': General Relativity and Quantum Cosmology;Computational Physics '2698': General Relativity and Quantum Cosmology;Condensed Matter '2699': General Relativity and Quantum Cosmology;Condensed Matter;High Energy Physics - Phenomenology '2700': General Relativity and Quantum Cosmology;Condensed Matter;High Energy Physics - Theory '2701': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics '2702': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '2703': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '2704': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Physics - Theory '2705': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '2706': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '2707': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;High Energy Physics - Theory '2708': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Theory '2709': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '2710': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '2711': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory '2712': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology;High Energy Physics - Theory;Quantum Physics '2713': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Theory '2714': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Theory;Quantum Physics '2715': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics '2716': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Quantum Physics '2717': General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '2718': General Relativity and Quantum Cosmology;Data Analysis, Statistics and Probability '2719': General Relativity and Quantum Cosmology;Differential Geometry '2720': General Relativity and Quantum Cosmology;Differential Geometry;Differential Geometry '2721': General Relativity and Quantum Cosmology;Dynamical Systems '2722': General Relativity and Quantum Cosmology;Earth and Planetary Astrophysics '2723': General Relativity and Quantum Cosmology;Earth and Planetary Astrophysics;Geophysics;Space Physics '2724': General Relativity and Quantum Cosmology;Earth and Planetary Astrophysics;High Energy Physics - Phenomenology;Space Physics '2725': General Relativity and Quantum Cosmology;Earth and Planetary Astrophysics;High Energy Physics - Theory '2726': General Relativity and Quantum Cosmology;Earth and Planetary Astrophysics;Space Physics '2727': General Relativity and Quantum Cosmology;Fluid Dynamics '2728': General Relativity and Quantum Cosmology;Geophysics '2729': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena '2730': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Computational Physics '2731': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '2732': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;High Energy Physics - Theory '2733': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;Nuclear Theory '2734': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;High Energy Physics - Theory '2735': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '2736': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Nuclear Theory '2737': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Quantum Physics '2738': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '2739': General Relativity and Quantum Cosmology;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;High Energy Physics - Theory '2740': General Relativity and Quantum Cosmology;High Energy Physics - Lattice '2741': General Relativity and Quantum Cosmology;High Energy Physics - Lattice;High Energy Physics - Theory '2742': General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '2743': General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '2744': General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory;Quantum Physics '2745': General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Quantum Physics '2746': General Relativity and Quantum Cosmology;High Energy Physics - Theory '2747': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Analysis of PDEs '2748': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Chaotic Dynamics '2749': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Classical Physics '2750': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Computational Physics '2751': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Differential Geometry '2752': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Exactly Solvable and Integrable Systems '2753': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Fluid Dynamics '2754': General Relativity and Quantum Cosmology;High Energy Physics - Theory;History and Philosophy of Physics '2755': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Quantum Algebra '2756': General Relativity and Quantum Cosmology;High Energy Physics - Theory;Quantum Physics '2757': General Relativity and Quantum Cosmology;History and Philosophy of Physics '2758': General Relativity and Quantum Cosmology;History and Philosophy of Physics;Quantum Physics '2759': General Relativity and Quantum Cosmology;Instrumentation and Detectors '2760': General Relativity and Quantum Cosmology;Instrumentation and Methods for Astrophysics '2761': General Relativity and Quantum Cosmology;Instrumentation and Methods for Astrophysics;Data Analysis, Statistics and Probability '2762': General Relativity and Quantum Cosmology;Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '2763': General Relativity and Quantum Cosmology;Instrumentation and Methods for Astrophysics;Optics '2764': General Relativity and Quantum Cosmology;Nuclear Theory '2765': General Relativity and Quantum Cosmology;Optics '2766': General Relativity and Quantum Cosmology;Optics;Quantum Physics '2767': General Relativity and Quantum Cosmology;Other Condensed Matter '2768': General Relativity and Quantum Cosmology;Other Condensed Matter;High Energy Physics - Phenomenology '2769': General Relativity and Quantum Cosmology;Other Condensed Matter;High Energy Physics - Theory '2770': General Relativity and Quantum Cosmology;Physics Education '2771': General Relativity and Quantum Cosmology;Popular Physics '2772': General Relativity and Quantum Cosmology;Quantum Gases '2773': General Relativity and Quantum Cosmology;Quantum Gases;High Energy Physics - Theory '2774': General Relativity and Quantum Cosmology;Quantum Physics '2775': General Relativity and Quantum Cosmology;Solar and Stellar Astrophysics '2776': General Relativity and Quantum Cosmology;Solar and Stellar Astrophysics;High Energy Physics - Theory '2777': General Relativity and Quantum Cosmology;Space Physics '2778': General Relativity and Quantum Cosmology;Statistical Mechanics '2779': General Relativity and Quantum Cosmology;Statistical Mechanics;High Energy Physics - Theory '2780': General Relativity and Quantum Cosmology;Statistical Mechanics;High Energy Physics - Theory;Quantum Physics '2781': General Topology '2782': General Topology;Algebraic Topology '2783': General Topology;Category Theory '2784': General Topology;Combinatorics '2785': General Topology;Combinatorics;Logic '2786': General Topology;Dynamical Systems '2787': General Topology;Functional Analysis '2788': General Topology;Functional Analysis;Group Theory '2789': General Topology;Functional Analysis;Logic '2790': General Topology;Geometric Topology '2791': General Topology;Group Theory '2792': General Topology;Logic '2793': General Topology;Logic in Computer Science '2794': General Topology;Metric Geometry '2795': General Topology;Probability '2796': General Topology;Rings and Algebras '2797': Genomics '2798': Genomics;Applications '2799': Genomics;Artificial Intelligence '2800': Genomics;Artificial Intelligence;Machine Learning '2801': Genomics;Biological Physics '2802': Genomics;Biomolecules '2803': Genomics;Cell Behavior '2804': Genomics;Computational Engineering, Finance, and Science '2805': Genomics;Data Structures and Algorithms '2806': Genomics;Machine Learning '2807': Genomics;Machine Learning;Machine Learning '2808': Genomics;Machine Learning;Quantitative Methods '2809': Genomics;Methodology '2810': Genomics;Molecular Networks '2811': Genomics;Populations and Evolution '2812': Genomics;Quantitative Methods '2813': Geometric Topology '2814': Geometric Topology;Algebraic Geometry '2815': Geometric Topology;Algebraic Geometry;Algebraic Topology '2816': Geometric Topology;Algebraic Geometry;Complex Variables '2817': Geometric Topology;Algebraic Geometry;Differential Geometry '2818': Geometric Topology;Algebraic Geometry;Group Theory '2819': Geometric Topology;Algebraic Geometry;Number Theory '2820': Geometric Topology;Algebraic Geometry;Symplectic Geometry '2821': Geometric Topology;Algebraic Topology '2822': Geometric Topology;Algebraic Topology;Combinatorics '2823': Geometric Topology;Algebraic Topology;Differential Geometry '2824': Geometric Topology;Algebraic Topology;General Topology '2825': Geometric Topology;Algebraic Topology;Group Theory '2826': Geometric Topology;Algebraic Topology;K-Theory and Homology '2827': Geometric Topology;Algebraic Topology;Quantum Algebra '2828': Geometric Topology;Algebraic Topology;Representation Theory '2829': Geometric Topology;Algebraic Topology;Symplectic Geometry '2830': Geometric Topology;Category Theory '2831': Geometric Topology;Combinatorics '2832': Geometric Topology;Combinatorics;Group Theory '2833': Geometric Topology;Combinatorics;Metric Geometry '2834': Geometric Topology;Combinatorics;Probability '2835': Geometric Topology;Combinatorics;Quantum Algebra '2836': Geometric Topology;Complex Variables '2837': Geometric Topology;Complex Variables;Differential Geometry '2838': Geometric Topology;Computational Geometry '2839': Geometric Topology;Computational Geometry;Combinatorics '2840': Geometric Topology;Differential Geometry '2841': Geometric Topology;Differential Geometry;Dynamical Systems '2842': Geometric Topology;Differential Geometry;Group Theory '2843': Geometric Topology;Differential Geometry;Metric Geometry '2844': Geometric Topology;Differential Geometry;Number Theory '2845': Geometric Topology;Differential Geometry;Symplectic Geometry '2846': Geometric Topology;Dynamical Systems '2847': Geometric Topology;Dynamical Systems;Group Theory '2848': Geometric Topology;General Topology '2849': Geometric Topology;Group Theory '2850': Geometric Topology;Group Theory;Metric Geometry '2851': Geometric Topology;Group Theory;Number Theory '2852': Geometric Topology;High Energy Physics - Theory '2853': Geometric Topology;High Energy Physics - Theory;Quantum Algebra '2854': Geometric Topology;History and Overview '2855': Geometric Topology;K-Theory and Homology '2856': Geometric Topology;Metric Geometry '2857': Geometric Topology;Number Theory '2858': Geometric Topology;Probability '2859': Geometric Topology;Quantum Algebra '2860': Geometric Topology;Quantum Algebra;Representation Theory '2861': Geometric Topology;Quantum Algebra;Symplectic Geometry '2862': Geometric Topology;Representation Theory '2863': Geometric Topology;Rings and Algebras '2864': Geometric Topology;Symplectic Geometry '2865': Geophysics '2866': Geophysics;Adaptation and Self-Organizing Systems '2867': Geophysics;Applications '2868': Geophysics;Applied Physics '2869': Geophysics;Atmospheric and Oceanic Physics '2870': Geophysics;Atmospheric and Oceanic Physics;Space Physics '2871': Geophysics;Classical Physics '2872': Geophysics;Computational Engineering, Finance, and Science '2873': Geophysics;Computational Physics '2874': Geophysics;Computational Physics;Fluid Dynamics '2875': Geophysics;Computer Vision and Pattern Recognition '2876': Geophysics;Data Analysis, Statistics and Probability '2877': Geophysics;Disordered Systems and Neural Networks '2878': Geophysics;Earth and Planetary Astrophysics '2879': Geophysics;Earth and Planetary Astrophysics;Fluid Dynamics '2880': Geophysics;Fluid Dynamics '2881': Geophysics;General Physics '2882': Geophysics;Image and Video Processing '2883': Geophysics;Instrumentation and Detectors '2884': Geophysics;Instrumentation and Methods for Astrophysics '2885': Geophysics;Machine Learning '2886': Geophysics;Machine Learning;Signal Processing '2887': Geophysics;Materials Science '2888': Geophysics;Numerical Analysis '2889': Geophysics;Numerical Analysis;Numerical Analysis '2890': Geophysics;Other Condensed Matter '2891': Geophysics;Physics and Society '2892': Geophysics;Signal Processing '2893': Geophysics;Soft Condensed Matter '2894': Geophysics;Space Physics '2895': Geophysics;Statistical Mechanics '2896': Graphics '2897': Graphics;Artificial Intelligence '2898': Graphics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '2899': Graphics;Artificial Intelligence;Machine Learning '2900': Graphics;Computational Geometry '2901': Graphics;Computational Geometry;Computer Vision and Pattern Recognition '2902': Graphics;Computer Vision and Pattern Recognition '2903': Graphics;Computer Vision and Pattern Recognition;Image and Video Processing '2904': Graphics;Computer Vision and Pattern Recognition;Machine Learning '2905': Graphics;Distributed, Parallel, and Cluster Computing '2906': Graphics;Human-Computer Interaction '2907': Graphics;Image and Video Processing '2908': Graphics;Machine Learning '2909': Graphics;Machine Learning;Machine Learning '2910': Graphics;Multimedia '2911': Graphics;Numerical Analysis '2912': Graphics;Robotics '2913': Group Theory '2914': Group Theory;Algebraic Geometry '2915': Group Theory;Algebraic Geometry;Number Theory '2916': Group Theory;Algebraic Geometry;Representation Theory '2917': Group Theory;Algebraic Topology '2918': Group Theory;Algebraic Topology;Combinatorics '2919': Group Theory;Algebraic Topology;Geometric Topology '2920': Group Theory;Algebraic Topology;K-Theory and Homology '2921': Group Theory;Algebraic Topology;Representation Theory '2922': Group Theory;Category Theory '2923': Group Theory;Combinatorics '2924': Group Theory;Combinatorics;Dynamical Systems '2925': Group Theory;Combinatorics;Geometric Topology '2926': Group Theory;Combinatorics;Metric Geometry '2927': Group Theory;Combinatorics;Number Theory '2928': Group Theory;Combinatorics;Probability '2929': Group Theory;Combinatorics;Representation Theory '2930': Group Theory;Combinatorics;Rings and Algebras '2931': Group Theory;Commutative Algebra '2932': Group Theory;Computational Complexity '2933': Group Theory;Cryptography and Security '2934': Group Theory;Data Structures and Algorithms '2935': Group Theory;Differential Geometry '2936': Group Theory;Differential Geometry;Geometric Topology '2937': Group Theory;Differential Geometry;Metric Geometry '2938': Group Theory;Discrete Mathematics '2939': Group Theory;Dynamical Systems '2940': Group Theory;Dynamical Systems;Geometric Topology '2941': Group Theory;Dynamical Systems;Metric Geometry '2942': Group Theory;Dynamical Systems;Operator Algebras '2943': Group Theory;Dynamical Systems;Probability '2944': Group Theory;Dynamical Systems;Representation Theory '2945': Group Theory;Formal Languages and Automata Theory '2946': Group Theory;Functional Analysis '2947': Group Theory;Functional Analysis;Metric Geometry '2948': Group Theory;Functional Analysis;Operator Algebras '2949': Group Theory;General Topology '2950': Group Theory;Geometric Topology '2951': Group Theory;Geometric Topology;Metric Geometry '2952': Group Theory;Geometric Topology;Number Theory '2953': Group Theory;Geometric Topology;Operator Algebras '2954': Group Theory;Geometric Topology;Probability '2955': Group Theory;History and Overview '2956': Group Theory;K-Theory and Homology '2957': Group Theory;Logic '2958': Group Theory;Logic;Rings and Algebras '2959': Group Theory;Metric Geometry '2960': Group Theory;Number Theory '2961': Group Theory;Operator Algebras '2962': Group Theory;Operator Algebras;Representation Theory '2963': Group Theory;Probability '2964': Group Theory;Quantum Algebra '2965': Group Theory;Quantum Algebra;Rings and Algebras '2966': Group Theory;Representation Theory '2967': Group Theory;Rings and Algebras '2968': Group Theory;Rings and Algebras;Representation Theory '2969': Hardware Architecture '2970': Hardware Architecture;Artificial Intelligence '2971': Hardware Architecture;Artificial Intelligence;Machine Learning '2972': Hardware Architecture;Computer Vision and Pattern Recognition '2973': Hardware Architecture;Computer Vision and Pattern Recognition;Machine Learning '2974': Hardware Architecture;Cryptography and Security '2975': Hardware Architecture;Distributed, Parallel, and Cluster Computing '2976': Hardware Architecture;Distributed, Parallel, and Cluster Computing;Machine Learning '2977': Hardware Architecture;Distributed, Parallel, and Cluster Computing;Performance '2978': Hardware Architecture;Emerging Technologies '2979': Hardware Architecture;Emerging Technologies;Machine Learning '2980': Hardware Architecture;Machine Learning '2981': Hardware Architecture;Machine Learning;Neural and Evolutionary Computing '2982': Hardware Architecture;Networking and Internet Architecture '2983': Hardware Architecture;Neural and Evolutionary Computing '2984': Hardware Architecture;Operating Systems '2985': Hardware Architecture;Performance '2986': Hardware Architecture;Programming Languages '2987': Hardware Architecture;Quantum Physics '2988': Hardware Architecture;Signal Processing '2989': Hardware Architecture;Systems and Control;Systems and Control '2990': High Energy Astrophysical Phenomena '2991': High Energy Astrophysical Phenomena;Astrophysics of Galaxies '2992': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;General Relativity and Quantum Cosmology '2993': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;High Energy Physics - Experiment;High Energy Physics - Phenomenology '2994': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;High Energy Physics - Phenomenology '2995': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '2996': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '2997': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;Plasma Physics '2998': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;Solar and Stellar Astrophysics '2999': High Energy Astrophysical Phenomena;Astrophysics of Galaxies;Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology '3000': High Energy Astrophysical Phenomena;Atomic Physics '3001': High Energy Astrophysical Phenomena;Computational Physics '3002': High Energy Astrophysical Phenomena;Computational Physics;Plasma Physics '3003': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics '3004': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '3005': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology '3006': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Physics - Phenomenology '3007': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '3008': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '3009': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3010': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '3011': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3012': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '3013': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics '3014': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Plasma Physics '3015': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '3016': High Energy Astrophysical Phenomena;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology '3017': High Energy Astrophysical Phenomena;Data Analysis, Statistics and Probability '3018': High Energy Astrophysical Phenomena;Earth and Planetary Astrophysics '3019': High Energy Astrophysical Phenomena;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '3020': High Energy Astrophysical Phenomena;Fluid Dynamics '3021': High Energy Astrophysical Phenomena;Fluid Dynamics;Plasma Physics '3022': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '3023': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3024': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '3025': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Nuclear Theory '3026': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3027': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;Nuclear Theory '3028': High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;Plasma Physics '3029': High Energy Astrophysical Phenomena;High Energy Physics - Experiment '3030': High Energy Astrophysical Phenomena;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3031': High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '3032': High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;High Energy Physics - Theory '3033': High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;Nuclear Theory '3034': High Energy Astrophysical Phenomena;High Energy Physics - Theory '3035': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '3036': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;Data Analysis, Statistics and Probability '3037': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology '3038': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment '3039': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;High Energy Physics - Phenomenology '3040': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '3041': High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '3042': High Energy Astrophysical Phenomena;Nuclear Experiment '3043': High Energy Astrophysical Phenomena;Nuclear Experiment;Nuclear Theory '3044': High Energy Astrophysical Phenomena;Nuclear Theory '3045': High Energy Astrophysical Phenomena;Plasma Physics '3046': High Energy Astrophysical Phenomena;Plasma Physics;Space Physics '3047': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3048': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Fluid Dynamics '3049': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology '3050': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology;Nuclear Theory '3051': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;High Energy Physics - Phenomenology '3052': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;High Energy Physics - Phenomenology;Nuclear Theory '3053': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Nuclear Experiment;Nuclear Theory '3054': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Nuclear Theory '3055': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Plasma Physics '3056': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Plasma Physics;Space Physics '3057': High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Space Physics '3058': High Energy Astrophysical Phenomena;Space Physics '3059': High Energy Physics - Experiment '3060': High Energy Physics - Experiment;Accelerator Physics '3061': High Energy Physics - Experiment;Accelerator Physics;Instrumentation and Detectors '3062': High Energy Physics - Experiment;Astrophysics '3063': High Energy Physics - Experiment;Astrophysics;High Energy Physics - Phenomenology '3064': High Energy Physics - Experiment;Atomic Physics '3065': High Energy Physics - Experiment;Computational Physics '3066': High Energy Physics - Experiment;Cosmology and Nongalactic Astrophysics '3067': High Energy Physics - Experiment;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '3068': High Energy Physics - Experiment;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology;Instrumentation and Detectors '3069': High Energy Physics - Experiment;Cosmology and Nongalactic Astrophysics;Instrumentation and Detectors '3070': High Energy Physics - Experiment;Data Analysis, Statistics and Probability '3071': High Energy Physics - Experiment;High Energy Astrophysical Phenomena '3072': High Energy Physics - Experiment;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '3073': High Energy Physics - Experiment;High Energy Astrophysical Phenomena;Instrumentation and Detectors '3074': High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3075': High Energy Physics - Experiment;High Energy Physics - Phenomenology '3076': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Accelerator Physics '3077': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Data Analysis, Statistics and Probability '3078': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Instrumentation and Detectors '3079': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment '3080': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment;Instrumentation and Detectors '3081': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '3082': High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Theory '3083': High Energy Physics - Experiment;Instrumentation and Detectors '3084': High Energy Physics - Experiment;Instrumentation and Methods for Astrophysics '3085': High Energy Physics - Experiment;Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '3086': High Energy Physics - Experiment;Machine Learning '3087': High Energy Physics - Experiment;Machine Learning;High Energy Physics - Phenomenology '3088': High Energy Physics - Experiment;Nuclear Experiment '3089': High Energy Physics - Experiment;Nuclear Experiment;Instrumentation and Detectors '3090': High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory '3091': High Energy Physics - Experiment;Quantum Physics '3092': High Energy Physics - Lattice '3093': High Energy Physics - Lattice;Computational Physics '3094': High Energy Physics - Lattice;Condensed Matter '3095': High Energy Physics - Lattice;Condensed Matter;High Energy Physics - Phenomenology '3096': High Energy Physics - Lattice;Condensed Matter;High Energy Physics - Phenomenology;High Energy Physics - Theory '3097': High Energy Physics - Lattice;Condensed Matter;High Energy Physics - Theory '3098': High Energy Physics - Lattice;Disordered Systems and Neural Networks '3099': High Energy Physics - Lattice;Disordered Systems and Neural Networks;High Energy Physics - Theory '3100': High Energy Physics - Lattice;General Relativity and Quantum Cosmology '3101': High Energy Physics - Lattice;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3102': High Energy Physics - Lattice;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3103': High Energy Physics - Lattice;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '3104': High Energy Physics - Lattice;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Theory '3105': High Energy Physics - Lattice;High Energy Physics - Phenomenology '3106': High Energy Physics - Lattice;High Energy Physics - Phenomenology;High Energy Physics - Theory '3107': High Energy Physics - Lattice;High Energy Physics - Phenomenology;High Energy Physics - Theory;Nuclear Theory '3108': High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '3109': High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Theory '3110': High Energy Physics - Lattice;High Energy Physics - Theory '3111': High Energy Physics - Lattice;High Energy Physics - Theory;Nuclear Theory '3112': High Energy Physics - Lattice;High Energy Physics - Theory;Quantum Physics '3113': High Energy Physics - Lattice;Machine Learning '3114': High Energy Physics - Lattice;Nuclear Experiment;Nuclear Theory '3115': High Energy Physics - Lattice;Nuclear Theory '3116': High Energy Physics - Lattice;Quantum Physics '3117': High Energy Physics - Lattice;Statistical Mechanics '3118': High Energy Physics - Lattice;Statistical Mechanics;Computational Physics '3119': High Energy Physics - Lattice;Statistical Mechanics;High Energy Physics - Phenomenology '3120': High Energy Physics - Lattice;Statistical Mechanics;High Energy Physics - Theory '3121': High Energy Physics - Lattice;Statistical Mechanics;Machine Learning '3122': High Energy Physics - Lattice;Statistical Mechanics;Quantum Physics '3123': High Energy Physics - Lattice;Strongly Correlated Electrons '3124': High Energy Physics - Lattice;Strongly Correlated Electrons;High Energy Physics - Phenomenology;High Energy Physics - Theory '3125': High Energy Physics - Lattice;Strongly Correlated Electrons;High Energy Physics - Theory '3126': High Energy Physics - Lattice;Strongly Correlated Electrons;High Energy Physics - Theory;Quantum Physics '3127': High Energy Physics - Lattice;Strongly Correlated Electrons;Quantum Physics '3128': High Energy Physics - Lattice;Superconductivity;High Energy Physics - Phenomenology '3129': High Energy Physics - Phenomenology '3130': High Energy Physics - Phenomenology;Accelerator Physics '3131': High Energy Physics - Phenomenology;Astrophysics '3132': High Energy Physics - Phenomenology;Astrophysics of Galaxies '3133': High Energy Physics - Phenomenology;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '3134': High Energy Physics - Phenomenology;Astrophysics;Condensed Matter '3135': High Energy Physics - Phenomenology;Astrophysics;General Relativity and Quantum Cosmology '3136': High Energy Physics - Phenomenology;Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Experiment;High Energy Physics - Theory '3137': High Energy Physics - Phenomenology;Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3138': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Experiment '3139': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Experiment;High Energy Physics - Theory '3140': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Experiment;Nuclear Experiment '3141': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory '3142': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Experiment;Nuclear Theory '3143': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Theory '3144': High Energy Physics - Phenomenology;Astrophysics;High Energy Physics - Theory;Nuclear Theory '3145': High Energy Physics - Phenomenology;Astrophysics;Nuclear Experiment;Nuclear Theory '3146': High Energy Physics - Phenomenology;Astrophysics;Nuclear Theory '3147': High Energy Physics - Phenomenology;Atomic Physics '3148': High Energy Physics - Phenomenology;Atomic Physics;Quantum Physics '3149': High Energy Physics - Phenomenology;Computational Physics '3150': High Energy Physics - Phenomenology;Condensed Matter '3151': High Energy Physics - Phenomenology;Condensed Matter;High Energy Physics - Lattice '3152': High Energy Physics - Phenomenology;Condensed Matter;High Energy Physics - Lattice;High Energy Physics - Theory '3153': High Energy Physics - Phenomenology;Condensed Matter;High Energy Physics - Theory '3154': High Energy Physics - Phenomenology;Condensed Matter;High Energy Physics - Theory;Nuclear Theory '3155': High Energy Physics - Phenomenology;Condensed Matter;Nuclear Theory '3156': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics '3157': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '3158': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '3159': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;Atomic Physics '3160': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '3161': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Experiment '3162': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3163': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '3164': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '3165': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3166': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Experiment '3167': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Theory '3168': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Nuclear Theory '3169': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3170': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment '3171': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;High Energy Physics - Theory '3172': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;Nuclear Experiment '3173': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory '3174': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Lattice '3175': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Lattice;High Energy Physics - Theory '3176': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Theory '3177': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Theory;Nuclear Theory '3178': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;Nuclear Theory '3179': High Energy Physics - Phenomenology;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '3180': High Energy Physics - Phenomenology;Data Analysis, Statistics and Probability '3181': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology '3182': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology;High Energy Physics - Experiment '3183': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology;High Energy Physics - Experiment;High Energy Physics - Theory '3184': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3185': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology;High Energy Physics - Theory;Nuclear Theory '3186': High Energy Physics - Phenomenology;General Relativity and Quantum Cosmology;Nuclear Theory '3187': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena '3188': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '3189': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3190': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;High Energy Physics - Experiment '3191': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;High Energy Physics - Experiment;High Energy Physics - Theory '3192': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory '3193': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;High Energy Physics - Theory '3194': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;High Energy Physics - Theory;Nuclear Theory '3195': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;Nuclear Theory '3196': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3197': High Energy Physics - Phenomenology;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;High Energy Physics - Experiment '3198': High Energy Physics - Phenomenology;High Energy Physics - Experiment '3199': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Accelerator Physics '3200': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Atomic Physics '3201': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Computational Physics '3202': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Data Analysis, Statistics and Probability '3203': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Data Analysis, Statistics and Probability;Machine Learning '3204': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Lattice '3205': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Theory '3206': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Theory;Nuclear Theory '3207': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Lattice;Nuclear Experiment;Nuclear Theory '3208': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Lattice;Nuclear Theory '3209': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Theory '3210': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Theory;Nuclear Experiment;Nuclear Theory '3211': High Energy Physics - Phenomenology;High Energy Physics - Experiment;High Energy Physics - Theory;Nuclear Theory '3212': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Instrumentation and Detectors '3213': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Machine Learning '3214': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Nuclear Experiment '3215': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory '3216': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Nuclear Theory '3217': High Energy Physics - Phenomenology;High Energy Physics - Experiment;Quantum Physics '3218': High Energy Physics - Phenomenology;High Energy Physics - Lattice '3219': High Energy Physics - Phenomenology;High Energy Physics - Lattice;High Energy Physics - Theory '3220': High Energy Physics - Phenomenology;High Energy Physics - Lattice;High Energy Physics - Theory;Nuclear Theory '3221': High Energy Physics - Phenomenology;High Energy Physics - Lattice;Nuclear Experiment;Nuclear Theory '3222': High Energy Physics - Phenomenology;High Energy Physics - Lattice;Nuclear Theory '3223': High Energy Physics - Phenomenology;High Energy Physics - Theory '3224': High Energy Physics - Phenomenology;High Energy Physics - Theory;Atomic Physics '3225': High Energy Physics - Phenomenology;High Energy Physics - Theory;Computational Physics '3226': High Energy Physics - Phenomenology;High Energy Physics - Theory;Nuclear Experiment;Nuclear Theory '3227': High Energy Physics - Phenomenology;High Energy Physics - Theory;Nuclear Theory '3228': High Energy Physics - Phenomenology;High Energy Physics - Theory;Nuclear Theory;Quantum Physics '3229': High Energy Physics - Phenomenology;High Energy Physics - Theory;Quantum Physics '3230': High Energy Physics - Phenomenology;History and Philosophy of Physics '3231': High Energy Physics - Phenomenology;Instrumentation and Detectors '3232': High Energy Physics - Phenomenology;Instrumentation and Methods for Astrophysics '3233': High Energy Physics - Phenomenology;Machine Learning '3234': High Energy Physics - Phenomenology;Machine Learning;High Energy Physics - Experiment '3235': High Energy Physics - Phenomenology;Machine Learning;High Energy Physics - Experiment;Data Analysis, Statistics and Probability '3236': High Energy Physics - Phenomenology;Mesoscale and Nanoscale Physics '3237': High Energy Physics - Phenomenology;Mesoscale and Nanoscale Physics;High Energy Physics - Theory '3238': High Energy Physics - Phenomenology;Nuclear Experiment '3239': High Energy Physics - Phenomenology;Nuclear Experiment;Nuclear Theory '3240': High Energy Physics - Phenomenology;Nuclear Theory '3241': High Energy Physics - Phenomenology;Nuclear Theory;Atomic Physics '3242': High Energy Physics - Phenomenology;Nuclear Theory;Plasma Physics '3243': High Energy Physics - Phenomenology;Nuclear Theory;Quantum Physics '3244': High Energy Physics - Phenomenology;Optics '3245': High Energy Physics - Phenomenology;Optics;Quantum Physics '3246': High Energy Physics - Phenomenology;Other Condensed Matter '3247': High Energy Physics - Phenomenology;Other Condensed Matter;High Energy Physics - Theory '3248': High Energy Physics - Phenomenology;Other Condensed Matter;Nuclear Theory '3249': High Energy Physics - Phenomenology;Plasma Physics '3250': High Energy Physics - Phenomenology;Quantum Gases '3251': High Energy Physics - Phenomenology;Quantum Physics '3252': High Energy Physics - Phenomenology;Solar and Stellar Astrophysics '3253': High Energy Physics - Phenomenology;Solar and Stellar Astrophysics;High Energy Physics - Experiment '3254': High Energy Physics - Phenomenology;Solar and Stellar Astrophysics;Nuclear Theory '3255': High Energy Physics - Phenomenology;Statistical Mechanics '3256': High Energy Physics - Phenomenology;Statistical Mechanics;High Energy Physics - Theory '3257': High Energy Physics - Phenomenology;Statistical Mechanics;High Energy Physics - Theory;Nuclear Theory '3258': High Energy Physics - Phenomenology;Statistical Mechanics;Nuclear Theory '3259': High Energy Physics - Phenomenology;Strongly Correlated Electrons '3260': High Energy Physics - Phenomenology;Strongly Correlated Electrons;High Energy Physics - Theory '3261': High Energy Physics - Phenomenology;Strongly Correlated Electrons;Nuclear Theory '3262': High Energy Physics - Phenomenology;Superconductivity '3263': High Energy Physics - Phenomenology;Superconductivity;High Energy Physics - Theory '3264': High Energy Physics - Phenomenology;Superconductivity;High Energy Physics - Theory;Nuclear Theory '3265': High Energy Physics - Phenomenology;Superconductivity;Nuclear Theory '3266': High Energy Physics - Theory '3267': High Energy Physics - Theory;Algebraic Geometry '3268': High Energy Physics - Theory;Algebraic Geometry;Algebraic Geometry '3269': High Energy Physics - Theory;Algebraic Geometry;Algebraic Geometry;Quantum Algebra;Quantum Algebra '3270': High Energy Physics - Theory;Algebraic Geometry;Combinatorics '3271': High Energy Physics - Theory;Algebraic Geometry;Differential Geometry '3272': High Energy Physics - Theory;Algebraic Geometry;Differential Geometry;Algebraic Geometry;Differential Geometry '3273': High Energy Physics - Theory;Algebraic Geometry;Geometric Topology '3274': High Energy Physics - Theory;Algebraic Geometry;Number Theory '3275': High Energy Physics - Theory;Algebraic Geometry;Quantum Algebra '3276': High Energy Physics - Theory;Algebraic Geometry;Quantum Algebra;Representation Theory '3277': High Energy Physics - Theory;Algebraic Geometry;Representation Theory '3278': High Energy Physics - Theory;Algebraic Geometry;Symplectic Geometry '3279': High Energy Physics - Theory;Algebraic Topology '3280': High Energy Physics - Theory;Algebraic Topology;Differential Geometry '3281': High Energy Physics - Theory;Astrophysics '3282': High Energy Physics - Theory;Astrophysics;General Relativity and Quantum Cosmology '3283': High Energy Physics - Theory;Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3284': High Energy Physics - Theory;Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Quantum Physics '3285': High Energy Physics - Theory;Astrophysics;General Relativity and Quantum Cosmology;Quantum Physics '3286': High Energy Physics - Theory;Astrophysics;High Energy Physics - Phenomenology '3287': High Energy Physics - Theory;Astrophysics;Quantum Physics '3288': High Energy Physics - Theory;Category Theory;Quantum Algebra '3289': High Energy Physics - Theory;Chaotic Dynamics '3290': High Energy Physics - Theory;Chaotic Dynamics;Condensed Matter;Chaotic Dynamics '3291': High Energy Physics - Theory;Chaotic Dynamics;Quantum Physics '3292': High Energy Physics - Theory;Classical Analysis and ODEs '3293': High Energy Physics - Theory;Classical Physics '3294': High Energy Physics - Theory;Combinatorics '3295': High Energy Physics - Theory;Complex Variables '3296': High Energy Physics - Theory;Condensed Matter '3297': High Energy Physics - Theory;Condensed Matter;Exactly Solvable and Integrable Systems '3298': High Energy Physics - Theory;Condensed Matter;Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '3299': High Energy Physics - Theory;Condensed Matter;General Relativity and Quantum Cosmology '3300': High Energy Physics - Theory;Condensed Matter;High Energy Physics - Lattice '3301': High Energy Physics - Theory;Condensed Matter;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3302': High Energy Physics - Theory;Condensed Matter;High Energy Physics - Phenomenology '3303': High Energy Physics - Theory;Condensed Matter;Quantum Algebra;Quantum Algebra '3304': High Energy Physics - Theory;Condensed Matter;Quantum Physics '3305': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics '3306': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '3307': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3308': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Quantum Physics '3309': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;Quantum Physics '3310': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3311': High Energy Physics - Theory;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '3312': High Energy Physics - Theory;Differential Geometry '3313': High Energy Physics - Theory;Differential Geometry;Differential Geometry '3314': High Energy Physics - Theory;Differential Geometry;General Relativity and Quantum Cosmology;Differential Geometry '3315': High Energy Physics - Theory;Differential Geometry;Quantum Algebra '3316': High Energy Physics - Theory;Disordered Systems and Neural Networks '3317': High Energy Physics - Theory;Exactly Solvable and Integrable Systems '3318': High Energy Physics - Theory;Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '3319': High Energy Physics - Theory;Fluid Dynamics '3320': High Energy Physics - Theory;Functional Analysis;Functional Analysis '3321': High Energy Physics - Theory;General Relativity and Quantum Cosmology '3322': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Differential Geometry '3323': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Exactly Solvable and Integrable Systems '3324': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Fluid Dynamics '3325': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3326': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Lattice '3327': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3328': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3329': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Nuclear Theory '3330': High Energy Physics - Theory;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Quantum Physics '3331': High Energy Physics - Theory;General Relativity and Quantum Cosmology;History and Philosophy of Physics '3332': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Nuclear Theory '3333': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Quantum Algebra '3334': High Energy Physics - Theory;General Relativity and Quantum Cosmology;Quantum Physics '3335': High Energy Physics - Theory;Geometric Topology '3336': High Energy Physics - Theory;Geometric Topology;Quantum Algebra '3337': High Energy Physics - Theory;High Energy Astrophysical Phenomena '3338': High Energy Physics - Theory;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '3339': High Energy Physics - Theory;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3340': High Energy Physics - Theory;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '3341': High Energy Physics - Theory;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;Nuclear Theory '3342': High Energy Physics - Theory;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3343': High Energy Physics - Theory;High Energy Physics - Lattice '3344': High Energy Physics - Theory;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3345': High Energy Physics - Theory;High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Theory '3346': High Energy Physics - Theory;High Energy Physics - Lattice;Nuclear Theory '3347': High Energy Physics - Theory;High Energy Physics - Lattice;Quantum Physics '3348': High Energy Physics - Theory;High Energy Physics - Phenomenology '3349': High Energy Physics - Theory;High Energy Physics - Phenomenology;Algebraic Geometry '3350': High Energy Physics - Theory;High Energy Physics - Phenomenology;Exactly Solvable and Integrable Systems '3351': High Energy Physics - Theory;High Energy Physics - Phenomenology;Nuclear Theory '3352': High Energy Physics - Theory;High Energy Physics - Phenomenology;Nuclear Theory;Fluid Dynamics '3353': High Energy Physics - Theory;High Energy Physics - Phenomenology;Nuclear Theory;Quantum Physics '3354': High Energy Physics - Theory;High Energy Physics - Phenomenology;Pattern Formation and Solitons '3355': High Energy Physics - Theory;High Energy Physics - Phenomenology;Quantum Algebra '3356': High Energy Physics - Theory;High Energy Physics - Phenomenology;Quantum Algebra;Quantum Algebra '3357': High Energy Physics - Theory;High Energy Physics - Phenomenology;Quantum Physics '3358': High Energy Physics - Theory;History and Philosophy of Physics '3359': High Energy Physics - Theory;K-Theory and Homology '3360': High Energy Physics - Theory;Materials Science '3361': High Energy Physics - Theory;Materials Science;General Relativity and Quantum Cosmology '3362': High Energy Physics - Theory;Mesoscale and Nanoscale Physics '3363': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;General Relativity and Quantum Cosmology '3364': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;High Energy Physics - Phenomenology '3365': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;High Energy Physics - Phenomenology;Nuclear Theory '3366': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;Nuclear Theory '3367': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;Quantum Physics '3368': High Energy Physics - Theory;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '3369': High Energy Physics - Theory;Nuclear Theory '3370': High Energy Physics - Theory;Nuclear Theory;Quantum Physics '3371': High Energy Physics - Theory;Number Theory '3372': High Energy Physics - Theory;Number Theory;Representation Theory '3373': High Energy Physics - Theory;Operator Algebras '3374': High Energy Physics - Theory;Other Condensed Matter '3375': High Energy Physics - Theory;Other Condensed Matter;General Relativity and Quantum Cosmology '3376': High Energy Physics - Theory;Other Condensed Matter;High Energy Physics - Phenomenology '3377': High Energy Physics - Theory;Other Condensed Matter;Quantum Physics '3378': High Energy Physics - Theory;Pattern Formation and Solitons '3379': High Energy Physics - Theory;Quantum Algebra '3380': High Energy Physics - Theory;Quantum Algebra;Exactly Solvable and Integrable Systems '3381': High Energy Physics - Theory;Quantum Algebra;Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '3382': High Energy Physics - Theory;Quantum Algebra;Exactly Solvable and Integrable Systems;Quantum Algebra;Exactly Solvable and Integrable Systems '3383': High Energy Physics - Theory;Quantum Algebra;Quantum Algebra '3384': High Energy Physics - Theory;Quantum Algebra;Representation Theory '3385': High Energy Physics - Theory;Quantum Gases '3386': High Energy Physics - Theory;Quantum Gases;Strongly Correlated Electrons '3387': High Energy Physics - Theory;Quantum Physics '3388': High Energy Physics - Theory;Representation Theory '3389': High Energy Physics - Theory;Rings and Algebras '3390': High Energy Physics - Theory;Soft Condensed Matter '3391': High Energy Physics - Theory;Soft Condensed Matter;High Energy Physics - Phenomenology '3392': High Energy Physics - Theory;Soft Condensed Matter;Strongly Correlated Electrons '3393': High Energy Physics - Theory;Statistical Mechanics '3394': High Energy Physics - Theory;Statistical Mechanics;Exactly Solvable and Integrable Systems '3395': High Energy Physics - Theory;Statistical Mechanics;General Relativity and Quantum Cosmology '3396': High Energy Physics - Theory;Statistical Mechanics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;Quantum Physics '3397': High Energy Physics - Theory;Statistical Mechanics;General Relativity and Quantum Cosmology;Quantum Physics '3398': High Energy Physics - Theory;Statistical Mechanics;High Energy Physics - Lattice '3399': High Energy Physics - Theory;Statistical Mechanics;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3400': High Energy Physics - Theory;Statistical Mechanics;High Energy Physics - Phenomenology '3401': High Energy Physics - Theory;Statistical Mechanics;High Energy Physics - Phenomenology;Nuclear Theory '3402': High Energy Physics - Theory;Statistical Mechanics;Quantum Physics '3403': High Energy Physics - Theory;Statistical Mechanics;Strongly Correlated Electrons '3404': High Energy Physics - Theory;Statistical Mechanics;Strongly Correlated Electrons;General Relativity and Quantum Cosmology '3405': High Energy Physics - Theory;Statistical Mechanics;Strongly Correlated Electrons;High Energy Physics - Lattice '3406': High Energy Physics - Theory;Statistical Mechanics;Strongly Correlated Electrons;High Energy Physics - Phenomenology '3407': High Energy Physics - Theory;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '3408': High Energy Physics - Theory;Strongly Correlated Electrons '3409': High Energy Physics - Theory;Strongly Correlated Electrons;Category Theory '3410': High Energy Physics - Theory;Strongly Correlated Electrons;General Relativity and Quantum Cosmology '3411': High Energy Physics - Theory;Strongly Correlated Electrons;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '3412': High Energy Physics - Theory;Strongly Correlated Electrons;General Relativity and Quantum Cosmology;Quantum Physics '3413': High Energy Physics - Theory;Strongly Correlated Electrons;High Energy Physics - Lattice '3414': High Energy Physics - Theory;Strongly Correlated Electrons;High Energy Physics - Lattice;High Energy Physics - Phenomenology '3415': High Energy Physics - Theory;Strongly Correlated Electrons;High Energy Physics - Phenomenology '3416': High Energy Physics - Theory;Strongly Correlated Electrons;High Energy Physics - Phenomenology;Nuclear Theory '3417': High Energy Physics - Theory;Strongly Correlated Electrons;Nuclear Theory '3418': High Energy Physics - Theory;Strongly Correlated Electrons;Quantum Algebra '3419': High Energy Physics - Theory;Strongly Correlated Electrons;Quantum Physics '3420': High Energy Physics - Theory;Strongly Correlated Electrons;Superconductivity '3421': High Energy Physics - Theory;Strongly Correlated Electrons;Superconductivity;General Relativity and Quantum Cosmology '3422': High Energy Physics - Theory;Superconductivity '3423': High Energy Physics - Theory;Superconductivity;General Relativity and Quantum Cosmology '3424': High Energy Physics - Theory;Superconductivity;High Energy Physics - Phenomenology '3425': High Energy Physics - Theory;Symplectic Geometry '3426': History and Overview '3427': History and Overview;Algebraic Geometry '3428': History and Overview;Classical Analysis and ODEs '3429': History and Overview;Classical Analysis and ODEs;Logic '3430': History and Overview;Combinatorics '3431': History and Overview;Commutative Algebra '3432': History and Overview;Complex Variables '3433': History and Overview;Differential Geometry '3434': History and Overview;Dynamical Systems '3435': History and Overview;Functional Analysis '3436': History and Overview;General Mathematics '3437': History and Overview;Geometric Topology '3438': History and Overview;Group Theory '3439': History and Overview;History and Philosophy of Physics '3440': History and Overview;Logic '3441': History and Overview;Metric Geometry '3442': History and Overview;Number Theory '3443': History and Overview;Optimization and Control '3444': History and Overview;Physics Education '3445': History and Overview;Probability '3446': History and Overview;Rings and Algebras '3447': History and Philosophy of Physics '3448': History and Philosophy of Physics;Astrophysics of Galaxies '3449': History and Philosophy of Physics;Atomic Physics '3450': History and Philosophy of Physics;Classical Physics '3451': History and Philosophy of Physics;Cosmology and Nongalactic Astrophysics '3452': History and Philosophy of Physics;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '3453': History and Philosophy of Physics;Earth and Planetary Astrophysics '3454': History and Philosophy of Physics;General Physics '3455': History and Philosophy of Physics;General Relativity and Quantum Cosmology '3456': History and Philosophy of Physics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '3457': History and Philosophy of Physics;General Relativity and Quantum Cosmology;Quantum Physics '3458': History and Philosophy of Physics;High Energy Astrophysical Phenomena '3459': History and Philosophy of Physics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3460': History and Philosophy of Physics;High Energy Physics - Phenomenology '3461': History and Philosophy of Physics;High Energy Physics - Phenomenology;High Energy Physics - Theory '3462': History and Philosophy of Physics;High Energy Physics - Theory '3463': History and Philosophy of Physics;High Energy Physics - Theory;Quantum Physics '3464': History and Philosophy of Physics;History and Overview '3465': History and Philosophy of Physics;Instrumentation and Methods for Astrophysics '3466': History and Philosophy of Physics;Physics Education '3467': History and Philosophy of Physics;Physics and Society '3468': History and Philosophy of Physics;Popular Physics '3469': History and Philosophy of Physics;Quantum Physics '3470': History and Philosophy of Physics;Solar and Stellar Astrophysics '3471': History and Philosophy of Physics;Statistical Mechanics '3472': History and Philosophy of Physics;Statistical Mechanics;Quantum Physics '3473': History and Philosophy of Physics;Superconductivity '3474': Human-Computer Interaction '3475': Human-Computer Interaction;Applications '3476': Human-Computer Interaction;Artificial Intelligence '3477': Human-Computer Interaction;Artificial Intelligence;Computation and Language '3478': Human-Computer Interaction;Artificial Intelligence;Computation and Language;Machine Learning '3479': Human-Computer Interaction;Artificial Intelligence;Computer Vision and Pattern Recognition '3480': Human-Computer Interaction;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '3481': Human-Computer Interaction;Artificial Intelligence;Computers and Society '3482': Human-Computer Interaction;Artificial Intelligence;Information Retrieval '3483': Human-Computer Interaction;Artificial Intelligence;Machine Learning '3484': Human-Computer Interaction;Artificial Intelligence;Robotics '3485': Human-Computer Interaction;Computation and Language '3486': Human-Computer Interaction;Computation and Language;Computers and Society '3487': Human-Computer Interaction;Computation and Language;Machine Learning '3488': Human-Computer Interaction;Computer Science and Game Theory '3489': Human-Computer Interaction;Computer Vision and Pattern Recognition '3490': Human-Computer Interaction;Computer Vision and Pattern Recognition;Graphics '3491': Human-Computer Interaction;Computer Vision and Pattern Recognition;Image and Video Processing '3492': Human-Computer Interaction;Computer Vision and Pattern Recognition;Machine Learning '3493': Human-Computer Interaction;Computers and Society '3494': Human-Computer Interaction;Computers and Society;Machine Learning '3495': Human-Computer Interaction;Computers and Society;Multimedia '3496': Human-Computer Interaction;Computers and Society;Social and Information Networks '3497': Human-Computer Interaction;Cryptography and Security '3498': Human-Computer Interaction;Cryptography and Security;Computers and Society '3499': Human-Computer Interaction;Databases '3500': Human-Computer Interaction;Digital Libraries '3501': Human-Computer Interaction;Graphics '3502': Human-Computer Interaction;Image and Video Processing '3503': Human-Computer Interaction;Information Retrieval '3504': Human-Computer Interaction;Information Retrieval;Machine Learning '3505': Human-Computer Interaction;Machine Learning '3506': Human-Computer Interaction;Machine Learning;Machine Learning '3507': Human-Computer Interaction;Machine Learning;Multimedia '3508': Human-Computer Interaction;Machine Learning;Signal Processing '3509': Human-Computer Interaction;Machine Learning;Sound;Audio and Speech Processing '3510': Human-Computer Interaction;Multimedia '3511': Human-Computer Interaction;Networking and Internet Architecture '3512': Human-Computer Interaction;Neural and Evolutionary Computing '3513': Human-Computer Interaction;Neurons and Cognition '3514': Human-Computer Interaction;Programming Languages '3515': Human-Computer Interaction;Robotics '3516': Human-Computer Interaction;Signal Processing '3517': Human-Computer Interaction;Social and Information Networks '3518': Human-Computer Interaction;Software Engineering '3519': Human-Computer Interaction;Sound;Audio and Speech Processing '3520': Human-Computer Interaction;Systems and Control;Systems and Control '3521': Image and Video Processing '3522': Image and Video Processing;Applications '3523': Image and Video Processing;Applied Physics '3524': Image and Video Processing;Applied Physics;Optics '3525': Image and Video Processing;Artificial Intelligence '3526': Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition '3527': Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '3528': Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics '3529': Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Quantitative Methods '3530': Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Medical Physics '3531': Image and Video Processing;Artificial Intelligence;Machine Learning '3532': Image and Video Processing;Computer Vision and Pattern Recognition '3533': Image and Video Processing;Computer Vision and Pattern Recognition;Graphics '3534': Image and Video Processing;Computer Vision and Pattern Recognition;Human-Computer Interaction '3535': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning '3536': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning '3537': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics '3538': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Multimedia '3539': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing '3540': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Neurons and Cognition '3541': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Quantitative Methods '3542': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Signal Processing '3543': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Signal Processing;Medical Physics '3544': Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Tissues and Organs '3545': Image and Video Processing;Computer Vision and Pattern Recognition;Medical Physics '3546': Image and Video Processing;Computer Vision and Pattern Recognition;Multimedia '3547': Image and Video Processing;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing '3548': Image and Video Processing;Computer Vision and Pattern Recognition;Neurons and Cognition '3549': Image and Video Processing;Computer Vision and Pattern Recognition;Numerical Analysis;Numerical Analysis '3550': Image and Video Processing;Computer Vision and Pattern Recognition;Optics '3551': Image and Video Processing;Computer Vision and Pattern Recognition;Quantitative Methods '3552': Image and Video Processing;Computer Vision and Pattern Recognition;Robotics '3553': Image and Video Processing;Computer Vision and Pattern Recognition;Signal Processing '3554': Image and Video Processing;Computer Vision and Pattern Recognition;Tissues and Organs '3555': Image and Video Processing;Cryptography and Security;Computer Vision and Pattern Recognition '3556': Image and Video Processing;Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning '3557': Image and Video Processing;Data Analysis, Statistics and Probability '3558': Image and Video Processing;Graphics '3559': Image and Video Processing;Instrumentation and Detectors '3560': Image and Video Processing;Machine Learning '3561': Image and Video Processing;Machine Learning;Machine Learning '3562': Image and Video Processing;Machine Learning;Medical Physics '3563': Image and Video Processing;Machine Learning;Quantitative Methods '3564': Image and Video Processing;Machine Learning;Signal Processing '3565': Image and Video Processing;Machine Learning;Signal Processing;Machine Learning '3566': Image and Video Processing;Medical Physics '3567': Image and Video Processing;Medical Physics;Optics '3568': Image and Video Processing;Multimedia '3569': Image and Video Processing;Neural and Evolutionary Computing '3570': Image and Video Processing;Neurons and Cognition '3571': Image and Video Processing;Numerical Analysis;Numerical Analysis '3572': Image and Video Processing;Optics '3573': Image and Video Processing;Optimization and Control '3574': Image and Video Processing;Quantitative Methods '3575': Image and Video Processing;Signal Processing '3576': Image and Video Processing;Signal Processing;Optics '3577': Information Retrieval '3578': Information Retrieval;Applications '3579': Information Retrieval;Artificial Intelligence '3580': Information Retrieval;Artificial Intelligence;Computation and Language '3581': Information Retrieval;Artificial Intelligence;Computation and Language;Machine Learning '3582': Information Retrieval;Artificial Intelligence;Computer Vision and Pattern Recognition '3583': Information Retrieval;Artificial Intelligence;Cryptography and Security;Machine Learning '3584': Information Retrieval;Artificial Intelligence;Databases '3585': Information Retrieval;Artificial Intelligence;Human-Computer Interaction '3586': Information Retrieval;Artificial Intelligence;Machine Learning '3587': Information Retrieval;Artificial Intelligence;Machine Learning;Machine Learning '3588': Information Retrieval;Artificial Intelligence;Machine Learning;Social and Information Networks '3589': Information Retrieval;Artificial Intelligence;Social and Information Networks '3590': Information Retrieval;Computation and Language '3591': Information Retrieval;Computation and Language;Digital Libraries '3592': Information Retrieval;Computation and Language;Machine Learning '3593': Information Retrieval;Computation and Language;Machine Learning;Machine Learning '3594': Information Retrieval;Computation and Language;Social and Information Networks '3595': Information Retrieval;Computer Vision and Pattern Recognition '3596': Information Retrieval;Computer Vision and Pattern Recognition;Machine Learning '3597': Information Retrieval;Computer Vision and Pattern Recognition;Multimedia '3598': Information Retrieval;Computers and Society '3599': Information Retrieval;Computers and Society;Machine Learning '3600': Information Retrieval;Cryptography and Security '3601': Information Retrieval;Cryptography and Security;Machine Learning '3602': Information Retrieval;Data Structures and Algorithms '3603': Information Retrieval;Databases '3604': Information Retrieval;Digital Libraries '3605': Information Retrieval;Digital Libraries;Human-Computer Interaction '3606': Information Retrieval;Digital Libraries;Machine Learning '3607': Information Retrieval;Distributed, Parallel, and Cluster Computing '3608': Information Retrieval;Human-Computer Interaction '3609': Information Retrieval;Human-Computer Interaction;Machine Learning '3610': Information Retrieval;Machine Learning '3611': Information Retrieval;Machine Learning;Machine Learning '3612': Information Retrieval;Machine Learning;Multimedia '3613': Information Retrieval;Machine Learning;Neural and Evolutionary Computing '3614': Information Retrieval;Machine Learning;Social and Information Networks '3615': Information Retrieval;Machine Learning;Social and Information Networks;Machine Learning '3616': Information Retrieval;Machine Learning;Sound;Audio and Speech Processing '3617': Information Retrieval;Multimedia '3618': Information Retrieval;Neural and Evolutionary Computing '3619': Information Retrieval;Physics and Society '3620': Information Retrieval;Social and Information Networks '3621': Information Retrieval;Social and Information Networks;Physics and Society '3622': Information Retrieval;Software Engineering '3623': Information Retrieval;Sound;Audio and Speech Processing '3624': Instrumentation and Detectors '3625': Instrumentation and Detectors;Accelerator Physics '3626': Instrumentation and Detectors;Applied Physics '3627': Instrumentation and Detectors;Applied Physics;Optics '3628': Instrumentation and Detectors;Applied Physics;Quantum Physics '3629': Instrumentation and Detectors;Astrophysics '3630': Instrumentation and Detectors;Astrophysics;High Energy Physics - Experiment '3631': Instrumentation and Detectors;Atomic Physics '3632': Instrumentation and Detectors;Atomic Physics;Optics '3633': Instrumentation and Detectors;Biological Physics '3634': Instrumentation and Detectors;Chemical Physics '3635': Instrumentation and Detectors;Classical Physics '3636': Instrumentation and Detectors;Computational Physics '3637': Instrumentation and Detectors;Cosmology and Nongalactic Astrophysics '3638': Instrumentation and Detectors;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment '3639': Instrumentation and Detectors;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3640': Instrumentation and Detectors;Cosmology and Nongalactic Astrophysics;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment '3641': Instrumentation and Detectors;Data Analysis, Statistics and Probability '3642': Instrumentation and Detectors;Fluid Dynamics '3643': Instrumentation and Detectors;General Physics '3644': Instrumentation and Detectors;General Relativity and Quantum Cosmology '3645': Instrumentation and Detectors;Geophysics '3646': Instrumentation and Detectors;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '3647': Instrumentation and Detectors;High Energy Physics - Experiment '3648': Instrumentation and Detectors;High Energy Physics - Experiment;Accelerator Physics '3649': Instrumentation and Detectors;High Energy Physics - Experiment;Computational Physics '3650': Instrumentation and Detectors;High Energy Physics - Experiment;Data Analysis, Statistics and Probability '3651': Instrumentation and Detectors;High Energy Physics - Experiment;High Energy Physics - Phenomenology '3652': Instrumentation and Detectors;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment '3653': Instrumentation and Detectors;High Energy Physics - Experiment;Medical Physics '3654': Instrumentation and Detectors;High Energy Physics - Experiment;Nuclear Experiment '3655': Instrumentation and Detectors;High Energy Physics - Phenomenology '3656': Instrumentation and Detectors;Image and Video Processing '3657': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics '3658': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology '3659': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment '3660': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment;Nuclear Experiment '3661': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics;Nuclear Experiment '3662': Instrumentation and Detectors;Instrumentation and Methods for Astrophysics;Optics '3663': Instrumentation and Detectors;Machine Learning '3664': Instrumentation and Detectors;Machine Learning;High Energy Physics - Experiment '3665': Instrumentation and Detectors;Machine Learning;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Data Analysis, Statistics and Probability '3666': Instrumentation and Detectors;Materials Science '3667': Instrumentation and Detectors;Materials Science;Optics '3668': Instrumentation and Detectors;Medical Physics '3669': Instrumentation and Detectors;Mesoscale and Nanoscale Physics '3670': Instrumentation and Detectors;Mesoscale and Nanoscale Physics;Applied Physics '3671': Instrumentation and Detectors;Mesoscale and Nanoscale Physics;Materials Science '3672': Instrumentation and Detectors;Mesoscale and Nanoscale Physics;Optics '3673': Instrumentation and Detectors;Nuclear Experiment '3674': Instrumentation and Detectors;Nuclear Experiment;Accelerator Physics '3675': Instrumentation and Detectors;Nuclear Experiment;Applied Physics '3676': Instrumentation and Detectors;Nuclear Experiment;Atomic Physics '3677': Instrumentation and Detectors;Nuclear Experiment;Medical Physics '3678': Instrumentation and Detectors;Optics '3679': Instrumentation and Detectors;Optics;Quantum Physics '3680': Instrumentation and Detectors;Other Condensed Matter '3681': Instrumentation and Detectors;Plasma Physics '3682': Instrumentation and Detectors;Quantum Physics '3683': Instrumentation and Detectors;Signal Processing '3684': Instrumentation and Detectors;Soft Condensed Matter '3685': Instrumentation and Detectors;Space Physics '3686': Instrumentation and Detectors;Strongly Correlated Electrons '3687': Instrumentation and Detectors;Superconductivity '3688': Instrumentation and Detectors;Systems and Control;Systems and Control '3689': Instrumentation and Methods for Astrophysics '3690': Instrumentation and Methods for Astrophysics;Applications '3691': Instrumentation and Methods for Astrophysics;Artificial Intelligence '3692': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies '3693': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;Chemical Physics '3694': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;Computational Physics '3695': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;General Relativity and Quantum Cosmology '3696': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '3697': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3698': Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '3699': Instrumentation and Methods for Astrophysics;Atmospheric and Oceanic Physics '3700': Instrumentation and Methods for Astrophysics;Atmospheric and Oceanic Physics;Instrumentation and Detectors '3701': Instrumentation and Methods for Astrophysics;Atomic Physics '3702': Instrumentation and Methods for Astrophysics;Chemical Physics '3703': Instrumentation and Methods for Astrophysics;Computational Physics '3704': Instrumentation and Methods for Astrophysics;Computational Physics;Fluid Dynamics '3705': Instrumentation and Methods for Astrophysics;Computer Vision and Pattern Recognition '3706': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics '3707': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '3708': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '3709': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3710': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '3711': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Computational Physics '3712': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Earth and Planetary Astrophysics '3713': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '3714': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology '3715': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '3716': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;High Energy Physics - Experiment;Instrumentation and Detectors '3717': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Instrumentation and Detectors '3718': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Machine Learning '3719': Instrumentation and Methods for Astrophysics;Cosmology and Nongalactic Astrophysics;Solar and Stellar Astrophysics '3720': Instrumentation and Methods for Astrophysics;Data Analysis, Statistics and Probability '3721': Instrumentation and Methods for Astrophysics;Databases '3722': Instrumentation and Methods for Astrophysics;Digital Libraries '3723': Instrumentation and Methods for Astrophysics;Digital Libraries;Physics and Society '3724': Instrumentation and Methods for Astrophysics;Distributed, Parallel, and Cluster Computing '3725': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics '3726': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies '3727': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies;Solar and Stellar Astrophysics '3728': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Machine Learning '3729': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Popular Physics '3730': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '3731': Instrumentation and Methods for Astrophysics;Earth and Planetary Astrophysics;Space Physics '3732': Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology '3733': Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology;Data Analysis, Statistics and Probability '3734': Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology;Instrumentation and Detectors '3735': Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology;Optics '3736': Instrumentation and Methods for Astrophysics;Geophysics '3737': Instrumentation and Methods for Astrophysics;Graphics '3738': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena '3739': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;Data Analysis, Statistics and Probability '3740': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '3741': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Experiment '3742': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Experiment;Instrumentation and Detectors '3743': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '3744': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;Instrumentation and Detectors '3745': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;Machine Learning '3746': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '3747': Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Plasma Physics;Space Physics '3748': Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment '3749': Instrumentation and Methods for Astrophysics;High Energy Physics - Experiment;Instrumentation and Detectors '3750': Instrumentation and Methods for Astrophysics;High Energy Physics - Phenomenology '3751': Instrumentation and Methods for Astrophysics;History and Philosophy of Physics '3752': Instrumentation and Methods for Astrophysics;Human-Computer Interaction '3753': Instrumentation and Methods for Astrophysics;Image and Video Processing '3754': Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '3755': Instrumentation and Methods for Astrophysics;Instrumentation and Detectors;Optics '3756': Instrumentation and Methods for Astrophysics;Machine Learning '3757': Instrumentation and Methods for Astrophysics;Methodology '3758': Instrumentation and Methods for Astrophysics;Nuclear Experiment '3759': Instrumentation and Methods for Astrophysics;Numerical Analysis '3760': Instrumentation and Methods for Astrophysics;Optics '3761': Instrumentation and Methods for Astrophysics;Physics Education '3762': Instrumentation and Methods for Astrophysics;Physics and Society '3763': Instrumentation and Methods for Astrophysics;Popular Physics '3764': Instrumentation and Methods for Astrophysics;Quantum Physics '3765': Instrumentation and Methods for Astrophysics;Robotics '3766': Instrumentation and Methods for Astrophysics;Signal Processing '3767': Instrumentation and Methods for Astrophysics;Software Engineering '3768': Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics '3769': Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics;Data Analysis, Statistics and Probability '3770': Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics;Space Physics '3771': Instrumentation and Methods for Astrophysics;Space Physics '3772': Instrumentation and Methods for Astrophysics;Superconductivity '3773': Instrumentation and Methods for Astrophysics;Superconductivity;Instrumentation and Detectors '3774': Instrumentation and Methods for Astrophysics;Systems and Control;Systems and Control '3775': K-Theory and Homology '3776': K-Theory and Homology;Algebraic Geometry '3777': K-Theory and Homology;Algebraic Geometry;Algebraic Topology '3778': K-Theory and Homology;Algebraic Geometry;Algebraic Topology;Category Theory '3779': K-Theory and Homology;Algebraic Topology '3780': K-Theory and Homology;Algebraic Topology;Category Theory '3781': K-Theory and Homology;Algebraic Topology;Differential Geometry '3782': K-Theory and Homology;Algebraic Topology;Geometric Topology '3783': K-Theory and Homology;Algebraic Topology;Operator Algebras '3784': K-Theory and Homology;Category Theory '3785': K-Theory and Homology;Commutative Algebra '3786': K-Theory and Homology;Differential Geometry '3787': K-Theory and Homology;Differential Geometry;Operator Algebras '3788': K-Theory and Homology;Dynamical Systems;Operator Algebras '3789': K-Theory and Homology;Functional Analysis '3790': K-Theory and Homology;Functional Analysis;Operator Algebras '3791': K-Theory and Homology;Geometric Topology '3792': K-Theory and Homology;Group Theory '3793': K-Theory and Homology;Number Theory '3794': K-Theory and Homology;Operator Algebras '3795': K-Theory and Homology;Operator Algebras;Quantum Algebra '3796': K-Theory and Homology;Quantum Algebra '3797': K-Theory and Homology;Representation Theory '3798': K-Theory and Homology;Rings and Algebras '3799': K-Theory and Homology;Rings and Algebras;Representation Theory '3800': Logic '3801': Logic in Computer Science '3802': Logic in Computer Science;Algebraic Topology '3803': Logic in Computer Science;Artificial Intelligence '3804': Logic in Computer Science;Artificial Intelligence;Computational Complexity '3805': Logic in Computer Science;Artificial Intelligence;Databases '3806': Logic in Computer Science;Artificial Intelligence;Logic '3807': Logic in Computer Science;Artificial Intelligence;Machine Learning '3808': Logic in Computer Science;Artificial Intelligence;Multiagent Systems '3809': Logic in Computer Science;Artificial Intelligence;Programming Languages '3810': Logic in Computer Science;Category Theory '3811': Logic in Computer Science;Category Theory;Logic '3812': Logic in Computer Science;Category Theory;Quantum Physics '3813': Logic in Computer Science;Combinatorics '3814': Logic in Computer Science;Computation and Language '3815': Logic in Computer Science;Computational Complexity '3816': Logic in Computer Science;Computational Complexity;Logic '3817': Logic in Computer Science;Computational Complexity;Programming Languages '3818': Logic in Computer Science;Computer Science and Game Theory '3819': Logic in Computer Science;Cryptography and Security '3820': Logic in Computer Science;Data Structures and Algorithms '3821': Logic in Computer Science;Databases '3822': Logic in Computer Science;Discrete Mathematics '3823': Logic in Computer Science;Discrete Mathematics;Combinatorics '3824': Logic in Computer Science;Distributed, Parallel, and Cluster Computing '3825': Logic in Computer Science;Distributed, Parallel, and Cluster Computing;Formal Languages and Automata Theory '3826': Logic in Computer Science;Distributed, Parallel, and Cluster Computing;Programming Languages '3827': Logic in Computer Science;Formal Languages and Automata Theory '3828': Logic in Computer Science;Formal Languages and Automata Theory;Computer Science and Game Theory '3829': Logic in Computer Science;Formal Languages and Automata Theory;Logic '3830': Logic in Computer Science;Formal Languages and Automata Theory;Programming Languages '3831': Logic in Computer Science;General Topology '3832': Logic in Computer Science;Human-Computer Interaction '3833': Logic in Computer Science;Logic '3834': Logic in Computer Science;Machine Learning '3835': Logic in Computer Science;Machine Learning;Programming Languages '3836': Logic in Computer Science;Mathematical Software '3837': Logic in Computer Science;Multiagent Systems '3838': Logic in Computer Science;Number Theory '3839': Logic in Computer Science;Numerical Analysis '3840': Logic in Computer Science;Optimization and Control '3841': Logic in Computer Science;Performance '3842': Logic in Computer Science;Programming Languages '3843': Logic in Computer Science;Programming Languages;Category Theory '3844': Logic in Computer Science;Programming Languages;Logic '3845': Logic in Computer Science;Programming Languages;Software Engineering '3846': Logic in Computer Science;Programming Languages;Symbolic Computation '3847': Logic in Computer Science;Quantum Physics '3848': Logic in Computer Science;Rings and Algebras '3849': Logic in Computer Science;Software Engineering '3850': Logic in Computer Science;Symbolic Computation '3851': Logic in Computer Science;Systems and Control '3852': Logic in Computer Science;Systems and Control;Systems and Control '3853': Logic;Algebraic Geometry '3854': Logic;Algebraic Geometry;Number Theory '3855': Logic;Algebraic Topology '3856': Logic;Artificial Intelligence '3857': Logic;Category Theory '3858': Logic;Classical Analysis and ODEs '3859': Logic;Combinatorics '3860': Logic;Combinatorics;Dynamical Systems '3861': Logic;Combinatorics;General Topology '3862': Logic;Combinatorics;Group Theory '3863': Logic;Commutative Algebra '3864': Logic;Complex Variables '3865': Logic;Computational Complexity '3866': Logic;Computational Complexity;Logic in Computer Science '3867': Logic;Dynamical Systems '3868': Logic;Functional Analysis '3869': Logic;General Topology '3870': Logic;General Topology;Group Theory '3871': Logic;General Topology;Rings and Algebras '3872': Logic;Group Theory '3873': Logic;History and Overview '3874': Logic;Logic in Computer Science '3875': Logic;Logic in Computer Science;Category Theory '3876': Logic;Logic in Computer Science;Combinatorics '3877': Logic;Metric Geometry '3878': Logic;Number Theory '3879': Logic;Operator Algebras '3880': Logic;Probability '3881': Logic;Rings and Algebras '3882': Machine Learning '3883': Machine Learning;Algebraic Topology '3884': Machine Learning;Algebraic Topology;Machine Learning '3885': Machine Learning;Analysis of PDEs;Machine Learning '3886': Machine Learning;Applications '3887': Machine Learning;Applications;Computation '3888': Machine Learning;Applications;Machine Learning '3889': Machine Learning;Applications;Methodology '3890': Machine Learning;Applications;Methodology;Machine Learning '3891': Machine Learning;Applied Physics '3892': Machine Learning;Artificial Intelligence '3893': Machine Learning;Artificial Intelligence;Applications '3894': Machine Learning;Artificial Intelligence;Applications;Machine Learning '3895': Machine Learning;Artificial Intelligence;Atmospheric and Oceanic Physics '3896': Machine Learning;Artificial Intelligence;Biomolecules '3897': Machine Learning;Artificial Intelligence;Chemical Physics '3898': Machine Learning;Artificial Intelligence;Computation and Language '3899': Machine Learning;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition '3900': Machine Learning;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition;Machine Learning '3901': Machine Learning;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition;Multimedia '3902': Machine Learning;Artificial Intelligence;Computation and Language;Computers and Society '3903': Machine Learning;Artificial Intelligence;Computation and Language;Human-Computer Interaction '3904': Machine Learning;Artificial Intelligence;Computation and Language;Information Retrieval '3905': Machine Learning;Artificial Intelligence;Computation and Language;Machine Learning '3906': Machine Learning;Artificial Intelligence;Computation and Language;Neural and Evolutionary Computing '3907': Machine Learning;Artificial Intelligence;Computational Complexity '3908': Machine Learning;Artificial Intelligence;Computational Engineering, Finance, and Science '3909': Machine Learning;Artificial Intelligence;Computational Physics '3910': Machine Learning;Artificial Intelligence;Computer Science and Game Theory '3911': Machine Learning;Artificial Intelligence;Computer Science and Game Theory;Machine Learning '3912': Machine Learning;Artificial Intelligence;Computer Science and Game Theory;Multiagent Systems '3913': Machine Learning;Artificial Intelligence;Computer Science and Game Theory;Multiagent Systems;Machine Learning '3914': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition '3915': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Computers and Society '3916': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing '3917': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Image and Video Processing '3918': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '3919': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing '3920': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Machine Learning '3921': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Robotics '3922': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics '3923': Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics;Machine Learning '3924': Machine Learning;Artificial Intelligence;Computers and Society '3925': Machine Learning;Artificial Intelligence;Computers and Society;Human-Computer Interaction '3926': Machine Learning;Artificial Intelligence;Computers and Society;Machine Learning '3927': Machine Learning;Artificial Intelligence;Cryptography and Security '3928': Machine Learning;Artificial Intelligence;Cryptography and Security;Computer Vision and Pattern Recognition '3929': Machine Learning;Artificial Intelligence;Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning '3930': Machine Learning;Artificial Intelligence;Cryptography and Security;Computers and Society '3931': Machine Learning;Artificial Intelligence;Cryptography and Security;Distributed, Parallel, and Cluster Computing '3932': Machine Learning;Artificial Intelligence;Cryptography and Security;Machine Learning '3933': Machine Learning;Artificial Intelligence;Data Structures and Algorithms '3934': Machine Learning;Artificial Intelligence;Data Structures and Algorithms;Machine Learning '3935': Machine Learning;Artificial Intelligence;Databases '3936': Machine Learning;Artificial Intelligence;Databases;Machine Learning '3937': Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing '3938': Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Machine Learning '3939': Machine Learning;Artificial Intelligence;Dynamical Systems '3940': Machine Learning;Artificial Intelligence;Emerging Technologies '3941': Machine Learning;Artificial Intelligence;Genomics '3942': Machine Learning;Artificial Intelligence;Hardware Architecture '3943': Machine Learning;Artificial Intelligence;Human-Computer Interaction '3944': Machine Learning;Artificial Intelligence;Human-Computer Interaction;Machine Learning '3945': Machine Learning;Artificial Intelligence;Image and Video Processing '3946': Machine Learning;Artificial Intelligence;Information Retrieval '3947': Machine Learning;Artificial Intelligence;Information Retrieval;Machine Learning '3948': Machine Learning;Artificial Intelligence;Information Retrieval;Social and Information Networks '3949': Machine Learning;Artificial Intelligence;Logic in Computer Science '3950': Machine Learning;Artificial Intelligence;Logic in Computer Science;Machine Learning '3951': Machine Learning;Artificial Intelligence;Machine Learning '3952': Machine Learning;Artificial Intelligence;Machine Learning;Applications '3953': Machine Learning;Artificial Intelligence;Machine Learning;Computation '3954': Machine Learning;Artificial Intelligence;Machine Learning;Methodology '3955': Machine Learning;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '3956': Machine Learning;Artificial Intelligence;Machine Learning;Optimization and Control '3957': Machine Learning;Artificial Intelligence;Machine Learning;Probability '3958': Machine Learning;Artificial Intelligence;Methodology '3959': Machine Learning;Artificial Intelligence;Methodology;Machine Learning '3960': Machine Learning;Artificial Intelligence;Multiagent Systems '3961': Machine Learning;Artificial Intelligence;Multiagent Systems;Machine Learning '3962': Machine Learning;Artificial Intelligence;Multiagent Systems;Robotics '3963': Machine Learning;Artificial Intelligence;Multiagent Systems;Systems and Control;Systems and Control '3964': Machine Learning;Artificial Intelligence;Networking and Internet Architecture '3965': Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing '3966': Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing;Machine Learning '3967': Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing;Robotics '3968': Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing;Robotics;Machine Learning '3969': Machine Learning;Artificial Intelligence;Neurons and Cognition '3970': Machine Learning;Artificial Intelligence;Neurons and Cognition;Machine Learning '3971': Machine Learning;Artificial Intelligence;Numerical Analysis;Numerical Analysis '3972': Machine Learning;Artificial Intelligence;Optimization and Control '3973': Machine Learning;Artificial Intelligence;Optimization and Control;Machine Learning '3974': Machine Learning;Artificial Intelligence;Performance '3975': Machine Learning;Artificial Intelligence;Programming Languages '3976': Machine Learning;Artificial Intelligence;Programming Languages;Machine Learning '3977': Machine Learning;Artificial Intelligence;Programming Languages;Software Engineering '3978': Machine Learning;Artificial Intelligence;Quantitative Methods '3979': Machine Learning;Artificial Intelligence;Quantum Physics '3980': Machine Learning;Artificial Intelligence;Robotics '3981': Machine Learning;Artificial Intelligence;Robotics;Machine Learning '3982': Machine Learning;Artificial Intelligence;Robotics;Systems and Control;Systems and Control '3983': Machine Learning;Artificial Intelligence;Signal Processing '3984': Machine Learning;Artificial Intelligence;Signal Processing;Machine Learning '3985': Machine Learning;Artificial Intelligence;Social and Information Networks '3986': Machine Learning;Artificial Intelligence;Social and Information Networks;Machine Learning '3987': Machine Learning;Artificial Intelligence;Software Engineering '3988': Machine Learning;Artificial Intelligence;Software Engineering;Machine Learning '3989': Machine Learning;Artificial Intelligence;Sound;Audio and Speech Processing '3990': Machine Learning;Artificial Intelligence;Symbolic Computation '3991': Machine Learning;Artificial Intelligence;Systems and Control;Systems and Control '3992': Machine Learning;Artificial Intelligence;Systems and Control;Systems and Control;Machine Learning '3993': Machine Learning;Artificial Intelligence;Systems and Control;Systems and Control;Optimization and Control '3994': Machine Learning;Atmospheric and Oceanic Physics '3995': Machine Learning;Atmospheric and Oceanic Physics;Machine Learning '3996': Machine Learning;Audio and Speech Processing '3997': Machine Learning;Audio and Speech Processing;Machine Learning '3998': Machine Learning;Biomolecules '3999': Machine Learning;Biomolecules;Machine Learning '4000': Machine Learning;Biomolecules;Quantitative Methods '4001': Machine Learning;Chaotic Dynamics '4002': Machine Learning;Chemical Physics '4003': Machine Learning;Chemical Physics;Biomolecules '4004': Machine Learning;Chemical Physics;Machine Learning '4005': Machine Learning;Chemical Physics;Quantitative Methods '4006': Machine Learning;Combinatorics '4007': Machine Learning;Combinatorics;Machine Learning '4008': Machine Learning;Computation '4009': Machine Learning;Computation and Language '4010': Machine Learning;Computation and Language;Computer Vision and Pattern Recognition '4011': Machine Learning;Computation and Language;Computer Vision and Pattern Recognition;Machine Learning '4012': Machine Learning;Computation and Language;Computers and Society '4013': Machine Learning;Computation and Language;Cryptography and Security '4014': Machine Learning;Computation and Language;Cryptography and Security;Machine Learning '4015': Machine Learning;Computation and Language;Distributed, Parallel, and Cluster Computing '4016': Machine Learning;Computation and Language;Information Retrieval '4017': Machine Learning;Computation and Language;Information Retrieval;Machine Learning '4018': Machine Learning;Computation and Language;Machine Learning '4019': Machine Learning;Computation and Language;Neural and Evolutionary Computing '4020': Machine Learning;Computation and Language;Neural and Evolutionary Computing;Machine Learning '4021': Machine Learning;Computation and Language;Social and Information Networks '4022': Machine Learning;Computation and Language;Software Engineering '4023': Machine Learning;Computation and Language;Sound;Audio and Speech Processing '4024': Machine Learning;Computation and Language;Sound;Audio and Speech Processing;Machine Learning '4025': Machine Learning;Computation;Machine Learning '4026': Machine Learning;Computation;Methodology '4027': Machine Learning;Computational Complexity '4028': Machine Learning;Computational Complexity;Data Structures and Algorithms '4029': Machine Learning;Computational Complexity;Data Structures and Algorithms;Machine Learning '4030': Machine Learning;Computational Complexity;Machine Learning '4031': Machine Learning;Computational Engineering, Finance, and Science '4032': Machine Learning;Computational Engineering, Finance, and Science;Machine Learning '4033': Machine Learning;Computational Finance '4034': Machine Learning;Computational Finance;Machine Learning '4035': Machine Learning;Computational Geometry '4036': Machine Learning;Computational Geometry;Machine Learning '4037': Machine Learning;Computational Physics '4038': Machine Learning;Computational Physics;Machine Learning '4039': Machine Learning;Computer Science and Game Theory '4040': Machine Learning;Computer Science and Game Theory;Machine Learning '4041': Machine Learning;Computer Science and Game Theory;Multiagent Systems '4042': Machine Learning;Computer Science and Game Theory;Multiagent Systems;Machine Learning '4043': Machine Learning;Computer Science and Game Theory;Optimization and Control '4044': Machine Learning;Computer Science and Game Theory;Optimization and Control;Machine Learning '4045': Machine Learning;Computer Vision and Pattern Recognition '4046': Machine Learning;Computer Vision and Pattern Recognition;Computers and Society '4047': Machine Learning;Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing '4048': Machine Learning;Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing;Machine Learning '4049': Machine Learning;Computer Vision and Pattern Recognition;Graphics '4050': Machine Learning;Computer Vision and Pattern Recognition;Graphics;Machine Learning '4051': Machine Learning;Computer Vision and Pattern Recognition;Human-Computer Interaction '4052': Machine Learning;Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning '4053': Machine Learning;Computer Vision and Pattern Recognition;Image and Video Processing '4054': Machine Learning;Computer Vision and Pattern Recognition;Image and Video Processing;Machine Learning '4055': Machine Learning;Computer Vision and Pattern Recognition;Information Retrieval '4056': Machine Learning;Computer Vision and Pattern Recognition;Information Retrieval;Machine Learning '4057': Machine Learning;Computer Vision and Pattern Recognition;Machine Learning '4058': Machine Learning;Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing '4059': Machine Learning;Computer Vision and Pattern Recognition;Multimedia '4060': Machine Learning;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing '4061': Machine Learning;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Machine Learning '4062': Machine Learning;Computer Vision and Pattern Recognition;Neurons and Cognition '4063': Machine Learning;Computer Vision and Pattern Recognition;Numerical Analysis;Numerical Analysis '4064': Machine Learning;Computer Vision and Pattern Recognition;Optimization and Control '4065': Machine Learning;Computer Vision and Pattern Recognition;Optimization and Control;Machine Learning '4066': Machine Learning;Computer Vision and Pattern Recognition;Robotics '4067': Machine Learning;Computer Vision and Pattern Recognition;Robotics;Machine Learning '4068': Machine Learning;Computer Vision and Pattern Recognition;Signal Processing '4069': Machine Learning;Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing '4070': Machine Learning;Computers and Society '4071': Machine Learning;Computers and Society;Applications '4072': Machine Learning;Computers and Society;Human-Computer Interaction '4073': Machine Learning;Computers and Society;Information Retrieval '4074': Machine Learning;Computers and Society;Machine Learning '4075': Machine Learning;Computers and Society;Social and Information Networks '4076': Machine Learning;Computers and Society;Software Engineering '4077': Machine Learning;Cryptography and Security '4078': Machine Learning;Cryptography and Security;Computer Science and Game Theory '4079': Machine Learning;Cryptography and Security;Computer Vision and Pattern Recognition '4080': Machine Learning;Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning '4081': Machine Learning;Cryptography and Security;Computers and Society '4082': Machine Learning;Cryptography and Security;Computers and Society;Machine Learning '4083': Machine Learning;Cryptography and Security;Data Structures and Algorithms '4084': Machine Learning;Cryptography and Security;Data Structures and Algorithms;Machine Learning '4085': Machine Learning;Cryptography and Security;Databases '4086': Machine Learning;Cryptography and Security;Distributed, Parallel, and Cluster Computing '4087': Machine Learning;Cryptography and Security;Distributed, Parallel, and Cluster Computing;Machine Learning '4088': Machine Learning;Cryptography and Security;Information Retrieval '4089': Machine Learning;Cryptography and Security;Machine Learning '4090': Machine Learning;Cryptography and Security;Networking and Internet Architecture '4091': Machine Learning;Cryptography and Security;Neural and Evolutionary Computing '4092': Machine Learning;Cryptography and Security;Optimization and Control;Machine Learning '4093': Machine Learning;Cryptography and Security;Signal Processing '4094': Machine Learning;Cryptography and Security;Social and Information Networks '4095': Machine Learning;Cryptography and Security;Social and Information Networks;Machine Learning '4096': Machine Learning;Cryptography and Security;Software Engineering '4097': Machine Learning;Data Analysis, Statistics and Probability '4098': Machine Learning;Data Analysis, Statistics and Probability;Machine Learning '4099': Machine Learning;Data Structures and Algorithms '4100': Machine Learning;Data Structures and Algorithms;Computer Science and Game Theory '4101': Machine Learning;Data Structures and Algorithms;Computer Science and Game Theory;Machine Learning '4102': Machine Learning;Data Structures and Algorithms;Machine Learning '4103': Machine Learning;Data Structures and Algorithms;Optimization and Control '4104': Machine Learning;Data Structures and Algorithms;Optimization and Control;Machine Learning '4105': Machine Learning;Databases '4106': Machine Learning;Databases;Distributed, Parallel, and Cluster Computing '4107': Machine Learning;Databases;Information Retrieval '4108': Machine Learning;Databases;Information Retrieval;Machine Learning '4109': Machine Learning;Databases;Machine Learning '4110': Machine Learning;Discrete Mathematics '4111': Machine Learning;Discrete Mathematics;Machine Learning '4112': Machine Learning;Disordered Systems and Neural Networks '4113': Machine Learning;Disordered Systems and Neural Networks;Machine Learning '4114': Machine Learning;Disordered Systems and Neural Networks;Statistical Mechanics '4115': Machine Learning;Disordered Systems and Neural Networks;Statistical Mechanics;Machine Learning '4116': Machine Learning;Distributed, Parallel, and Cluster Computing '4117': Machine Learning;Distributed, Parallel, and Cluster Computing;Machine Learning '4118': Machine Learning;Distributed, Parallel, and Cluster Computing;Machine Learning;Optimization and Control '4119': Machine Learning;Distributed, Parallel, and Cluster Computing;Multiagent Systems '4120': Machine Learning;Distributed, Parallel, and Cluster Computing;Networking and Internet Architecture '4121': Machine Learning;Distributed, Parallel, and Cluster Computing;Neural and Evolutionary Computing '4122': Machine Learning;Distributed, Parallel, and Cluster Computing;Optimization and Control '4123': Machine Learning;Distributed, Parallel, and Cluster Computing;Optimization and Control;Machine Learning '4124': Machine Learning;Distributed, Parallel, and Cluster Computing;Performance '4125': Machine Learning;Distributed, Parallel, and Cluster Computing;Signal Processing '4126': Machine Learning;Distributed, Parallel, and Cluster Computing;Software Engineering '4127': Machine Learning;Distributed, Parallel, and Cluster Computing;Systems and Control;Systems and Control '4128': Machine Learning;Dynamical Systems '4129': Machine Learning;Dynamical Systems;Machine Learning '4130': Machine Learning;Dynamical Systems;Optimization and Control '4131': Machine Learning;Econometrics '4132': Machine Learning;Econometrics;Machine Learning '4133': Machine Learning;Emerging Technologies '4134': Machine Learning;Fluid Dynamics '4135': Machine Learning;Formal Languages and Automata Theory '4136': Machine Learning;Formal Languages and Automata Theory;Machine Learning '4137': Machine Learning;Functional Analysis '4138': Machine Learning;Functional Analysis;Machine Learning '4139': Machine Learning;Genomics '4140': Machine Learning;Genomics;Machine Learning '4141': Machine Learning;Geophysics '4142': Machine Learning;Graphics '4143': Machine Learning;Graphics;Machine Learning '4144': Machine Learning;Hardware Architecture '4145': Machine Learning;Hardware Architecture;Computer Vision and Pattern Recognition '4146': Machine Learning;Hardware Architecture;Distributed, Parallel, and Cluster Computing '4147': Machine Learning;Hardware Architecture;Machine Learning '4148': Machine Learning;Human-Computer Interaction '4149': Machine Learning;Human-Computer Interaction;Machine Learning '4150': Machine Learning;Human-Computer Interaction;Robotics '4151': Machine Learning;Human-Computer Interaction;Signal Processing '4152': Machine Learning;Image and Video Processing '4153': Machine Learning;Image and Video Processing;Machine Learning '4154': Machine Learning;Image and Video Processing;Signal Processing '4155': Machine Learning;Information Retrieval '4156': Machine Learning;Information Retrieval;Machine Learning '4157': Machine Learning;Information Retrieval;Social and Information Networks '4158': Machine Learning;Information Retrieval;Social and Information Networks;Machine Learning '4159': Machine Learning;Instrumentation and Methods for Astrophysics '4160': Machine Learning;Instrumentation and Methods for Astrophysics;Machine Learning '4161': Machine Learning;Logic in Computer Science '4162': Machine Learning;Logic in Computer Science;Machine Learning '4163': Machine Learning;Machine Learning '4164': Machine Learning;Machine Learning;Algebraic Topology '4165': Machine Learning;Machine Learning;Applications '4166': Machine Learning;Machine Learning;Applications;Computation '4167': Machine Learning;Machine Learning;Applications;Computation;Methodology '4168': Machine Learning;Machine Learning;Applications;Methodology '4169': Machine Learning;Machine Learning;Atmospheric and Oceanic Physics '4170': Machine Learning;Machine Learning;Biomolecules '4171': Machine Learning;Machine Learning;Chemical Physics '4172': Machine Learning;Machine Learning;Computation '4173': Machine Learning;Machine Learning;Computation;Methodology '4174': Machine Learning;Machine Learning;Computational Physics '4175': Machine Learning;Machine Learning;Data Analysis, Statistics and Probability '4176': Machine Learning;Machine Learning;Dynamical Systems '4177': Machine Learning;Machine Learning;Econometrics '4178': Machine Learning;Machine Learning;Econometrics;Methodology '4179': Machine Learning;Machine Learning;Functional Analysis '4180': Machine Learning;Machine Learning;Genomics '4181': Machine Learning;Machine Learning;Geophysics '4182': Machine Learning;Machine Learning;Image and Video Processing '4183': Machine Learning;Machine Learning;Methodology '4184': Machine Learning;Machine Learning;Neural and Evolutionary Computing '4185': Machine Learning;Machine Learning;Neurons and Cognition '4186': Machine Learning;Machine Learning;Numerical Analysis '4187': Machine Learning;Machine Learning;Numerical Analysis;Numerical Analysis '4188': Machine Learning;Machine Learning;Optimization and Control '4189': Machine Learning;Machine Learning;Optimization and Control;Computation '4190': Machine Learning;Machine Learning;Optimization and Control;Methodology '4191': Machine Learning;Machine Learning;Optimization and Control;Probability '4192': Machine Learning;Machine Learning;Probability '4193': Machine Learning;Machine Learning;Quantitative Methods '4194': Machine Learning;Machine Learning;Robotics '4195': Machine Learning;Machine Learning;Signal Processing '4196': Machine Learning;Machine Learning;Social and Information Networks '4197': Machine Learning;Machine Learning;Sound '4198': Machine Learning;Machine Learning;Sound;Audio and Speech Processing '4199': Machine Learning;Machine Learning;Systems and Control '4200': Machine Learning;Machine Learning;Systems and Control;Systems and Control '4201': Machine Learning;Materials Science '4202': Machine Learning;Materials Science;Machine Learning '4203': Machine Learning;Mathematical Software '4204': Machine Learning;Mathematical Software;Machine Learning '4205': Machine Learning;Methodology '4206': Machine Learning;Methodology;Machine Learning '4207': Machine Learning;Multiagent Systems '4208': Machine Learning;Multiagent Systems;Machine Learning '4209': Machine Learning;Multiagent Systems;Optimization and Control '4210': Machine Learning;Multimedia '4211': Machine Learning;Networking and Internet Architecture '4212': Machine Learning;Networking and Internet Architecture;Machine Learning '4213': Machine Learning;Networking and Internet Architecture;Signal Processing '4214': Machine Learning;Networking and Internet Architecture;Signal Processing;Machine Learning '4215': Machine Learning;Neural and Evolutionary Computing '4216': Machine Learning;Neural and Evolutionary Computing;Machine Learning '4217': Machine Learning;Neural and Evolutionary Computing;Neurons and Cognition '4218': Machine Learning;Neural and Evolutionary Computing;Neurons and Cognition;Machine Learning '4219': Machine Learning;Neural and Evolutionary Computing;Optimization and Control '4220': Machine Learning;Neural and Evolutionary Computing;Optimization and Control;Machine Learning '4221': Machine Learning;Neural and Evolutionary Computing;Signal Processing '4222': Machine Learning;Neural and Evolutionary Computing;Signal Processing;Machine Learning '4223': Machine Learning;Neural and Evolutionary Computing;Systems and Control;Systems and Control '4224': Machine Learning;Neurons and Cognition '4225': Machine Learning;Neurons and Cognition;Machine Learning '4226': Machine Learning;Numerical Analysis '4227': Machine Learning;Numerical Analysis;Dynamical Systems;Numerical Analysis '4228': Machine Learning;Numerical Analysis;Machine Learning '4229': Machine Learning;Numerical Analysis;Numerical Analysis '4230': Machine Learning;Numerical Analysis;Numerical Analysis;Computational Physics '4231': Machine Learning;Numerical Analysis;Numerical Analysis;Machine Learning '4232': Machine Learning;Numerical Analysis;Numerical Analysis;Optimization and Control '4233': Machine Learning;Numerical Analysis;Numerical Analysis;Optimization and Control;Machine Learning '4234': Machine Learning;Optics '4235': Machine Learning;Optimization and Control '4236': Machine Learning;Optimization and Control;Machine Learning '4237': Machine Learning;Optimization and Control;Probability;Machine Learning '4238': Machine Learning;Performance '4239': Machine Learning;Performance;Machine Learning '4240': Machine Learning;Physics and Society '4241': Machine Learning;Populations and Evolution '4242': Machine Learning;Probability '4243': Machine Learning;Probability;Machine Learning '4244': Machine Learning;Programming Languages '4245': Machine Learning;Programming Languages;Machine Learning '4246': Machine Learning;Programming Languages;Software Engineering '4247': Machine Learning;Programming Languages;Software Engineering;Machine Learning '4248': Machine Learning;Quantitative Methods '4249': Machine Learning;Quantitative Methods;Applications '4250': Machine Learning;Quantitative Methods;Machine Learning '4251': Machine Learning;Quantum Physics '4252': Machine Learning;Quantum Physics;Machine Learning '4253': Machine Learning;Robotics '4254': Machine Learning;Robotics;Machine Learning '4255': Machine Learning;Robotics;Systems and Control;Systems and Control '4256': Machine Learning;Robotics;Systems and Control;Systems and Control;Machine Learning '4257': Machine Learning;Signal Processing '4258': Machine Learning;Signal Processing;Applications '4259': Machine Learning;Signal Processing;Applications;Machine Learning '4260': Machine Learning;Signal Processing;Machine Learning '4261': Machine Learning;Signal Processing;Neurons and Cognition '4262': Machine Learning;Signal Processing;Optimization and Control '4263': Machine Learning;Signal Processing;Optimization and Control;Machine Learning '4264': Machine Learning;Social and Information Networks '4265': Machine Learning;Social and Information Networks;Machine Learning '4266': Machine Learning;Social and Information Networks;Physics and Society '4267': Machine Learning;Software Engineering '4268': Machine Learning;Software Engineering;Machine Learning '4269': Machine Learning;Sound '4270': Machine Learning;Sound;Audio and Speech Processing '4271': Machine Learning;Sound;Audio and Speech Processing;Machine Learning '4272': Machine Learning;Sound;Machine Learning '4273': Machine Learning;Statistical Finance '4274': Machine Learning;Statistical Finance;Machine Learning '4275': Machine Learning;Statistical Mechanics '4276': Machine Learning;Statistical Mechanics;Machine Learning '4277': Machine Learning;Symbolic Computation '4278': Machine Learning;Systems and Control '4279': Machine Learning;Systems and Control;Machine Learning '4280': Machine Learning;Systems and Control;Optimization and Control;Machine Learning '4281': Machine Learning;Systems and Control;Signal Processing;Systems and Control '4282': Machine Learning;Systems and Control;Systems and Control '4283': Machine Learning;Systems and Control;Systems and Control;Machine Learning '4284': Machine Learning;Systems and Control;Systems and Control;Optimization and Control '4285': Machine Learning;Systems and Control;Systems and Control;Optimization and Control;Machine Learning '4286': Machine Learning;Trading and Market Microstructure '4287': Materials Science '4288': Materials Science;Accelerator Physics '4289': Materials Science;Applied Physics '4290': Materials Science;Applied Physics;Chemical Physics '4291': Materials Science;Applied Physics;Computational Physics '4292': Materials Science;Applied Physics;Instrumentation and Detectors '4293': Materials Science;Applied Physics;Optics '4294': Materials Science;Artificial Intelligence '4295': Materials Science;Artificial Intelligence;Machine Learning '4296': Materials Science;Atomic Physics '4297': Materials Science;Atomic Physics;Quantum Physics '4298': Materials Science;Atomic and Molecular Clusters '4299': Materials Science;Atomic and Molecular Clusters;Chemical Physics '4300': Materials Science;Biological Physics '4301': Materials Science;Chemical Physics '4302': Materials Science;Chemical Physics;Computational Physics '4303': Materials Science;Chemical Physics;Materials Science '4304': Materials Science;Chemical Physics;Optics '4305': Materials Science;Chemical Physics;Quantum Physics '4306': Materials Science;Classical Physics '4307': Materials Science;Computational Engineering, Finance, and Science '4308': Materials Science;Computational Physics '4309': Materials Science;Computational Physics;Data Analysis, Statistics and Probability '4310': Materials Science;Computational Physics;Optics '4311': Materials Science;Computational Physics;Quantum Physics '4312': Materials Science;Data Analysis, Statistics and Probability '4313': Materials Science;Disordered Systems and Neural Networks '4314': Materials Science;Disordered Systems and Neural Networks;Computational Physics '4315': Materials Science;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '4316': Materials Science;Disordered Systems and Neural Networks;Soft Condensed Matter '4317': Materials Science;Disordered Systems and Neural Networks;Statistical Mechanics '4318': Materials Science;Disordered Systems and Neural Networks;Strongly Correlated Electrons '4319': Materials Science;Earth and Planetary Astrophysics '4320': Materials Science;Emerging Technologies '4321': Materials Science;Fluid Dynamics '4322': Materials Science;Geophysics '4323': Materials Science;Image and Video Processing '4324': Materials Science;Instrumentation and Detectors '4325': Materials Science;Instrumentation and Detectors;Optics '4326': Materials Science;Machine Learning '4327': Materials Science;Machine Learning;Chemical Physics '4328': Materials Science;Machine Learning;Computational Physics '4329': Materials Science;Materials Science '4330': Materials Science;Mesoscale and Nanoscale Physics '4331': Materials Science;Mesoscale and Nanoscale Physics;Applied Physics '4332': Materials Science;Mesoscale and Nanoscale Physics;Applied Physics;Chemical Physics '4333': Materials Science;Mesoscale and Nanoscale Physics;Applied Physics;Computational Physics '4334': Materials Science;Mesoscale and Nanoscale Physics;Applied Physics;Optics '4335': Materials Science;Mesoscale and Nanoscale Physics;Chemical Physics '4336': Materials Science;Mesoscale and Nanoscale Physics;Chemical Physics;Computational Physics '4337': Materials Science;Mesoscale and Nanoscale Physics;Computational Physics '4338': Materials Science;Mesoscale and Nanoscale Physics;Computational Physics;Quantum Physics '4339': Materials Science;Mesoscale and Nanoscale Physics;Instrumentation and Detectors '4340': Materials Science;Mesoscale and Nanoscale Physics;Optics '4341': Materials Science;Mesoscale and Nanoscale Physics;Optics;Quantum Physics '4342': Materials Science;Mesoscale and Nanoscale Physics;Other Condensed Matter '4343': Materials Science;Mesoscale and Nanoscale Physics;Pattern Formation and Solitons '4344': Materials Science;Mesoscale and Nanoscale Physics;Quantum Physics '4345': Materials Science;Mesoscale and Nanoscale Physics;Soft Condensed Matter '4346': Materials Science;Mesoscale and Nanoscale Physics;Statistical Mechanics '4347': Materials Science;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '4348': Materials Science;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Superconductivity '4349': Materials Science;Mesoscale and Nanoscale Physics;Superconductivity '4350': Materials Science;Nuclear Experiment '4351': Materials Science;Nuclear Theory '4352': Materials Science;Numerical Analysis;Numerical Analysis '4353': Materials Science;Optics '4354': Materials Science;Optics;Quantum Physics '4355': Materials Science;Other Condensed Matter '4356': Materials Science;Other Condensed Matter;Chemical Physics '4357': Materials Science;Other Condensed Matter;Chemical Physics;Computational Physics '4358': Materials Science;Other Condensed Matter;Computational Physics '4359': Materials Science;Other Condensed Matter;Optics '4360': Materials Science;Other Condensed Matter;Quantum Physics '4361': Materials Science;Other Condensed Matter;Strongly Correlated Electrons '4362': Materials Science;Pattern Formation and Solitons '4363': Materials Science;Plasma Physics '4364': Materials Science;Quantum Gases '4365': Materials Science;Quantum Physics '4366': Materials Science;Soft Condensed Matter '4367': Materials Science;Soft Condensed Matter;Applied Physics '4368': Materials Science;Soft Condensed Matter;Chemical Physics '4369': Materials Science;Soft Condensed Matter;Computational Physics '4370': Materials Science;Soft Condensed Matter;Optics '4371': Materials Science;Soft Condensed Matter;Statistical Mechanics '4372': Materials Science;Statistical Mechanics '4373': Materials Science;Statistical Mechanics;Chemical Physics '4374': Materials Science;Statistical Mechanics;Chemical Physics;Computational Physics '4375': Materials Science;Statistical Mechanics;Computational Physics '4376': Materials Science;Statistical Mechanics;Strongly Correlated Electrons '4377': Materials Science;Strongly Correlated Electrons '4378': Materials Science;Strongly Correlated Electrons;Applied Physics '4379': Materials Science;Strongly Correlated Electrons;Chemical Physics '4380': Materials Science;Strongly Correlated Electrons;Computational Physics '4381': Materials Science;Strongly Correlated Electrons;Quantum Physics '4382': Materials Science;Strongly Correlated Electrons;Superconductivity '4383': Materials Science;Superconductivity '4384': Materials Science;Superconductivity;Computational Physics '4385': Mathematical Finance '4386': Mathematical Finance;Computational Finance '4387': Mathematical Finance;Computational Finance;Pricing of Securities '4388': Mathematical Finance;General Finance '4389': Mathematical Finance;Optimization and Control '4390': Mathematical Finance;Optimization and Control;Probability '4391': Mathematical Finance;Portfolio Management '4392': Mathematical Finance;Pricing of Securities '4393': Mathematical Finance;Probability '4394': Mathematical Finance;Probability;Portfolio Management '4395': Mathematical Finance;Probability;Pricing of Securities '4396': Mathematical Finance;Risk Management '4397': Mathematical Finance;Trading and Market Microstructure '4398': Mathematical Software '4399': Mathematical Software;Computation '4400': Mathematical Software;Computational Engineering, Finance, and Science '4401': Mathematical Software;Computational Physics '4402': Mathematical Software;Data Structures and Algorithms '4403': Mathematical Software;Distributed, Parallel, and Cluster Computing '4404': Mathematical Software;Distributed, Parallel, and Cluster Computing;Numerical Analysis '4405': Mathematical Software;Distributed, Parallel, and Cluster Computing;Numerical Analysis;Numerical Analysis '4406': Mathematical Software;Distributed, Parallel, and Cluster Computing;Performance '4407': Mathematical Software;Machine Learning '4408': Mathematical Software;Numerical Analysis '4409': Mathematical Software;Numerical Analysis;Numerical Analysis '4410': Mathematical Software;Performance '4411': Mathematical Software;Programming Languages '4412': Mathematical Software;Symbolic Computation '4413': Medical Physics '4414': Medical Physics;Accelerator Physics '4415': Medical Physics;Applications '4416': Medical Physics;Applied Physics '4417': Medical Physics;Applied Physics;Instrumentation and Detectors '4418': Medical Physics;Artificial Intelligence '4419': Medical Physics;Biological Physics '4420': Medical Physics;Biological Physics;Optics '4421': Medical Physics;Biological Physics;Quantitative Methods '4422': Medical Physics;Biological Physics;Tissues and Organs '4423': Medical Physics;Classical Physics '4424': Medical Physics;Computational Engineering, Finance, and Science '4425': Medical Physics;Computational Physics '4426': Medical Physics;Computer Vision and Pattern Recognition '4427': Medical Physics;Computer Vision and Pattern Recognition;Image and Video Processing '4428': Medical Physics;Computer Vision and Pattern Recognition;Machine Learning '4429': Medical Physics;Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing '4430': Medical Physics;Data Analysis, Statistics and Probability '4431': Medical Physics;Fluid Dynamics '4432': Medical Physics;Image and Video Processing '4433': Medical Physics;Image and Video Processing;Optics '4434': Medical Physics;Instrumentation and Detectors '4435': Medical Physics;Machine Learning '4436': Medical Physics;Machine Learning;Image and Video Processing '4437': Medical Physics;Materials Science '4438': Medical Physics;Neurons and Cognition '4439': Medical Physics;Numerical Analysis '4440': Medical Physics;Numerical Analysis;Numerical Analysis '4441': Medical Physics;Optics '4442': Medical Physics;Optimization and Control '4443': Medical Physics;Quantitative Methods '4444': Medical Physics;Quantum Physics '4445': Medical Physics;Robotics '4446': Medical Physics;Signal Processing '4447': Medical Physics;Soft Condensed Matter '4448': Medical Physics;Systems and Control;Systems and Control '4449': Medical Physics;Tissues and Organs '4450': Mesoscale and Nanoscale Physics '4451': Mesoscale and Nanoscale Physics;Applied Physics '4452': Mesoscale and Nanoscale Physics;Applied Physics;Optics '4453': Mesoscale and Nanoscale Physics;Applied Physics;Optics;Quantum Physics '4454': Mesoscale and Nanoscale Physics;Applied Physics;Quantum Physics '4455': Mesoscale and Nanoscale Physics;Atomic Physics '4456': Mesoscale and Nanoscale Physics;Atomic Physics;Optics;Quantum Physics '4457': Mesoscale and Nanoscale Physics;Atomic Physics;Quantum Physics '4458': Mesoscale and Nanoscale Physics;Atomic and Molecular Clusters '4459': Mesoscale and Nanoscale Physics;Biological Physics '4460': Mesoscale and Nanoscale Physics;Chaotic Dynamics '4461': Mesoscale and Nanoscale Physics;Chaotic Dynamics;Chaotic Dynamics '4462': Mesoscale and Nanoscale Physics;Chaotic Dynamics;Quantum Physics '4463': Mesoscale and Nanoscale Physics;Chemical Physics '4464': Mesoscale and Nanoscale Physics;Chemical Physics;Computational Physics '4465': Mesoscale and Nanoscale Physics;Chemical Physics;Optics '4466': Mesoscale and Nanoscale Physics;Chemical Physics;Quantum Physics '4467': Mesoscale and Nanoscale Physics;Classical Physics '4468': Mesoscale and Nanoscale Physics;Computational Physics '4469': Mesoscale and Nanoscale Physics;Computational Physics;Optics '4470': Mesoscale and Nanoscale Physics;Computational Physics;Quantum Physics '4471': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks '4472': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Chaotic Dynamics '4473': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;High Energy Physics - Theory '4474': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Materials Science '4475': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Materials Science;Strongly Correlated Electrons '4476': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Optics '4477': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Other Condensed Matter '4478': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Quantum Physics '4479': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Statistical Mechanics '4480': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Strongly Correlated Electrons '4481': Mesoscale and Nanoscale Physics;Disordered Systems and Neural Networks;Superconductivity '4482': Mesoscale and Nanoscale Physics;Emerging Technologies '4483': Mesoscale and Nanoscale Physics;Fluid Dynamics '4484': Mesoscale and Nanoscale Physics;General Relativity and Quantum Cosmology '4485': Mesoscale and Nanoscale Physics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '4486': Mesoscale and Nanoscale Physics;High Energy Physics - Phenomenology '4487': Mesoscale and Nanoscale Physics;High Energy Physics - Phenomenology;High Energy Physics - Theory '4488': Mesoscale and Nanoscale Physics;High Energy Physics - Theory '4489': Mesoscale and Nanoscale Physics;High Energy Physics - Theory;Quantum Physics '4490': Mesoscale and Nanoscale Physics;Instrumentation and Detectors '4491': Mesoscale and Nanoscale Physics;Instrumentation and Detectors;Optics '4492': Mesoscale and Nanoscale Physics;Instrumentation and Detectors;Quantum Physics '4493': Mesoscale and Nanoscale Physics;Materials Science '4494': Mesoscale and Nanoscale Physics;Materials Science;Applied Physics '4495': Mesoscale and Nanoscale Physics;Materials Science;Applied Physics;Computational Physics '4496': Mesoscale and Nanoscale Physics;Materials Science;Applied Physics;Optics '4497': Mesoscale and Nanoscale Physics;Materials Science;Applied Physics;Quantum Physics '4498': Mesoscale and Nanoscale Physics;Materials Science;Atomic and Molecular Clusters '4499': Mesoscale and Nanoscale Physics;Materials Science;Chemical Physics '4500': Mesoscale and Nanoscale Physics;Materials Science;Chemical Physics;Computational Physics '4501': Mesoscale and Nanoscale Physics;Materials Science;Computational Physics '4502': Mesoscale and Nanoscale Physics;Materials Science;High Energy Physics - Theory '4503': Mesoscale and Nanoscale Physics;Materials Science;Instrumentation and Detectors '4504': Mesoscale and Nanoscale Physics;Materials Science;Optics '4505': Mesoscale and Nanoscale Physics;Materials Science;Optics;Quantum Physics '4506': Mesoscale and Nanoscale Physics;Materials Science;Other Condensed Matter '4507': Mesoscale and Nanoscale Physics;Materials Science;Quantum Gases '4508': Mesoscale and Nanoscale Physics;Materials Science;Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons '4509': Mesoscale and Nanoscale Physics;Materials Science;Quantum Physics '4510': Mesoscale and Nanoscale Physics;Materials Science;Soft Condensed Matter '4511': Mesoscale and Nanoscale Physics;Materials Science;Statistical Mechanics '4512': Mesoscale and Nanoscale Physics;Materials Science;Strongly Correlated Electrons '4513': Mesoscale and Nanoscale Physics;Materials Science;Strongly Correlated Electrons;Quantum Physics '4514': Mesoscale and Nanoscale Physics;Materials Science;Strongly Correlated Electrons;Superconductivity '4515': Mesoscale and Nanoscale Physics;Materials Science;Strongly Correlated Electrons;Superconductivity;Quantum Physics '4516': Mesoscale and Nanoscale Physics;Materials Science;Superconductivity '4517': Mesoscale and Nanoscale Physics;Materials Science;Superconductivity;Quantum Physics '4518': Mesoscale and Nanoscale Physics;Nuclear Theory '4519': Mesoscale and Nanoscale Physics;Optics '4520': Mesoscale and Nanoscale Physics;Optics;Quantum Physics '4521': Mesoscale and Nanoscale Physics;Other Condensed Matter '4522': Mesoscale and Nanoscale Physics;Other Condensed Matter;Applied Physics '4523': Mesoscale and Nanoscale Physics;Other Condensed Matter;Optics '4524': Mesoscale and Nanoscale Physics;Other Condensed Matter;Quantum Physics '4525': Mesoscale and Nanoscale Physics;Other Condensed Matter;Strongly Correlated Electrons '4526': Mesoscale and Nanoscale Physics;Pattern Formation and Solitons '4527': Mesoscale and Nanoscale Physics;Plasma Physics '4528': Mesoscale and Nanoscale Physics;Quantum Gases '4529': Mesoscale and Nanoscale Physics;Quantum Gases;Optics '4530': Mesoscale and Nanoscale Physics;Quantum Gases;Optics;Quantum Physics '4531': Mesoscale and Nanoscale Physics;Quantum Gases;Quantum Physics '4532': Mesoscale and Nanoscale Physics;Quantum Gases;Statistical Mechanics '4533': Mesoscale and Nanoscale Physics;Quantum Gases;Strongly Correlated Electrons '4534': Mesoscale and Nanoscale Physics;Quantum Gases;Strongly Correlated Electrons;Quantum Physics '4535': Mesoscale and Nanoscale Physics;Quantum Gases;Superconductivity '4536': Mesoscale and Nanoscale Physics;Quantum Physics '4537': Mesoscale and Nanoscale Physics;Soft Condensed Matter '4538': Mesoscale and Nanoscale Physics;Soft Condensed Matter;Statistical Mechanics '4539': Mesoscale and Nanoscale Physics;Statistical Mechanics '4540': Mesoscale and Nanoscale Physics;Statistical Mechanics;Chemical Physics '4541': Mesoscale and Nanoscale Physics;Statistical Mechanics;Quantum Physics '4542': Mesoscale and Nanoscale Physics;Statistical Mechanics;Strongly Correlated Electrons '4543': Mesoscale and Nanoscale Physics;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '4544': Mesoscale and Nanoscale Physics;Statistical Mechanics;Superconductivity '4545': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '4546': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;High Energy Physics - Theory '4547': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;High Energy Physics - Theory;Quantum Physics '4548': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Nuclear Theory '4549': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Nuclear Theory;Atomic Physics '4550': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Quantum Physics '4551': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Superconductivity '4552': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Superconductivity;High Energy Physics - Theory '4553': Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Superconductivity;Quantum Physics '4554': Mesoscale and Nanoscale Physics;Superconductivity '4555': Mesoscale and Nanoscale Physics;Superconductivity;High Energy Physics - Theory '4556': Mesoscale and Nanoscale Physics;Superconductivity;Quantum Physics '4557': Methodology '4558': Methodology;Applications '4559': Methodology;Applications;Computation '4560': Methodology;Applications;Computation;Machine Learning '4561': Methodology;Applications;Machine Learning '4562': Methodology;Applications;Other Statistics '4563': Methodology;Artificial Intelligence '4564': Methodology;Artificial Intelligence;Machine Learning '4565': Methodology;Artificial Intelligence;Machine Learning;Machine Learning '4566': Methodology;Computation '4567': Methodology;Computation;Machine Learning '4568': Methodology;Cryptography and Security '4569': Methodology;Data Analysis, Statistics and Probability '4570': Methodology;Econometrics '4571': Methodology;Econometrics;Applications '4572': Methodology;Econometrics;Machine Learning '4573': Methodology;Genomics '4574': Methodology;Genomics;Applications '4575': Methodology;Machine Learning '4576': Methodology;Machine Learning;Applications '4577': Methodology;Machine Learning;Applications;Machine Learning '4578': Methodology;Machine Learning;Computation '4579': Methodology;Machine Learning;Computation;Machine Learning '4580': Methodology;Machine Learning;Econometrics '4581': Methodology;Machine Learning;Econometrics;Machine Learning '4582': Methodology;Machine Learning;Machine Learning '4583': Methodology;Neurons and Cognition '4584': Methodology;Numerical Analysis '4585': Methodology;Numerical Analysis;Numerical Analysis '4586': Methodology;Optimization and Control '4587': Methodology;Other Statistics '4588': Methodology;Physics and Society '4589': Methodology;Populations and Evolution '4590': Methodology;Probability '4591': Methodology;Probability;Computation '4592': Methodology;Quantitative Methods '4593': Methodology;Quantitative Methods;Applications '4594': Methodology;Risk Management '4595': Methodology;Signal Processing '4596': Methodology;Social and Information Networks '4597': Methodology;Social and Information Networks;Physics and Society '4598': Methodology;Statistical Finance '4599': Methodology;Statistical Finance;Applications '4600': Methodology;Systems and Control;Systems and Control '4601': Metric Geometry '4602': Metric Geometry;Algebraic Geometry '4603': Metric Geometry;Algebraic Geometry;Combinatorics '4604': Metric Geometry;Algebraic Topology '4605': Metric Geometry;Algebraic Topology;Combinatorics '4606': Metric Geometry;Analysis of PDEs '4607': Metric Geometry;Analysis of PDEs;Differential Geometry '4608': Metric Geometry;Analysis of PDEs;Functional Analysis '4609': Metric Geometry;Classical Analysis and ODEs '4610': Metric Geometry;Classical Analysis and ODEs;Differential Geometry '4611': Metric Geometry;Classical Analysis and ODEs;Dynamical Systems '4612': Metric Geometry;Classical Analysis and ODEs;Functional Analysis '4613': Metric Geometry;Classical Analysis and ODEs;Probability '4614': Metric Geometry;Combinatorics '4615': Metric Geometry;Combinatorics;Functional Analysis '4616': Metric Geometry;Combinatorics;Geometric Topology '4617': Metric Geometry;Combinatorics;Group Theory '4618': Metric Geometry;Combinatorics;Number Theory '4619': Metric Geometry;Combinatorics;Optimization and Control '4620': Metric Geometry;Combinatorics;Probability '4621': Metric Geometry;Complex Variables '4622': Metric Geometry;Complex Variables;Differential Geometry '4623': Metric Geometry;Computational Geometry '4624': Metric Geometry;Computational Geometry;Combinatorics '4625': Metric Geometry;Differential Geometry '4626': Metric Geometry;Differential Geometry;Functional Analysis '4627': Metric Geometry;Differential Geometry;Geometric Topology '4628': Metric Geometry;Differential Geometry;Group Theory '4629': Metric Geometry;Dynamical Systems '4630': Metric Geometry;Functional Analysis '4631': Metric Geometry;Functional Analysis;Group Theory '4632': Metric Geometry;Functional Analysis;Probability '4633': Metric Geometry;General Topology '4634': Metric Geometry;General Topology;Geometric Topology '4635': Metric Geometry;Geometric Topology '4636': Metric Geometry;Group Theory '4637': Metric Geometry;Group Theory;Geometric Topology '4638': Metric Geometry;History and Overview '4639': Metric Geometry;Number Theory '4640': Metric Geometry;Operator Algebras '4641': Metric Geometry;Optimization and Control '4642': Metric Geometry;Probability '4643': Metric Geometry;Rings and Algebras '4644': Metric Geometry;Spectral Theory '4645': Metric Geometry;Symplectic Geometry '4646': Molecular Networks '4647': Molecular Networks;Algebraic Geometry '4648': Molecular Networks;Algebraic Geometry;Dynamical Systems '4649': Molecular Networks;Biological Physics '4650': Molecular Networks;Biological Physics;Quantitative Methods '4651': Molecular Networks;Biological Physics;Subcellular Processes '4652': Molecular Networks;Biomolecules '4653': Molecular Networks;Cell Behavior '4654': Molecular Networks;Computational Engineering, Finance, and Science '4655': Molecular Networks;Disordered Systems and Neural Networks '4656': Molecular Networks;Disordered Systems and Neural Networks;Biological Physics '4657': Molecular Networks;Dynamical Systems '4658': Molecular Networks;Genomics '4659': Molecular Networks;Machine Learning '4660': Molecular Networks;Populations and Evolution '4661': Molecular Networks;Quantitative Methods '4662': Molecular Networks;Soft Condensed Matter '4663': Molecular Networks;Statistical Mechanics '4664': Molecular Networks;Statistical Mechanics;Biological Physics '4665': Molecular Networks;Statistical Mechanics;Quantitative Methods '4666': Molecular Networks;Subcellular Processes '4667': Multiagent Systems '4668': Multiagent Systems;Artificial Intelligence '4669': Multiagent Systems;Artificial Intelligence;Computer Science and Game Theory '4670': Multiagent Systems;Artificial Intelligence;Computer Science and Game Theory;Machine Learning '4671': Multiagent Systems;Artificial Intelligence;Computers and Society '4672': Multiagent Systems;Artificial Intelligence;Machine Learning '4673': Multiagent Systems;Artificial Intelligence;Machine Learning;Robotics '4674': Multiagent Systems;Artificial Intelligence;Robotics '4675': Multiagent Systems;Computational Engineering, Finance, and Science '4676': Multiagent Systems;Computer Science and Game Theory '4677': Multiagent Systems;Computer Science and Game Theory;Machine Learning '4678': Multiagent Systems;Computers and Society '4679': Multiagent Systems;Distributed, Parallel, and Cluster Computing '4680': Multiagent Systems;Logic in Computer Science '4681': Multiagent Systems;Machine Learning '4682': Multiagent Systems;Networking and Internet Architecture '4683': Multiagent Systems;Optimization and Control '4684': Multiagent Systems;Physics and Society '4685': Multiagent Systems;Robotics '4686': Multiagent Systems;Robotics;Systems and Control;Systems and Control '4687': Multiagent Systems;Social and Information Networks '4688': Multiagent Systems;Software Engineering '4689': Multiagent Systems;Systems and Control '4690': Multiagent Systems;Systems and Control;Optimization and Control '4691': Multiagent Systems;Systems and Control;Systems and Control '4692': Multimedia '4693': Multimedia;Artificial Intelligence '4694': Multimedia;Artificial Intelligence;Computer Vision and Pattern Recognition '4695': Multimedia;Computation and Language '4696': Multimedia;Computation and Language;Computer Vision and Pattern Recognition '4697': Multimedia;Computer Vision and Pattern Recognition '4698': Multimedia;Computer Vision and Pattern Recognition;Image and Video Processing '4699': Multimedia;Computer Vision and Pattern Recognition;Information Retrieval '4700': Multimedia;Computer Vision and Pattern Recognition;Machine Learning '4701': Multimedia;Computer Vision and Pattern Recognition;Sound;Audio and Speech Processing '4702': Multimedia;Cryptography and Security '4703': Multimedia;Graphics '4704': Multimedia;Human-Computer Interaction '4705': Multimedia;Image and Video Processing '4706': Multimedia;Information Retrieval '4707': Multimedia;Machine Learning '4708': Multimedia;Networking and Internet Architecture '4709': Multimedia;Sound '4710': Multimedia;Sound;Audio and Speech Processing '4711': Networking and Internet Architecture '4712': Networking and Internet Architecture;Applications '4713': Networking and Internet Architecture;Artificial Intelligence '4714': Networking and Internet Architecture;Artificial Intelligence;Distributed, Parallel, and Cluster Computing '4715': Networking and Internet Architecture;Artificial Intelligence;Machine Learning '4716': Networking and Internet Architecture;Artificial Intelligence;Signal Processing '4717': Networking and Internet Architecture;Computational Geometry '4718': Networking and Internet Architecture;Computer Science and Game Theory '4719': Networking and Internet Architecture;Computer Vision and Pattern Recognition '4720': Networking and Internet Architecture;Computers and Society '4721': Networking and Internet Architecture;Cryptography and Security '4722': Networking and Internet Architecture;Cryptography and Security;Distributed, Parallel, and Cluster Computing '4723': Networking and Internet Architecture;Cryptography and Security;Machine Learning '4724': Networking and Internet Architecture;Data Structures and Algorithms '4725': Networking and Internet Architecture;Databases '4726': Networking and Internet Architecture;Discrete Mathematics '4727': Networking and Internet Architecture;Distributed, Parallel, and Cluster Computing '4728': Networking and Internet Architecture;Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms '4729': Networking and Internet Architecture;Distributed, Parallel, and Cluster Computing;Machine Learning '4730': Networking and Internet Architecture;Distributed, Parallel, and Cluster Computing;Performance '4731': Networking and Internet Architecture;Emerging Technologies '4732': Networking and Internet Architecture;Hardware Architecture '4733': Networking and Internet Architecture;Human-Computer Interaction '4734': Networking and Internet Architecture;Information Retrieval '4735': Networking and Internet Architecture;Logic in Computer Science '4736': Networking and Internet Architecture;Machine Learning '4737': Networking and Internet Architecture;Machine Learning;Machine Learning '4738': Networking and Internet Architecture;Machine Learning;Multiagent Systems '4739': Networking and Internet Architecture;Machine Learning;Performance '4740': Networking and Internet Architecture;Machine Learning;Signal Processing '4741': Networking and Internet Architecture;Machine Learning;Systems and Control;Systems and Control '4742': Networking and Internet Architecture;Multiagent Systems '4743': Networking and Internet Architecture;Multimedia '4744': Networking and Internet Architecture;Neural and Evolutionary Computing '4745': Networking and Internet Architecture;Operating Systems '4746': Networking and Internet Architecture;Optimization and Control '4747': Networking and Internet Architecture;Performance '4748': Networking and Internet Architecture;Physics and Society '4749': Networking and Internet Architecture;Probability '4750': Networking and Internet Architecture;Programming Languages '4751': Networking and Internet Architecture;Quantum Physics '4752': Networking and Internet Architecture;Robotics '4753': Networking and Internet Architecture;Signal Processing '4754': Networking and Internet Architecture;Social and Information Networks '4755': Networking and Internet Architecture;Software Engineering '4756': Networking and Internet Architecture;Statistical Mechanics '4757': Networking and Internet Architecture;Systems and Control '4758': Networking and Internet Architecture;Systems and Control;Optimization and Control '4759': Networking and Internet Architecture;Systems and Control;Systems and Control '4760': Neural and Evolutionary Computing '4761': Neural and Evolutionary Computing;Adaptation and Self-Organizing Systems '4762': Neural and Evolutionary Computing;Artificial Intelligence '4763': Neural and Evolutionary Computing;Artificial Intelligence;Computer Vision and Pattern Recognition '4764': Neural and Evolutionary Computing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '4765': Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning '4766': Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning;Machine Learning '4767': Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning;Neurons and Cognition '4768': Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning;Robotics '4769': Neural and Evolutionary Computing;Artificial Intelligence;Neurons and Cognition '4770': Neural and Evolutionary Computing;Artificial Intelligence;Optimization and Control '4771': Neural and Evolutionary Computing;Artificial Intelligence;Robotics '4772': Neural and Evolutionary Computing;Computation and Language '4773': Neural and Evolutionary Computing;Computation and Language;Machine Learning '4774': Neural and Evolutionary Computing;Computational Complexity '4775': Neural and Evolutionary Computing;Computational Engineering, Finance, and Science '4776': Neural and Evolutionary Computing;Computer Vision and Pattern Recognition '4777': Neural and Evolutionary Computing;Computer Vision and Pattern Recognition;Image and Video Processing '4778': Neural and Evolutionary Computing;Computer Vision and Pattern Recognition;Machine Learning '4779': Neural and Evolutionary Computing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning '4780': Neural and Evolutionary Computing;Cryptography and Security '4781': Neural and Evolutionary Computing;Data Structures and Algorithms '4782': Neural and Evolutionary Computing;Distributed, Parallel, and Cluster Computing '4783': Neural and Evolutionary Computing;Emerging Technologies '4784': Neural and Evolutionary Computing;Emerging Technologies;Machine Learning '4785': Neural and Evolutionary Computing;Hardware Architecture '4786': Neural and Evolutionary Computing;Hardware Architecture;Emerging Technologies '4787': Neural and Evolutionary Computing;Hardware Architecture;Machine Learning '4788': Neural and Evolutionary Computing;Human-Computer Interaction '4789': Neural and Evolutionary Computing;Machine Learning '4790': Neural and Evolutionary Computing;Machine Learning;Machine Learning '4791': Neural and Evolutionary Computing;Machine Learning;Neurons and Cognition '4792': Neural and Evolutionary Computing;Machine Learning;Optimization and Control '4793': Neural and Evolutionary Computing;Machine Learning;Robotics '4794': Neural and Evolutionary Computing;Machine Learning;Signal Processing '4795': Neural and Evolutionary Computing;Multiagent Systems '4796': Neural and Evolutionary Computing;Networking and Internet Architecture '4797': Neural and Evolutionary Computing;Neurons and Cognition '4798': Neural and Evolutionary Computing;Optimization and Control '4799': Neural and Evolutionary Computing;Populations and Evolution '4800': Neural and Evolutionary Computing;Probability '4801': Neural and Evolutionary Computing;Robotics '4802': Neural and Evolutionary Computing;Signal Processing '4803': Neural and Evolutionary Computing;Software Engineering '4804': Neural and Evolutionary Computing;Systems and Control '4805': Neural and Evolutionary Computing;Systems and Control;Systems and Control '4806': Neurons and Cognition '4807': Neurons and Cognition;Adaptation and Self-Organizing Systems '4808': Neurons and Cognition;Adaptation and Self-Organizing Systems;Biological Physics '4809': Neurons and Cognition;Applications '4810': Neurons and Cognition;Artificial Intelligence '4811': Neurons and Cognition;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '4812': Neurons and Cognition;Artificial Intelligence;Machine Learning '4813': Neurons and Cognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '4814': Neurons and Cognition;Artificial Intelligence;Neural and Evolutionary Computing '4815': Neurons and Cognition;Biological Physics '4816': Neurons and Cognition;Cell Behavior '4817': Neurons and Cognition;Chaotic Dynamics '4818': Neurons and Cognition;Computation and Language '4819': Neurons and Cognition;Computer Vision and Pattern Recognition '4820': Neurons and Cognition;Computer Vision and Pattern Recognition;Image and Video Processing '4821': Neurons and Cognition;Computer Vision and Pattern Recognition;Machine Learning '4822': Neurons and Cognition;Data Analysis, Statistics and Probability '4823': Neurons and Cognition;Disordered Systems and Neural Networks '4824': Neurons and Cognition;Disordered Systems and Neural Networks;Adaptation and Self-Organizing Systems '4825': Neurons and Cognition;Disordered Systems and Neural Networks;Biological Physics '4826': Neurons and Cognition;Disordered Systems and Neural Networks;Statistical Mechanics '4827': Neurons and Cognition;Dynamical Systems '4828': Neurons and Cognition;Dynamical Systems;Adaptation and Self-Organizing Systems '4829': Neurons and Cognition;Human-Computer Interaction '4830': Neurons and Cognition;Image and Video Processing '4831': Neurons and Cognition;Machine Learning '4832': Neurons and Cognition;Machine Learning;Image and Video Processing '4833': Neurons and Cognition;Machine Learning;Machine Learning '4834': Neurons and Cognition;Machine Learning;Neural and Evolutionary Computing '4835': Neurons and Cognition;Machine Learning;Signal Processing '4836': Neurons and Cognition;Medical Physics '4837': Neurons and Cognition;Methodology '4838': Neurons and Cognition;Molecular Networks '4839': Neurons and Cognition;Neural and Evolutionary Computing '4840': Neurons and Cognition;Neural and Evolutionary Computing;Machine Learning '4841': Neurons and Cognition;Other Quantitative Biology '4842': Neurons and Cognition;Pattern Formation and Solitons '4843': Neurons and Cognition;Physics and Society '4844': Neurons and Cognition;Populations and Evolution '4845': Neurons and Cognition;Probability '4846': Neurons and Cognition;Quantitative Methods '4847': Neurons and Cognition;Quantitative Methods;Applications '4848': Neurons and Cognition;Quantum Physics '4849': Neurons and Cognition;Robotics '4850': Neurons and Cognition;Signal Processing '4851': Neurons and Cognition;Statistical Mechanics '4852': Neurons and Cognition;Subcellular Processes '4853': Neurons and Cognition;Systems and Control;Systems and Control '4854': Neurons and Cognition;Tissues and Organs '4855': Nuclear Experiment '4856': Nuclear Experiment;Accelerator Physics '4857': Nuclear Experiment;Astrophysics '4858': Nuclear Experiment;Astrophysics;Nuclear Theory '4859': Nuclear Experiment;Atomic Physics '4860': Nuclear Experiment;Cosmology and Nongalactic Astrophysics '4861': Nuclear Experiment;Data Analysis, Statistics and Probability '4862': Nuclear Experiment;High Energy Astrophysical Phenomena '4863': Nuclear Experiment;High Energy Physics - Experiment '4864': Nuclear Experiment;High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Theory '4865': Nuclear Experiment;High Energy Physics - Experiment;High Energy Physics - Phenomenology '4866': Nuclear Experiment;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Theory '4867': Nuclear Experiment;High Energy Physics - Experiment;Instrumentation and Detectors '4868': Nuclear Experiment;High Energy Physics - Experiment;Nuclear Theory '4869': Nuclear Experiment;High Energy Physics - Phenomenology '4870': Nuclear Experiment;High Energy Physics - Phenomenology;Nuclear Theory '4871': Nuclear Experiment;Instrumentation and Detectors '4872': Nuclear Experiment;Instrumentation and Methods for Astrophysics '4873': Nuclear Experiment;Nuclear Theory '4874': Nuclear Experiment;Solar and Stellar Astrophysics '4875': Nuclear Experiment;Solar and Stellar Astrophysics;Nuclear Theory '4876': Nuclear Theory '4877': Nuclear Theory;Astrophysics '4878': Nuclear Theory;Astrophysics;High Energy Physics - Phenomenology '4879': Nuclear Theory;Astrophysics;High Energy Physics - Phenomenology;Nuclear Experiment '4880': Nuclear Theory;Astrophysics;Nuclear Experiment '4881': Nuclear Theory;Atomic Physics '4882': Nuclear Theory;Atomic Physics;Quantum Physics '4883': Nuclear Theory;Atomic and Molecular Clusters '4884': Nuclear Theory;Chaotic Dynamics '4885': Nuclear Theory;Chaotic Dynamics;Chaotic Dynamics '4886': Nuclear Theory;Chemical Physics '4887': Nuclear Theory;Computational Physics '4888': Nuclear Theory;Condensed Matter '4889': Nuclear Theory;Condensed Matter;High Energy Physics - Phenomenology '4890': Nuclear Theory;Cosmology and Nongalactic Astrophysics '4891': Nuclear Theory;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology '4892': Nuclear Theory;Data Analysis, Statistics and Probability '4893': Nuclear Theory;General Relativity and Quantum Cosmology '4894': Nuclear Theory;High Energy Astrophysical Phenomena '4895': Nuclear Theory;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '4896': Nuclear Theory;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '4897': Nuclear Theory;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '4898': Nuclear Theory;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;Nuclear Experiment '4899': Nuclear Theory;High Energy Astrophysical Phenomena;Nuclear Experiment '4900': Nuclear Theory;High Energy Astrophysical Phenomena;Quantum Gases '4901': Nuclear Theory;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '4902': Nuclear Theory;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;High Energy Physics - Phenomenology '4903': Nuclear Theory;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Nuclear Experiment '4904': Nuclear Theory;High Energy Physics - Experiment '4905': Nuclear Theory;High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Phenomenology '4906': Nuclear Theory;High Energy Physics - Experiment;High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Experiment '4907': Nuclear Theory;High Energy Physics - Experiment;High Energy Physics - Phenomenology '4908': Nuclear Theory;High Energy Physics - Experiment;High Energy Physics - Phenomenology;Nuclear Experiment '4909': Nuclear Theory;High Energy Physics - Experiment;Nuclear Experiment '4910': Nuclear Theory;High Energy Physics - Lattice '4911': Nuclear Theory;High Energy Physics - Lattice;High Energy Physics - Phenomenology '4912': Nuclear Theory;High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Experiment '4913': Nuclear Theory;High Energy Physics - Lattice;Nuclear Experiment '4914': Nuclear Theory;High Energy Physics - Phenomenology '4915': Nuclear Theory;High Energy Physics - Phenomenology;Atomic Physics '4916': Nuclear Theory;High Energy Physics - Phenomenology;Fluid Dynamics '4917': Nuclear Theory;High Energy Physics - Phenomenology;High Energy Physics - Theory '4918': Nuclear Theory;High Energy Physics - Phenomenology;High Energy Physics - Theory;Nuclear Experiment '4919': Nuclear Theory;High Energy Physics - Phenomenology;Nuclear Experiment '4920': Nuclear Theory;High Energy Physics - Phenomenology;Nuclear Experiment;Atomic Physics '4921': Nuclear Theory;High Energy Physics - Phenomenology;Quantum Physics '4922': Nuclear Theory;High Energy Physics - Theory '4923': Nuclear Theory;Mesoscale and Nanoscale Physics '4924': Nuclear Theory;Nuclear Experiment '4925': Nuclear Theory;Nuclear Experiment;Atomic Physics '4926': Nuclear Theory;Nuclear Experiment;Data Analysis, Statistics and Probability '4927': Nuclear Theory;Nuclear Experiment;Quantum Physics '4928': Nuclear Theory;Other Condensed Matter '4929': Nuclear Theory;Other Condensed Matter;High Energy Physics - Phenomenology '4930': Nuclear Theory;Quantum Gases '4931': Nuclear Theory;Quantum Gases;High Energy Physics - Phenomenology '4932': Nuclear Theory;Quantum Gases;Nuclear Experiment '4933': Nuclear Theory;Quantum Physics '4934': Nuclear Theory;Solar and Stellar Astrophysics '4935': Nuclear Theory;Solar and Stellar Astrophysics;High Energy Physics - Phenomenology '4936': Nuclear Theory;Solar and Stellar Astrophysics;High Energy Physics - Phenomenology;Nuclear Experiment '4937': Nuclear Theory;Solar and Stellar Astrophysics;Nuclear Experiment '4938': Nuclear Theory;Statistical Mechanics '4939': Nuclear Theory;Statistical Mechanics;High Energy Physics - Phenomenology '4940': Nuclear Theory;Strongly Correlated Electrons '4941': Nuclear Theory;Strongly Correlated Electrons;High Energy Physics - Phenomenology '4942': Nuclear Theory;Superconductivity '4943': Nuclear Theory;Superconductivity;High Energy Physics - Phenomenology '4944': Nuclear Theory;Superconductivity;Nuclear Experiment '4945': Number Theory '4946': Number Theory;Algebraic Geometry '4947': Number Theory;Algebraic Geometry;Algebraic Topology '4948': Number Theory;Algebraic Geometry;Combinatorics '4949': Number Theory;Algebraic Geometry;Complex Variables '4950': Number Theory;Algebraic Geometry;Dynamical Systems '4951': Number Theory;Algebraic Geometry;Group Theory '4952': Number Theory;Algebraic Geometry;K-Theory and Homology '4953': Number Theory;Algebraic Geometry;Logic '4954': Number Theory;Algebraic Geometry;Representation Theory '4955': Number Theory;Algebraic Geometry;Rings and Algebras '4956': Number Theory;Algebraic Topology '4957': Number Theory;Analysis of PDEs '4958': Number Theory;Classical Analysis and ODEs '4959': Number Theory;Classical Analysis and ODEs;Combinatorics '4960': Number Theory;Combinatorics '4961': Number Theory;Combinatorics;Dynamical Systems '4962': Number Theory;Combinatorics;Group Theory '4963': Number Theory;Combinatorics;Metric Geometry '4964': Number Theory;Combinatorics;Probability '4965': Number Theory;Commutative Algebra '4966': Number Theory;Commutative Algebra;Algebraic Geometry '4967': Number Theory;Commutative Algebra;Combinatorics '4968': Number Theory;Commutative Algebra;Rings and Algebras '4969': Number Theory;Complex Variables '4970': Number Theory;Computational Complexity '4971': Number Theory;Cryptography and Security '4972': Number Theory;Data Structures and Algorithms '4973': Number Theory;Differential Geometry '4974': Number Theory;Discrete Mathematics '4975': Number Theory;Discrete Mathematics;Combinatorics '4976': Number Theory;Dynamical Systems '4977': Number Theory;Dynamical Systems;Metric Geometry '4978': Number Theory;Dynamical Systems;Probability '4979': Number Theory;Formal Languages and Automata Theory '4980': Number Theory;Formal Languages and Automata Theory;Combinatorics '4981': Number Theory;Functional Analysis '4982': Number Theory;General Mathematics '4983': Number Theory;Geometric Topology '4984': Number Theory;Group Theory '4985': Number Theory;Group Theory;Representation Theory '4986': Number Theory;High Energy Physics - Theory '4987': Number Theory;High Energy Physics - Theory;Algebraic Geometry '4988': Number Theory;High Energy Physics - Theory;Representation Theory '4989': Number Theory;History and Overview '4990': Number Theory;K-Theory and Homology '4991': Number Theory;Logic '4992': Number Theory;Metric Geometry '4993': Number Theory;Numerical Analysis '4994': Number Theory;Numerical Analysis;Numerical Analysis '4995': Number Theory;Operator Algebras '4996': Number Theory;Probability '4997': Number Theory;Quantum Algebra '4998': Number Theory;Representation Theory '4999': Number Theory;Rings and Algebras '5000': Number Theory;Spectral Theory '5001': Number Theory;Symbolic Computation '5002': Numerical Analysis '5003': Numerical Analysis;Algebraic Geometry '5004': Numerical Analysis;Analysis of PDEs '5005': Numerical Analysis;Analysis of PDEs;Classical Physics '5006': Numerical Analysis;Analysis of PDEs;Optimization and Control '5007': Numerical Analysis;Analysis of PDEs;Probability '5008': Numerical Analysis;Artificial Intelligence;Machine Learning;Numerical Analysis '5009': Numerical Analysis;Artificial Intelligence;Numerical Analysis '5010': Numerical Analysis;Atmospheric and Oceanic Physics '5011': Numerical Analysis;Classical Analysis and ODEs '5012': Numerical Analysis;Classical Physics '5013': Numerical Analysis;Combinatorics '5014': Numerical Analysis;Complex Variables '5015': Numerical Analysis;Computation '5016': Numerical Analysis;Computational Complexity '5017': Numerical Analysis;Computational Complexity;Numerical Analysis '5018': Numerical Analysis;Computational Engineering, Finance, and Science '5019': Numerical Analysis;Computational Engineering, Finance, and Science;Computational Physics '5020': Numerical Analysis;Computational Engineering, Finance, and Science;Numerical Analysis '5021': Numerical Analysis;Computational Engineering, Finance, and Science;Numerical Analysis;Computational Physics '5022': Numerical Analysis;Computational Engineering, Finance, and Science;Numerical Analysis;Fluid Dynamics '5023': Numerical Analysis;Computational Finance '5024': Numerical Analysis;Computational Geometry '5025': Numerical Analysis;Computational Geometry;Numerical Analysis '5026': Numerical Analysis;Computational Physics '5027': Numerical Analysis;Computational Physics;Fluid Dynamics '5028': Numerical Analysis;Computer Vision and Pattern Recognition '5029': Numerical Analysis;Computer Vision and Pattern Recognition;Numerical Analysis '5030': Numerical Analysis;Data Structures and Algorithms '5031': Numerical Analysis;Data Structures and Algorithms;Numerical Analysis '5032': Numerical Analysis;Differential Geometry '5033': Numerical Analysis;Discrete Mathematics '5034': Numerical Analysis;Distributed, Parallel, and Cluster Computing '5035': Numerical Analysis;Distributed, Parallel, and Cluster Computing;Mathematical Software;Numerical Analysis '5036': Numerical Analysis;Distributed, Parallel, and Cluster Computing;Numerical Analysis '5037': Numerical Analysis;Dynamical Systems '5038': Numerical Analysis;Fluid Dynamics '5039': Numerical Analysis;Functional Analysis '5040': Numerical Analysis;Geophysics '5041': Numerical Analysis;Graphics '5042': Numerical Analysis;Graphics;Numerical Analysis '5043': Numerical Analysis;Machine Learning '5044': Numerical Analysis;Machine Learning;Machine Learning '5045': Numerical Analysis;Machine Learning;Numerical Analysis '5046': Numerical Analysis;Machine Learning;Numerical Analysis;Analysis of PDEs '5047': Numerical Analysis;Machine Learning;Numerical Analysis;Computational Physics '5048': Numerical Analysis;Machine Learning;Numerical Analysis;Dynamical Systems '5049': Numerical Analysis;Machine Learning;Numerical Analysis;Machine Learning '5050': Numerical Analysis;Machine Learning;Numerical Analysis;Optimization and Control '5051': Numerical Analysis;Materials Science '5052': Numerical Analysis;Materials Science;Numerical Analysis '5053': Numerical Analysis;Mathematical Software '5054': Numerical Analysis;Mathematical Software;Numerical Analysis '5055': Numerical Analysis;Medical Physics '5056': Numerical Analysis;Methodology '5057': Numerical Analysis;Metric Geometry '5058': Numerical Analysis;Number Theory '5059': Numerical Analysis;Numerical Analysis '5060': Numerical Analysis;Numerical Analysis;Algebraic Geometry '5061': Numerical Analysis;Numerical Analysis;Analysis of PDEs '5062': Numerical Analysis;Numerical Analysis;Analysis of PDEs;Optimization and Control '5063': Numerical Analysis;Numerical Analysis;Analysis of PDEs;Probability '5064': Numerical Analysis;Numerical Analysis;Applied Physics '5065': Numerical Analysis;Numerical Analysis;Atmospheric and Oceanic Physics '5066': Numerical Analysis;Numerical Analysis;Classical Analysis and ODEs '5067': Numerical Analysis;Numerical Analysis;Complex Variables '5068': Numerical Analysis;Numerical Analysis;Computation '5069': Numerical Analysis;Numerical Analysis;Computational Finance '5070': Numerical Analysis;Numerical Analysis;Computational Physics '5071': Numerical Analysis;Numerical Analysis;Computational Physics;Fluid Dynamics '5072': Numerical Analysis;Numerical Analysis;Computational Physics;Plasma Physics '5073': Numerical Analysis;Numerical Analysis;Data Analysis, Statistics and Probability '5074': Numerical Analysis;Numerical Analysis;Differential Geometry '5075': Numerical Analysis;Numerical Analysis;Dynamical Systems '5076': Numerical Analysis;Numerical Analysis;Fluid Dynamics '5077': Numerical Analysis;Numerical Analysis;Functional Analysis '5078': Numerical Analysis;Numerical Analysis;Geophysics '5079': Numerical Analysis;Numerical Analysis;Image and Video Processing '5080': Numerical Analysis;Numerical Analysis;Machine Learning '5081': Numerical Analysis;Numerical Analysis;Methodology '5082': Numerical Analysis;Numerical Analysis;Number Theory '5083': Numerical Analysis;Numerical Analysis;Optimization and Control '5084': Numerical Analysis;Numerical Analysis;Plasma Physics '5085': Numerical Analysis;Numerical Analysis;Populations and Evolution '5086': Numerical Analysis;Numerical Analysis;Probability '5087': Numerical Analysis;Numerical Analysis;Quantitative Methods '5088': Numerical Analysis;Numerical Analysis;Quantum Physics '5089': Numerical Analysis;Numerical Analysis;Signal Processing '5090': Numerical Analysis;Numerical Analysis;Spectral Theory '5091': Numerical Analysis;Numerical Analysis;Systems and Control;Systems and Control '5092': Numerical Analysis;Numerical Analysis;Tissues and Organs '5093': Numerical Analysis;Optimization and Control '5094': Numerical Analysis;Plasma Physics '5095': Numerical Analysis;Probability '5096': Numerical Analysis;Quantum Physics '5097': Numerical Analysis;Rings and Algebras '5098': Numerical Analysis;Soft Condensed Matter;Numerical Analysis '5099': Numerical Analysis;Spectral Theory '5100': Numerical Analysis;Symbolic Computation '5101': Numerical Analysis;Systems and Control '5102': Operating Systems '5103': Operating Systems;Cryptography and Security '5104': Operating Systems;Distributed, Parallel, and Cluster Computing '5105': Operating Systems;Performance '5106': Operating Systems;Software Engineering '5107': Operator Algebras '5108': Operator Algebras;Algebraic Geometry '5109': Operator Algebras;Algebraic Topology '5110': Operator Algebras;Analysis of PDEs '5111': Operator Algebras;Category Theory '5112': Operator Algebras;Category Theory;Quantum Algebra '5113': Operator Algebras;Combinatorics '5114': Operator Algebras;Combinatorics;Probability '5115': Operator Algebras;Complex Variables '5116': Operator Algebras;Complex Variables;Functional Analysis '5117': Operator Algebras;Differential Geometry '5118': Operator Algebras;Dynamical Systems '5119': Operator Algebras;Dynamical Systems;Functional Analysis '5120': Operator Algebras;Dynamical Systems;Group Theory '5121': Operator Algebras;Functional Analysis '5122': Operator Algebras;Functional Analysis;Group Theory '5123': Operator Algebras;Functional Analysis;K-Theory and Homology '5124': Operator Algebras;Functional Analysis;Logic '5125': Operator Algebras;Functional Analysis;Probability '5126': Operator Algebras;Functional Analysis;Quantum Algebra '5127': Operator Algebras;Functional Analysis;Quantum Physics '5128': Operator Algebras;Functional Analysis;Representation Theory '5129': Operator Algebras;Functional Analysis;Rings and Algebras '5130': Operator Algebras;Functional Analysis;Spectral Theory '5131': Operator Algebras;General Topology '5132': Operator Algebras;Group Theory '5133': Operator Algebras;Group Theory;K-Theory and Homology '5134': Operator Algebras;Group Theory;Quantum Algebra '5135': Operator Algebras;K-Theory and Homology '5136': Operator Algebras;K-Theory and Homology;Quantum Algebra '5137': Operator Algebras;Logic '5138': Operator Algebras;Metric Geometry '5139': Operator Algebras;Number Theory '5140': Operator Algebras;Probability '5141': Operator Algebras;Quantum Algebra '5142': Operator Algebras;Quantum Physics '5143': Operator Algebras;Representation Theory '5144': Operator Algebras;Rings and Algebras '5145': Operator Algebras;Spectral Theory '5146': Optics '5147': Optics;Accelerator Physics '5148': Optics;Adaptation and Self-Organizing Systems '5149': Optics;Analysis of PDEs '5150': Optics;Applied Physics '5151': Optics;Applied Physics;Chemical Physics '5152': Optics;Applied Physics;Classical Physics '5153': Optics;Applied Physics;Computational Physics '5154': Optics;Applied Physics;Instrumentation and Detectors '5155': Optics;Applied Physics;Plasma Physics '5156': Optics;Applied Physics;Quantum Physics '5157': Optics;Atmospheric and Oceanic Physics '5158': Optics;Atomic Physics '5159': Optics;Atomic Physics;Chemical Physics '5160': Optics;Atomic Physics;Instrumentation and Detectors '5161': Optics;Atomic Physics;Quantum Physics '5162': Optics;Atomic and Molecular Clusters '5163': Optics;Biological Physics '5164': Optics;Biological Physics;Instrumentation and Detectors '5165': Optics;Chaotic Dynamics '5166': Optics;Chaotic Dynamics;Quantum Physics '5167': Optics;Chemical Physics '5168': Optics;Chemical Physics;Quantum Physics '5169': Optics;Classical Physics '5170': Optics;Classical Physics;Computational Physics '5171': Optics;Classical Physics;Quantum Physics '5172': Optics;Computational Physics '5173': Optics;Computer Vision and Pattern Recognition '5174': Optics;Condensed Matter '5175': Optics;Data Analysis, Statistics and Probability '5176': Optics;Disordered Systems and Neural Networks '5177': Optics;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '5178': Optics;Disordered Systems and Neural Networks;Quantum Physics '5179': Optics;Emerging Technologies '5180': Optics;Emerging Technologies;Applied Physics '5181': Optics;Fluid Dynamics '5182': Optics;General Physics '5183': Optics;General Physics;Quantum Physics '5184': Optics;General Relativity and Quantum Cosmology '5185': Optics;High Energy Physics - Phenomenology '5186': Optics;Image and Video Processing '5187': Optics;Image and Video Processing;Applied Physics '5188': Optics;Image and Video Processing;Biological Physics '5189': Optics;Instrumentation and Detectors '5190': Optics;Instrumentation and Detectors;Quantum Physics '5191': Optics;Instrumentation and Methods for Astrophysics '5192': Optics;Instrumentation and Methods for Astrophysics;Instrumentation and Detectors '5193': Optics;Machine Learning '5194': Optics;Materials Science '5195': Optics;Materials Science;Applied Physics '5196': Optics;Materials Science;Instrumentation and Detectors '5197': Optics;Materials Science;Other Condensed Matter '5198': Optics;Materials Science;Pattern Formation and Solitons '5199': Optics;Materials Science;Quantum Physics '5200': Optics;Medical Physics '5201': Optics;Mesoscale and Nanoscale Physics '5202': Optics;Mesoscale and Nanoscale Physics;Applied Physics '5203': Optics;Mesoscale and Nanoscale Physics;Applied Physics;Quantum Physics '5204': Optics;Mesoscale and Nanoscale Physics;Chemical Physics '5205': Optics;Mesoscale and Nanoscale Physics;Computational Physics '5206': Optics;Mesoscale and Nanoscale Physics;Instrumentation and Detectors '5207': Optics;Mesoscale and Nanoscale Physics;Materials Science '5208': Optics;Mesoscale and Nanoscale Physics;Materials Science;Applied Physics '5209': Optics;Mesoscale and Nanoscale Physics;Materials Science;Quantum Physics '5210': Optics;Mesoscale and Nanoscale Physics;Pattern Formation and Solitons '5211': Optics;Mesoscale and Nanoscale Physics;Quantum Gases '5212': Optics;Mesoscale and Nanoscale Physics;Quantum Physics '5213': Optics;Optimization and Control '5214': Optics;Other Condensed Matter '5215': Optics;Other Condensed Matter;Applied Physics '5216': Optics;Other Condensed Matter;Quantum Physics '5217': Optics;Pattern Formation and Solitons '5218': Optics;Pattern Formation and Solitons;Applied Physics '5219': Optics;Pattern Formation and Solitons;Quantum Physics '5220': Optics;Plasma Physics '5221': Optics;Popular Physics '5222': Optics;Quantitative Methods '5223': Optics;Quantum Gases '5224': Optics;Quantum Gases;Atomic Physics '5225': Optics;Quantum Gases;Pattern Formation and Solitons '5226': Optics;Quantum Gases;Quantum Physics '5227': Optics;Quantum Physics '5228': Optics;Signal Processing '5229': Optics;Signal Processing;Applied Physics '5230': Optics;Soft Condensed Matter '5231': Optics;Space Physics '5232': Optics;Statistical Mechanics '5233': Optics;Strongly Correlated Electrons '5234': Optimization and Control '5235': Optimization and Control;Adaptation and Self-Organizing Systems '5236': Optimization and Control;Algebraic Geometry '5237': Optimization and Control;Analysis of PDEs '5238': Optimization and Control;Analysis of PDEs;Dynamical Systems '5239': Optimization and Control;Analysis of PDEs;Functional Analysis '5240': Optimization and Control;Analysis of PDEs;Numerical Analysis '5241': Optimization and Control;Analysis of PDEs;Probability '5242': Optimization and Control;Applications '5243': Optimization and Control;Artificial Intelligence '5244': Optimization and Control;Artificial Intelligence;Machine Learning '5245': Optimization and Control;Artificial Intelligence;Machine Learning;Machine Learning '5246': Optimization and Control;Artificial Intelligence;Systems and Control;Systems and Control '5247': Optimization and Control;Classical Analysis and ODEs '5248': Optimization and Control;Combinatorics '5249': Optimization and Control;Combinatorics;Metric Geometry '5250': Optimization and Control;Commutative Algebra '5251': Optimization and Control;Computation '5252': Optimization and Control;Computation;Machine Learning '5253': Optimization and Control;Computational Complexity '5254': Optimization and Control;Computational Complexity;Data Structures and Algorithms '5255': Optimization and Control;Computational Complexity;Machine Learning '5256': Optimization and Control;Computational Engineering, Finance, and Science '5257': Optimization and Control;Computational Finance '5258': Optimization and Control;Computational Geometry '5259': Optimization and Control;Computational Physics '5260': Optimization and Control;Computer Science and Game Theory '5261': Optimization and Control;Computer Science and Game Theory;Machine Learning '5262': Optimization and Control;Computer Science and Game Theory;Machine Learning;Machine Learning '5263': Optimization and Control;Computer Science and Game Theory;Multiagent Systems '5264': Optimization and Control;Computer Science and Game Theory;Probability '5265': Optimization and Control;Computer Science and Game Theory;Systems and Control '5266': Optimization and Control;Computer Science and Game Theory;Systems and Control;Systems and Control '5267': Optimization and Control;Computer Vision and Pattern Recognition '5268': Optimization and Control;Computer Vision and Pattern Recognition;Machine Learning '5269': Optimization and Control;Computers and Society '5270': Optimization and Control;Cryptography and Security '5271': Optimization and Control;Data Structures and Algorithms '5272': Optimization and Control;Data Structures and Algorithms;Machine Learning '5273': Optimization and Control;Data Structures and Algorithms;Machine Learning;Machine Learning '5274': Optimization and Control;Differential Geometry '5275': Optimization and Control;Differential Geometry;Dynamical Systems '5276': Optimization and Control;Discrete Mathematics '5277': Optimization and Control;Discrete Mathematics;Combinatorics '5278': Optimization and Control;Discrete Mathematics;Data Structures and Algorithms;Combinatorics '5279': Optimization and Control;Distributed, Parallel, and Cluster Computing '5280': Optimization and Control;Distributed, Parallel, and Cluster Computing;Machine Learning '5281': Optimization and Control;Distributed, Parallel, and Cluster Computing;Machine Learning;Machine Learning '5282': Optimization and Control;Distributed, Parallel, and Cluster Computing;Multiagent Systems '5283': Optimization and Control;Distributed, Parallel, and Cluster Computing;Systems and Control '5284': Optimization and Control;Dynamical Systems '5285': Optimization and Control;Dynamical Systems;Functional Analysis '5286': Optimization and Control;Dynamical Systems;Probability '5287': Optimization and Control;Fluid Dynamics '5288': Optimization and Control;Functional Analysis '5289': Optimization and Control;Functional Analysis;Numerical Analysis '5290': Optimization and Control;General Economics;Economics '5291': Optimization and Control;General Finance '5292': Optimization and Control;Image and Video Processing '5293': Optimization and Control;Machine Learning '5294': Optimization and Control;Machine Learning;Machine Learning '5295': Optimization and Control;Machine Learning;Multiagent Systems '5296': Optimization and Control;Machine Learning;Neural and Evolutionary Computing '5297': Optimization and Control;Machine Learning;Numerical Analysis '5298': Optimization and Control;Machine Learning;Numerical Analysis;Numerical Analysis '5299': Optimization and Control;Machine Learning;Numerical Analysis;Numerical Analysis;Machine Learning '5300': Optimization and Control;Machine Learning;Probability '5301': Optimization and Control;Machine Learning;Signal Processing '5302': Optimization and Control;Machine Learning;Systems and Control '5303': Optimization and Control;Machine Learning;Systems and Control;Systems and Control '5304': Optimization and Control;Machine Learning;Systems and Control;Systems and Control;Dynamical Systems '5305': Optimization and Control;Machine Learning;Systems and Control;Systems and Control;Machine Learning '5306': Optimization and Control;Mathematical Finance '5307': Optimization and Control;Mathematical Software '5308': Optimization and Control;Medical Physics '5309': Optimization and Control;Methodology '5310': Optimization and Control;Metric Geometry '5311': Optimization and Control;Molecular Networks '5312': Optimization and Control;Multiagent Systems '5313': Optimization and Control;Multiagent Systems;Systems and Control '5314': Optimization and Control;Multiagent Systems;Systems and Control;Systems and Control '5315': Optimization and Control;Networking and Internet Architecture '5316': Optimization and Control;Neural and Evolutionary Computing '5317': Optimization and Control;Number Theory '5318': Optimization and Control;Numerical Analysis '5319': Optimization and Control;Numerical Analysis;Analysis of PDEs;Numerical Analysis '5320': Optimization and Control;Numerical Analysis;Dynamical Systems;Numerical Analysis '5321': Optimization and Control;Numerical Analysis;Machine Learning '5322': Optimization and Control;Numerical Analysis;Numerical Analysis '5323': Optimization and Control;Numerical Analysis;Numerical Analysis;Machine Learning '5324': Optimization and Control;Numerical Analysis;Numerical Analysis;Probability '5325': Optimization and Control;Numerical Analysis;Systems and Control;Systems and Control;Numerical Analysis '5326': Optimization and Control;Performance '5327': Optimization and Control;Physics and Society '5328': Optimization and Control;Physics and Society;Populations and Evolution '5329': Optimization and Control;Populations and Evolution '5330': Optimization and Control;Portfolio Management '5331': Optimization and Control;Probability '5332': Optimization and Control;Probability;Computational Finance '5333': Optimization and Control;Probability;Mathematical Finance '5334': Optimization and Control;Probability;Risk Management '5335': Optimization and Control;Quantitative Methods '5336': Optimization and Control;Quantum Physics '5337': Optimization and Control;Rings and Algebras '5338': Optimization and Control;Risk Management '5339': Optimization and Control;Robotics '5340': Optimization and Control;Robotics;Systems and Control '5341': Optimization and Control;Robotics;Systems and Control;Systems and Control '5342': Optimization and Control;Signal Processing '5343': Optimization and Control;Social and Information Networks '5344': Optimization and Control;Social and Information Networks;Physics and Society '5345': Optimization and Control;Spectral Theory '5346': Optimization and Control;Systems and Control '5347': Optimization and Control;Systems and Control;Analysis of PDEs '5348': Optimization and Control;Systems and Control;Dynamical Systems '5349': Optimization and Control;Systems and Control;Probability '5350': Optimization and Control;Systems and Control;Quantum Physics '5351': Optimization and Control;Systems and Control;Signal Processing;Systems and Control '5352': Optimization and Control;Systems and Control;Systems and Control '5353': Optimization and Control;Systems and Control;Systems and Control;Dynamical Systems '5354': Optimization and Control;Theoretical Economics '5355': Other Computer Science '5356': Other Computer Science;Artificial Intelligence '5357': Other Computer Science;Computers and Society '5358': Other Computer Science;Hardware Architecture '5359': Other Computer Science;Signal Processing '5360': Other Condensed Matter '5361': Other Condensed Matter;Applied Physics '5362': Other Condensed Matter;Atomic Physics '5363': Other Condensed Matter;Atomic Physics;Quantum Physics '5364': Other Condensed Matter;Atomic and Molecular Clusters '5365': Other Condensed Matter;Chaotic Dynamics '5366': Other Condensed Matter;Chaotic Dynamics;Quantum Physics '5367': Other Condensed Matter;Chemical Physics '5368': Other Condensed Matter;Classical Physics '5369': Other Condensed Matter;Computational Physics '5370': Other Condensed Matter;Disordered Systems and Neural Networks '5371': Other Condensed Matter;Fluid Dynamics '5372': Other Condensed Matter;General Finance '5373': Other Condensed Matter;General Relativity and Quantum Cosmology '5374': Other Condensed Matter;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '5375': Other Condensed Matter;Geophysics '5376': Other Condensed Matter;High Energy Physics - Phenomenology '5377': Other Condensed Matter;High Energy Physics - Phenomenology;Nuclear Theory '5378': Other Condensed Matter;High Energy Physics - Theory '5379': Other Condensed Matter;High Energy Physics - Theory;Quantum Physics '5380': Other Condensed Matter;Instrumentation and Detectors '5381': Other Condensed Matter;Materials Science '5382': Other Condensed Matter;Mesoscale and Nanoscale Physics '5383': Other Condensed Matter;Mesoscale and Nanoscale Physics;Quantum Physics '5384': Other Condensed Matter;Nuclear Theory '5385': Other Condensed Matter;Optics '5386': Other Condensed Matter;Optics;Quantum Physics '5387': Other Condensed Matter;Pattern Formation and Solitons '5388': Other Condensed Matter;Pricing of Securities '5389': Other Condensed Matter;Quantum Gases '5390': Other Condensed Matter;Quantum Physics '5391': Other Condensed Matter;Soft Condensed Matter '5392': Other Condensed Matter;Statistical Finance '5393': Other Condensed Matter;Statistical Mechanics '5394': Other Condensed Matter;Statistical Mechanics;Quantum Physics '5395': Other Condensed Matter;Strongly Correlated Electrons '5396': Other Condensed Matter;Superconductivity '5397': Other Quantitative Biology '5398': Other Quantitative Biology;Biological Physics '5399': Other Quantitative Biology;Machine Learning '5400': Other Quantitative Biology;Populations and Evolution '5401': Other Quantitative Biology;Quantitative Methods '5402': Other Statistics '5403': Other Statistics;Applications '5404': Other Statistics;Computation '5405': Other Statistics;Methodology '5406': Other Statistics;Probability '5407': Pattern Formation and Solitons '5408': Pattern Formation and Solitons;Adaptation and Self-Organizing Systems '5409': Pattern Formation and Solitons;Analysis of PDEs '5410': Pattern Formation and Solitons;Analysis of PDEs;Dynamical Systems '5411': Pattern Formation and Solitons;Biological Physics '5412': Pattern Formation and Solitons;Cell Behavior '5413': Pattern Formation and Solitons;Chaotic Dynamics '5414': Pattern Formation and Solitons;Classical Physics '5415': Pattern Formation and Solitons;Condensed Matter '5416': Pattern Formation and Solitons;Condensed Matter;Pattern Formation and Solitons '5417': Pattern Formation and Solitons;Dynamical Systems '5418': Pattern Formation and Solitons;Exactly Solvable and Integrable Systems '5419': Pattern Formation and Solitons;Exactly Solvable and Integrable Systems;Fluid Dynamics '5420': Pattern Formation and Solitons;Exactly Solvable and Integrable Systems;Optics '5421': Pattern Formation and Solitons;Fluid Dynamics '5422': Pattern Formation and Solitons;High Energy Physics - Theory '5423': Pattern Formation and Solitons;Materials Science '5424': Pattern Formation and Solitons;Mesoscale and Nanoscale Physics '5425': Pattern Formation and Solitons;Neurons and Cognition '5426': Pattern Formation and Solitons;Optics '5427': Pattern Formation and Solitons;Other Condensed Matter '5428': Pattern Formation and Solitons;Pattern Formation and Solitons '5429': Pattern Formation and Solitons;Plasma Physics '5430': Pattern Formation and Solitons;Populations and Evolution '5431': Pattern Formation and Solitons;Quantum Gases '5432': Pattern Formation and Solitons;Quantum Gases;Optics '5433': Pattern Formation and Solitons;Quantum Physics '5434': Pattern Formation and Solitons;Soft Condensed Matter '5435': Pattern Formation and Solitons;Soft Condensed Matter;Adaptation and Self-Organizing Systems '5436': Pattern Formation and Solitons;Statistical Mechanics '5437': Pattern Formation and Solitons;Superconductivity '5438': Performance '5439': Performance;Distributed, Parallel, and Cluster Computing '5440': Performance;Hardware Architecture '5441': Performance;Machine Learning '5442': Performance;Mathematical Software '5443': Performance;Networking and Internet Architecture '5444': Performance;Operating Systems '5445': Performance;Probability '5446': Performance;Programming Languages '5447': Performance;Software Engineering '5448': Physics Education '5449': Physics Education;Classical Physics '5450': Physics Education;Computational Physics '5451': Physics Education;Computers and Society '5452': Physics Education;General Physics '5453': Physics Education;General Relativity and Quantum Cosmology '5454': Physics Education;High Energy Physics - Experiment '5455': Physics Education;History and Philosophy of Physics '5456': Physics Education;Instrumentation and Detectors '5457': Physics Education;Instrumentation and Methods for Astrophysics '5458': Physics Education;Optics '5459': Physics Education;Physics and Society '5460': Physics Education;Popular Physics '5461': Physics Education;Popular Physics;Physics and Society '5462': Physics Education;Quantum Physics '5463': Physics and Society '5464': Physics and Society;Adaptation and Self-Organizing Systems '5465': Physics and Society;Adaptation and Self-Organizing Systems;Data Analysis, Statistics and Probability '5466': Physics and Society;Adaptation and Self-Organizing Systems;Populations and Evolution '5467': Physics and Society;Applications '5468': Physics and Society;Applied Physics '5469': Physics and Society;Atmospheric and Oceanic Physics '5470': Physics and Society;Biological Physics '5471': Physics and Society;Biological Physics;Populations and Evolution '5472': Physics and Society;Cellular Automata and Lattice Gases '5473': Physics and Society;Chaotic Dynamics '5474': Physics and Society;Combinatorics '5475': Physics and Society;Computation and Language '5476': Physics and Society;Computation and Language;Data Analysis, Statistics and Probability '5477': Physics and Society;Computation and Language;Social and Information Networks '5478': Physics and Society;Computational Physics '5479': Physics and Society;Computer Science and Game Theory '5480': Physics and Society;Computer Science and Game Theory;Populations and Evolution '5481': Physics and Society;Computers and Society '5482': Physics and Society;Computers and Society;Social and Information Networks '5483': Physics and Society;Computers and Society;Social and Information Networks;Data Analysis, Statistics and Probability '5484': Physics and Society;Data Analysis, Statistics and Probability '5485': Physics and Society;Data Analysis, Statistics and Probability;Applications '5486': Physics and Society;Data Analysis, Statistics and Probability;General Finance '5487': Physics and Society;Data Analysis, Statistics and Probability;Popular Physics '5488': Physics and Society;Data Analysis, Statistics and Probability;Statistical Finance '5489': Physics and Society;Digital Libraries '5490': Physics and Society;Digital Libraries;Data Analysis, Statistics and Probability '5491': Physics and Society;Digital Libraries;Social and Information Networks '5492': Physics and Society;Disordered Systems and Neural Networks '5493': Physics and Society;Disordered Systems and Neural Networks;Adaptation and Self-Organizing Systems '5494': Physics and Society;Disordered Systems and Neural Networks;Computational Physics '5495': Physics and Society;Disordered Systems and Neural Networks;Data Analysis, Statistics and Probability '5496': Physics and Society;Disordered Systems and Neural Networks;Social and Information Networks '5497': Physics and Society;Disordered Systems and Neural Networks;Statistical Mechanics '5498': Physics and Society;Disordered Systems and Neural Networks;Statistical Mechanics;Social and Information Networks '5499': Physics and Society;Dynamical Systems '5500': Physics and Society;Dynamical Systems;Populations and Evolution '5501': Physics and Society;Economics '5502': Physics and Society;General Economics;Economics '5503': Physics and Society;General Finance '5504': Physics and Society;General Physics '5505': Physics and Society;High Energy Physics - Experiment '5506': Physics and Society;History and Philosophy of Physics '5507': Physics and Society;Information Retrieval;Social and Information Networks '5508': Physics and Society;Instrumentation and Methods for Astrophysics '5509': Physics and Society;Machine Learning '5510': Physics and Society;Machine Learning;Social and Information Networks '5511': Physics and Society;Methodology '5512': Physics and Society;Multiagent Systems '5513': Physics and Society;Multiagent Systems;Adaptation and Self-Organizing Systems '5514': Physics and Society;Neurons and Cognition '5515': Physics and Society;Numerical Analysis;Numerical Analysis '5516': Physics and Society;Optimization and Control '5517': Physics and Society;Physics Education '5518': Physics and Society;Popular Physics '5519': Physics and Society;Populations and Evolution '5520': Physics and Society;Populations and Evolution;Applications '5521': Physics and Society;Populations and Evolution;Quantitative Methods '5522': Physics and Society;Probability '5523': Physics and Society;Quantitative Methods '5524': Physics and Society;Quantum Physics '5525': Physics and Society;Risk Management '5526': Physics and Society;Signal Processing '5527': Physics and Society;Social and Information Networks '5528': Physics and Society;Social and Information Networks;Adaptation and Self-Organizing Systems '5529': Physics and Society;Social and Information Networks;Adaptation and Self-Organizing Systems;Data Analysis, Statistics and Probability '5530': Physics and Society;Social and Information Networks;Applications '5531': Physics and Society;Social and Information Networks;Computational Physics '5532': Physics and Society;Social and Information Networks;Data Analysis, Statistics and Probability '5533': Physics and Society;Social and Information Networks;Data Analysis, Statistics and Probability;Applications '5534': Physics and Society;Social and Information Networks;Dynamical Systems '5535': Physics and Society;Social and Information Networks;General Finance '5536': Physics and Society;Social and Information Networks;Neurons and Cognition '5537': Physics and Society;Social and Information Networks;Populations and Evolution '5538': Physics and Society;Social and Information Networks;Probability '5539': Physics and Society;Statistical Finance '5540': Physics and Society;Statistical Mechanics '5541': Physics and Society;Statistical Mechanics;Adaptation and Self-Organizing Systems '5542': Physics and Society;Statistical Mechanics;Computational Physics '5543': Physics and Society;Statistical Mechanics;Computer Science and Game Theory;Populations and Evolution '5544': Physics and Society;Statistical Mechanics;Data Analysis, Statistics and Probability '5545': Physics and Society;Statistical Mechanics;General Finance '5546': Physics and Society;Statistical Mechanics;Populations and Evolution '5547': Physics and Society;Statistical Mechanics;Probability '5548': Physics and Society;Statistical Mechanics;Social and Information Networks '5549': Physics and Society;Statistical Mechanics;Social and Information Networks;Adaptation and Self-Organizing Systems '5550': Physics and Society;Statistical Mechanics;Social and Information Networks;Data Analysis, Statistics and Probability '5551': Physics and Society;Statistical Mechanics;Social and Information Networks;Populations and Evolution '5552': Physics and Society;Statistical Mechanics;Statistical Finance '5553': Physics and Society;Systems and Control '5554': Physics and Society;Systems and Control;Systems and Control '5555': Physics and Society;Trading and Market Microstructure '5556': Plasma Physics '5557': Plasma Physics;Accelerator Physics '5558': Plasma Physics;Accelerator Physics;Computational Physics '5559': Plasma Physics;Accelerator Physics;Optics '5560': Plasma Physics;Adaptation and Self-Organizing Systems '5561': Plasma Physics;Applied Physics '5562': Plasma Physics;Astrophysics '5563': Plasma Physics;Astrophysics of Galaxies '5564': Plasma Physics;Astrophysics;Space Physics '5565': Plasma Physics;Atmospheric and Oceanic Physics '5566': Plasma Physics;Atomic Physics '5567': Plasma Physics;Atomic and Molecular Clusters '5568': Plasma Physics;Chaotic Dynamics '5569': Plasma Physics;Chemical Physics '5570': Plasma Physics;Classical Physics '5571': Plasma Physics;Computational Physics '5572': Plasma Physics;Computational Physics;Fluid Dynamics '5573': Plasma Physics;Computational Physics;Space Physics '5574': Plasma Physics;Data Analysis, Statistics and Probability '5575': Plasma Physics;Earth and Planetary Astrophysics '5576': Plasma Physics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Space Physics '5577': Plasma Physics;Earth and Planetary Astrophysics;Space Physics '5578': Plasma Physics;Fluid Dynamics '5579': Plasma Physics;Fluid Dynamics;Space Physics '5580': Plasma Physics;General Physics '5581': Plasma Physics;Geophysics '5582': Plasma Physics;High Energy Astrophysical Phenomena '5583': Plasma Physics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '5584': Plasma Physics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics;Space Physics '5585': Plasma Physics;High Energy Astrophysical Phenomena;Space Physics '5586': Plasma Physics;High Energy Physics - Experiment '5587': Plasma Physics;High Energy Physics - Phenomenology '5588': Plasma Physics;High Energy Physics - Theory '5589': Plasma Physics;Instrumentation and Detectors '5590': Plasma Physics;Machine Learning '5591': Plasma Physics;Machine Learning;Computational Physics '5592': Plasma Physics;Materials Science '5593': Plasma Physics;Medical Physics '5594': Plasma Physics;Mesoscale and Nanoscale Physics '5595': Plasma Physics;Nuclear Experiment '5596': Plasma Physics;Nuclear Theory '5597': Plasma Physics;Optics '5598': Plasma Physics;Other Condensed Matter '5599': Plasma Physics;Pattern Formation and Solitons '5600': Plasma Physics;Quantum Physics '5601': Plasma Physics;Soft Condensed Matter '5602': Plasma Physics;Solar and Stellar Astrophysics '5603': Plasma Physics;Solar and Stellar Astrophysics;Fluid Dynamics '5604': Plasma Physics;Solar and Stellar Astrophysics;Fluid Dynamics;Space Physics '5605': Plasma Physics;Solar and Stellar Astrophysics;Space Physics '5606': Plasma Physics;Space Physics '5607': Plasma Physics;Statistical Mechanics '5608': Plasma Physics;Strongly Correlated Electrons '5609': Popular Physics '5610': Popular Physics;Astrophysics '5611': Popular Physics;Astrophysics of Galaxies '5612': Popular Physics;Classical Physics '5613': Popular Physics;Cosmology and Nongalactic Astrophysics '5614': Popular Physics;Earth and Planetary Astrophysics '5615': Popular Physics;Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics '5616': Popular Physics;Fluid Dynamics '5617': Popular Physics;General Physics '5618': Popular Physics;General Relativity and Quantum Cosmology '5619': Popular Physics;High Energy Physics - Phenomenology '5620': Popular Physics;High Energy Physics - Theory '5621': Popular Physics;History and Philosophy of Physics '5622': Popular Physics;Instrumentation and Methods for Astrophysics '5623': Popular Physics;Physics Education '5624': Popular Physics;Physics and Society '5625': Popular Physics;Quantum Physics '5626': Popular Physics;Space Physics '5627': Populations and Evolution '5628': Populations and Evolution;Adaptation and Self-Organizing Systems '5629': Populations and Evolution;Adaptation and Self-Organizing Systems;Biological Physics '5630': Populations and Evolution;Adaptation and Self-Organizing Systems;Physics and Society '5631': Populations and Evolution;Algebraic Geometry '5632': Populations and Evolution;Analysis of PDEs '5633': Populations and Evolution;Applications '5634': Populations and Evolution;Atmospheric and Oceanic Physics '5635': Populations and Evolution;Biological Physics '5636': Populations and Evolution;Biological Physics;Physics and Society '5637': Populations and Evolution;Biomolecules '5638': Populations and Evolution;Cell Behavior '5639': Populations and Evolution;Chaotic Dynamics '5640': Populations and Evolution;Classical Analysis and ODEs '5641': Populations and Evolution;Combinatorics '5642': Populations and Evolution;Computation '5643': Populations and Evolution;Computational Engineering, Finance, and Science '5644': Populations and Evolution;Computer Science and Game Theory;Physics and Society '5645': Populations and Evolution;Data Analysis, Statistics and Probability '5646': Populations and Evolution;Data Structures and Algorithms '5647': Populations and Evolution;Data Structures and Algorithms;Combinatorics '5648': Populations and Evolution;Disordered Systems and Neural Networks '5649': Populations and Evolution;Disordered Systems and Neural Networks;Statistical Mechanics '5650': Populations and Evolution;Dynamical Systems '5651': Populations and Evolution;Dynamical Systems;Adaptation and Self-Organizing Systems '5652': Populations and Evolution;Dynamical Systems;Physics and Society '5653': Populations and Evolution;Dynamical Systems;Probability '5654': Populations and Evolution;Genomics '5655': Populations and Evolution;Machine Learning '5656': Populations and Evolution;Machine Learning;Machine Learning '5657': Populations and Evolution;Machine Learning;Physics and Society '5658': Populations and Evolution;Methodology '5659': Populations and Evolution;Molecular Networks '5660': Populations and Evolution;Multiagent Systems '5661': Populations and Evolution;Neural and Evolutionary Computing '5662': Populations and Evolution;Neurons and Cognition '5663': Populations and Evolution;Optimization and Control '5664': Populations and Evolution;Other Quantitative Biology '5665': Populations and Evolution;Pattern Formation and Solitons '5666': Populations and Evolution;Physics and Society '5667': Populations and Evolution;Physics and Society;Applications '5668': Populations and Evolution;Physics and Society;Quantitative Methods '5669': Populations and Evolution;Probability '5670': Populations and Evolution;Probability;Applications '5671': Populations and Evolution;Quantitative Methods '5672': Populations and Evolution;Quantitative Methods;Applications '5673': Populations and Evolution;Social and Information Networks;Physics and Society '5674': Populations and Evolution;Statistical Mechanics '5675': Populations and Evolution;Statistical Mechanics;Adaptation and Self-Organizing Systems '5676': Populations and Evolution;Statistical Mechanics;Adaptation and Self-Organizing Systems;Biological Physics '5677': Populations and Evolution;Statistical Mechanics;Adaptation and Self-Organizing Systems;Physics and Society '5678': Populations and Evolution;Statistical Mechanics;Biological Physics '5679': Populations and Evolution;Statistical Mechanics;Physics and Society '5680': Populations and Evolution;Statistical Mechanics;Quantitative Methods '5681': Populations and Evolution;Tissues and Organs '5682': Portfolio Management '5683': Portfolio Management;Applications '5684': Portfolio Management;Computational Engineering, Finance, and Science '5685': Portfolio Management;Computational Finance '5686': Portfolio Management;General Finance '5687': Portfolio Management;Machine Learning '5688': Portfolio Management;Mathematical Finance '5689': Portfolio Management;Optimization and Control '5690': Portfolio Management;Optimization and Control;Probability '5691': Portfolio Management;Probability '5692': Portfolio Management;Risk Management '5693': Portfolio Management;Statistical Finance '5694': Pricing of Securities '5695': Pricing of Securities;Analysis of PDEs '5696': Pricing of Securities;Computational Finance '5697': Pricing of Securities;Computational Finance;Mathematical Finance '5698': Pricing of Securities;General Finance '5699': Pricing of Securities;Machine Learning '5700': Pricing of Securities;Mathematical Finance '5701': Pricing of Securities;Mathematical Finance;Risk Management '5702': Pricing of Securities;Optimization and Control '5703': Pricing of Securities;Optimization and Control;Probability '5704': Pricing of Securities;Probability '5705': Pricing of Securities;Probability;Computational Finance '5706': Pricing of Securities;Probability;Mathematical Finance '5707': Pricing of Securities;Risk Management '5708': Pricing of Securities;Statistical Finance '5709': Probability '5710': Probability;Adaptation and Self-Organizing Systems '5711': Probability;Algebraic Topology '5712': Probability;Algebraic Topology;Combinatorics '5713': Probability;Analysis of PDEs '5714': Probability;Analysis of PDEs;Differential Geometry '5715': Probability;Analysis of PDEs;Dynamical Systems '5716': Probability;Analysis of PDEs;Functional Analysis '5717': Probability;Analysis of PDEs;Numerical Analysis '5718': Probability;Analysis of PDEs;Optimization and Control '5719': Probability;Analysis of PDEs;Spectral Theory '5720': Probability;Applications '5721': Probability;Classical Analysis and ODEs '5722': Probability;Classical Analysis and ODEs;Combinatorics '5723': Probability;Classical Analysis and ODEs;Complex Variables '5724': Probability;Classical Analysis and ODEs;Dynamical Systems '5725': Probability;Classical Analysis and ODEs;Functional Analysis '5726': Probability;Combinatorics '5727': Probability;Combinatorics;Dynamical Systems '5728': Probability;Combinatorics;Functional Analysis '5729': Probability;Combinatorics;Group Theory '5730': Probability;Combinatorics;Metric Geometry '5731': Probability;Combinatorics;Number Theory '5732': Probability;Combinatorics;Operator Algebras '5733': Probability;Combinatorics;Representation Theory '5734': Probability;Complex Variables '5735': Probability;Computation '5736': Probability;Computational Complexity '5737': Probability;Computational Finance '5738': Probability;Computer Science and Game Theory '5739': Probability;Data Analysis, Statistics and Probability '5740': Probability;Data Structures and Algorithms '5741': Probability;Data Structures and Algorithms;Combinatorics '5742': Probability;Differential Geometry '5743': Probability;Differential Geometry;Functional Analysis '5744': Probability;Discrete Mathematics '5745': Probability;Discrete Mathematics;Combinatorics '5746': Probability;Disordered Systems and Neural Networks '5747': Probability;Distributed, Parallel, and Cluster Computing '5748': Probability;Dynamical Systems '5749': Probability;Dynamical Systems;Populations and Evolution '5750': Probability;Functional Analysis '5751': Probability;Functional Analysis;Metric Geometry '5752': Probability;Functional Analysis;Operator Algebras '5753': Probability;Functional Analysis;Spectral Theory '5754': Probability;General Finance '5755': Probability;Geometric Topology '5756': Probability;Group Theory '5757': Probability;History and Overview '5758': Probability;Logic '5759': Probability;Machine Learning '5760': Probability;Machine Learning;Machine Learning '5761': Probability;Mathematical Finance '5762': Probability;Methodology '5763': Probability;Metric Geometry '5764': Probability;Molecular Networks '5765': Probability;Networking and Internet Architecture '5766': Probability;Neurons and Cognition '5767': Probability;Number Theory '5768': Probability;Numerical Analysis '5769': Probability;Numerical Analysis;Analysis of PDEs;Numerical Analysis '5770': Probability;Numerical Analysis;Numerical Analysis '5771': Probability;Operator Algebras '5772': Probability;Optimization and Control '5773': Probability;Optimization and Control;Computational Finance '5774': Probability;Optimization and Control;Mathematical Finance '5775': Probability;Optimization and Control;Pricing of Securities '5776': Probability;Other Statistics '5777': Probability;Performance '5778': Probability;Physics and Society '5779': Probability;Populations and Evolution '5780': Probability;Portfolio Management '5781': Probability;Pricing of Securities '5782': Probability;Quantitative Methods '5783': Probability;Quantum Physics '5784': Probability;Representation Theory '5785': Probability;Rings and Algebras '5786': Probability;Risk Management '5787': Probability;Social and Information Networks '5788': Probability;Social and Information Networks;Physics and Society '5789': Probability;Spectral Theory '5790': Probability;Statistical Finance '5791': Probability;Statistical Mechanics '5792': Probability;Statistical Mechanics;Analysis of PDEs '5793': Probability;Statistical Mechanics;Combinatorics '5794': Probability;Systems and Control;Systems and Control '5795': Probability;Trading and Market Microstructure '5796': Programming Languages '5797': Programming Languages;Artificial Intelligence '5798': Programming Languages;Artificial Intelligence;Logic in Computer Science '5799': Programming Languages;Artificial Intelligence;Machine Learning '5800': Programming Languages;Artificial Intelligence;Software Engineering '5801': Programming Languages;Category Theory '5802': Programming Languages;Computation and Language '5803': Programming Languages;Computers and Society '5804': Programming Languages;Cryptography and Security '5805': Programming Languages;Cryptography and Security;Logic in Computer Science '5806': Programming Languages;Data Structures and Algorithms '5807': Programming Languages;Databases '5808': Programming Languages;Distributed, Parallel, and Cluster Computing '5809': Programming Languages;Distributed, Parallel, and Cluster Computing;Logic in Computer Science '5810': Programming Languages;Distributed, Parallel, and Cluster Computing;Performance '5811': Programming Languages;Distributed, Parallel, and Cluster Computing;Software Engineering '5812': Programming Languages;Formal Languages and Automata Theory '5813': Programming Languages;Formal Languages and Automata Theory;Logic in Computer Science '5814': Programming Languages;Hardware Architecture '5815': Programming Languages;Human-Computer Interaction '5816': Programming Languages;Logic in Computer Science '5817': Programming Languages;Logic in Computer Science;Category Theory '5818': Programming Languages;Logic in Computer Science;Software Engineering '5819': Programming Languages;Machine Learning '5820': Programming Languages;Machine Learning;Machine Learning '5821': Programming Languages;Machine Learning;Software Engineering '5822': Programming Languages;Mathematical Software '5823': Programming Languages;Performance '5824': Programming Languages;Quantum Physics '5825': Programming Languages;Software Engineering '5826': Programming Languages;Symbolic Computation '5827': Quantitative Methods '5828': Quantitative Methods;Applications '5829': Quantitative Methods;Artificial Intelligence '5830': Quantitative Methods;Artificial Intelligence;Machine Learning '5831': Quantitative Methods;Biological Physics '5832': Quantitative Methods;Biological Physics;Biomolecules '5833': Quantitative Methods;Biomolecules '5834': Quantitative Methods;Cell Behavior '5835': Quantitative Methods;Chemical Physics '5836': Quantitative Methods;Computation '5837': Quantitative Methods;Computational Engineering, Finance, and Science '5838': Quantitative Methods;Computational Engineering, Finance, and Science;Machine Learning '5839': Quantitative Methods;Computer Vision and Pattern Recognition '5840': Quantitative Methods;Computer Vision and Pattern Recognition;Image and Video Processing '5841': Quantitative Methods;Computer Vision and Pattern Recognition;Machine Learning '5842': Quantitative Methods;Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing '5843': Quantitative Methods;Data Analysis, Statistics and Probability '5844': Quantitative Methods;Dynamical Systems '5845': Quantitative Methods;Genomics '5846': Quantitative Methods;Genomics;Applications '5847': Quantitative Methods;Image and Video Processing '5848': Quantitative Methods;Machine Learning '5849': Quantitative Methods;Machine Learning;Applications '5850': Quantitative Methods;Machine Learning;Biomolecules '5851': Quantitative Methods;Machine Learning;Genomics '5852': Quantitative Methods;Machine Learning;Image and Video Processing '5853': Quantitative Methods;Machine Learning;Machine Learning '5854': Quantitative Methods;Machine Learning;Signal Processing '5855': Quantitative Methods;Medical Physics '5856': Quantitative Methods;Methodology '5857': Quantitative Methods;Molecular Networks '5858': Quantitative Methods;Neurons and Cognition '5859': Quantitative Methods;Other Quantitative Biology '5860': Quantitative Methods;Physics and Society '5861': Quantitative Methods;Physics and Society;Populations and Evolution '5862': Quantitative Methods;Populations and Evolution '5863': Quantitative Methods;Populations and Evolution;Applications '5864': Quantitative Methods;Probability '5865': Quantitative Methods;Signal Processing '5866': Quantitative Methods;Soft Condensed Matter '5867': Quantitative Methods;Soft Condensed Matter;Biological Physics '5868': Quantitative Methods;Soft Condensed Matter;Biomolecules '5869': Quantitative Methods;Statistical Mechanics '5870': Quantitative Methods;Statistical Mechanics;Biological Physics '5871': Quantitative Methods;Subcellular Processes '5872': Quantitative Methods;Tissues and Organs '5873': Quantum Algebra '5874': Quantum Algebra;Algebraic Geometry '5875': Quantum Algebra;Algebraic Geometry;Combinatorics '5876': Quantum Algebra;Algebraic Geometry;High Energy Physics - Theory;Algebraic Geometry;Quantum Algebra '5877': Quantum Algebra;Algebraic Geometry;Representation Theory '5878': Quantum Algebra;Algebraic Geometry;Rings and Algebras '5879': Quantum Algebra;Algebraic Topology '5880': Quantum Algebra;Algebraic Topology;Category Theory '5881': Quantum Algebra;Category Theory '5882': Quantum Algebra;Category Theory;Operator Algebras '5883': Quantum Algebra;Category Theory;Representation Theory '5884': Quantum Algebra;Category Theory;Rings and Algebras '5885': Quantum Algebra;Classical Analysis and ODEs '5886': Quantum Algebra;Combinatorics '5887': Quantum Algebra;Combinatorics;Representation Theory '5888': Quantum Algebra;Combinatorics;Rings and Algebras '5889': Quantum Algebra;Differential Geometry '5890': Quantum Algebra;Differential Geometry;Operator Algebras '5891': Quantum Algebra;Functional Analysis '5892': Quantum Algebra;General Relativity and Quantum Cosmology '5893': Quantum Algebra;Geometric Topology '5894': Quantum Algebra;Geometric Topology;Representation Theory '5895': Quantum Algebra;Group Theory '5896': Quantum Algebra;Group Theory;Representation Theory '5897': Quantum Algebra;High Energy Physics - Theory '5898': Quantum Algebra;High Energy Physics - Theory;Algebraic Geometry '5899': Quantum Algebra;High Energy Physics - Theory;Category Theory '5900': Quantum Algebra;High Energy Physics - Theory;Differential Geometry '5901': Quantum Algebra;High Energy Physics - Theory;Number Theory '5902': Quantum Algebra;High Energy Physics - Theory;Quantum Algebra '5903': Quantum Algebra;High Energy Physics - Theory;Representation Theory '5904': Quantum Algebra;K-Theory and Homology '5905': Quantum Algebra;K-Theory and Homology;Representation Theory '5906': Quantum Algebra;Number Theory '5907': Quantum Algebra;Operator Algebras '5908': Quantum Algebra;Probability '5909': Quantum Algebra;Quantum Algebra '5910': Quantum Algebra;Representation Theory '5911': Quantum Algebra;Rings and Algebras '5912': Quantum Algebra;Rings and Algebras;Representation Theory '5913': Quantum Algebra;Symplectic Geometry '5914': Quantum Gases '5915': Quantum Gases;Atomic Physics '5916': Quantum Gases;Atomic Physics;Chemical Physics '5917': Quantum Gases;Atomic Physics;Chemical Physics;Quantum Physics '5918': Quantum Gases;Atomic Physics;Optics '5919': Quantum Gases;Atomic Physics;Optics;Quantum Physics '5920': Quantum Gases;Atomic Physics;Quantum Physics '5921': Quantum Gases;Chaotic Dynamics '5922': Quantum Gases;Chaotic Dynamics;Quantum Physics '5923': Quantum Gases;Computational Physics '5924': Quantum Gases;Computational Physics;Quantum Physics '5925': Quantum Gases;Disordered Systems and Neural Networks '5926': Quantum Gases;Disordered Systems and Neural Networks;Quantum Physics '5927': Quantum Gases;Disordered Systems and Neural Networks;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '5928': Quantum Gases;Disordered Systems and Neural Networks;Strongly Correlated Electrons '5929': Quantum Gases;Disordered Systems and Neural Networks;Strongly Correlated Electrons;Quantum Physics '5930': Quantum Gases;Exactly Solvable and Integrable Systems '5931': Quantum Gases;Fluid Dynamics '5932': Quantum Gases;General Relativity and Quantum Cosmology '5933': Quantum Gases;General Relativity and Quantum Cosmology;High Energy Physics - Theory '5934': Quantum Gases;General Relativity and Quantum Cosmology;Quantum Physics '5935': Quantum Gases;High Energy Physics - Lattice '5936': Quantum Gases;High Energy Physics - Lattice;Nuclear Theory '5937': Quantum Gases;High Energy Physics - Lattice;Quantum Physics '5938': Quantum Gases;High Energy Physics - Phenomenology '5939': Quantum Gases;High Energy Physics - Phenomenology;High Energy Physics - Theory '5940': Quantum Gases;High Energy Physics - Phenomenology;Nuclear Theory '5941': Quantum Gases;High Energy Physics - Phenomenology;Quantum Physics '5942': Quantum Gases;High Energy Physics - Theory '5943': Quantum Gases;High Energy Physics - Theory;Nuclear Theory '5944': Quantum Gases;High Energy Physics - Theory;Quantum Physics '5945': Quantum Gases;Materials Science '5946': Quantum Gases;Mesoscale and Nanoscale Physics '5947': Quantum Gases;Mesoscale and Nanoscale Physics;Atomic Physics;Quantum Physics '5948': Quantum Gases;Mesoscale and Nanoscale Physics;Optics '5949': Quantum Gases;Mesoscale and Nanoscale Physics;Quantum Physics '5950': Quantum Gases;Mesoscale and Nanoscale Physics;Statistical Mechanics '5951': Quantum Gases;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '5952': Quantum Gases;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Quantum Physics '5953': Quantum Gases;Mesoscale and Nanoscale Physics;Superconductivity '5954': Quantum Gases;Nuclear Theory '5955': Quantum Gases;Nuclear Theory;Atomic Physics '5956': Quantum Gases;Nuclear Theory;Quantum Physics '5957': Quantum Gases;Optics '5958': Quantum Gases;Optics;Quantum Physics '5959': Quantum Gases;Other Condensed Matter '5960': Quantum Gases;Other Condensed Matter;Quantum Physics '5961': Quantum Gases;Pattern Formation and Solitons '5962': Quantum Gases;Pattern Formation and Solitons;Atomic Physics '5963': Quantum Gases;Pattern Formation and Solitons;Atomic Physics;Quantum Physics '5964': Quantum Gases;Pattern Formation and Solitons;Optics '5965': Quantum Gases;Pattern Formation and Solitons;Quantum Physics '5966': Quantum Gases;Quantum Physics '5967': Quantum Gases;Statistical Mechanics '5968': Quantum Gases;Statistical Mechanics;Atomic Physics '5969': Quantum Gases;Statistical Mechanics;Atomic Physics;Quantum Physics '5970': Quantum Gases;Statistical Mechanics;High Energy Physics - Theory '5971': Quantum Gases;Statistical Mechanics;Quantum Physics '5972': Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons '5973': Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '5974': Quantum Gases;Strongly Correlated Electrons '5975': Quantum Gases;Strongly Correlated Electrons;Atomic Physics '5976': Quantum Gases;Strongly Correlated Electrons;Atomic Physics;Quantum Physics '5977': Quantum Gases;Strongly Correlated Electrons;High Energy Physics - Lattice;Quantum Physics '5978': Quantum Gases;Strongly Correlated Electrons;Quantum Physics '5979': Quantum Gases;Strongly Correlated Electrons;Superconductivity '5980': Quantum Gases;Strongly Correlated Electrons;Superconductivity;Nuclear Theory '5981': Quantum Gases;Strongly Correlated Electrons;Superconductivity;Quantum Physics '5982': Quantum Gases;Superconductivity '5983': Quantum Gases;Superconductivity;Nuclear Theory '5984': Quantum Gases;Superconductivity;Quantum Physics '5985': Quantum Physics '5986': Quantum Physics;Accelerator Physics;Optics '5987': Quantum Physics;Adaptation and Self-Organizing Systems '5988': Quantum Physics;Applications '5989': Quantum Physics;Applied Physics '5990': Quantum Physics;Applied Physics;Atomic Physics '5991': Quantum Physics;Applied Physics;Atomic Physics;Optics '5992': Quantum Physics;Applied Physics;Optics '5993': Quantum Physics;Artificial Intelligence '5994': Quantum Physics;Artificial Intelligence;Machine Learning '5995': Quantum Physics;Artificial Intelligence;Machine Learning;Machine Learning '5996': Quantum Physics;Atomic Physics '5997': Quantum Physics;Atomic Physics;Chemical Physics '5998': Quantum Physics;Atomic Physics;Computational Physics '5999': Quantum Physics;Atomic Physics;Instrumentation and Detectors '6000': Quantum Physics;Atomic Physics;Optics '6001': Quantum Physics;Atomic and Molecular Clusters '6002': Quantum Physics;Atomic and Molecular Clusters;Atomic Physics '6003': Quantum Physics;Biological Physics '6004': Quantum Physics;Biological Physics;Chemical Physics '6005': Quantum Physics;Biomolecules '6006': Quantum Physics;Category Theory '6007': Quantum Physics;Chaotic Dynamics '6008': Quantum Physics;Chaotic Dynamics;Atomic Physics '6009': Quantum Physics;Chaotic Dynamics;Chaotic Dynamics '6010': Quantum Physics;Chaotic Dynamics;Optics '6011': Quantum Physics;Chemical Physics '6012': Quantum Physics;Chemical Physics;Computational Physics '6013': Quantum Physics;Chemical Physics;Optics '6014': Quantum Physics;Classical Physics '6015': Quantum Physics;Combinatorics '6016': Quantum Physics;Computation and Language '6017': Quantum Physics;Computational Complexity '6018': Quantum Physics;Computational Complexity;Combinatorics '6019': Quantum Physics;Computational Complexity;Cryptography and Security '6020': Quantum Physics;Computational Complexity;Data Structures and Algorithms '6021': Quantum Physics;Computational Complexity;Formal Languages and Automata Theory '6022': Quantum Physics;Computational Complexity;Machine Learning '6023': Quantum Physics;Computational Finance '6024': Quantum Physics;Computational Physics '6025': Quantum Physics;Computational Physics;Optics '6026': Quantum Physics;Computer Science and Game Theory '6027': Quantum Physics;Computer Vision and Pattern Recognition '6028': Quantum Physics;Computer Vision and Pattern Recognition;Machine Learning '6029': Quantum Physics;Condensed Matter '6030': Quantum Physics;Condensed Matter;Chaotic Dynamics '6031': Quantum Physics;Condensed Matter;High Energy Physics - Theory '6032': Quantum Physics;Cryptography and Security '6033': Quantum Physics;Cryptography and Security;Emerging Technologies '6034': Quantum Physics;Cryptography and Security;Machine Learning '6035': Quantum Physics;Cryptography and Security;Optics '6036': Quantum Physics;Data Analysis, Statistics and Probability '6037': Quantum Physics;Data Structures and Algorithms '6038': Quantum Physics;Data Structures and Algorithms;Emerging Technologies '6039': Quantum Physics;Data Structures and Algorithms;Machine Learning '6040': Quantum Physics;Data Structures and Algorithms;Optimization and Control '6041': Quantum Physics;Discrete Mathematics '6042': Quantum Physics;Discrete Mathematics;Combinatorics '6043': Quantum Physics;Disordered Systems and Neural Networks '6044': Quantum Physics;Disordered Systems and Neural Networks;Artificial Intelligence;Machine Learning '6045': Quantum Physics;Disordered Systems and Neural Networks;Chaotic Dynamics '6046': Quantum Physics;Disordered Systems and Neural Networks;Computational Physics '6047': Quantum Physics;Disordered Systems and Neural Networks;High Energy Physics - Theory '6048': Quantum Physics;Disordered Systems and Neural Networks;Machine Learning '6049': Quantum Physics;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '6050': Quantum Physics;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Statistical Mechanics '6051': Quantum Physics;Disordered Systems and Neural Networks;Optics '6052': Quantum Physics;Disordered Systems and Neural Networks;Other Condensed Matter '6053': Quantum Physics;Disordered Systems and Neural Networks;Quantum Gases '6054': Quantum Physics;Disordered Systems and Neural Networks;Quantum Gases;Statistical Mechanics '6055': Quantum Physics;Disordered Systems and Neural Networks;Statistical Mechanics '6056': Quantum Physics;Disordered Systems and Neural Networks;Statistical Mechanics;High Energy Physics - Theory '6057': Quantum Physics;Disordered Systems and Neural Networks;Statistical Mechanics;Strongly Correlated Electrons '6058': Quantum Physics;Disordered Systems and Neural Networks;Strongly Correlated Electrons '6059': Quantum Physics;Distributed, Parallel, and Cluster Computing '6060': Quantum Physics;Dynamical Systems '6061': Quantum Physics;Emerging Technologies '6062': Quantum Physics;Emerging Technologies;Machine Learning '6063': Quantum Physics;Emerging Technologies;Programming Languages '6064': Quantum Physics;Exactly Solvable and Integrable Systems '6065': Quantum Physics;Fluid Dynamics '6066': Quantum Physics;Functional Analysis '6067': Quantum Physics;Functional Analysis;Operator Algebras '6068': Quantum Physics;Functional Analysis;Probability '6069': Quantum Physics;General Physics '6070': Quantum Physics;General Relativity and Quantum Cosmology '6071': Quantum Physics;General Relativity and Quantum Cosmology;Atomic Physics '6072': Quantum Physics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology '6073': Quantum Physics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory '6074': Quantum Physics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '6075': Quantum Physics;General Relativity and Quantum Cosmology;History and Philosophy of Physics '6076': Quantum Physics;General Relativity and Quantum Cosmology;Optics '6077': Quantum Physics;Geometric Topology '6078': Quantum Physics;Group Theory '6079': Quantum Physics;Hardware Architecture '6080': Quantum Physics;Hardware Architecture;Emerging Technologies '6081': Quantum Physics;High Energy Physics - Experiment '6082': Quantum Physics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '6083': Quantum Physics;High Energy Physics - Lattice '6084': Quantum Physics;High Energy Physics - Lattice;High Energy Physics - Phenomenology '6085': Quantum Physics;High Energy Physics - Lattice;High Energy Physics - Phenomenology;Nuclear Theory '6086': Quantum Physics;High Energy Physics - Lattice;High Energy Physics - Theory '6087': Quantum Physics;High Energy Physics - Phenomenology '6088': Quantum Physics;High Energy Physics - Phenomenology;High Energy Physics - Theory '6089': Quantum Physics;High Energy Physics - Phenomenology;Nuclear Theory '6090': Quantum Physics;High Energy Physics - Phenomenology;Optics '6091': Quantum Physics;High Energy Physics - Theory '6092': Quantum Physics;High Energy Physics - Theory;Atomic Physics '6093': Quantum Physics;High Energy Physics - Theory;History and Philosophy of Physics '6094': Quantum Physics;High Energy Physics - Theory;Nuclear Theory '6095': Quantum Physics;History and Philosophy of Physics '6096': Quantum Physics;History and Philosophy of Physics;Popular Physics '6097': Quantum Physics;Instrumentation and Detectors '6098': Quantum Physics;Instrumentation and Detectors;Optics '6099': Quantum Physics;Instrumentation and Methods for Astrophysics '6100': Quantum Physics;Logic '6101': Quantum Physics;Logic in Computer Science '6102': Quantum Physics;Logic in Computer Science;Category Theory '6103': Quantum Physics;Machine Learning '6104': Quantum Physics;Machine Learning;Data Analysis, Statistics and Probability '6105': Quantum Physics;Machine Learning;Machine Learning '6106': Quantum Physics;Machine Learning;Neural and Evolutionary Computing '6107': Quantum Physics;Machine Learning;Optimization and Control '6108': Quantum Physics;Materials Science '6109': Quantum Physics;Materials Science;Applied Physics '6110': Quantum Physics;Materials Science;Atomic Physics '6111': Quantum Physics;Materials Science;Chemical Physics '6112': Quantum Physics;Materials Science;Optics '6113': Quantum Physics;Materials Science;Strongly Correlated Electrons '6114': Quantum Physics;Mesoscale and Nanoscale Physics '6115': Quantum Physics;Mesoscale and Nanoscale Physics;Applied Physics '6116': Quantum Physics;Mesoscale and Nanoscale Physics;Applied Physics;Optics '6117': Quantum Physics;Mesoscale and Nanoscale Physics;Atomic Physics '6118': Quantum Physics;Mesoscale and Nanoscale Physics;Atomic Physics;Optics '6119': Quantum Physics;Mesoscale and Nanoscale Physics;Chaotic Dynamics '6120': Quantum Physics;Mesoscale and Nanoscale Physics;Chemical Physics '6121': Quantum Physics;Mesoscale and Nanoscale Physics;Computational Physics '6122': Quantum Physics;Mesoscale and Nanoscale Physics;General Relativity and Quantum Cosmology '6123': Quantum Physics;Mesoscale and Nanoscale Physics;High Energy Physics - Theory '6124': Quantum Physics;Mesoscale and Nanoscale Physics;Instrumentation and Detectors '6125': Quantum Physics;Mesoscale and Nanoscale Physics;Materials Science '6126': Quantum Physics;Mesoscale and Nanoscale Physics;Materials Science;Optics '6127': Quantum Physics;Mesoscale and Nanoscale Physics;Optics '6128': Quantum Physics;Mesoscale and Nanoscale Physics;Other Condensed Matter '6129': Quantum Physics;Mesoscale and Nanoscale Physics;Quantum Gases '6130': Quantum Physics;Mesoscale and Nanoscale Physics;Quantum Gases;Optics '6131': Quantum Physics;Mesoscale and Nanoscale Physics;Quantum Gases;Strongly Correlated Electrons '6132': Quantum Physics;Mesoscale and Nanoscale Physics;Quantum Gases;Superconductivity '6133': Quantum Physics;Mesoscale and Nanoscale Physics;Statistical Mechanics '6134': Quantum Physics;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '6135': Quantum Physics;Mesoscale and Nanoscale Physics;Superconductivity '6136': Quantum Physics;Networking and Internet Architecture '6137': Quantum Physics;Neural and Evolutionary Computing '6138': Quantum Physics;Neurons and Cognition '6139': Quantum Physics;Nuclear Theory '6140': Quantum Physics;Nuclear Theory;Atomic Physics '6141': Quantum Physics;Number Theory '6142': Quantum Physics;Numerical Analysis '6143': Quantum Physics;Numerical Analysis;Numerical Analysis '6144': Quantum Physics;Numerical Analysis;Numerical Analysis;Computational Physics '6145': Quantum Physics;Operator Algebras '6146': Quantum Physics;Optics '6147': Quantum Physics;Optimization and Control '6148': Quantum Physics;Other Computer Science '6149': Quantum Physics;Other Condensed Matter '6150': Quantum Physics;Other Condensed Matter;Atomic Physics '6151': Quantum Physics;Other Condensed Matter;Chaotic Dynamics '6152': Quantum Physics;Other Condensed Matter;Chemical Physics '6153': Quantum Physics;Other Condensed Matter;Computational Physics '6154': Quantum Physics;Other Condensed Matter;High Energy Physics - Phenomenology '6155': Quantum Physics;Other Condensed Matter;High Energy Physics - Theory '6156': Quantum Physics;Other Condensed Matter;Optics '6157': Quantum Physics;Other Condensed Matter;Quantum Gases '6158': Quantum Physics;Other Condensed Matter;Statistical Mechanics '6159': Quantum Physics;Other Condensed Matter;Strongly Correlated Electrons '6160': Quantum Physics;Pattern Formation and Solitons '6161': Quantum Physics;Performance '6162': Quantum Physics;Physics Education '6163': Quantum Physics;Physics and Society '6164': Quantum Physics;Plasma Physics '6165': Quantum Physics;Popular Physics '6166': Quantum Physics;Probability '6167': Quantum Physics;Programming Languages '6168': Quantum Physics;Quantum Algebra '6169': Quantum Physics;Quantum Gases '6170': Quantum Physics;Quantum Gases;Atomic Physics '6171': Quantum Physics;Quantum Gases;Atomic Physics;Optics '6172': Quantum Physics;Quantum Gases;Chaotic Dynamics '6173': Quantum Physics;Quantum Gases;General Relativity and Quantum Cosmology '6174': Quantum Physics;Quantum Gases;High Energy Physics - Lattice;High Energy Physics - Theory '6175': Quantum Physics;Quantum Gases;High Energy Physics - Theory '6176': Quantum Physics;Quantum Gases;Optics '6177': Quantum Physics;Quantum Gases;Statistical Mechanics '6178': Quantum Physics;Quantum Gases;Statistical Mechanics;Strongly Correlated Electrons '6179': Quantum Physics;Quantum Gases;Strongly Correlated Electrons '6180': Quantum Physics;Representation Theory '6181': Quantum Physics;Signal Processing '6182': Quantum Physics;Soft Condensed Matter '6183': Quantum Physics;Software Engineering '6184': Quantum Physics;Statistical Mechanics '6185': Quantum Physics;Statistical Mechanics;Atomic Physics '6186': Quantum Physics;Statistical Mechanics;Chaotic Dynamics '6187': Quantum Physics;Statistical Mechanics;Chemical Physics '6188': Quantum Physics;Statistical Mechanics;Computational Complexity '6189': Quantum Physics;Statistical Mechanics;Computational Physics '6190': Quantum Physics;Statistical Mechanics;Exactly Solvable and Integrable Systems '6191': Quantum Physics;Statistical Mechanics;General Relativity and Quantum Cosmology '6192': Quantum Physics;Statistical Mechanics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '6193': Quantum Physics;Statistical Mechanics;High Energy Physics - Lattice '6194': Quantum Physics;Statistical Mechanics;High Energy Physics - Theory '6195': Quantum Physics;Statistical Mechanics;High Energy Physics - Theory;Chaotic Dynamics '6196': Quantum Physics;Statistical Mechanics;Optics '6197': Quantum Physics;Statistical Mechanics;Strongly Correlated Electrons '6198': Quantum Physics;Statistical Mechanics;Strongly Correlated Electrons;High Energy Physics - Theory '6199': Quantum Physics;Strongly Correlated Electrons '6200': Quantum Physics;Strongly Correlated Electrons;Atomic Physics '6201': Quantum Physics;Strongly Correlated Electrons;Chemical Physics '6202': Quantum Physics;Strongly Correlated Electrons;Computational Physics '6203': Quantum Physics;Strongly Correlated Electrons;High Energy Physics - Lattice '6204': Quantum Physics;Strongly Correlated Electrons;High Energy Physics - Lattice;High Energy Physics - Theory '6205': Quantum Physics;Strongly Correlated Electrons;High Energy Physics - Theory '6206': Quantum Physics;Strongly Correlated Electrons;Machine Learning '6207': Quantum Physics;Strongly Correlated Electrons;Nuclear Theory '6208': Quantum Physics;Strongly Correlated Electrons;Optics '6209': Quantum Physics;Strongly Correlated Electrons;Superconductivity '6210': Quantum Physics;Superconductivity '6211': Quantum Physics;Superconductivity;Applied Physics '6212': Quantum Physics;Superconductivity;Instrumentation and Detectors '6213': Quantum Physics;Systems and Control '6214': Quantum Physics;Systems and Control;Optimization and Control '6215': Quantum Physics;Systems and Control;Systems and Control '6216': Quantum Physics;Systems and Control;Systems and Control;Optimization and Control '6217': Representation Theory '6218': Representation Theory;Algebraic Geometry '6219': Representation Theory;Algebraic Geometry;Category Theory '6220': Representation Theory;Algebraic Geometry;Combinatorics '6221': Representation Theory;Algebraic Geometry;Group Theory '6222': Representation Theory;Algebraic Geometry;Number Theory '6223': Representation Theory;Algebraic Geometry;Quantum Algebra '6224': Representation Theory;Algebraic Geometry;Rings and Algebras '6225': Representation Theory;Algebraic Geometry;Symplectic Geometry '6226': Representation Theory;Algebraic Topology '6227': Representation Theory;Algebraic Topology;Combinatorics '6228': Representation Theory;Algebraic Topology;Group Theory '6229': Representation Theory;Analysis of PDEs '6230': Representation Theory;Category Theory '6231': Representation Theory;Category Theory;Group Theory '6232': Representation Theory;Category Theory;K-Theory and Homology '6233': Representation Theory;Category Theory;Quantum Algebra '6234': Representation Theory;Category Theory;Rings and Algebras '6235': Representation Theory;Classical Analysis and ODEs '6236': Representation Theory;Combinatorics '6237': Representation Theory;Combinatorics;Category Theory '6238': Representation Theory;Combinatorics;Group Theory '6239': Representation Theory;Combinatorics;Number Theory '6240': Representation Theory;Combinatorics;Probability '6241': Representation Theory;Combinatorics;Quantum Algebra '6242': Representation Theory;Combinatorics;Rings and Algebras '6243': Representation Theory;Commutative Algebra '6244': Representation Theory;Commutative Algebra;Algebraic Geometry '6245': Representation Theory;Commutative Algebra;Combinatorics '6246': Representation Theory;Commutative Algebra;Rings and Algebras '6247': Representation Theory;Complex Variables '6248': Representation Theory;Differential Geometry '6249': Representation Theory;Dynamical Systems '6250': Representation Theory;Functional Analysis '6251': Representation Theory;Geometric Topology '6252': Representation Theory;Geometric Topology;Quantum Algebra '6253': Representation Theory;Group Theory '6254': Representation Theory;Group Theory;Number Theory '6255': Representation Theory;Group Theory;Quantum Algebra '6256': Representation Theory;Group Theory;Rings and Algebras '6257': Representation Theory;High Energy Physics - Theory '6258': Representation Theory;High Energy Physics - Theory;Algebraic Geometry;Quantum Algebra '6259': Representation Theory;High Energy Physics - Theory;Number Theory '6260': Representation Theory;High Energy Physics - Theory;Quantum Algebra '6261': Representation Theory;K-Theory and Homology '6262': Representation Theory;K-Theory and Homology;Rings and Algebras '6263': Representation Theory;Logic '6264': Representation Theory;Number Theory '6265': Representation Theory;Number Theory;Quantum Algebra '6266': Representation Theory;Operator Algebras '6267': Representation Theory;Probability '6268': Representation Theory;Quantum Algebra '6269': Representation Theory;Quantum Algebra;Rings and Algebras '6270': Representation Theory;Rings and Algebras '6271': Representation Theory;Symplectic Geometry '6272': Rings and Algebras '6273': Rings and Algebras;Algebraic Geometry '6274': Rings and Algebras;Algebraic Geometry;Number Theory '6275': Rings and Algebras;Algebraic Geometry;Quantum Algebra '6276': Rings and Algebras;Algebraic Geometry;Representation Theory '6277': Rings and Algebras;Algebraic Topology '6278': Rings and Algebras;Category Theory '6279': Rings and Algebras;Category Theory;Representation Theory '6280': Rings and Algebras;Classical Analysis and ODEs '6281': Rings and Algebras;Combinatorics '6282': Rings and Algebras;Combinatorics;Group Theory '6283': Rings and Algebras;Combinatorics;Quantum Algebra '6284': Rings and Algebras;Combinatorics;Representation Theory '6285': Rings and Algebras;Commutative Algebra '6286': Rings and Algebras;Commutative Algebra;Algebraic Geometry '6287': Rings and Algebras;Commutative Algebra;Combinatorics '6288': Rings and Algebras;Commutative Algebra;Representation Theory '6289': Rings and Algebras;Differential Geometry '6290': Rings and Algebras;Dynamical Systems '6291': Rings and Algebras;Functional Analysis '6292': Rings and Algebras;Functional Analysis;Operator Algebras '6293': Rings and Algebras;General Topology '6294': Rings and Algebras;Group Theory '6295': Rings and Algebras;Group Theory;Quantum Algebra '6296': Rings and Algebras;Group Theory;Representation Theory '6297': Rings and Algebras;K-Theory and Homology '6298': Rings and Algebras;K-Theory and Homology;Representation Theory '6299': Rings and Algebras;Logic '6300': Rings and Algebras;Logic in Computer Science '6301': Rings and Algebras;Number Theory '6302': Rings and Algebras;Numerical Analysis '6303': Rings and Algebras;Numerical Analysis;Numerical Analysis '6304': Rings and Algebras;Operator Algebras '6305': Rings and Algebras;Quantum Algebra '6306': Rings and Algebras;Quantum Algebra;Representation Theory '6307': Rings and Algebras;Representation Theory '6308': Rings and Algebras;Spectral Theory '6309': Rings and Algebras;Symbolic Computation '6310': Rings and Algebras;Symplectic Geometry '6311': Risk Management '6312': Risk Management;Applications '6313': Risk Management;Computational Finance '6314': Risk Management;General Finance '6315': Risk Management;Machine Learning '6316': Risk Management;Mathematical Finance '6317': Risk Management;Optimization and Control '6318': Risk Management;Optimization and Control;Probability '6319': Risk Management;Physics and Society '6320': Risk Management;Portfolio Management '6321': Risk Management;Pricing of Securities '6322': Risk Management;Probability '6323': Risk Management;Probability;Mathematical Finance '6324': Risk Management;Statistical Finance '6325': Robotics '6326': Robotics;Algebraic Geometry '6327': Robotics;Applications '6328': Robotics;Applied Physics '6329': Robotics;Artificial Intelligence '6330': Robotics;Artificial Intelligence;Computation and Language '6331': Robotics;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition '6332': Robotics;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition;Machine Learning '6333': Robotics;Artificial Intelligence;Computation and Language;Machine Learning '6334': Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition '6335': Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '6336': Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Multiagent Systems '6337': Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Systems and Control;Systems and Control '6338': Robotics;Artificial Intelligence;Human-Computer Interaction '6339': Robotics;Artificial Intelligence;Human-Computer Interaction;Machine Learning '6340': Robotics;Artificial Intelligence;Machine Learning '6341': Robotics;Artificial Intelligence;Machine Learning;Machine Learning '6342': Robotics;Artificial Intelligence;Machine Learning;Multiagent Systems '6343': Robotics;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing '6344': Robotics;Artificial Intelligence;Machine Learning;Systems and Control '6345': Robotics;Artificial Intelligence;Machine Learning;Systems and Control;Systems and Control '6346': Robotics;Artificial Intelligence;Multiagent Systems '6347': Robotics;Artificial Intelligence;Neural and Evolutionary Computing '6348': Robotics;Artificial Intelligence;Systems and Control '6349': Robotics;Artificial Intelligence;Systems and Control;Systems and Control '6350': Robotics;Classical Physics '6351': Robotics;Computation and Language '6352': Robotics;Computation and Language;Computer Vision and Pattern Recognition '6353': Robotics;Computation and Language;Human-Computer Interaction '6354': Robotics;Computational Geometry '6355': Robotics;Computer Science and Game Theory '6356': Robotics;Computer Vision and Pattern Recognition '6357': Robotics;Computer Vision and Pattern Recognition;Human-Computer Interaction '6358': Robotics;Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning '6359': Robotics;Computer Vision and Pattern Recognition;Image and Video Processing '6360': Robotics;Computer Vision and Pattern Recognition;Machine Learning '6361': Robotics;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning '6362': Robotics;Computer Vision and Pattern Recognition;Machine Learning;Systems and Control;Systems and Control '6363': Robotics;Computer Vision and Pattern Recognition;Multiagent Systems '6364': Robotics;Computer Vision and Pattern Recognition;Systems and Control;Systems and Control '6365': Robotics;Computers and Society '6366': Robotics;Computers and Society;Human-Computer Interaction '6367': Robotics;Cryptography and Security '6368': Robotics;Data Structures and Algorithms '6369': Robotics;Distributed, Parallel, and Cluster Computing '6370': Robotics;Dynamical Systems '6371': Robotics;Graphics '6372': Robotics;Hardware Architecture '6373': Robotics;Human-Computer Interaction '6374': Robotics;Human-Computer Interaction;Machine Learning '6375': Robotics;Human-Computer Interaction;Systems and Control;Systems and Control '6376': Robotics;Image and Video Processing '6377': Robotics;Instrumentation and Methods for Astrophysics '6378': Robotics;Logic in Computer Science '6379': Robotics;Machine Learning '6380': Robotics;Machine Learning;Machine Learning '6381': Robotics;Machine Learning;Multiagent Systems '6382': Robotics;Machine Learning;Neural and Evolutionary Computing '6383': Robotics;Machine Learning;Optimization and Control '6384': Robotics;Machine Learning;Systems and Control '6385': Robotics;Machine Learning;Systems and Control;Systems and Control '6386': Robotics;Machine Learning;Systems and Control;Systems and Control;Optimization and Control '6387': Robotics;Medical Physics '6388': Robotics;Multiagent Systems '6389': Robotics;Multiagent Systems;Systems and Control;Systems and Control '6390': Robotics;Networking and Internet Architecture '6391': Robotics;Neural and Evolutionary Computing '6392': Robotics;Neurons and Cognition '6393': Robotics;Optimization and Control '6394': Robotics;Programming Languages '6395': Robotics;Signal Processing '6396': Robotics;Software Engineering '6397': Robotics;Systems and Control '6398': Robotics;Systems and Control;Optimization and Control '6399': Robotics;Systems and Control;Signal Processing;Systems and Control '6400': Robotics;Systems and Control;Systems and Control '6401': Robotics;Systems and Control;Systems and Control;Optimization and Control '6402': Signal Processing '6403': Signal Processing;Applications '6404': Signal Processing;Applied Physics '6405': Signal Processing;Applied Physics;Optics '6406': Signal Processing;Artificial Intelligence '6407': Signal Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning '6408': Signal Processing;Artificial Intelligence;Human-Computer Interaction;Machine Learning '6409': Signal Processing;Artificial Intelligence;Machine Learning '6410': Signal Processing;Artificial Intelligence;Machine Learning;Machine Learning '6411': Signal Processing;Artificial Intelligence;Systems and Control;Systems and Control '6412': Signal Processing;Audio and Speech Processing '6413': Signal Processing;Chaotic Dynamics '6414': Signal Processing;Computational Engineering, Finance, and Science '6415': Signal Processing;Computer Vision and Pattern Recognition '6416': Signal Processing;Computer Vision and Pattern Recognition;Image and Video Processing '6417': Signal Processing;Computer Vision and Pattern Recognition;Machine Learning '6418': Signal Processing;Computers and Society '6419': Signal Processing;Computers and Society;Machine Learning '6420': Signal Processing;Cryptography and Security '6421': Signal Processing;Cryptography and Security;Machine Learning '6422': Signal Processing;Data Analysis, Statistics and Probability '6423': Signal Processing;Distributed, Parallel, and Cluster Computing '6424': Signal Processing;Emerging Technologies '6425': Signal Processing;Geophysics '6426': Signal Processing;Hardware Architecture '6427': Signal Processing;Human-Computer Interaction '6428': Signal Processing;Human-Computer Interaction;Machine Learning '6429': Signal Processing;Image and Video Processing '6430': Signal Processing;Instrumentation and Detectors '6431': Signal Processing;Instrumentation and Methods for Astrophysics '6432': Signal Processing;Machine Learning '6433': Signal Processing;Machine Learning;Applications '6434': Signal Processing;Machine Learning;Image and Video Processing '6435': Signal Processing;Machine Learning;Machine Learning '6436': Signal Processing;Machine Learning;Networking and Internet Architecture '6437': Signal Processing;Machine Learning;Networking and Internet Architecture;Machine Learning '6438': Signal Processing;Machine Learning;Neural and Evolutionary Computing '6439': Signal Processing;Machine Learning;Neurons and Cognition '6440': Signal Processing;Machine Learning;Optimization and Control '6441': Signal Processing;Machine Learning;Quantitative Methods '6442': Signal Processing;Machine Learning;Robotics '6443': Signal Processing;Machine Learning;Sound;Audio and Speech Processing '6444': Signal Processing;Machine Learning;Systems and Control;Systems and Control '6445': Signal Processing;Medical Physics '6446': Signal Processing;Methodology '6447': Signal Processing;Multiagent Systems '6448': Signal Processing;Multimedia '6449': Signal Processing;Networking and Internet Architecture '6450': Signal Processing;Networking and Internet Architecture;Systems and Control;Systems and Control '6451': Signal Processing;Neural and Evolutionary Computing '6452': Signal Processing;Neurons and Cognition '6453': Signal Processing;Numerical Analysis '6454': Signal Processing;Numerical Analysis;Numerical Analysis '6455': Signal Processing;Optics '6456': Signal Processing;Optimization and Control '6457': Signal Processing;Performance '6458': Signal Processing;Quantitative Methods '6459': Signal Processing;Quantum Physics '6460': Signal Processing;Robotics '6461': Signal Processing;Robotics;Systems and Control;Systems and Control '6462': Signal Processing;Social and Information Networks '6463': Signal Processing;Sound;Audio and Speech Processing '6464': Signal Processing;Systems and Control '6465': Signal Processing;Systems and Control;Systems and Control '6466': Signal Processing;Systems and Control;Systems and Control;Optimization and Control '6467': Social and Information Networks '6468': Social and Information Networks;Adaptation and Self-Organizing Systems;Physics and Society '6469': Social and Information Networks;Applications '6470': Social and Information Networks;Artificial Intelligence '6471': Social and Information Networks;Artificial Intelligence;Computation and Language '6472': Social and Information Networks;Artificial Intelligence;Computers and Society '6473': Social and Information Networks;Artificial Intelligence;Information Retrieval '6474': Social and Information Networks;Artificial Intelligence;Machine Learning '6475': Social and Information Networks;Artificial Intelligence;Physics and Society '6476': Social and Information Networks;Combinatorics '6477': Social and Information Networks;Combinatorics;Physics and Society '6478': Social and Information Networks;Computation and Language '6479': Social and Information Networks;Computation and Language;Computers and Society '6480': Social and Information Networks;Computation and Language;Information Retrieval '6481': Social and Information Networks;Computation and Language;Machine Learning '6482': Social and Information Networks;Computation and Language;Physics and Society '6483': Social and Information Networks;Computer Science and Game Theory '6484': Social and Information Networks;Computer Science and Game Theory;Physics and Society '6485': Social and Information Networks;Computer Vision and Pattern Recognition '6486': Social and Information Networks;Computers and Society '6487': Social and Information Networks;Computers and Society;Applications '6488': Social and Information Networks;Computers and Society;Data Analysis, Statistics and Probability;Physics and Society '6489': Social and Information Networks;Computers and Society;Human-Computer Interaction '6490': Social and Information Networks;Computers and Society;Human-Computer Interaction;Physics and Society '6491': Social and Information Networks;Computers and Society;Information Retrieval '6492': Social and Information Networks;Computers and Society;Machine Learning '6493': Social and Information Networks;Computers and Society;Physics and Society '6494': Social and Information Networks;Cryptography and Security '6495': Social and Information Networks;Cryptography and Security;Machine Learning '6496': Social and Information Networks;Data Analysis, Statistics and Probability '6497': Social and Information Networks;Data Analysis, Statistics and Probability;Physics and Society '6498': Social and Information Networks;Data Analysis, Statistics and Probability;Physics and Society;Machine Learning '6499': Social and Information Networks;Data Structures and Algorithms '6500': Social and Information Networks;Data Structures and Algorithms;Machine Learning '6501': Social and Information Networks;Data Structures and Algorithms;Physics and Society '6502': Social and Information Networks;Databases '6503': Social and Information Networks;Digital Libraries '6504': Social and Information Networks;Digital Libraries;Physics and Society '6505': Social and Information Networks;Discrete Mathematics '6506': Social and Information Networks;Discrete Mathematics;Combinatorics '6507': Social and Information Networks;Discrete Mathematics;Data Structures and Algorithms '6508': Social and Information Networks;Discrete Mathematics;Physics and Society '6509': Social and Information Networks;Distributed, Parallel, and Cluster Computing '6510': Social and Information Networks;Human-Computer Interaction '6511': Social and Information Networks;Human-Computer Interaction;Physics and Society '6512': Social and Information Networks;Information Retrieval '6513': Social and Information Networks;Information Retrieval;Machine Learning '6514': Social and Information Networks;Information Retrieval;Physics and Society '6515': Social and Information Networks;Machine Learning '6516': Social and Information Networks;Machine Learning;Machine Learning '6517': Social and Information Networks;Machine Learning;Physics and Society '6518': Social and Information Networks;Machine Learning;Physics and Society;Machine Learning '6519': Social and Information Networks;Methodology '6520': Social and Information Networks;Multiagent Systems '6521': Social and Information Networks;Multimedia '6522': Social and Information Networks;Networking and Internet Architecture '6523': Social and Information Networks;Networking and Internet Architecture;Physics and Society '6524': Social and Information Networks;Optimization and Control '6525': Social and Information Networks;Optimization and Control;Physics and Society '6526': Social and Information Networks;Physics and Society '6527': Social and Information Networks;Physics and Society;Applications '6528': Social and Information Networks;Physics and Society;Machine Learning '6529': Social and Information Networks;Physics and Society;Methodology '6530': Social and Information Networks;Physics and Society;Populations and Evolution '6531': Social and Information Networks;Probability '6532': Social and Information Networks;Probability;Physics and Society '6533': Social and Information Networks;Signal Processing '6534': Social and Information Networks;Software Engineering '6535': Social and Information Networks;Statistical Mechanics;Physics and Society '6536': Social and Information Networks;Systems and Control;Systems and Control '6537': Soft Condensed Matter '6538': Soft Condensed Matter;Adaptation and Self-Organizing Systems '6539': Soft Condensed Matter;Analysis of PDEs '6540': Soft Condensed Matter;Applied Physics '6541': Soft Condensed Matter;Applied Physics;Fluid Dynamics '6542': Soft Condensed Matter;Atomic Physics '6543': Soft Condensed Matter;Biological Physics '6544': Soft Condensed Matter;Biological Physics;Biomolecules '6545': Soft Condensed Matter;Biological Physics;Cell Behavior '6546': Soft Condensed Matter;Biological Physics;Chemical Physics '6547': Soft Condensed Matter;Biological Physics;Chemical Physics;Biomolecules '6548': Soft Condensed Matter;Biological Physics;Chemical Physics;Computational Physics '6549': Soft Condensed Matter;Biological Physics;Computational Physics '6550': Soft Condensed Matter;Biological Physics;Fluid Dynamics '6551': Soft Condensed Matter;Biological Physics;Quantitative Methods '6552': Soft Condensed Matter;Biological Physics;Subcellular Processes '6553': Soft Condensed Matter;Biological Physics;Tissues and Organs '6554': Soft Condensed Matter;Biomolecules '6555': Soft Condensed Matter;Cell Behavior '6556': Soft Condensed Matter;Chaotic Dynamics '6557': Soft Condensed Matter;Chaotic Dynamics;Fluid Dynamics '6558': Soft Condensed Matter;Chemical Physics '6559': Soft Condensed Matter;Chemical Physics;Computational Physics '6560': Soft Condensed Matter;Chemical Physics;Fluid Dynamics '6561': Soft Condensed Matter;Classical Physics '6562': Soft Condensed Matter;Classical Physics;Fluid Dynamics '6563': Soft Condensed Matter;Computational Engineering, Finance, and Science '6564': Soft Condensed Matter;Computational Physics '6565': Soft Condensed Matter;Computational Physics;Fluid Dynamics '6566': Soft Condensed Matter;Data Analysis, Statistics and Probability '6567': Soft Condensed Matter;Disordered Systems and Neural Networks '6568': Soft Condensed Matter;Disordered Systems and Neural Networks;Materials Science '6569': Soft Condensed Matter;Disordered Systems and Neural Networks;Materials Science;Statistical Mechanics '6570': Soft Condensed Matter;Disordered Systems and Neural Networks;Statistical Mechanics '6571': Soft Condensed Matter;Fluid Dynamics '6572': Soft Condensed Matter;Fluid Dynamics;Geophysics '6573': Soft Condensed Matter;Geophysics '6574': Soft Condensed Matter;High Energy Physics - Theory '6575': Soft Condensed Matter;Instrumentation and Detectors '6576': Soft Condensed Matter;Machine Learning '6577': Soft Condensed Matter;Materials Science '6578': Soft Condensed Matter;Materials Science;Applied Physics '6579': Soft Condensed Matter;Materials Science;Biological Physics '6580': Soft Condensed Matter;Materials Science;Biomolecules '6581': Soft Condensed Matter;Materials Science;Chemical Physics '6582': Soft Condensed Matter;Materials Science;Chemical Physics;Fluid Dynamics '6583': Soft Condensed Matter;Materials Science;Computational Physics '6584': Soft Condensed Matter;Materials Science;Fluid Dynamics '6585': Soft Condensed Matter;Materials Science;Optics '6586': Soft Condensed Matter;Materials Science;Other Condensed Matter '6587': Soft Condensed Matter;Materials Science;Pattern Formation and Solitons '6588': Soft Condensed Matter;Materials Science;Statistical Mechanics '6589': Soft Condensed Matter;Materials Science;Statistical Mechanics;Chemical Physics '6590': Soft Condensed Matter;Materials Science;Statistical Mechanics;Computational Physics '6591': Soft Condensed Matter;Materials Science;Statistical Mechanics;Fluid Dynamics '6592': Soft Condensed Matter;Medical Physics '6593': Soft Condensed Matter;Mesoscale and Nanoscale Physics '6594': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Biological Physics '6595': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Chemical Physics '6596': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Fluid Dynamics '6597': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Materials Science '6598': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Materials Science;Chemical Physics '6599': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Materials Science;Statistical Mechanics '6600': Soft Condensed Matter;Mesoscale and Nanoscale Physics;Statistical Mechanics '6601': Soft Condensed Matter;Optics '6602': Soft Condensed Matter;Other Condensed Matter '6603': Soft Condensed Matter;Pattern Formation and Solitons '6604': Soft Condensed Matter;Pattern Formation and Solitons;Fluid Dynamics '6605': Soft Condensed Matter;Plasma Physics '6606': Soft Condensed Matter;Quantitative Methods '6607': Soft Condensed Matter;Quantum Physics '6608': Soft Condensed Matter;Statistical Mechanics '6609': Soft Condensed Matter;Statistical Mechanics;Biological Physics '6610': Soft Condensed Matter;Statistical Mechanics;Biological Physics;Biomolecules '6611': Soft Condensed Matter;Statistical Mechanics;Biological Physics;Chemical Physics '6612': Soft Condensed Matter;Statistical Mechanics;Biological Physics;Computational Physics '6613': Soft Condensed Matter;Statistical Mechanics;Biological Physics;Fluid Dynamics '6614': Soft Condensed Matter;Statistical Mechanics;Biomolecules '6615': Soft Condensed Matter;Statistical Mechanics;Cell Behavior '6616': Soft Condensed Matter;Statistical Mechanics;Chemical Physics '6617': Soft Condensed Matter;Statistical Mechanics;Computational Physics '6618': Soft Condensed Matter;Statistical Mechanics;Fluid Dynamics '6619': Soft Condensed Matter;Statistical Mechanics;High Energy Physics - Theory '6620': Soft Condensed Matter;Statistical Mechanics;Pattern Formation and Solitons '6621': Soft Condensed Matter;Statistical Mechanics;Subcellular Processes '6622': Soft Condensed Matter;Strongly Correlated Electrons '6623': Soft Condensed Matter;Subcellular Processes '6624': Soft Condensed Matter;Superconductivity '6625': Soft Condensed Matter;Tissues and Organs '6626': Software Engineering '6627': Software Engineering;Artificial Intelligence '6628': Software Engineering;Artificial Intelligence;Computation and Language '6629': Software Engineering;Artificial Intelligence;Computation and Language;Machine Learning '6630': Software Engineering;Artificial Intelligence;Cryptography and Security '6631': Software Engineering;Artificial Intelligence;Human-Computer Interaction '6632': Software Engineering;Artificial Intelligence;Machine Learning '6633': Software Engineering;Artificial Intelligence;Machine Learning;Programming Languages '6634': Software Engineering;Artificial Intelligence;Neural and Evolutionary Computing '6635': Software Engineering;Artificial Intelligence;Programming Languages '6636': Software Engineering;Computation and Language '6637': Software Engineering;Computation and Language;Machine Learning '6638': Software Engineering;Computational Engineering, Finance, and Science '6639': Software Engineering;Computer Vision and Pattern Recognition '6640': Software Engineering;Computers and Society '6641': Software Engineering;Computers and Society;Human-Computer Interaction '6642': Software Engineering;Cryptography and Security '6643': Software Engineering;Cryptography and Security;Machine Learning '6644': Software Engineering;Cryptography and Security;Programming Languages '6645': Software Engineering;Databases '6646': Software Engineering;Digital Libraries '6647': Software Engineering;Distributed, Parallel, and Cluster Computing '6648': Software Engineering;Formal Languages and Automata Theory '6649': Software Engineering;Human-Computer Interaction '6650': Software Engineering;Information Retrieval '6651': Software Engineering;Information Retrieval;Machine Learning '6652': Software Engineering;Logic in Computer Science '6653': Software Engineering;Logic in Computer Science;Programming Languages '6654': Software Engineering;Machine Learning '6655': Software Engineering;Machine Learning;Machine Learning '6656': Software Engineering;Machine Learning;Programming Languages '6657': Software Engineering;Multiagent Systems '6658': Software Engineering;Networking and Internet Architecture '6659': Software Engineering;Neural and Evolutionary Computing '6660': Software Engineering;Operating Systems '6661': Software Engineering;Performance '6662': Software Engineering;Programming Languages '6663': Software Engineering;Quantum Physics '6664': Software Engineering;Robotics '6665': Software Engineering;Social and Information Networks '6666': Software Engineering;Systems and Control '6667': Software Engineering;Systems and Control;Systems and Control '6668': Solar and Stellar Astrophysics '6669': Solar and Stellar Astrophysics;Astrophysics of Galaxies '6670': Solar and Stellar Astrophysics;Astrophysics of Galaxies;Atomic Physics '6671': Solar and Stellar Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '6672': Solar and Stellar Astrophysics;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '6673': Solar and Stellar Astrophysics;Astrophysics of Galaxies;Plasma Physics '6674': Solar and Stellar Astrophysics;Atomic Physics '6675': Solar and Stellar Astrophysics;Atomic Physics;Plasma Physics '6676': Solar and Stellar Astrophysics;Chaotic Dynamics '6677': Solar and Stellar Astrophysics;Chemical Physics '6678': Solar and Stellar Astrophysics;Computational Physics '6679': Solar and Stellar Astrophysics;Cosmology and Nongalactic Astrophysics '6680': Solar and Stellar Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies '6681': Solar and Stellar Astrophysics;Cosmology and Nongalactic Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '6682': Solar and Stellar Astrophysics;Cosmology and Nongalactic Astrophysics;High Energy Astrophysical Phenomena '6683': Solar and Stellar Astrophysics;Data Analysis, Statistics and Probability '6684': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics '6685': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies '6686': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies;High Energy Astrophysical Phenomena '6687': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics '6688': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Fluid Dynamics '6689': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;High Energy Astrophysical Phenomena '6690': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics '6691': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Plasma Physics;Space Physics '6692': Solar and Stellar Astrophysics;Earth and Planetary Astrophysics;Space Physics '6693': Solar and Stellar Astrophysics;Fluid Dynamics '6694': Solar and Stellar Astrophysics;Fluid Dynamics;Plasma Physics '6695': Solar and Stellar Astrophysics;Fluid Dynamics;Plasma Physics;Space Physics '6696': Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology '6697': Solar and Stellar Astrophysics;General Relativity and Quantum Cosmology;Nuclear Theory '6698': Solar and Stellar Astrophysics;Geophysics '6699': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena '6700': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Fluid Dynamics '6701': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;General Relativity and Quantum Cosmology '6702': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology '6703': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;High Energy Physics - Phenomenology;Nuclear Theory '6704': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Instrumentation and Methods for Astrophysics '6705': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Nuclear Experiment;Nuclear Theory '6706': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Nuclear Theory '6707': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Plasma Physics '6708': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Plasma Physics;Space Physics '6709': Solar and Stellar Astrophysics;High Energy Astrophysical Phenomena;Space Physics '6710': Solar and Stellar Astrophysics;High Energy Physics - Experiment;High Energy Physics - Phenomenology '6711': Solar and Stellar Astrophysics;High Energy Physics - Phenomenology '6712': Solar and Stellar Astrophysics;High Energy Physics - Phenomenology;Nuclear Theory '6713': Solar and Stellar Astrophysics;History and Philosophy of Physics '6714': Solar and Stellar Astrophysics;Instrumentation and Methods for Astrophysics '6715': Solar and Stellar Astrophysics;Instrumentation and Methods for Astrophysics;Machine Learning '6716': Solar and Stellar Astrophysics;Instrumentation and Methods for Astrophysics;Space Physics '6717': Solar and Stellar Astrophysics;Machine Learning '6718': Solar and Stellar Astrophysics;Nuclear Experiment '6719': Solar and Stellar Astrophysics;Nuclear Experiment;Nuclear Theory '6720': Solar and Stellar Astrophysics;Nuclear Theory '6721': Solar and Stellar Astrophysics;Plasma Physics '6722': Solar and Stellar Astrophysics;Plasma Physics;Space Physics '6723': Solar and Stellar Astrophysics;Space Physics '6724': Sound '6725': Sound;Artificial Intelligence '6726': Sound;Artificial Intelligence;Audio and Speech Processing '6727': Sound;Artificial Intelligence;Computation and Language;Audio and Speech Processing '6728': Sound;Artificial Intelligence;Computation and Language;Machine Learning;Audio and Speech Processing '6729': Sound;Artificial Intelligence;Computation and Language;Machine Learning;Multimedia;Audio and Speech Processing '6730': Sound;Artificial Intelligence;Machine Learning '6731': Sound;Artificial Intelligence;Machine Learning;Audio and Speech Processing '6732': Sound;Artificial Intelligence;Machine Learning;Multimedia;Audio and Speech Processing '6733': Sound;Artificial Intelligence;Multimedia;Audio and Speech Processing '6734': Sound;Audio and Speech Processing '6735': Sound;Audio and Speech Processing;Machine Learning '6736': Sound;Audio and Speech Processing;Signal Processing '6737': Sound;Computation and Language '6738': Sound;Computation and Language;Audio and Speech Processing '6739': Sound;Computation and Language;Machine Learning '6740': Sound;Computation and Language;Machine Learning;Audio and Speech Processing '6741': Sound;Computer Vision and Pattern Recognition '6742': Sound;Computer Vision and Pattern Recognition;Audio and Speech Processing '6743': Sound;Computer Vision and Pattern Recognition;Machine Learning;Audio and Speech Processing '6744': Sound;Computer Vision and Pattern Recognition;Machine Learning;Multimedia;Audio and Speech Processing '6745': Sound;Computer Vision and Pattern Recognition;Multimedia;Audio and Speech Processing '6746': Sound;Cryptography and Security;Audio and Speech Processing '6747': Sound;Cryptography and Security;Machine Learning;Audio and Speech Processing '6748': Sound;Human-Computer Interaction;Audio and Speech Processing '6749': Sound;Human-Computer Interaction;Machine Learning;Audio and Speech Processing '6750': Sound;Information Retrieval;Audio and Speech Processing '6751': Sound;Information Retrieval;Machine Learning;Audio and Speech Processing '6752': Sound;Information Retrieval;Multimedia;Audio and Speech Processing '6753': Sound;Machine Learning '6754': Sound;Machine Learning;Audio and Speech Processing '6755': Sound;Machine Learning;Audio and Speech Processing;Machine Learning '6756': Sound;Machine Learning;Audio and Speech Processing;Signal Processing '6757': Sound;Machine Learning;Machine Learning '6758': Sound;Machine Learning;Multimedia;Audio and Speech Processing '6759': Sound;Machine Learning;Neural and Evolutionary Computing '6760': Sound;Multimedia '6761': Sound;Multimedia;Audio and Speech Processing '6762': Sound;Neural and Evolutionary Computing '6763': Sound;Robotics;Audio and Speech Processing '6764': Space Physics '6765': Space Physics;Astrophysics '6766': Space Physics;Atmospheric and Oceanic Physics '6767': Space Physics;Data Analysis, Statistics and Probability '6768': Space Physics;Earth and Planetary Astrophysics '6769': Space Physics;Earth and Planetary Astrophysics;Plasma Physics '6770': Space Physics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics '6771': Space Physics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Geophysics;Plasma Physics '6772': Space Physics;Earth and Planetary Astrophysics;Solar and Stellar Astrophysics;Plasma Physics '6773': Space Physics;General Physics '6774': Space Physics;Geophysics '6775': Space Physics;High Energy Astrophysical Phenomena '6776': Space Physics;High Energy Astrophysical Phenomena;Solar and Stellar Astrophysics '6777': Space Physics;Instrumentation and Methods for Astrophysics '6778': Space Physics;Plasma Physics '6779': Space Physics;Solar and Stellar Astrophysics '6780': Space Physics;Solar and Stellar Astrophysics;Plasma Physics '6781': Spectral Theory '6782': Spectral Theory;Analysis of PDEs '6783': Spectral Theory;Analysis of PDEs;Differential Geometry '6784': Spectral Theory;Analysis of PDEs;Functional Analysis '6785': Spectral Theory;Analysis of PDEs;Probability '6786': Spectral Theory;Classical Analysis and ODEs '6787': Spectral Theory;Classical Analysis and ODEs;Functional Analysis '6788': Spectral Theory;Combinatorics '6789': Spectral Theory;Complex Variables '6790': Spectral Theory;Differential Geometry '6791': Spectral Theory;Dynamical Systems '6792': Spectral Theory;Functional Analysis '6793': Spectral Theory;Number Theory '6794': Spectral Theory;Numerical Analysis '6795': Spectral Theory;Operator Algebras '6796': Spectral Theory;Optimization and Control '6797': Spectral Theory;Probability '6798': Spectral Theory;Representation Theory '6799': Spectral Theory;Rings and Algebras '6800': Statistical Finance '6801': Statistical Finance;Applications '6802': Statistical Finance;Applications;Methodology '6803': Statistical Finance;Artificial Intelligence;Machine Learning '6804': Statistical Finance;Computational Engineering, Finance, and Science '6805': Statistical Finance;Computational Finance '6806': Statistical Finance;Data Analysis, Statistics and Probability '6807': Statistical Finance;Data Analysis, Statistics and Probability;Applications '6808': Statistical Finance;Data Analysis, Statistics and Probability;Physics and Society '6809': Statistical Finance;Econometrics '6810': Statistical Finance;General Finance '6811': Statistical Finance;Machine Learning '6812': Statistical Finance;Machine Learning;Machine Learning '6813': Statistical Finance;Mathematical Finance '6814': Statistical Finance;Methodology '6815': Statistical Finance;Physics and Society '6816': Statistical Finance;Portfolio Management '6817': Statistical Finance;Probability '6818': Statistical Finance;Risk Management '6819': Statistical Finance;Statistical Mechanics '6820': Statistical Finance;Trading and Market Microstructure '6821': Statistical Mechanics '6822': Statistical Mechanics;Adaptation and Self-Organizing Systems '6823': Statistical Mechanics;Adaptation and Self-Organizing Systems;Biological Physics '6824': Statistical Mechanics;Adaptation and Self-Organizing Systems;Physics and Society '6825': Statistical Mechanics;Adaptation, Noise, and Self-Organizing Systems;Adaptation and Self-Organizing Systems '6826': Statistical Mechanics;Applied Physics '6827': Statistical Mechanics;Astrophysics '6828': Statistical Mechanics;Astrophysics of Galaxies '6829': Statistical Mechanics;Atmospheric and Oceanic Physics '6830': Statistical Mechanics;Atomic Physics '6831': Statistical Mechanics;Atomic Physics;Quantum Physics '6832': Statistical Mechanics;Biological Physics '6833': Statistical Mechanics;Biological Physics;Biomolecules '6834': Statistical Mechanics;Biological Physics;Cell Behavior '6835': Statistical Mechanics;Biological Physics;Chemical Physics '6836': Statistical Mechanics;Biological Physics;Molecular Networks '6837': Statistical Mechanics;Biological Physics;Populations and Evolution '6838': Statistical Mechanics;Biological Physics;Quantitative Methods '6839': Statistical Mechanics;Biological Physics;Subcellular Processes '6840': Statistical Mechanics;Biomolecules '6841': Statistical Mechanics;Cell Behavior '6842': Statistical Mechanics;Cellular Automata and Lattice Gases '6843': Statistical Mechanics;Chaotic Dynamics '6844': Statistical Mechanics;Chaotic Dynamics;Chaotic Dynamics '6845': Statistical Mechanics;Chaotic Dynamics;Classical Physics '6846': Statistical Mechanics;Chaotic Dynamics;Computational Physics '6847': Statistical Mechanics;Chaotic Dynamics;Fluid Dynamics '6848': Statistical Mechanics;Chaotic Dynamics;Quantum Physics '6849': Statistical Mechanics;Chemical Physics '6850': Statistical Mechanics;Chemical Physics;Computational Physics '6851': Statistical Mechanics;Chemical Physics;Quantum Physics '6852': Statistical Mechanics;Classical Physics '6853': Statistical Mechanics;Combinatorics '6854': Statistical Mechanics;Computational Complexity '6855': Statistical Mechanics;Computational Physics '6856': Statistical Mechanics;Computational Physics;Data Analysis, Statistics and Probability '6857': Statistical Mechanics;Computational Physics;Fluid Dynamics '6858': Statistical Mechanics;Computational Physics;Quantum Physics '6859': Statistical Mechanics;Cosmology and Nongalactic Astrophysics '6860': Statistical Mechanics;Data Analysis, Statistics and Probability '6861': Statistical Mechanics;Disordered Systems and Neural Networks '6862': Statistical Mechanics;Disordered Systems and Neural Networks;Adaptation and Self-Organizing Systems '6863': Statistical Mechanics;Disordered Systems and Neural Networks;Chaotic Dynamics '6864': Statistical Mechanics;Disordered Systems and Neural Networks;Computational Complexity '6865': Statistical Mechanics;Disordered Systems and Neural Networks;Computational Physics '6866': Statistical Mechanics;Disordered Systems and Neural Networks;High Energy Physics - Lattice '6867': Statistical Mechanics;Disordered Systems and Neural Networks;High Energy Physics - Theory '6868': Statistical Mechanics;Disordered Systems and Neural Networks;Machine Learning '6869': Statistical Mechanics;Disordered Systems and Neural Networks;Materials Science '6870': Statistical Mechanics;Disordered Systems and Neural Networks;Materials Science;Soft Condensed Matter '6871': Statistical Mechanics;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '6872': Statistical Mechanics;Disordered Systems and Neural Networks;Neurons and Cognition '6873': Statistical Mechanics;Disordered Systems and Neural Networks;Physics and Society '6874': Statistical Mechanics;Disordered Systems and Neural Networks;Probability '6875': Statistical Mechanics;Disordered Systems and Neural Networks;Quantum Gases '6876': Statistical Mechanics;Disordered Systems and Neural Networks;Quantum Gases;Quantum Physics '6877': Statistical Mechanics;Disordered Systems and Neural Networks;Quantum Gases;Strongly Correlated Electrons;Quantum Physics '6878': Statistical Mechanics;Disordered Systems and Neural Networks;Quantum Physics '6879': Statistical Mechanics;Disordered Systems and Neural Networks;Soft Condensed Matter '6880': Statistical Mechanics;Disordered Systems and Neural Networks;Statistical Finance '6881': Statistical Mechanics;Disordered Systems and Neural Networks;Strongly Correlated Electrons '6882': Statistical Mechanics;Disordered Systems and Neural Networks;Strongly Correlated Electrons;Quantum Physics '6883': Statistical Mechanics;Disordered Systems and Neural Networks;Superconductivity '6884': Statistical Mechanics;Disordered Systems and Neural Networks;Trading and Market Microstructure '6885': Statistical Mechanics;Dynamical Systems '6886': Statistical Mechanics;Exactly Solvable and Integrable Systems '6887': Statistical Mechanics;Fluid Dynamics '6888': Statistical Mechanics;General Finance '6889': Statistical Mechanics;General Relativity and Quantum Cosmology '6890': Statistical Mechanics;General Relativity and Quantum Cosmology;High Energy Physics - Theory '6891': Statistical Mechanics;Geophysics '6892': Statistical Mechanics;High Energy Physics - Lattice '6893': Statistical Mechanics;High Energy Physics - Lattice;Combinatorics '6894': Statistical Mechanics;High Energy Physics - Lattice;Computational Physics '6895': Statistical Mechanics;High Energy Physics - Lattice;High Energy Physics - Phenomenology;High Energy Physics - Theory '6896': Statistical Mechanics;High Energy Physics - Lattice;High Energy Physics - Theory '6897': Statistical Mechanics;High Energy Physics - Lattice;High Energy Physics - Theory;Quantum Physics '6898': Statistical Mechanics;High Energy Physics - Lattice;Quantum Physics '6899': Statistical Mechanics;High Energy Physics - Phenomenology '6900': Statistical Mechanics;High Energy Physics - Phenomenology;High Energy Physics - Theory '6901': Statistical Mechanics;High Energy Physics - Phenomenology;Nuclear Theory '6902': Statistical Mechanics;High Energy Physics - Phenomenology;Quantum Physics '6903': Statistical Mechanics;High Energy Physics - Theory '6904': Statistical Mechanics;High Energy Physics - Theory;Chaotic Dynamics;Quantum Physics '6905': Statistical Mechanics;High Energy Physics - Theory;Combinatorics '6906': Statistical Mechanics;High Energy Physics - Theory;Computational Physics '6907': Statistical Mechanics;High Energy Physics - Theory;Exactly Solvable and Integrable Systems '6908': Statistical Mechanics;High Energy Physics - Theory;Exactly Solvable and Integrable Systems;Exactly Solvable and Integrable Systems '6909': Statistical Mechanics;High Energy Physics - Theory;Quantum Physics '6910': Statistical Mechanics;History and Philosophy of Physics '6911': Statistical Mechanics;Machine Learning '6912': Statistical Mechanics;Machine Learning;Machine Learning '6913': Statistical Mechanics;Materials Science '6914': Statistical Mechanics;Materials Science;Chemical Physics '6915': Statistical Mechanics;Materials Science;Computational Physics '6916': Statistical Mechanics;Materials Science;Quantum Physics '6917': Statistical Mechanics;Materials Science;Soft Condensed Matter '6918': Statistical Mechanics;Materials Science;Soft Condensed Matter;Chemical Physics '6919': Statistical Mechanics;Mesoscale and Nanoscale Physics '6920': Statistical Mechanics;Mesoscale and Nanoscale Physics;Materials Science '6921': Statistical Mechanics;Mesoscale and Nanoscale Physics;Quantum Gases '6922': Statistical Mechanics;Mesoscale and Nanoscale Physics;Quantum Physics '6923': Statistical Mechanics;Mesoscale and Nanoscale Physics;Soft Condensed Matter '6924': Statistical Mechanics;Mesoscale and Nanoscale Physics;Soft Condensed Matter;Chemical Physics '6925': Statistical Mechanics;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '6926': Statistical Mechanics;Mesoscale and Nanoscale Physics;Superconductivity '6927': Statistical Mechanics;Molecular Networks '6928': Statistical Mechanics;Neurons and Cognition '6929': Statistical Mechanics;Nuclear Theory '6930': Statistical Mechanics;Nuclear Theory;Quantum Physics '6931': Statistical Mechanics;Optics '6932': Statistical Mechanics;Optics;Quantum Physics '6933': Statistical Mechanics;Other Condensed Matter '6934': Statistical Mechanics;Other Condensed Matter;Quantum Physics '6935': Statistical Mechanics;Other Condensed Matter;Soft Condensed Matter '6936': Statistical Mechanics;Pattern Formation and Solitons '6937': Statistical Mechanics;Pattern Formation and Solitons;Pattern Formation and Solitons '6938': Statistical Mechanics;Physics and Society '6939': Statistical Mechanics;Physics and Society;Populations and Evolution '6940': Statistical Mechanics;Physics and Society;Statistical Finance '6941': Statistical Mechanics;Plasma Physics '6942': Statistical Mechanics;Populations and Evolution '6943': Statistical Mechanics;Portfolio Management '6944': Statistical Mechanics;Pricing of Securities '6945': Statistical Mechanics;Probability '6946': Statistical Mechanics;Quantitative Methods '6947': Statistical Mechanics;Quantum Gases '6948': Statistical Mechanics;Quantum Gases;High Energy Physics - Theory '6949': Statistical Mechanics;Quantum Gases;High Energy Physics - Theory;Quantum Physics '6950': Statistical Mechanics;Quantum Gases;Quantum Physics '6951': Statistical Mechanics;Quantum Gases;Strongly Correlated Electrons '6952': Statistical Mechanics;Quantum Gases;Strongly Correlated Electrons;High Energy Physics - Theory;Quantum Physics '6953': Statistical Mechanics;Quantum Gases;Strongly Correlated Electrons;Quantum Physics '6954': Statistical Mechanics;Quantum Physics '6955': Statistical Mechanics;Social and Information Networks;Physics and Society '6956': Statistical Mechanics;Soft Condensed Matter '6957': Statistical Mechanics;Soft Condensed Matter;Biological Physics '6958': Statistical Mechanics;Soft Condensed Matter;Biological Physics;Biomolecules '6959': Statistical Mechanics;Soft Condensed Matter;Biological Physics;Chemical Physics '6960': Statistical Mechanics;Soft Condensed Matter;Biomolecules '6961': Statistical Mechanics;Soft Condensed Matter;Chemical Physics '6962': Statistical Mechanics;Soft Condensed Matter;Computational Physics '6963': Statistical Mechanics;Soft Condensed Matter;Data Analysis, Statistics and Probability '6964': Statistical Mechanics;Soft Condensed Matter;Fluid Dynamics '6965': Statistical Mechanics;Soft Condensed Matter;High Energy Physics - Theory '6966': Statistical Mechanics;Soft Condensed Matter;Metric Geometry '6967': Statistical Mechanics;Soft Condensed Matter;Pattern Formation and Solitons '6968': Statistical Mechanics;Soft Condensed Matter;Quantum Physics '6969': Statistical Mechanics;Statistical Finance '6970': Statistical Mechanics;Strongly Correlated Electrons '6971': Statistical Mechanics;Strongly Correlated Electrons;Computational Physics '6972': Statistical Mechanics;Strongly Correlated Electrons;High Energy Physics - Theory '6973': Statistical Mechanics;Strongly Correlated Electrons;High Energy Physics - Theory;Quantum Physics '6974': Statistical Mechanics;Strongly Correlated Electrons;Quantum Physics '6975': Statistical Mechanics;Subcellular Processes '6976': Statistical Mechanics;Superconductivity '6977': Statistical Mechanics;Superconductivity;Quantum Physics '6978': Statistical Mechanics;Trading and Market Microstructure '6979': Strongly Correlated Electrons '6980': Strongly Correlated Electrons;Applied Physics '6981': Strongly Correlated Electrons;Atomic Physics '6982': Strongly Correlated Electrons;Chemical Physics '6983': Strongly Correlated Electrons;Chemical Physics;Computational Physics '6984': Strongly Correlated Electrons;Chemical Physics;Quantum Physics '6985': Strongly Correlated Electrons;Computational Physics '6986': Strongly Correlated Electrons;Computational Physics;Quantum Physics '6987': Strongly Correlated Electrons;Disordered Systems and Neural Networks '6988': Strongly Correlated Electrons;Disordered Systems and Neural Networks;High Energy Physics - Theory '6989': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Materials Science '6990': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '6991': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Quantum Gases '6992': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Quantum Physics '6993': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Statistical Mechanics '6994': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Statistical Mechanics;High Energy Physics - Theory '6995': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Statistical Mechanics;Quantum Physics '6996': Strongly Correlated Electrons;Disordered Systems and Neural Networks;Superconductivity '6997': Strongly Correlated Electrons;High Energy Physics - Lattice '6998': Strongly Correlated Electrons;High Energy Physics - Lattice;High Energy Physics - Phenomenology '6999': Strongly Correlated Electrons;High Energy Physics - Lattice;High Energy Physics - Theory '7000': Strongly Correlated Electrons;High Energy Physics - Lattice;High Energy Physics - Theory;Quantum Physics '7001': Strongly Correlated Electrons;High Energy Physics - Lattice;Quantum Physics '7002': Strongly Correlated Electrons;High Energy Physics - Phenomenology '7003': Strongly Correlated Electrons;High Energy Physics - Phenomenology;High Energy Physics - Theory '7004': Strongly Correlated Electrons;High Energy Physics - Theory '7005': Strongly Correlated Electrons;High Energy Physics - Theory;Quantum Physics '7006': Strongly Correlated Electrons;Materials Science '7007': Strongly Correlated Electrons;Materials Science;Applied Physics '7008': Strongly Correlated Electrons;Materials Science;Chemical Physics '7009': Strongly Correlated Electrons;Materials Science;Computational Physics '7010': Strongly Correlated Electrons;Materials Science;High Energy Physics - Theory '7011': Strongly Correlated Electrons;Materials Science;Other Condensed Matter '7012': Strongly Correlated Electrons;Materials Science;Quantum Gases '7013': Strongly Correlated Electrons;Materials Science;Quantum Physics '7014': Strongly Correlated Electrons;Materials Science;Statistical Mechanics '7015': Strongly Correlated Electrons;Materials Science;Superconductivity '7016': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics '7017': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;High Energy Physics - Lattice '7018': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;High Energy Physics - Theory '7019': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;High Energy Physics - Theory;Quantum Physics '7020': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Materials Science '7021': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Materials Science;Other Condensed Matter '7022': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Materials Science;Superconductivity '7023': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Quantum Gases '7024': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Quantum Gases;Quantum Physics '7025': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Quantum Physics '7026': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Statistical Mechanics '7027': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Statistical Mechanics;High Energy Physics - Theory '7028': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Statistical Mechanics;Quantum Physics '7029': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Superconductivity '7030': Strongly Correlated Electrons;Mesoscale and Nanoscale Physics;Superconductivity;Quantum Physics '7031': Strongly Correlated Electrons;Nuclear Theory '7032': Strongly Correlated Electrons;Optics '7033': Strongly Correlated Electrons;Other Condensed Matter '7034': Strongly Correlated Electrons;Other Condensed Matter;Quantum Physics '7035': Strongly Correlated Electrons;Other Condensed Matter;Statistical Mechanics '7036': Strongly Correlated Electrons;Plasma Physics '7037': Strongly Correlated Electrons;Quantum Gases '7038': Strongly Correlated Electrons;Quantum Gases;High Energy Physics - Theory '7039': Strongly Correlated Electrons;Quantum Gases;Quantum Physics '7040': Strongly Correlated Electrons;Quantum Gases;Statistical Mechanics '7041': Strongly Correlated Electrons;Quantum Gases;Statistical Mechanics;Quantum Physics '7042': Strongly Correlated Electrons;Quantum Gases;Superconductivity '7043': Strongly Correlated Electrons;Quantum Gases;Superconductivity;Quantum Physics '7044': Strongly Correlated Electrons;Quantum Physics '7045': Strongly Correlated Electrons;Soft Condensed Matter '7046': Strongly Correlated Electrons;Statistical Mechanics '7047': Strongly Correlated Electrons;Statistical Mechanics;Computational Physics '7048': Strongly Correlated Electrons;Statistical Mechanics;High Energy Physics - Lattice '7049': Strongly Correlated Electrons;Statistical Mechanics;High Energy Physics - Theory '7050': Strongly Correlated Electrons;Statistical Mechanics;High Energy Physics - Theory;Quantum Physics '7051': Strongly Correlated Electrons;Statistical Mechanics;Quantum Physics '7052': Strongly Correlated Electrons;Statistical Mechanics;Superconductivity '7053': Strongly Correlated Electrons;Superconductivity '7054': Strongly Correlated Electrons;Superconductivity;High Energy Physics - Theory '7055': Strongly Correlated Electrons;Superconductivity;Quantum Physics '7056': Subcellular Processes '7057': Subcellular Processes;Biological Physics '7058': Subcellular Processes;Biomolecules '7059': Subcellular Processes;Cell Behavior '7060': Subcellular Processes;Molecular Networks '7061': Subcellular Processes;Quantitative Methods '7062': Subcellular Processes;Soft Condensed Matter '7063': Subcellular Processes;Soft Condensed Matter;Biological Physics '7064': Subcellular Processes;Statistical Mechanics '7065': Subcellular Processes;Statistical Mechanics;Biological Physics '7066': Superconductivity '7067': Superconductivity;Accelerator Physics '7068': Superconductivity;Applied Physics '7069': Superconductivity;Computational Physics '7070': Superconductivity;Disordered Systems and Neural Networks '7071': Superconductivity;Disordered Systems and Neural Networks;Materials Science '7072': Superconductivity;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics '7073': Superconductivity;Disordered Systems and Neural Networks;Statistical Mechanics '7074': Superconductivity;Disordered Systems and Neural Networks;Strongly Correlated Electrons '7075': Superconductivity;High Energy Physics - Phenomenology '7076': Superconductivity;High Energy Physics - Phenomenology;High Energy Physics - Theory '7077': Superconductivity;High Energy Physics - Theory '7078': Superconductivity;Instrumentation and Detectors '7079': Superconductivity;Materials Science '7080': Superconductivity;Materials Science;Applied Physics '7081': Superconductivity;Materials Science;Other Condensed Matter '7082': Superconductivity;Materials Science;Strongly Correlated Electrons '7083': Superconductivity;Mesoscale and Nanoscale Physics '7084': Superconductivity;Mesoscale and Nanoscale Physics;Applied Physics '7085': Superconductivity;Mesoscale and Nanoscale Physics;Materials Science '7086': Superconductivity;Mesoscale and Nanoscale Physics;Materials Science;Strongly Correlated Electrons '7087': Superconductivity;Mesoscale and Nanoscale Physics;Quantum Gases '7088': Superconductivity;Mesoscale and Nanoscale Physics;Quantum Physics '7089': Superconductivity;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons '7090': Superconductivity;Mesoscale and Nanoscale Physics;Strongly Correlated Electrons;Quantum Physics '7091': Superconductivity;Nuclear Theory '7092': Superconductivity;Optics '7093': Superconductivity;Other Condensed Matter '7094': Superconductivity;Pattern Formation and Solitons '7095': Superconductivity;Quantum Gases '7096': Superconductivity;Quantum Gases;Strongly Correlated Electrons '7097': Superconductivity;Quantum Physics '7098': Superconductivity;Soft Condensed Matter '7099': Superconductivity;Soft Condensed Matter;Statistical Mechanics '7100': Superconductivity;Statistical Mechanics '7101': Superconductivity;Statistical Mechanics;Strongly Correlated Electrons '7102': Superconductivity;Strongly Correlated Electrons '7103': Superconductivity;Strongly Correlated Electrons;High Energy Physics - Theory '7104': Superconductivity;Strongly Correlated Electrons;Nuclear Theory '7105': Superconductivity;Strongly Correlated Electrons;Quantum Physics '7106': Superconductivity;Superconductivity '7107': Symbolic Computation '7108': Symbolic Computation;Algebraic Geometry '7109': Symbolic Computation;Classical Analysis and ODEs '7110': Symbolic Computation;Combinatorics '7111': Symbolic Computation;Commutative Algebra '7112': Symbolic Computation;Commutative Algebra;Algebraic Geometry '7113': Symbolic Computation;Computational Complexity '7114': Symbolic Computation;Computational Geometry '7115': Symbolic Computation;Data Structures and Algorithms '7116': Symbolic Computation;Logic in Computer Science '7117': Symbolic Computation;Machine Learning '7118': Symbolic Computation;Mathematical Software '7119': Symbolic Computation;Number Theory '7120': Symbolic Computation;Numerical Analysis '7121': Symbolic Computation;Numerical Analysis;Numerical Analysis '7122': Symbolic Computation;Rings and Algebras '7123': Symplectic Geometry '7124': Symplectic Geometry;Algebraic Geometry '7125': Symplectic Geometry;Algebraic Geometry;Combinatorics '7126': Symplectic Geometry;Algebraic Geometry;Differential Geometry '7127': Symplectic Geometry;Algebraic Geometry;Geometric Topology '7128': Symplectic Geometry;Algebraic Geometry;Representation Theory '7129': Symplectic Geometry;Algebraic Topology '7130': Symplectic Geometry;Algebraic Topology;Differential Geometry '7131': Symplectic Geometry;Algebraic Topology;Geometric Topology '7132': Symplectic Geometry;Analysis of PDEs '7133': Symplectic Geometry;Analysis of PDEs;Differential Geometry '7134': Symplectic Geometry;Combinatorics '7135': Symplectic Geometry;Complex Variables '7136': Symplectic Geometry;Differential Geometry '7137': Symplectic Geometry;Differential Geometry;Dynamical Systems '7138': Symplectic Geometry;Differential Geometry;Geometric Topology '7139': Symplectic Geometry;Dynamical Systems '7140': Symplectic Geometry;Dynamical Systems;Geometric Topology '7141': Symplectic Geometry;Functional Analysis '7142': Symplectic Geometry;Geometric Topology '7143': Symplectic Geometry;Group Theory '7144': Symplectic Geometry;High Energy Physics - Theory;Algebraic Geometry '7145': Symplectic Geometry;K-Theory and Homology '7146': Symplectic Geometry;Metric Geometry '7147': Symplectic Geometry;Quantum Algebra '7148': Symplectic Geometry;Representation Theory '7149': Systems and Control '7150': Systems and Control;Adaptation and Self-Organizing Systems '7151': Systems and Control;Applications '7152': Systems and Control;Artificial Intelligence '7153': Systems and Control;Artificial Intelligence;Machine Learning;Robotics;Systems and Control '7154': Systems and Control;Artificial Intelligence;Machine Learning;Systems and Control '7155': Systems and Control;Artificial Intelligence;Machine Learning;Systems and Control;Optimization and Control '7156': Systems and Control;Artificial Intelligence;Optimization and Control '7157': Systems and Control;Artificial Intelligence;Robotics;Systems and Control '7158': Systems and Control;Artificial Intelligence;Systems and Control '7159': Systems and Control;Artificial Intelligence;Systems and Control;Optimization and Control '7160': Systems and Control;Computational Engineering, Finance, and Science '7161': Systems and Control;Computational Engineering, Finance, and Science;Systems and Control '7162': Systems and Control;Computer Science and Game Theory '7163': Systems and Control;Computer Science and Game Theory;Optimization and Control '7164': Systems and Control;Computer Science and Game Theory;Systems and Control '7165': Systems and Control;Computer Science and Game Theory;Systems and Control;Optimization and Control '7166': Systems and Control;Computer Vision and Pattern Recognition;Systems and Control '7167': Systems and Control;Computers and Society;Systems and Control '7168': Systems and Control;Cryptography and Security '7169': Systems and Control;Cryptography and Security;Systems and Control '7170': Systems and Control;Distributed, Parallel, and Cluster Computing '7171': Systems and Control;Distributed, Parallel, and Cluster Computing;Optimization and Control '7172': Systems and Control;Distributed, Parallel, and Cluster Computing;Systems and Control '7173': Systems and Control;Dynamical Systems '7174': Systems and Control;Dynamical Systems;Chaotic Dynamics '7175': Systems and Control;Dynamical Systems;Optimization and Control '7176': Systems and Control;Formal Languages and Automata Theory '7177': Systems and Control;Formal Languages and Automata Theory;Systems and Control '7178': Systems and Control;Hardware Architecture;Systems and Control '7179': Systems and Control;Human-Computer Interaction;Systems and Control '7180': Systems and Control;Logic in Computer Science '7181': Systems and Control;Logic in Computer Science;Systems and Control '7182': Systems and Control;Machine Learning '7183': Systems and Control;Machine Learning;Machine Learning '7184': Systems and Control;Machine Learning;Multiagent Systems;Systems and Control '7185': Systems and Control;Machine Learning;Neural and Evolutionary Computing;Systems and Control '7186': Systems and Control;Machine Learning;Optimization and Control '7187': Systems and Control;Machine Learning;Robotics;Systems and Control '7188': Systems and Control;Machine Learning;Systems and Control '7189': Systems and Control;Machine Learning;Systems and Control;Dynamical Systems;Optimization and Control '7190': Systems and Control;Machine Learning;Systems and Control;Machine Learning '7191': Systems and Control;Machine Learning;Systems and Control;Optimization and Control '7192': Systems and Control;Machine Learning;Systems and Control;Optimization and Control;Machine Learning '7193': Systems and Control;Machine Learning;Systems and Control;Signal Processing '7194': Systems and Control;Multiagent Systems '7195': Systems and Control;Multiagent Systems;Optimization and Control '7196': Systems and Control;Multiagent Systems;Robotics '7197': Systems and Control;Multiagent Systems;Robotics;Systems and Control '7198': Systems and Control;Multiagent Systems;Systems and Control '7199': Systems and Control;Multiagent Systems;Systems and Control;Optimization and Control '7200': Systems and Control;Networking and Internet Architecture '7201': Systems and Control;Networking and Internet Architecture;Systems and Control '7202': Systems and Control;Neural and Evolutionary Computing '7203': Systems and Control;Neural and Evolutionary Computing;Systems and Control '7204': Systems and Control;Numerical Analysis '7205': Systems and Control;Numerical Analysis;Systems and Control;Numerical Analysis '7206': Systems and Control;Numerical Analysis;Systems and Control;Numerical Analysis;Optimization and Control '7207': Systems and Control;Optimization and Control '7208': Systems and Control;Optimization and Control;Machine Learning '7209': Systems and Control;Optimization and Control;Probability '7210': Systems and Control;Robotics '7211': Systems and Control;Robotics;Optimization and Control '7212': Systems and Control;Robotics;Systems and Control '7213': Systems and Control;Robotics;Systems and Control;Dynamical Systems '7214': Systems and Control;Robotics;Systems and Control;Optimization and Control '7215': Systems and Control;Signal Processing '7216': Systems and Control;Social and Information Networks;Optimization and Control '7217': Systems and Control;Social and Information Networks;Systems and Control '7218': Systems and Control;Software Engineering '7219': Systems and Control;Software Engineering;Systems and Control '7220': Systems and Control;Systems and Control '7221': Systems and Control;Systems and Control;Adaptation and Self-Organizing Systems '7222': Systems and Control;Systems and Control;Applications '7223': Systems and Control;Systems and Control;Applied Physics '7224': Systems and Control;Systems and Control;Chaotic Dynamics '7225': Systems and Control;Systems and Control;Dynamical Systems '7226': Systems and Control;Systems and Control;Dynamical Systems;Optimization and Control '7227': Systems and Control;Systems and Control;General Economics;Economics '7228': Systems and Control;Systems and Control;Instrumentation and Detectors '7229': Systems and Control;Systems and Control;Machine Learning '7230': Systems and Control;Systems and Control;Neurons and Cognition '7231': Systems and Control;Systems and Control;Optimization and Control '7232': Systems and Control;Systems and Control;Physics and Society '7233': Systems and Control;Systems and Control;Signal Processing '7234': Systems and Control;Systems and Control;Signal Processing;Optimization and Control '7235': Theoretical Economics '7236': Theoretical Economics;Artificial Intelligence '7237': Theoretical Economics;Computer Science and Game Theory '7238': Theoretical Economics;Dynamical Systems '7239': Theoretical Economics;Econometrics '7240': Theoretical Economics;General Economics;Economics '7241': Theoretical Economics;Optimization and Control '7242': Theoretical Economics;Physics and Society '7243': Tissues and Organs '7244': Tissues and Organs;Biological Physics '7245': Tissues and Organs;Biomolecules '7246': Tissues and Organs;Cell Behavior '7247': Tissues and Organs;Dynamical Systems '7248': Tissues and Organs;Medical Physics '7249': Tissues and Organs;Molecular Networks '7250': Tissues and Organs;Populations and Evolution '7251': Tissues and Organs;Quantitative Methods '7252': Tissues and Organs;Soft Condensed Matter '7253': Tissues and Organs;Soft Condensed Matter;Biological Physics '7254': Trading and Market Microstructure '7255': Trading and Market Microstructure;Artificial Intelligence;Machine Learning '7256': Trading and Market Microstructure;Computational Finance '7257': Trading and Market Microstructure;Data Analysis, Statistics and Probability;Physics and Society '7258': Trading and Market Microstructure;General Finance '7259': Trading and Market Microstructure;Machine Learning '7260': Trading and Market Microstructure;Mathematical Finance '7261': Trading and Market Microstructure;Optimization and Control '7262': Trading and Market Microstructure;Physics and Society '7263': Trading and Market Microstructure;Portfolio Management '7264': Trading and Market Microstructure;Probability '7265': Trading and Market Microstructure;Statistical Finance '7266': Trading and Market Microstructure;Statistical Mechanics '7267': longtail splits: - name: train num_bytes: 2152920109 num_examples: 2022879 - name: validation num_bytes: 71988398 num_examples: 67430 - name: test num_bytes: 167265378 num_examples: 157335 - name: simple_validation num_bytes: 7172900 num_examples: 6743 download_size: 1375587682 dataset_size: 2399346785 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - split: simple_validation path: data/simple_validation-* ---
alfredplpl/anime-with-gpt4v-caption-for-lora
--- license: cc-by-nc-4.0 language: - en --- # Anime style image - text by GPT4V small dataset ![cute1.png](cute1.png) ## The text is as follows: This is a charming anime-style illustration featuring a young girl as the main subject. The image predominantly uses a soft, pastel color palette, creating a gentle and whimsical ambiance. The main character has light blonde hair styled in two low twintails, secured with what could be interpreted as dark-colored hair ties or ribbons. She has large expressive blue eyes and a demure expression, with her mouth slightly open as if she is about to speak or is quietly admiring something. A black hairband is perched on top of her head. She is dressed in an outfit that radiates a youthful, almost springtime elegance. She wears a long-sleeved white coat, with the sleeves rolled up to just below the elbow, revealing a light green dress with a floral hem design underneath. The dress itself is a rich, green color with a subtle texture that suggests a fabric like cotton or linen. It is accented with small white, yellow-centered flowers near the hem, which also features a ruffled fringe hinting at layers beneath. Around her neck, she has a thin, green scarf or kerchief, and her feet are adorned with sturdy black boots with brown soles and notable detailing, including black laces tied in neat bows. In her right hand, the girl holds a glass of what appears to be a cold, whipped cream-topped beverage, the kind typically found at a cafe. On her left, she gently cradles a triangular-shaped pastry, possibly a slice of pie or cake, on a small, simple plate. To her right, the image shows a smaller rendition of the girl in a similar pose but without food or drink, emphasizing her adorable and innocent demeanor. Additionally, there are two cute white rabbits in the image, one sitting directly in front of the girl and the other to her left. The rabbit in front wears a collar with a bell, hinting at it being a pet. The one on the left appears to be free and unadorned. Both rabbits have their attention directed towards the girl, further amplifying the sweetness and serene nature of the scene. Leaf motifs and plant elements are scattered throughout the image, further establishing the connection to nature and spring. The overall composition is bordered by a teal background, which contrasts with the lighter colors and helps the central elements to stand out. The backdrop features subtle watercolor-effects, adding texture and visual interest. Lastly, text elements on the image read "MatsoTie, Mity Litite, Ianoiynote," and "magnolia kat," likely representing illustrative or fictional branding and the artist's signature, respectively. The chosen font for the main text is elegant and simple, maintaining the gentle aesthetics of the artwork. ## format - cute1.png+cute1.txt - [llava.json](llava.json) - [metadata.csv](metadata.csv) Thanks https://huggingface.co/datasets/p1atdev/niji-v5 . ## Restriction You may not develop models that compete with OpenAI because of [OpenAI's terms of use](https://openai.com/policies/terms-of-use).
open-llm-leaderboard/details_mahiatlinux__ShadowM7EXP-7B
--- pretty_name: Evaluation run of mahiatlinux/ShadowM7EXP-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mahiatlinux/ShadowM7EXP-7B](https://huggingface.co/mahiatlinux/ShadowM7EXP-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mahiatlinux__ShadowM7EXP-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-10T11:29:06.773232](https://huggingface.co/datasets/open-llm-leaderboard/details_mahiatlinux__ShadowM7EXP-7B/blob/main/results_2024-04-10T11-29-06.773232.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6505062857639512,\n\ \ \"acc_stderr\": 0.03209977108645598,\n \"acc_norm\": 0.6493944620730646,\n\ \ \"acc_norm_stderr\": 0.03277794129364625,\n \"mc1\": 0.6328029375764994,\n\ \ \"mc1_stderr\": 0.016874805001453184,\n \"mc2\": 0.7806197157886434,\n\ \ \"mc2_stderr\": 0.013676681727971188\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7175767918088737,\n \"acc_stderr\": 0.013155456884097224,\n\ \ \"acc_norm\": 0.7312286689419796,\n \"acc_norm_stderr\": 0.012955065963710695\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7180840470025891,\n\ \ \"acc_stderr\": 0.004490130691020433,\n \"acc_norm\": 0.8914558852818164,\n\ \ \"acc_norm_stderr\": 0.0031043064349724645\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\ \ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\ \ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.032400380867927465,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.032400380867927465\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778398,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778398\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \ \ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553346,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553346\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\ \ \"acc_stderr\": 0.013664230995834841,\n \"acc_norm\": 0.822477650063857,\n\ \ \"acc_norm_stderr\": 0.013664230995834841\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4301675977653631,\n\ \ \"acc_stderr\": 0.016558601636041035,\n \"acc_norm\": 0.4301675977653631,\n\ \ \"acc_norm_stderr\": 0.016558601636041035\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\ \ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142248,\n \ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142248\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4758800521512386,\n\ \ \"acc_stderr\": 0.012755368722863935,\n \"acc_norm\": 0.4758800521512386,\n\ \ \"acc_norm_stderr\": 0.012755368722863935\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6328029375764994,\n\ \ \"mc1_stderr\": 0.016874805001453184,\n \"mc2\": 0.7806197157886434,\n\ \ \"mc2_stderr\": 0.013676681727971188\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.850828729281768,\n \"acc_stderr\": 0.010012598805627297\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7028051554207733,\n \ \ \"acc_stderr\": 0.012588685966624182\n }\n}\n```" repo_url: https://huggingface.co/mahiatlinux/ShadowM7EXP-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|arc:challenge|25_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-10T11-29-06.773232.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|gsm8k|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hellaswag|10_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-29-06.773232.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-management|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-29-06.773232.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|truthfulqa:mc|0_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-10T11-29-06.773232.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_10T11_29_06.773232 path: - '**/details_harness|winogrande|5_2024-04-10T11-29-06.773232.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-10T11-29-06.773232.parquet' - config_name: results data_files: - split: 2024_04_10T11_29_06.773232 path: - results_2024-04-10T11-29-06.773232.parquet - split: latest path: - results_2024-04-10T11-29-06.773232.parquet --- # Dataset Card for Evaluation run of mahiatlinux/ShadowM7EXP-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mahiatlinux/ShadowM7EXP-7B](https://huggingface.co/mahiatlinux/ShadowM7EXP-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_mahiatlinux__ShadowM7EXP-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-10T11:29:06.773232](https://huggingface.co/datasets/open-llm-leaderboard/details_mahiatlinux__ShadowM7EXP-7B/blob/main/results_2024-04-10T11-29-06.773232.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6505062857639512, "acc_stderr": 0.03209977108645598, "acc_norm": 0.6493944620730646, "acc_norm_stderr": 0.03277794129364625, "mc1": 0.6328029375764994, "mc1_stderr": 0.016874805001453184, "mc2": 0.7806197157886434, "mc2_stderr": 0.013676681727971188 }, "harness|arc:challenge|25": { "acc": 0.7175767918088737, "acc_stderr": 0.013155456884097224, "acc_norm": 0.7312286689419796, "acc_norm_stderr": 0.012955065963710695 }, "harness|hellaswag|10": { "acc": 0.7180840470025891, "acc_stderr": 0.004490130691020433, "acc_norm": 0.8914558852818164, "acc_norm_stderr": 0.0031043064349724645 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.032400380867927465, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.032400380867927465 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778398, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778398 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677171, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677171 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.03287666758603491, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.03287666758603491 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.658974358974359, "acc_stderr": 0.02403548967633508, "acc_norm": 0.658974358974359, "acc_norm_stderr": 0.02403548967633508 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242741, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242741 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553346, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553346 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.822477650063857, "acc_stderr": 0.013664230995834841, "acc_norm": 0.822477650063857, "acc_norm_stderr": 0.013664230995834841 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500104, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500104 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4301675977653631, "acc_stderr": 0.016558601636041035, "acc_norm": 0.4301675977653631, "acc_norm_stderr": 0.016558601636041035 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885135, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885135 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7314814814814815, "acc_stderr": 0.024659685185967284, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.024659685185967284 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48936170212765956, "acc_stderr": 0.02982074719142248, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.02982074719142248 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4758800521512386, "acc_stderr": 0.012755368722863935, "acc_norm": 0.4758800521512386, "acc_norm_stderr": 0.012755368722863935 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6764705882352942, "acc_stderr": 0.018926082916083383, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.018926082916083383 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.6328029375764994, "mc1_stderr": 0.016874805001453184, "mc2": 0.7806197157886434, "mc2_stderr": 0.013676681727971188 }, "harness|winogrande|5": { "acc": 0.850828729281768, "acc_stderr": 0.010012598805627297 }, "harness|gsm8k|5": { "acc": 0.7028051554207733, "acc_stderr": 0.012588685966624182 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
jtatman/sciphi-micro-unsloth-processed
--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 584386995.8044599 num_examples: 84119 - name: val num_bytes: 64934976.04327544 num_examples: 9347 download_size: 254404662 dataset_size: 649321971.8477354 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---
rouskinlab/bpRNA-1m
--- license: mit language: - en tags: - chemistry - biology` author: Silvi Rouskin source: data.json date: 2024-03-16-18-10-44 --- # Data types - **sequence**: 66715 datapoints - **structure**: 66715 datapoints # Conversion report Over a total of 102318 datapoints, there are: ### OUTPUT - ALL: 66715 valid datapoints - INCLUDED: 1482 duplicate sequences with different structure / dms / shape ### MODIFIED - 0 multiple sequences with the same reference (renamed reference) ### FILTERED OUT - 5064 invalid datapoints (ex: sequence with non-regular characters) - 0 datapoints with bad structures - 30539 duplicate sequences with the same structure / dms / shape
amalina-k/marketing-mail
--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 7993 num_examples: 5 download_size: 16846 dataset_size: 7993 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "marketing-mail" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cacau/RequestedFiles
--- license: openrail ---
tyzhu/random_letter_find_passage_train30_eval10_num
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 6140 num_examples: 70 - name: validation num_bytes: 1120 num_examples: 10 download_size: 6514 dataset_size: 7260 --- # Dataset Card for "random_letter_find_passage_train30_eval10_num" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-one-sec-cv12/chunk_113
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1532172616 num_examples: 300898 download_size: 1561809638 dataset_size: 1532172616 --- # Dataset Card for "chunk_113" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/Hindi_Conversational_Speech_Data_by_Telephone
--- task_categories: - conversational language: - hi --- # Dataset Card for Nexdata/Hindi-Conversational-Speech-Data-by-Telephone ## Description The 760 Hours - Hindi Conversational Speech Data involved more than 1,000 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 16bit, uncompressed WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification. The accuracy rate of sentences is ≥ 95%. For more details, please refer to the link: https://www.nexdata.ai/datasets/1206?source=Huggingface ## Format 8kHz, 8bit, wav, mono channel; ## Recording Environment quiet indoor environment, without echo; ## Recording Content dozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed; ## Speaker 1,004 speakers totally, with 48% male and 52% female. ## Annotation annotating for the transcription text, speaker identification and gender ## Device Android mobile phone, iPhone; ## Language Hindi ## Application scenarios speech recognition; voiceprint recognition; ## Accuracy rate 95% # Licensing Information Commercial License
merve/tr-h4-norobots
--- configs: - config_name: default data_files: - split: train_sft path: train_sft.csv - split: test_sft path: test_sft.csv dataset_info: features: - name: idx dtype: int - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: category dtype: string splits: - name: train_sft - name: test_sft task_categories: - conversational - text-generation language: - tr pretty_name: No Robots license: cc-by-nc-4.0 --- # No Robots Veriseti Kartı 🙅‍♂️🤖 ### Özet No Robots 10000 komut ve gösterimden oluşan, profesyonel etiketleyiciler tarafından oluşturulmuş bir verisetidir. Çevirisi Google Cloud Platform Translation API ile yapıldı. Bu veriset LLM'lere komut takibi öğretmek için kullanılabilir. (Instruction Supervised Fine-tuning - SFT) No Robots veriseti OpenAI'ın [InstructGPT makalesinden](https://huggingface.co/papers/2203.02155) esinlenerek oluşturulmuştur ve aşağıdaki kategorilere sahiptir: | Kategori | Adet | |:-----------|--------:| | Generation | 4560 | | Open QA | 1240 | | Brainstorm | 1120 | | Chat | 850 | | Rewrite | 660 | | Summarize | 420 | | Coding | 350 | | Classify | 350 | | Closed QA | 260 | | Extract | 190 | ### Diller Bu verisetinde sadece Türkçe var. ## Veriseti Yapısı Bu verisetini CSV olarak yükledim. Örneklerin neye benzediğini görmek istiyorsanız widget'a bakın. ### Veri Alanları Kolonlar aşağıdaki gibidir: * `prompt`: Modelin takip etmesi gereken komutu belirler. * `prompt_id`: Unique identifier. * `messages`: Dictionary'ler içeren liste, her dictionary bir mesajı (key: content) ve o mesajı kimin gönderdiğini (key: role) açıklar. * `category`: Görevin kategorisi, bunu çevirmedim. ### Split'ler | | train_sft | test_sft | |---------------|------:| ---: | | no_robots | 9500 | 500 | ### Lisans Bu veriseti ne yazık ki açık kaynak değil açık erişimli. Lisansı [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). Eğer verisetinin kendisi açık kaynak olursa bu veriseti de açık kaynak olacaktır, çünkü çevirisini çeviriler üstünde fikri mülkiyet istemeyen GCP tarafından yaptım. ### Citation ``` @misc{no_robots, author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf}, title = {No Robots}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}} } ```
rbnuria/SentiMP-Gr
--- license: cc-by-sa-4.0 task_categories: - text-classification language: - el tags: - code size_categories: - n<1K --- # SentiMP-Gr Dataset The SentiMP-Gr Dataset is a greek sentiment analysis dataset based on tweets written by members of parliament in United Kingdom in 2021. It has been developed collaboratively by the [Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)](https://dasci.es/) research group from the [University of Granada](https://www.ugr.es/), the [SINAI](https://sinai.ujaen.es/) research group from the [University of Jaén](https://www.ujaen.es/) and the [Cardiff NLP](https://sites.google.com/view/cardiffnlp/) research group from the [University of Cardiff](https://isc.cardiff.ac.uk/). <div align="center", style="text-align:center; display:block"> <img style="float:left; padding-right:10px" src="https://dasci.es/wp-content/uploads/2018/12/DaSCI_logo_vertical.png" alt="DaSCI" width="150"/> <img style="float:left; padding-right:10px" src="https://www.ujaen.es/gobierno/viccom/sites/gobierno_viccom/files/uploads/inline-images/Marca%20Tradicional.png" alt="UJAEN" width="175"/> <img style="float:left;" src="https://upload.wikimedia.org/wikipedia/commons/e/ef/Cardiff_University_%28logo%29.svg" alt="Cardiff" width="125"/> </div> <div style="clear:both"></div> ## Dataset details The dataset containst 500 tweets in Greek. For each tweet we provide the following information: * **full_text**: Which containts the content of the tweet. * **fold**: Proposed partitions \{0,1,2,3,4\} in 5 folds for 5 fold cross-validation for the sake of reproducibility. * **label_i** : Annotator's i label (i in \{1,2,3\}). It takes values in \{-1,0,1\}. * **majority_vote**: The result after applying the majority vote strategy to the annotators' partial labelling. When there is a tie we use the label "TIE". It takes values in \{-1,0,1,TIE\}. * **tie_break**: We use this column to break ties in cases where there is a tie. Therefore, it is only completed when TIE appears in the *majority_vote* column. It takes values in \{-1,0,1\}. * **gold_label**: It represents the final label. It is a combination between the *majority_vote* abd the *tie_break* columns. It takes values in \{-1,0,1\}. ## Citation If you use this dataset, please cite: ## Contact Nuria Rodríguez Barroso - rbnuria@ugr.es ## Acknowledgements This work was partly supported by the grants PID2020-119478GB-I00, PID2020-116118GA-I00 and TED2021-130145B-I00 funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government. Shield: [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa] This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa]. [![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa] [cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/ [cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png [cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg
chansung/aaa
--- dataset_info: features: - name: a dtype: int64 splits: - name: ttt num_bytes: 24 num_examples: 3 download_size: 835 dataset_size: 24 configs: - config_name: default data_files: - split: ttt path: data/ttt-* --- # Dataset Card for "aaa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aston21/we_train_dataset
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 240700 num_examples: 660 download_size: 12292 dataset_size: 240700 configs: - config_name: default data_files: - split: train path: data/train-* ---
HuggingFaceM4/imagenet1k_support_1k_query_sets_part_1
Invalid username or password.
ghbacct/twitter-financial-news-sentiment-classification
--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 939352 num_examples: 9543 - name: test num_bytes: 237530 num_examples: 2388 download_size: 717579 dataset_size: 1176882 --- # Dataset Card for "twitter-financial-news-sentiment-classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zahrach0724/Attendify
--- license: mit ---
open-llm-leaderboard/details_openaccess-ai-collective__minotaur-13b
--- pretty_name: Evaluation run of openaccess-ai-collective/minotaur-13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openaccess-ai-collective/minotaur-13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openaccess-ai-collective__minotaur-13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T11:35:33.158218](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__minotaur-13b/blob/main/results_2023-09-17T11-35-33.158218.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2911073825503356,\n\ \ \"em_stderr\": 0.004652179762964262,\n \"f1\": 0.3533399748322166,\n\ \ \"f1_stderr\": 0.004582757562230151,\n \"acc\": 0.4453413859606396,\n\ \ \"acc_stderr\": 0.01050936577304381\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.2911073825503356,\n \"em_stderr\": 0.004652179762964262,\n\ \ \"f1\": 0.3533399748322166,\n \"f1_stderr\": 0.004582757562230151\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12509476876421532,\n \ \ \"acc_stderr\": 0.009112601439849629\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237988\n\ \ }\n}\n```" repo_url: https://huggingface.co/openaccess-ai-collective/minotaur-13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|arc:challenge|25_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T19:13:52.077510.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T11_35_33.158218 path: - '**/details_harness|drop|3_2023-09-17T11-35-33.158218.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T11-35-33.158218.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T11_35_33.158218 path: - '**/details_harness|gsm8k|5_2023-09-17T11-35-33.158218.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T11-35-33.158218.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hellaswag|10_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:13:52.077510.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:13:52.077510.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T19_13_52.077510 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:13:52.077510.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:13:52.077510.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T11_35_33.158218 path: - '**/details_harness|winogrande|5_2023-09-17T11-35-33.158218.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T11-35-33.158218.parquet' - config_name: results data_files: - split: 2023_07_19T19_13_52.077510 path: - results_2023-07-19T19:13:52.077510.parquet - split: 2023_09_17T11_35_33.158218 path: - results_2023-09-17T11-35-33.158218.parquet - split: latest path: - results_2023-09-17T11-35-33.158218.parquet --- # Dataset Card for Evaluation run of openaccess-ai-collective/minotaur-13b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/openaccess-ai-collective/minotaur-13b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [openaccess-ai-collective/minotaur-13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_openaccess-ai-collective__minotaur-13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T11:35:33.158218](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__minotaur-13b/blob/main/results_2023-09-17T11-35-33.158218.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.2911073825503356, "em_stderr": 0.004652179762964262, "f1": 0.3533399748322166, "f1_stderr": 0.004582757562230151, "acc": 0.4453413859606396, "acc_stderr": 0.01050936577304381 }, "harness|drop|3": { "em": 0.2911073825503356, "em_stderr": 0.004652179762964262, "f1": 0.3533399748322166, "f1_stderr": 0.004582757562230151 }, "harness|gsm8k|5": { "acc": 0.12509476876421532, "acc_stderr": 0.009112601439849629 }, "harness|winogrande|5": { "acc": 0.7655880031570639, "acc_stderr": 0.011906130106237988 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]