Datasets:
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CyberHarem/akagi_miria_theidolmastercinderellagirlsu149
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Akagi Miria This is the dataset of Akagi Miria, containing 200 images and their tags. 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)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 389 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 389 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 389 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 389 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
M4-ai/LDJnr_combined_inout_format
--- dataset_info: features: - name: source dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 84050141 num_examples: 48551 download_size: 44177228 dataset_size: 84050141 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering - text-generation - conversational pretty_name: LDJNR_combined size_categories: - 10K<n<100K --- # Dataset Card for "LDJnr_combined_inout_format" Dataset contains QA format versions of the data contained in the following datasets: - LDJnr/Capybara - LDJnr/Pure-Dove - LDJnr/Verified-Camel This consists of an exploded out converation list seperated into input and output params for each, while retaining the source information for attribution purposes.
communityai/apt_pretrain_textbook_16k-1k
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 101687189.03313944 num_examples: 1000 download_size: 51289141 dataset_size: 101687189.03313944 --- # Dataset Card for "apt_pretrain_textbook_16k-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akash418/germeval_2017
--- annotations_creators: [] language: - de language_creators: [] license: [] multilinguality: [] pretty_name: 'German Eval 2017 ' size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification task_ids: - multi-class-classification - topic-classification --- # Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback In the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way. Thus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year. Example: > “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“ As shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level). Consequently, we frame the task as aspect-based sentiment analysis with four sub tasks: ## Data format ``` ID <tab> Text <tab> Relevance <tab> Sentiment <tab> Aspect:Polarity (whitespace separated) ``` ## Links - http://ltdata1.informatik.uni-hamburg.de/germeval2017/ - https://sites.google.com/view/germeval2017-absa/ ## How to cite ```bibtex @inproceedings{germevaltask2017, title = {{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}}, author = {Michael Wojatzki and Eugen Ruppert and Sarah Holschneider and Torsten Zesch and Chris Biemann}, year = {2017}, booktitle = {Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}, address={Berlin, Germany}, pages={1--12} } ```
allenai/ms2_sparse_max
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-MS^2 - extended|other-Cochrane task_categories: - summarization - text2text-generation paperswithcode_id: multi-document-summarization pretty_name: MSLR Shared Task --- This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used: - __query__: The `background` field of each example - __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`. - __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings - __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25` Retrieval results on the `train` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.4333 | 0.2163 | 0.1746 | 0.2636 | Retrieval results on the `validation` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.378 | 0.1827 | 0.1559 | 0.2188 | Retrieval results on the `test` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.3928 | 0.1898 | 0.1672 | 0.2208 |
CyberHarem/kotohime_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kotohime/ことひめ/小兎姫 (Touhou) This is the dataset of kotohime/ことひめ/小兎姫 (Touhou), containing 78 images and their tags. The core tags of this character are `long_hair, red_hair, red_eyes, bow, hair_bow`, 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 | 78 | 65.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kotohime_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 78 | 46.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kotohime_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 142 | 79.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kotohime_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 78 | 60.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kotohime_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 142 | 98.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kotohime_touhou/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/kotohime_touhou', 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 | 20 | ![](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, kimono, solo, smile, ponytail, sash | | 1 | 7 | ![](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, long_sleeves, solo, wide_sleeves, bangs, looking_at_viewer, simple_background, smile, yellow_bow, closed_mouth, purple_kimono, white_background, white_kimono, obi, sidelocks | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | kimono | solo | smile | ponytail | sash | long_sleeves | wide_sleeves | bangs | looking_at_viewer | simple_background | yellow_bow | closed_mouth | purple_kimono | white_background | white_kimono | obi | sidelocks | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------|:--------|:-----------|:-------|:---------------|:---------------|:--------|:--------------------|:--------------------|:-------------|:---------------|:----------------|:-------------------|:---------------|:------|:------------| | 0 | 20 | ![](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 | | | | | | | | | | | | | | 1 | 7 | ![](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 |
Nexdata/93_Hours_Russian_Conversational_Speech_Data_by_Telephone
--- license: cc-by-nc-nd-4.0 --- ## Description Russian(Russia) Spontaneous Dialogue Telephony speech dataset, collected from dialogues based on given topics, covering 20+ domains. Transcribed with text content, speaker's ID, gender, age and other attributes. Our dataset was collected from extensive and diversify speakers(126 native speakers), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied. For more details, please refer to the link: https://www.nexdata.ai/dataset/1312?source=Huggingface ## Format 8kHz 8bit, a-law/u-law pcm, mono channel ## Content category Dialogue based on given topics ## Recording condition Low background noise (indoor) ## Recording device Telephony ## Country Russia(RUS) ## Language(Region) Code ru-RU ## Language Russian ## Speaker 126 people in total, 46% male and 54% female ## Features of annotation Transcription text, timestamp, speaker ID, gender, noise ## Accuracy rate Word accuracy rate(WAR) 98% # Licensing Information Commercial License
IlyaGusev/yandex_q_full
--- dataset_info: features: - name: id dtype: string - name: id2 dtype: int64 - name: title dtype: string - name: text_plain dtype: string - name: text_html dtype: string - name: author dtype: string - name: negative_votes dtype: int32 - name: positive_votes dtype: int32 - name: quality dtype: int8 - name: views dtype: uint64 - name: votes dtype: int32 - name: approved_answer dtype: string - name: timestamp dtype: uint64 - name: tags sequence: string - name: answers sequence: - name: id dtype: string - name: id2 dtype: int64 - name: text_plain dtype: string - name: text_html dtype: string - name: author dtype: string - name: negative_votes dtype: int32 - name: positive_votes dtype: int32 - name: votes dtype: int32 - name: quality dtype: int8 - name: views dtype: uint64 - name: reposts dtype: int32 - name: timestamp dtype: uint64 splits: - name: train num_bytes: 5468460217 num_examples: 1297670 download_size: 1130317937 dataset_size: 5468460217 --- Based on https://huggingface.co/datasets/its5Q/yandex-q, parsed full.jsonl.gz
anjunhu/naively_captioned_CUB2002011_test_8shot
--- dataset_info: features: - name: text dtype: string - name: text_cupl dtype: string - name: image dtype: image splits: - name: train num_bytes: 44087534.0 num_examples: 1600 download_size: 43955165 dataset_size: 44087534.0 --- # Dataset Card for "naively_captioned_CUB2002011_test_8shot" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kewu93/dreambooth_full
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: subject_name dtype: string splits: - name: train num_bytes: 111680598.0 num_examples: 158 download_size: 111587177 dataset_size: 111680598.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dreambooth_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_EleutherAI__pythia-12b
--- pretty_name: Evaluation run of EleutherAI/pythia-12b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 4 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_EleutherAI__pythia-12b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-12T13:49:53.203420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-12b/blob/main/results_2023-10-12T13-49-53.203420.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\": 0.0006291946308724832,\n\ \ \"em_stderr\": 0.0002568002749723885,\n \"f1\": 0.04447986577181216,\n\ \ \"f1_stderr\": 0.0010992181181045415,\n \"acc\": 0.32955534824940325,\n\ \ \"acc_stderr\": 0.008541034020282903\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0006291946308724832,\n \"em_stderr\": 0.0002568002749723885,\n\ \ \"f1\": 0.04447986577181216,\n \"f1_stderr\": 0.0010992181181045415\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \ \ \"acc_stderr\": 0.003605486867998272\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6416732438831886,\n \"acc_stderr\": 0.013476581172567535\n\ \ }\n}\n```" repo_url: https://huggingface.co/EleutherAI/pythia-12b 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_19T18_06_28.460226 path: - '**/details_harness|arc:challenge|25_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|arc:challenge|25_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-23T22:15:03.187761.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_12T13_49_53.203420 path: - '**/details_harness|drop|3_2023-10-12T13-49-53.203420.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-12T13-49-53.203420.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_12T13_49_53.203420 path: - '**/details_harness|gsm8k|5_2023-10-12T13-49-53.203420.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-12T13-49-53.203420.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hellaswag|10_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hellaswag|10_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:06:28.460226.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T22:15:03.187761.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T22:15:03.187761.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T18_06_28.460226 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T18:06:28.460226.parquet' - split: 2023_08_23T22_15_03.187761 path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T22:15:03.187761.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T22:15:03.187761.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_12T13_49_53.203420 path: - '**/details_harness|winogrande|5_2023-10-12T13-49-53.203420.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-12T13-49-53.203420.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:25:00.431107.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:management|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:25:00.431107.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_25_00.431107 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:25:00.431107.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:25:00.431107.parquet' - config_name: results data_files: - split: 2023_07_19T18_06_28.460226 path: - results_2023-07-19T18:06:28.460226.parquet - split: 2023_08_23T22_15_03.187761 path: - results_2023-08-23T22:15:03.187761.parquet - split: 2023_08_28T20_25_00.431107 path: - results_2023-08-28T20:25:00.431107.parquet - split: 2023_10_12T13_49_53.203420 path: - results_2023-10-12T13-49-53.203420.parquet - split: latest path: - results_2023-10-12T13-49-53.203420.parquet --- # Dataset Card for Evaluation run of EleutherAI/pythia-12b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/EleutherAI/pythia-12b - **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 [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 4 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_EleutherAI__pythia-12b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-12T13:49:53.203420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-12b/blob/main/results_2023-10-12T13-49-53.203420.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": 0.0006291946308724832, "em_stderr": 0.0002568002749723885, "f1": 0.04447986577181216, "f1_stderr": 0.0010992181181045415, "acc": 0.32955534824940325, "acc_stderr": 0.008541034020282903 }, "harness|drop|3": { "em": 0.0006291946308724832, "em_stderr": 0.0002568002749723885, "f1": 0.04447986577181216, "f1_stderr": 0.0010992181181045415 }, "harness|gsm8k|5": { "acc": 0.017437452615617893, "acc_stderr": 0.003605486867998272 }, "harness|winogrande|5": { "acc": 0.6416732438831886, "acc_stderr": 0.013476581172567535 } } ``` ### 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]
NathanGavenski/How-Resilient-are-Imitation-Learning-Methods-to-Sub-Optimal-Experts
--- annotations_creators: - machine-generated language_creators: - expert-generated language: [] license: - mit multilinguality: [] size_categories: - 100B<n<1T source_datasets: - original task_categories: - other task_ids: [] pretty_name: How Resilient are Imitation Learning Methods to Sub-Optimal Experts? tags: - Imitation Learning - Expert Trajectories - Classic Control --- # How Resilient are Imitation Learning Methods to Sub-Optimal Experts? ## Related Work Trajectories used in [How Resilient are Imitation Learning Methods to Sub-Optimal Experts?]() The code that uses this data is on GitHub: https://github.com/NathanGavenski/How-resilient-IL-methods-are # Structure These trajectories are formed by using [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/). Each file is a dictionary of a set of trajectories with the following keys: * actions: the action in the given timestamp `t` * obs: current state in the given timestamp `t` * rewards: reward retrieved after the action in the given timestamp `t` * episode_returns: The aggregated reward of each episode (each file consists of 5000 runs) * episode_Starts: Whether that `obs` is the first state of an episode (boolean list) ## Citation Information ``` @inproceedings{gavenski2022how, title={How Resilient are Imitation Learning Methods to Sub-Optimal Experts?}, author={Nathan Gavenski and Juarez Monteiro and Adilson Medronha and Rodrigo Barros}, booktitle={2022 Brazilian Conference on Intelligent Systems (BRACIS)}, year={2022}, organization={IEEE} } ``` ## Contact: - [Nathan Schneider Gavenski](nathan.gavenski@edu.pucrs.br) - [Juarez Monteiro](juarez.santos@edu.pucrs.br) - [Adilson Medronha](adilson.medronha@edu.pucrs.br) - [Rodrigo C. Barros](rodrigo.barros@pucrs.br)
caretech-owl/wikiquote-de-quotes
--- license: cc-by-sa-4.0 language: - de --- # Dataset Card for Wikiquotes German This dataset contains german quotes from [wikiquote](https://de.wikiquote.org/wiki/Hauptseite). It consists of two columns named 'author' and 'quote'. For regenerating the dataset we provided the source code in this repo. You can use it as follows: ```sh pip install bs4 pandas python CrawlingQuotes.py ``` For usag in python just include ```python from datasets import load_dataset training_data = load_dataset("caretech-owl/wikiquote-de-quotes", split="train") ``` after installing 🤗 datasets (```pip install datasets```). * **Attribution**: Raw data was retrieved from https://dumps.wikimedia.org/dewikiquote/ on November 27th. * **License**: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)
jayan098/ImageDataset_1
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Non-violence '1': Violence splits: - name: train num_bytes: 3222924.7333333334 num_examples: 114 - name: test num_bytes: 571143.2666666667 num_examples: 21 download_size: 1171145 dataset_size: 3794068.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
davozing223/xandeestudante
--- license: openrail ---
claudios/D2A
--- arxiv: 2102.07995 dataset_info: - config_name: code features: - name: id dtype: int64 - name: label dtype: int64 - name: bug_url dtype: string - name: bug_function dtype: string - name: functions dtype: string splits: - name: train num_bytes: 357876131 num_examples: 36719 - name: dev num_bytes: 48017743 num_examples: 4634 - name: test num_bytes: 43035964 num_examples: 4604 download_size: 139316551 dataset_size: 448929838 - config_name: code_trace features: - name: id dtype: int64 - name: label dtype: int64 - name: trace dtype: string - name: bug_url dtype: string - name: bug_function dtype: string - name: functions dtype: string splits: - name: train num_bytes: 531973771 num_examples: 36719 - name: dev num_bytes: 66958385 num_examples: 4634 - name: test num_bytes: 64518442 num_examples: 4604 download_size: 208837991 dataset_size: 663450598 - config_name: function features: - name: id dtype: int64 - name: label dtype: int64 - name: code dtype: string splits: - name: train num_bytes: 8913129 num_examples: 4643 - name: dev num_bytes: 1107843 num_examples: 596 - name: test num_bytes: 1193137 num_examples: 618 download_size: 4715682 dataset_size: 11214109 - config_name: trace features: - name: id dtype: int64 - name: label dtype: int64 - name: trace dtype: string splits: - name: train num_bytes: 174685144 num_examples: 36719 - name: dev num_bytes: 19014786 num_examples: 4634 - name: test num_bytes: 21556142 num_examples: 4604 download_size: 68014392 dataset_size: 215256072 configs: - config_name: code data_files: - split: train path: code/train-* - split: dev path: code/dev-* - split: test path: code/test-* - config_name: code_trace data_files: - split: train path: code_trace/train-* - split: dev path: code_trace/dev-* - split: test path: code_trace/test-* - config_name: function data_files: - split: train path: function/train-* - split: dev path: function/dev-* - split: test path: function/test-* - config_name: trace data_files: - split: train path: trace/train-* - split: dev path: trace/dev-* - split: test path: trace/test-* license: apache-2.0 task_categories: - text-classification tags: - code --- # D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis This is an unofficial HuggingFace upload of the D2A dataset from "[D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis](https://arxiv.org/abs/2102.07995)". "Test" splits have all labels as -1 as they are not provided. Usage: ```python from datasets import load_dataset # Use "code", "code_trace", "function", or "trace" to load the different variants. dataset = load_dataset("claudios/D2A", "code") ``` *** # D2A Leaderboard Data This document describes D2A V1 Leaderboard data. You can download them from the Leaderboard section of the [D2A Dataset](https://dax-cdn.cdn.appdomain.cloud/dax-d2a/1.1.0/d2a.html?cm_mc_uid=52096571630515722723826&cm_mc_sid_50200000=65851751618339788874&_ga=2.42786284.851757668.1618339789-1229357178.1617837310) page. To begin download directly you can click [here](https://dax-cdn.cdn.appdomain.cloud/dax-d2a/1.1.0/d2a_leaderboard_data.tar.gz). ## Source files: The files were created using the [default security errors](#default-security-errors) of datasets Libav, OpenSSL, Nginx, Httpd and Libtiff from [D2A](https://developer.ibm.com/exchanges/data/all/d2a/). There are 4 directories corresponding to 4 tasks of the leaderboard. Each directory contains 3 csv files corresponding to the train (80%), dev (10%) and test (10%) split. The columns in the split files are identical except the test split which does not contain the label column. ## Columns: 1. **id**: A unique id for every example in a task. 2. **label**: Values are 0 or 1. 1. Value 0: No vulnerability/defect in the example. 2. Value 1: Example contains some vulnerability/defect. 3. **trace**: Bug trace or bug report generated by Infer static analyzer. Infer predictions contain a lot of False positives which is why even 0 label examples have a bug report. 4. **bug_function/code**: Full source code of the function where the vulnerability originates. 5. **bug_url**: URL of the file which contains the bug_function. 6. **functions**: Full source code of all the functions in the bug trace, with the duplicates removed. This will include the function in bug_function. ## Default Security Errors: These are security errors enabled by default by Infer. * BIABD_USE_AFTER_FREE * BUFFER_OVERRUN_L1 * BUFFER_OVERRUN_L2 * BUFFER_OVERRUN_L3 * BUFFER_OVERRUN_R2 * BUFFER_OVERRUN_S2 * BUFFER_OVERRUN_T1 * INTEGER_OVERFLOW_L1 * INTEGER_OVERFLOW_L2 * INTEGER_OVERFLOW_R2 * MEMORY_LEAK * NULL_DEREFERENCE * RESOURCE_LEAK * LAB_RESOURCE_LEAK * UNINITIALIZED_VALUE * USE_AFTER_DELETE * USE_AFTER_FREE * USE_AFTER_LIFETIME ## Data Examples: 1. Trace: ```"test/bntest.c:1802: error: BUFFER_OVERRUN_L3 Offset: [4, +oo] (‚áê [0, +oo] + 4) Size: [0, 8388607] by call to `BN_mul`. Showing all 12 steps of the trace test/bntest.c:1798:10: Call 1796. 1797. /* Test that BN_mul never gives negative zero. */ 1798. if (!BN_set_word(a, 1)) ^ 1799. goto err; 1800. BN_set_negative(a, 1); crypto/bn/bn_lib.c:463:1: Parameter `*a->d` 461. } 462. 463. > int BN_set_word(BIGNUM *a, BN_ULONG w) 464. { 465. bn_check_top(a); crypto/bn/bn_lib.c:466:9: Call 464. { 465. bn_check_top(a); 466. if (bn_expand(a, (int)sizeof(BN_ULONG) * 8) == NULL) ^ 467. return (0); 468. a->neg = 0; crypto/bn/bn_lcl.h:676:1: Parameter `*a->d` 674. int bn_probable_prime_dh_coprime(BIGNUM *rnd, int bits, BN_CTX *ctx); 675. 676. > static ossl_inline BIGNUM *bn_expand(BIGNUM *a, int bits) 677. { 678. if (bits > (INT_MAX - BN_BITS2 + 1)) test/bntest.c:1802:10: Call 1800. BN_set_negative(a, 1); 1801. BN_zero(b); 1802. if (!BN_mul(c, a, b, ctx)) ^ 1803. goto err; 1804. if (!BN_is_zero(c) || BN_is_negative(c)) { crypto/bn/bn_mul.c:828:1: Parameter `*b->d` 826. #endif /* BN_RECURSION */ 827. 828. > int BN_mul(BIGNUM *r, const BIGNUM *a, const BIGNUM *b, BN_CTX *ctx) 829. { 830. int ret = 0; crypto/bn/bn_mul.c:909:17: Call 907. if (bn_wexpand(rr, k * 4) == NULL) 908. goto err; 909. bn_mul_part_recursive(rr->d, a->d, b->d, ^ 910. j, al - j, bl - j, t->d); 911. } else { /* al <= j || bl <= j */ crypto/bn/bn_mul.c:480:1: Parameter `*b` 478. */ 479. /* tnX may not be negative but less than n */ 480. > void bn_mul_part_recursive(BN_ULONG *r, BN_ULONG *a, BN_ULONG *b, int n, 481. int tna, int tnb, BN_ULONG *t) 482. { crypto/bn/bn_mul.c:488:9: Call 486. 487. if (n < 8) { 488. bn_mul_normal(r, a, n + tna, b, n + tnb); ^ 489. return; 490. } crypto/bn/bn_mul.c:983:1: <Length trace> 981. } 982. 983. > void bn_mul_normal(BN_ULONG *r, BN_ULONG *a, int na, BN_ULONG *b, int nb) 984. { 985. BN_ULONG *rr; crypto/bn/bn_mul.c:983:1: Parameter `*b` 981. } 982. 983. > void bn_mul_normal(BN_ULONG *r, BN_ULONG *a, int na, BN_ULONG *b, int nb) 984. { 985. BN_ULONG *rr; crypto/bn/bn_mul.c:1018:50: Array access: Offset: [4, +oo] (‚áê [0, +oo] + 4) Size: [0, 8388607] by call to `BN_mul` 1016. if (--nb <= 0) 1017. return; 1018. rr[4] = bn_mul_add_words(&(r[4]), a, na, b[4]); ^ 1019. rr += 4; 1020. r += 4; " ``` 2. Bug URL: ``` https://github.com/openssl/openssl/blob/0282aeb690d63fab73a07191b63300a2fe30d212/crypto/bn/bn_mul.c/#L1018 ``` 3. Bug Function: ``` "void bn_mul_normal(BN_ULONG *r, BN_ULONG *a, int na, BN_ULONG *b, int nb) { BN_ULONG *rr; if (na < nb) { int itmp; BN_ULONG *ltmp; itmp = na; na = nb; nb = itmp; ltmp = a; a = b; b = ltmp; } rr = &(r[na]); if (nb <= 0) { (void)bn_mul_words(r, a, na, 0); return; } else rr[0] = bn_mul_words(r, a, na, b[0]); for (;;) { if (--nb <= 0) return; rr[1] = bn_mul_add_words(&(r[1]), a, na, b[1]); if (--nb <= 0) return; rr[2] = bn_mul_add_words(&(r[2]), a, na, b[2]); if (--nb <= 0) return; rr[3] = bn_mul_add_words(&(r[3]), a, na, b[3]); if (--nb <= 0) return; rr[4] = bn_mul_add_words(&(r[4]), a, na, b[4]); rr += 4; r += 4; b += 4; } }" ``` 4. Functions: ``` [ 'static int test_negzero() { BIGNUM * a = BN_new(); BIGNUM * b = BN_new(); BIGNUM * c = BN_new(); BIGNUM * d = BN_new(); BIGNUM * numerator = NULL, * denominator = NULL; int consttime, st = 0; if (a == NULL || b == NULL || c == NULL || d == NULL) goto err; if (!BN_set_word(a, 1)) goto err; BN_set_negative(a, 1); BN_zero(b); if (!BN_mul(c, a, b, ctx)) goto err; if (!BN_is_zero(c) || BN_is_negative(c)) { fprintf(stderr, "Multiplication test failed!"); goto err; } for (consttime = 0; consttime < 2; consttime++) { numerator = BN_new(); denominator = BN_new(); if (numerator == NULL || denominator == NULL) goto err; if (consttime) { BN_set_flags(numerator, BN_FLG_CONSTTIME); BN_set_flags(denominator, BN_FLG_CONSTTIME); } if (!BN_set_word(numerator, 1) || !BN_set_word(denominator, 2)) goto err; BN_set_negative(numerator, 1); if (!BN_div(a, b, numerator, denominator, ctx)) goto err; if (!BN_is_zero(a) || BN_is_negative(a)) { fprintf(stderr, "Incorrect quotient (consttime = %d).", consttime); goto err; } if (!BN_set_word(denominator, 1)) goto err; if (!BN_div(a, b, numerator, denominator, ctx)) goto err; if (!BN_is_zero(b) || BN_is_negative(b)) { fprintf(stderr, "Incorrect remainder (consttime = %d).", consttime); goto err; } BN_free(numerator); BN_free(denominator); numerator = denominator = NULL; } BN_zero(a); BN_set_negative(a, 1); if (BN_is_negative(a)) { fprintf(stderr, "BN_set_negative produced a negative zero."); goto err; } st = 1; err: BN_free(a); BN_free(b); BN_free(c); BN_free(d); BN_free(numerator); BN_free(denominator); return st; }', 'int BN_set_word(BIGNUM * a, BN_ULONG w) { bn_check_top(a); if (bn_expand(a, (int) sizeof(BN_ULONG) * 8) == NULL) return (0); a -> neg = 0; a -> d[0] = w; a -> top = (w ? 1 : 0); bn_check_top(a); return (1); }', 'static ossl_inline BIGNUM * bn_expand(BIGNUM * a, int bits) { if (bits > (INT_MAX - BN_BITS2 + 1)) return NULL; if (((bits + BN_BITS2 - 1) / BN_BITS2) <= (a) -> dmax) return a; return bn_expand2((a), (bits + BN_BITS2 - 1) / BN_BITS2); }', 'int BN_mul(BIGNUM * r, const BIGNUM * a, const BIGNUM * b, BN_CTX * ctx) { int ret = 0; int top, al, bl; BIGNUM * rr; #if defined(BN_MUL_COMBA) || defined(BN_RECURSION) int i; #endif #ifdef BN_RECURSION BIGNUM * t = NULL; int j = 0, k; #endif bn_check_top(a); bn_check_top(b); bn_check_top(r); al = a -> top; bl = b -> top; if ((al == 0) || (bl == 0)) { BN_zero(r); return (1); } top = al + bl; BN_CTX_start(ctx); if ((r == a) || (r == b)) { if ((rr = BN_CTX_get(ctx)) == NULL) goto err; } else rr = r; rr -> neg = a -> neg ^ b -> neg; #if defined(BN_MUL_COMBA) || defined(BN_RECURSION) i = al - bl; #endif #ifdef BN_MUL_COMBA if (i == 0) { # if 0 if (al == 4) { if (bn_wexpand(rr, 8) == NULL) goto err; rr -> top = 8; bn_mul_comba4(rr -> d, a -> d, b -> d); goto end; } # endif if (al == 8) { if (bn_wexpand(rr, 16) == NULL) goto err; rr -> top = 16; bn_mul_comba8(rr -> d, a -> d, b -> d); goto end; } } #endif #ifdef BN_RECURSION if ((al >= BN_MULL_SIZE_NORMAL) && (bl >= BN_MULL_SIZE_NORMAL)) { if (i >= -1 && i <= 1) { if (i >= 0) { j = BN_num_bits_word((BN_ULONG) al); } if (i == -1) { j = BN_num_bits_word((BN_ULONG) bl); } j = 1 << (j - 1); assert(j <= al || j <= bl); k = j + j; t = BN_CTX_get(ctx); if (t == NULL) goto err; if (al > j || bl > j) { if (bn_wexpand(t, k * 4) == NULL) goto err; if (bn_wexpand(rr, k * 4) == NULL) goto err; bn_mul_part_recursive(rr -> d, a -> d, b -> d, j, al - j, bl - j, t -> d); } else { if (bn_wexpand(t, k * 2) == NULL) goto err; if (bn_wexpand(rr, k * 2) == NULL) goto err; bn_mul_recursive(rr -> d, a -> d, b -> d, j, al - j, bl - j, t -> d); } rr -> top = top; goto end; } # if 0 if (i == 1 && !BN_get_flags(b, BN_FLG_STATIC_DATA)) { BIGNUM * tmp_bn = (BIGNUM * ) b; if (bn_wexpand(tmp_bn, al) == NULL) goto err; tmp_bn -> d[bl] = 0; bl++; i--; } else if (i == -1 && !BN_get_flags(a, BN_FLG_STATIC_DATA)) { BIGNUM * tmp_bn = (BIGNUM * ) a; if (bn_wexpand(tmp_bn, bl) == NULL) goto err; tmp_bn -> d[al] = 0; al++; i++; } if (i == 0) { j = BN_num_bits_word((BN_ULONG) al); j = 1 << (j - 1); k = j + j; t = BN_CTX_get(ctx); if (al == j) { if (bn_wexpand(t, k * 2) == NULL) goto err; if (bn_wexpand(rr, k * 2) == NULL) goto err; bn_mul_recursive(rr -> d, a -> d, b -> d, al, t -> d); } else { if (bn_wexpand(t, k * 4) == NULL) goto err; if (bn_wexpand(rr, k * 4) == NULL) goto err; bn_mul_part_recursive(rr -> d, a -> d, b -> d, al - j, j, t -> d); } rr -> top = top; goto end; } # endif } #endif if (bn_wexpand(rr, top) == NULL) goto err; rr -> top = top; bn_mul_normal(rr -> d, a -> d, al, b -> d, bl); #if defined(BN_MUL_COMBA) || defined(BN_RECURSION) end: #endif bn_correct_top(rr); if (r != rr && BN_copy(r, rr) == NULL) goto err; ret = 1; err: bn_check_top(r); BN_CTX_end(ctx); return (ret); }', 'void bn_mul_part_recursive(BN_ULONG * r, BN_ULONG * a, BN_ULONG * b, int n, int tna, int tnb, BN_ULONG * t) { int i, j, n2 = n * 2; int c1, c2, neg; BN_ULONG ln, lo, * p; if (n < 8) { bn_mul_normal(r, a, n + tna, b, n + tnb); return; } c1 = bn_cmp_part_words(a, & (a[n]), tna, n - tna); c2 = bn_cmp_part_words( & (b[n]), b, tnb, tnb - n); neg = 0; switch (c1 * 3 + c2) { case -4: bn_sub_part_words(t, & (a[n]), a, tna, tna - n); bn_sub_part_words( & (t[n]), b, & (b[n]), tnb, n - tnb); break; case -3: case -2: bn_sub_part_words(t, & (a[n]), a, tna, tna - n); bn_sub_part_words( & (t[n]), & (b[n]), b, tnb, tnb - n); neg = 1; break; case -1: case 0: case 1: case 2: bn_sub_part_words(t, a, & (a[n]), tna, n - tna); bn_sub_part_words( & (t[n]), b, & (b[n]), tnb, n - tnb); neg = 1; break; case 3: case 4: bn_sub_part_words(t, a, & (a[n]), tna, n - tna); bn_sub_part_words( & (t[n]), & (b[n]), b, tnb, tnb - n); break; } # if 0 if (n == 4) { bn_mul_comba4( & (t[n2]), t, & (t[n])); bn_mul_comba4(r, a, b); bn_mul_normal( & (r[n2]), & (a[n]), tn, & (b[n]), tn); memset( & r[n2 + tn * 2], 0, sizeof( * r) * (n2 - tn * 2)); } else # endif if (n == 8) { bn_mul_comba8( & (t[n2]), t, & (t[n])); bn_mul_comba8(r, a, b); bn_mul_normal( & (r[n2]), & (a[n]), tna, & (b[n]), tnb); memset( & r[n2 + tna + tnb], 0, sizeof( * r) * (n2 - tna - tnb)); } else { p = & (t[n2 * 2]); bn_mul_recursive( & (t[n2]), t, & (t[n]), n, 0, 0, p); bn_mul_recursive(r, a, b, n, 0, 0, p); i = n / 2; if (tna > tnb) j = tna - i; else j = tnb - i; if (j == 0) { bn_mul_recursive( & (r[n2]), & (a[n]), & (b[n]), i, tna - i, tnb - i, p); memset( & r[n2 + i * 2], 0, sizeof( * r) * (n2 - i * 2)); } else if (j > 0) { bn_mul_part_recursive( & (r[n2]), & (a[n]), & (b[n]), i, tna - i, tnb - i, p); memset( & (r[n2 + tna + tnb]), 0, sizeof(BN_ULONG) * (n2 - tna - tnb)); } else { memset( & r[n2], 0, sizeof( * r) * n2); if (tna < BN_MUL_RECURSIVE_SIZE_NORMAL && tnb < BN_MUL_RECURSIVE_SIZE_NORMAL) { bn_mul_normal( & (r[n2]), & (a[n]), tna, & (b[n]), tnb); } else { for (;;) { i /= 2; if (i < tna || i < tnb) { bn_mul_part_recursive( & (r[n2]), & (a[n]), & (b[n]), i, tna - i, tnb - i, p); break; } else if (i == tna || i == tnb) { bn_mul_recursive( & (r[n2]), & (a[n]), & (b[n]), i, tna - i, tnb - i, p); break; } } } } } c1 = (int)(bn_add_words(t, r, & (r[n2]), n2)); if (neg) { c1 -= (int)(bn_sub_words( & (t[n2]), t, & (t[n2]), n2)); } else { c1 += (int)(bn_add_words( & (t[n2]), & (t[n2]), t, n2)); } c1 += (int)(bn_add_words( & (r[n]), & (r[n]), & (t[n2]), n2)); if (c1) { p = & (r[n + n2]); lo = * p; ln = (lo + c1) & BN_MASK2;* p = ln; if (ln < (BN_ULONG) c1) { do { p++; lo = * p; ln = (lo + 1) & BN_MASK2;* p = ln; } while (ln == 0); } } }', 'void bn_mul_normal(BN_ULONG * r, BN_ULONG * a, int na, BN_ULONG * b, int nb) { BN_ULONG * rr; if (na < nb) { int itmp; BN_ULONG * ltmp; itmp = na; na = nb; nb = itmp; ltmp = a; a = b; b = ltmp; } rr = & (r[na]); if (nb <= 0) { (void) bn_mul_words(r, a, na, 0); return; } else rr[0] = bn_mul_words(r, a, na, b[0]); for (;;) { if (--nb <= 0) return; rr[1] = bn_mul_add_words( & (r[1]), a, na, b[1]); if (--nb <= 0) return; rr[2] = bn_mul_add_words( & (r[2]), a, na, b[2]); if (--nb <= 0) return; rr[3] = bn_mul_add_words( & (r[3]), a, na, b[3]); if (--nb <= 0) return; rr[4] = bn_mul_add_words( & (r[4]), a, na, b[4]); rr += 4; r += 4; b += 4; } }' ] ``` [Leaderboard README](https://github.com/IBM/D2A/blob/main/leaderboard/README.md) || [Leaderboard page](https://ibm.github.io/D2A)
pvduy/ultra-feedback-9k
--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 46762065 num_examples: 9500 - name: test num_bytes: 440668 num_examples: 100 download_size: 22473913 dataset_size: 47202733 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
AfasdfAFASf/test
--- license: bigscience-openrail-m task_categories: - text-classification language: - en size_categories: - n<1K ---
nitinbhayana/spell_error_data_v1.1
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 77868 num_examples: 148 download_size: 41183 dataset_size: 77868 --- # Dataset Card for "spell_error_data_v1.1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TakanashiShiya/FamilyPlusLlama
--- license: apache-2.0 task_categories: - question-answering language: - en size_categories: - 10K<n<100K --- # 🤗 Dataset Card: TakanashiShiya/PlusLlama
manishiitg/squad_v2
--- dataset_info: features: - name: system dtype: string - name: instruction dtype: string - name: response dtype: string - name: lang dtype: string splits: - name: train num_bytes: 314202365 num_examples: 173518 download_size: 53117449 dataset_size: 314202365 configs: - config_name: default data_files: - split: train path: data/train-* ---
Graphcore/vqa
--- language: - en license: - cc-by-4.0 ---
salony/User_story
--- dataset_info: features: - name: input dtype: string - name: result dtype: string splits: - name: train num_bytes: 1796067.5034013605 num_examples: 1999 - name: test num_bytes: 449241.49659863947 num_examples: 500 download_size: 1135161 dataset_size: 2245309.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
premio-ai/TheArabicPile_Miscellaneous
--- language: - ar license: cc-by-nc-4.0 task_categories: - text-generation dataset_info: - config_name: dedup features: - name: text dtype: string splits: - name: train num_bytes: 85180751 num_examples: 216758 download_size: 41869082 dataset_size: 85180751 - config_name: default features: - name: text dtype: string splits: - name: original num_bytes: 93581658 num_examples: 235829 download_size: 46320612 dataset_size: 93581658 configs: - config_name: dedup data_files: - split: train path: dedup/train-* - config_name: default data_files: - split: original path: data/train-* --- # The Arabic Pile ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64da0fd923557cdce3e514c3/J0oY67lVvecV75SOlWpjc.png) ## Introduction: The Arabic Pile is a comprehensive dataset meticulously designed to parallel the structure of The Pile and The Nordic Pile. Focused on the Arabic language, the dataset encompasses a vast array of linguistic nuances, incorporating both Modern Standard Arabic (MSA) and various Levantine, North African, and Egyptian dialects. Tailored for the training and fine-tuning of large language models, the dataset consists of 13 subsets, each uniquely crafted to cater to different linguistic domains. ## The Misc Subset: This dataset has a collection of Arabic misc content. ## Other Subsets: 1. premio-ai/TheArabicPile 2. premio-ai/TheArabicPile_Web 3. premio-ai/TheArabicPile_Lyrics 4. premio-ai/TheArabicPile_Reviews 5. premio-ai/TheArabicPile_Dialects 6. premio-ai/TheArabicPile_Mathematics 7. premio-ai/TheArabicPile_Conversational 8. premio-ai/TheArabicPile_Articles 9. premio-ai/TheArabicPile_Poetry 10. premio-ai/TheArabicPile_Medical 11. premio-ai/TheArabicPile_Miscellaneous 12. premio-ai/TheArabicPile_SocialMedia 13. premio-ai/TheArabicPile_Translations 14. premio-ai/TheArabicPile_Books These subsets serve distinct purposes, ranging from mathematical content to conversational dialogue, medical texts, and more. Notably, there's a dedicated subset, "premio-ai/TheArabicPile_SocialMedia," emphasizing the inclusion of language commonly found in social media contexts. ## Dataset Description * Curated by: Premio.AI team * Language(s) (NLP): Arabic, multiple languages on the translation dataset. * License: CC BY-NC 4.0 Deed - Non Commercial. * For any commercial uses or licensing, please contact mo@premio.ai. ## Data Structure The datasets are divided into two main subsets: 1. Original Subset: The raw data as collected from sources, without modifications. 2. Deduplication Subset: A filtered and cleaned version, enhancing usability for large language models by reducing redundancy and noise. The Arabic Pile extends an invitation not only for training and fine-tuning large language models but also for diverse applications across linguistic domains. Whether for research, analysis, or other linguistic endeavors, The Arabic Pile stands as a rich resource for the exploration of Arabic language intricacies. ## Data Collection Please refer to the paper for more details on our data collection procedures. ## Data Format The dataset has one single column called text. The text should contain the required meta data and the body combined. This was done to make sure that it will be a good fit for direct training or fine-tuning of large language models. Please note that the meta data might require to be repeated if your training context window won’t fit the entire body of text. ## Potential Bias As with any large-scale dataset, The Arabic Pile is not immune to potential biases that may influence the training and performance of language models. It's crucial to transparently address these biases to ensure responsible usage and interpretation of the dataset. Here are some considerations: 1. Dialectal Imbalance: The dataset incorporates various Arabic dialects, with a focus on Levantine, North African, and Egyptian variants. However, there might be variations in the representation of these dialects, potentially leading to an imbalance in the training data. 2. Source Influence: Bias may arise from the sources of the original data. The dataset collects information from diverse platforms and domains, and biases inherent in those sources could transfer to the dataset. 3. Social Media Context: Some of our datasets have language from social media platforms and online platforms. This subset may introduce biases inherent in online discourse, such as informal language, colloquial expressions, and potential subjectivity in politics, religion or culture. 4. Genre and Domain Bias: Different subsets cater to distinct linguistic domains, such as medical texts, poetry, reviews, and more. Each domain carries its own linguistic characteristics, potentially leading to biases based on the genres represented. ## License Information for The Arabic Pile: No Commercial Use The Arabic Pile is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license is designed to facilitate the open sharing and collaboration of the dataset while ensuring responsible and non-commercial usage. Key Points of the License: * Attribution (BY): Users are free to share, adapt, and build upon the dataset, even commercially, as long as they provide appropriate attribution to the dataset creators. * Non-Commercial (NC): The dataset may not be used for commercial purposes. Any use for commercial gain requires explicit permission from the dataset creators. * No Additional Restrictions: The license allows for maximum freedom of use, provided the terms of attribution and non-commercial use are adhered to. How to Cite: When using The Arabic Pile in your work, please include a proper citation to acknowledge the dataset creators. A recommended citation can be found in the model card for easy reference. License Deed: For a comprehensive understanding of the terms and conditions, please refer to the CC BY-NC 4.0 License Deed. By adopting this license, we aim to foster a collaborative and open environment for the exploration and advancement of Arabic language understanding and natural language processing. ## Citation When utilizing The Arabic Pile in your research, development, or other projects, we kindly request that you cite the dataset using the following format: @article{alrefaie2024arabicpile, author = {Mohamed Taher Alrefaie, Mahmoud Ibrahim Barbary, Ahmed Yasser Hassanein, Shiref Khaled Elhalawany, Karim Ashraf Elsayed, Ahmed Yasser }, title = {The Arabic Pile: A Large Scale Dataset of Diverse Text for Large Language Modeling}, year = {2024}, url = {https://huggingface.co/datasets/premio-ai/TheArabicPile} }
ZHZisZZ/imdb_preference
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: responses sequence: string - name: scores sequence: float64 - name: chosen dtype: int64 splits: - name: train num_bytes: 26080900.0 num_examples: 20000 - name: validation num_bytes: 5216180.0 num_examples: 4000 - name: test num_bytes: 1304045.0 num_examples: 1000 download_size: 19096433 dataset_size: 32601125.0 --- # Dataset Card for "imdb_preference" SFT model: https://huggingface.co/lvwerra/gpt2-imdb Golden reward model (log_p(positive)-log_p(negative)): https://huggingface.co/lvwerra/distilbert-imdb
gilsonk12/THIAGOSCHUTZ
--- license: openrail ---
realbraga/tzdacoronel
--- license: openrail ---
alexavil/doppelbot_test
--- language: - en ---
mansichauhantyagi/complex_RAG
--- task_categories: - feature-extraction language: - en tags: - medical pretty_name: complex_rag size_categories: - n<1K ---
jilp00/youtoks-train-water-diplomacy
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 220110 num_examples: 283 download_size: 94414 dataset_size: 220110 configs: - config_name: default data_files: - split: train path: data/train-* ---
n3gflash/test
--- license: apache-2.0 ---
daokang/bs
--- license: afl-3.0 ---
c4iro/king-von
--- license: apache-2.0 task_categories: - conversational ---
biglam/gutenberg-poetry-corpus
--- annotations_creators: - no-annotation language: - en language_creators: - found license: - cc0-1.0 multilinguality: - monolingual pretty_name: Gutenberg Poetry Corpus size_categories: - 1M<n<10M source_datasets: [] tags: - poetry - stylistics - poems - gutenberg task_categories: - text-generation task_ids: - language-modeling --- # Allison Parrish's Gutenberg Poetry Corpus This corpus was originally published under the CC0 license by [Allison Parrish](https://www.decontextualize.com/). Please visit Allison's fantastic [accompanying GitHub repository](https://github.com/aparrish/gutenberg-poetry-corpus) for usage inspiration as well as more information on how the data was mined, how to create your own version of the corpus, and examples of projects using it. This dataset contains 3,085,117 lines of poetry from hundreds of Project Gutenberg books. Each line has a corresponding `gutenberg_id` (1191 unique values) from project Gutenberg. ```python Dataset({ features: ['line', 'gutenberg_id'], num_rows: 3085117 }) ``` A row of data looks like this: ```python {'line': 'And retreated, baffled, beaten,', 'gutenberg_id': 19} ```
Sampath1987/NER_cyber_3_test
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 120044 num_examples: 131 download_size: 29570 dataset_size: 120044 configs: - config_name: default data_files: - split: train path: data/train-* ---
ItzCrazyKns/mj-dataset
--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 41884816.0 num_examples: 10 download_size: 41890431 dataset_size: 41884816.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
RuterNorway/Fleurs-Alpaca-EN-NO
--- language: - no - en license: cc-by-4.0 task_categories: - translation pretty_name: Fleurs-Alpaca-EN-NO size_categories: - 1k<n<5k --- <p><h1>🦙 Alpaca Translate Norwegian 🦙</h1></p> This dataset is based on [Fleurs](https://huggingface.co/datasets/google/fleurs) from Google. We matched the English sentences with Norwegian sentences and formatted it to an Alpaca-style dataset. ## Dataset Structure ```json { "instruction": "Oversett teksten fra engelsk til norsk", "input": "English string", "output": "Norwegian string" } ``` This dataset was created by [Ruter](https://ruter.no) during Ruter's AI Lab effort to fine-tune LLaMA-2 models for Norwegian. ## License Following the original dataset from Google, this dataset is released under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license. <p><h1>🦙 Alpaca Translate Norsk 🦙</h1></p> Dette datasettet er basert på [Fleurs](https://huggingface.co/datasets/google/fleurs) utgitt av Google. Vi har sammenstilt de engelske setningene med norske setninger og formatert det til et Alpaca-stil datasett. ## Datasettstruktur ```json { "instruction": "Oversett teksten fra engelsk til norsk", "input": "English string", "output": "Norwegian string" } ``` Datasettet ble laget av [Ruter](https://ruter.no) AI Lab under arbeidet med å finjustere LLaMA-2-modeller for norsk. ## License Vi følger det originale datasettet fra Google sin lisens, som er utgitt under en [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
martinakaduc/ChEMBL_activities
--- license: cc-by-sa-4.0 ---
epts/kanji-serif
--- license: openrail dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 49143088.248 num_examples: 2136 download_size: 42660088 dataset_size: 49143088.248 ---
CyberHarem/fu_xuan_starrail
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of fu_xuan/符玄/符玄/부현 (Honkai: Star Rail) This is the dataset of fu_xuan/符玄/符玄/부현 (Honkai: Star Rail), containing 419 images and their tags. The core tags of this character are `long_hair, bangs, hair_ornament, pink_hair, parted_bangs, facial_mark, very_long_hair, yellow_eyes`, 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 | 419 | 957.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 419 | 419.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1091 | 935.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 419 | 778.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1091 | 1.45 GiB | [Download](https://huggingface.co/datasets/CyberHarem/fu_xuan_starrail/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/fu_xuan_starrail', 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 | 11 | ![](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, blush, 1boy, hetero, nipples, penis, sex, solo_focus, open_mouth, sweat, vaginal, small_breasts, spread_legs, completely_nude, looking_at_viewer, navel, pink_eyes, collarbone, lying, mosaic_censoring, pov, pussy_juice, bar_censor, jewelry | | 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, bare_shoulders, looking_at_viewer, solo, forehead_mark, hair_rings, white_background, simple_background, closed_mouth, parted_lips, purple_hair, white_dress, blush, upper_body | | 2 | 39 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, dress, solo, forehead_mark, looking_at_viewer, bare_shoulders, jewelry, closed_mouth | | 3 | 13 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, no_shoes, solo, dress, looking_at_viewer, sitting, toes, white_pantyhose, legs, full_body, soles, forehead_mark, blush, foot_focus, knees_up, purple_hair, hair_rings, indoors | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | 1boy | hetero | nipples | penis | sex | solo_focus | open_mouth | sweat | vaginal | small_breasts | spread_legs | completely_nude | looking_at_viewer | navel | pink_eyes | collarbone | lying | mosaic_censoring | pov | pussy_juice | bar_censor | jewelry | bare_shoulders | solo | forehead_mark | hair_rings | white_background | simple_background | closed_mouth | parted_lips | purple_hair | white_dress | upper_body | dress | no_shoes | sitting | toes | white_pantyhose | legs | full_body | soles | foot_focus | knees_up | indoors | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:---------|:----------|:--------|:------|:-------------|:-------------|:--------|:----------|:----------------|:--------------|:------------------|:--------------------|:--------|:------------|:-------------|:--------|:-------------------|:------|:--------------|:-------------|:----------|:-----------------|:-------|:----------------|:-------------|:-------------------|:--------------------|:---------------|:--------------|:--------------|:--------------|:-------------|:--------|:-----------|:----------|:-------|:------------------|:-------|:------------|:--------|:-------------|:-----------|:----------| | 0 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | 2 | 39 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | X | | | | | X | | | | | | | | | | | | 3 | 13 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X |
dahwinsingularity/experiment
--- license: apache-2.0 ---
Intuit-GenSRF/toxigen-train
--- dataset_info: features: - name: text dtype: string - name: labels sequence: string splits: - name: train num_bytes: 138945158 num_examples: 250951 download_size: 3070653 dataset_size: 138945158 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "toxigen-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Rimyy/problemMathGemma200K
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 228323386 num_examples: 200035 download_size: 82374192 dataset_size: 228323386 configs: - config_name: default data_files: - split: train path: data/train-* ---
aminoss/can
--- license: openrail ---
UnderstandLing/oasst1_es_threads
--- license: apache-2.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 13073679 num_examples: 9620 - name: validation num_bytes: 694634 num_examples: 503 download_size: 7284127 dataset_size: 13768313 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
Matsakitkat/Mobility_Future
--- license: afl-3.0 --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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]
Onegafer/e5_finetuning_dataset_cosine
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: score dtype: float64 - name: short dtype: string - name: query dtype: string splits: - name: train num_bytes: 85541460 num_examples: 33279 download_size: 1277308 dataset_size: 85541460 --- # Dataset Card for "e5_finetuning_dataset_cosine" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gsstein/25-percent-human-dataset-og
--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: generated dtype: bool - name: prompt dtype: string splits: - name: train num_bytes: 86206248 num_examples: 15326 - name: test num_bytes: 3060680 num_examples: 576 - name: validation num_bytes: 3258385 num_examples: 576 download_size: 57260110 dataset_size: 92525313 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
kuronomiki/anjeg
--- license: other ---
freshpearYoon/vr_val_free_5
--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7607757460 num_examples: 10000 download_size: 1271208714 dataset_size: 7607757460 configs: - config_name: default data_files: - split: train path: data/train-* ---
davanstrien/gahd
--- license: cc-by-4.0 task_categories: - text-classification language: - de pretty_name: GAHD configs: - config_name: default data_files: - split: train path: "data/gahd.csv" - config_name: gahd_disaggregated data_files: - split: train path: "data/gahd_disaggregated.csv" --- **NOTE** README copied from https://github.com/jagol/gahd This repository contains the dataset from our NAACL 2024 paper "Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset". `gahd.csv` contains the following columns: - `gahd_id`: unique identifier of the entry - `text`: text of the entry - `label`: `0` = "not-hate speech", `1` = "hate speech" - `round`: round in which the entry was created - `split`: "train", "dev", or "test" - `contrastive_gahd_id`: `gahd_id` of its contrastive example `gahd_disaggregated.csv` contains the following additional columns: - `source`: - if annotators entered the entry via the Dynabench interface: `dynabench` - if the entry was translated from the Vidgen et al. 2021 dataset: `translation` - if the entry stems from the Leipzit news corpus: `news` - `model_prediction`: label predicted by the target model, `0` or `1` - `annotator_id`: unique identifier of the annotator that created the entry - `annotator_labels`: a string containing a forward slash-separated list of all labels by annotators - `expert_labels`: `0` or `1` if an expert annotator annotated the entry, otherwise empty When using GAHD, please cite our preprint on Arxiv: ``` @misc{goldzycher2024improving, title={Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset}, author={Janis Goldzycher and Paul Röttger and Gerold Schneider}, year={2024}, eprint={2403.19559}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Lubub/locutorxxinews
--- license: apache-2.0 ---
MosenA/NaseejNews
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: date dtype: string - name: url dtype: string - name: body dtype: string splits: - name: train num_bytes: 1302696 num_examples: 291 download_size: 554546 dataset_size: 1302696 --- # Dataset Card for "Naseej" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlbaker361/test-league-data
--- dataset_info: features: - name: label dtype: string - name: optimal_prompt dtype: string - name: splash dtype: image - name: tile dtype: image - name: subject dtype: string splits: - name: train num_bytes: 237623.0 num_examples: 3 download_size: 241041 dataset_size: 237623.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Anurich/finance_dataset
--- license: mit task_categories: - question-answering language: - en tags: - finance size_categories: - 1K<n<10K ---
jgibb/billsum_abstracted_us_congress_117_bills
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: index dtype: int64 - name: id dtype: string - name: policy_areas dtype: string - name: cur_summary dtype: string - name: cur_text dtype: string - name: title dtype: string - name: titles_official dtype: string - name: titles_short dtype: string - name: sponsor_name dtype: string - name: sponsor_party dtype: string - name: sponsor_state dtype: string - name: cleaned_summary dtype: string - name: extracted_text dtype: string - name: extracted_text_375 dtype: string - name: extracted_text_750 dtype: string - name: extracted_text_1000 dtype: string - name: bertsum_extracted_250 dtype: string - name: bertsum_extracted_375 dtype: string - name: bertsum_extracted_375_1000 dtype: string - name: bertsum_extracted_250_1000 dtype: string - name: bertsum_extracted_375_750 dtype: string - name: bertsum_extracted_250_750 dtype: string - name: bertsum_extracted_375_500 dtype: string - name: bertsum_extracted_250_500 dtype: string - name: bertsum_extracted_375_375 dtype: string - name: bertsum_extracted_250_375 dtype: string - name: text_len dtype: int64 - name: billsum_abstracted_1000 dtype: string splits: - name: train num_bytes: 206866440 num_examples: 5627 - name: val num_bytes: 63760378 num_examples: 1713 - name: test num_bytes: 6706817 num_examples: 185 download_size: 119443598 dataset_size: 277333635 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
pratultandon/tokenized-recipe-nlg-gpt2
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: test num_bytes: 135944246 num_examples: 106202 - name: train num_bytes: 2582090838 num_examples: 2022671 download_size: 805955428 dataset_size: 2718035084 --- # Dataset Card for "tokenized-recipe-nlg-gpt2" This a tokenized version of the recipe-nlg database from https://recipenlg.cs.put.poznan.pl/. The preprocessing on the original csv was done using the methodology of the original paper (best as I could interpret) along with a similar 0.05 percent train test split. The tokenizer used has some special tokens, but all these parameters are accessible in https://huggingface.co/pratultandon/recipe-nlg-gpt2 if you want to recreate. This dataset will save you a lot of time getting started if you want to experiment with training GPT2 on the data yourself.
hujudev/spider-text-2-sql-train
--- license: cc-by-4.0 ---
breadlicker45/rlhf-prompt3
--- language: en dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 48261697 num_examples: 36958 download_size: 3977175 dataset_size: 48261697 --- # Dataset Card for "rlhf-prompt3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gauravsirola/sas_to_python_base_dataset
--- license: apache-2.0 ---
yuanmei424/fonts_en
--- dataset_info: features: - name: edit_prompt dtype: string - name: input_image dtype: image - name: edited_image dtype: image splits: - name: train num_bytes: 84494062422.25 num_examples: 19837823 download_size: 1463236645 dataset_size: 84494062422.25 --- # Dataset Card for "fonts_en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BhabhaAI/Aksharantar-hindi
--- language: - hi --- This is an improved subset version of Aksharantar hindi dataset. Find original [here](https://ai4bharat.iitm.ac.in/aksharantar-dataset/). Further improvements are in progress.
Resizable/FuckingEncrustedTesticle
--- license: openrail ---
alsunyu/CONSCENDI-guardrail-benchmark
--- dataset_info: features: - name: conversation dtype: string - name: rule_num dtype: string - name: rule dtype: string - name: scenario_num dtype: float64 - name: scenario dtype: string - name: english_num dtype: float64 - name: prompt dtype: string - name: time_elapsed dtype: float64 - name: cost dtype: float64 - name: prompt_tokens dtype: float64 - name: completion_tokens dtype: float64 - name: generation dtype: string - name: convo_length dtype: float64 - name: completion dtype: string - name: spliced_section dtype: string - name: rule_scenario dtype: string splits: - name: bus_test_id num_bytes: 386861 num_examples: 351 - name: bus_test_ood num_bytes: 288372 num_examples: 255 - name: bus_train num_bytes: 1020209 num_examples: 946 - name: flight_test_id num_bytes: 457393 num_examples: 347 - name: flight_test_ood num_bytes: 390962 num_examples: 302 - name: flight_train num_bytes: 1255875 num_examples: 937 - name: restaurant_test_id num_bytes: 426197 num_examples: 334 - name: restaurant_test_ood num_bytes: 324046 num_examples: 298 - name: restaurant_train num_bytes: 1162826 num_examples: 901 download_size: 1731265 dataset_size: 5712741 license: mit task_categories: - text-classification language: - en tags: - legal pretty_name: CONSCENDI Rule-based Guardrail Benchmark size_categories: - 1K<n<10K --- # Dataset Card for "CONSCENDI-guardrail-benchmark" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mbarnig/lb-de-fr-en-pt-12800-TTS-CORPUS
--- license: cc-by-nc-sa-4.0 language: - lb - de - fr - en - pt --- #### This custom multilingual-multispeaker TTS speech corpus contains 12.800 balanced samples with audio files (wav format sampled with 16000 Hz) and related transcriptions (csv format with two columns) from 18 speakers. The dataset has been assembled from the following sources: * [VCTK](https://datashare.ed.ac.uk/handle/10283/3443) : 428 + 426 + 426 english male samples (p259, p274, p286) (CC BY 4.0) * [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) : 1280 english female samples (public domain) * [m-ailabs](https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/) : 1280 french male samples (public free licence) * [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) : 1024 french female samples (CC BY 4.0) * [Rhasspy](https://github.com/rhasspy/dataset-voice-kerstin) : 1082 german female samples (CC0 1.0) * [Thorsten](https://www.thorsten-voice.de) : 1280 german male samples (CC0) * [TTS-Portuguese-Corpus](https://github.com/Edresson/TTS-Portuguese-Corpus) : 2560 portuguese male samples (CC BY 4.0) * [Marylux](https://github.com/marytts/marylux-data) : 663 luxembourgish & 198 german & 256 french female samples (CC BY-NC-SA 4.0) * [uni.lu](http://engelmann.uni.lu/dictee/index.php) : 409 luxembourgish female & 231 luxembourgish male samples (© uni.lu) * [rtl.lu](https://www.rtl.lu/meenung/commentaire) : 1257 luxembourgish male samples (© RTL-CLT-UFA) * Charel : 11 luxembourgish boy samples from my grandchild #### The dataset has been manually checked and the transcriptions have been expanded and eventually corrected to comply with the audio files. The data structure is equivalent to the mailabs format. The folder nesting is shown below: ``` mailabs language-1 by_book female speaker-1 wavs/ folder metadata.csv metadata-train.csv metadata-eval.csv speaker-2 wavs/ folder metadata.csv metadata-train.csv metadata-eval.csv ... male speaker-1 wavs/ folder metadata.csv metadata-train.csv metadata-eval.csv speaker-2 wavs/ folder metadata.csv metadata-train.csv metadata-eval.csv ... language-2 by_book ... language-3 by_book ... ... ``` #### Thanks to [RTL](https://www.rtl.lu/) and to the [University of Luxembourg](https://wwwen.uni.lu/) for permission to use and share selected copyrighted data.
alexredna/slim_orca_hermes_reasoning_sft
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 27241850.16600492 num_examples: 16433 download_size: 10075268 dataset_size: 27241850.16600492 configs: - config_name: default data_files: - split: train path: data/train-* license: cc-by-nc-4.0 --- # Dataset Card for "slim_orca_hermes_reasoning_sft" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mattma1970/SlimOrca100k
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 splits: - name: train num_bytes: 181795884 num_examples: 100000 download_size: 97226388 dataset_size: 181795884 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SlimOrca100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/quirky_squaring_increment0_alice_easy
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1595505.5 num_examples: 23000 - name: validation num_bytes: 67316.06 num_examples: 970 - name: test num_bytes: 68355.30625 num_examples: 985 download_size: 582231 dataset_size: 1731176.86625 --- # Dataset Card for "quirky_squaring_increment0_alice_easy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmayhem93/self-critiquing-base-topic-embeddings
--- dataset_info: features: - name: id dtype: string - name: split dtype: string - name: time dtype: float64 - name: labeler dtype: string - name: is_topic_based_summarization dtype: bool - name: prompt dtype: string - name: responses sequence: string - name: embedding sequence: float64 splits: - name: train num_bytes: 59346595 num_examples: 2758 download_size: 40171704 dataset_size: 59346595 --- # Dataset Card for "self-critiquing-base-topic-embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Hev832/RVC
--- license: mit ---
rokset3/slim_pajama_chunk_3
--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 257905858359 num_examples: 59060327 download_size: 150003305835 dataset_size: 257905858359 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "slim_pajama_chunk_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
youlive789/instructpix2pix
--- license: mit dataset_info: features: - name: original_image dtype: image - name: edited_image dtype: image - name: edit_promt dtype: string splits: - name: train num_bytes: 2478786161.568 num_examples: 2904 download_size: 2239120930 dataset_size: 2478786161.568 configs: - config_name: default data_files: - split: train path: data/train-* ---
hlt-lab/personachatsample-expansions
--- dataset_info: features: - name: context dtype: string - name: response dtype: string - name: reference dtype: string splits: - name: train num_bytes: 7250 num_examples: 20 download_size: 9048 dataset_size: 7250 --- # Dataset Card for "personachatsample-expansions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thangvip/luat-giao-thong
--- dataset_info: features: - name: content dtype: string - name: citation dtype: string - name: meta struct: - name: effective_date dtype: string - name: issuing_agency dtype: string - name: promulgation_date dtype: string - name: sign_number dtype: string - name: signer dtype: string - name: type dtype: string - name: url dtype: string - name: text dtype: string splits: - name: luat num_bytes: 439715.83635422064 num_examples: 160 download_size: 150341 dataset_size: 439715.83635422064 configs: - config_name: default data_files: - split: luat path: data/luat-* ---
introspector/llmvwmcts
--- license: mit ---
jonathan-roberts1/Airbus-Wind-Turbines-Patches
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': no wind turbine '1': wind turbine splits: - name: train num_bytes: 169946184.648 num_examples: 71504 download_size: 147716132 dataset_size: 169946184.648 license: other --- # Dataset Card for "Airbus-Wind-Turbines-Patches" ## Dataset Description - **Paper** [Airbus Wind Turbine Patches](https://www.kaggle.com/datasets/airbusgeo/airbus-wind-turbines-patches) - **Split** Validation ## Split Information This HuggingFace dataset repository contains just the Validation split. ### Licensing Information [CC BY-NC-SA 4.0](https://www.kaggle.com/datasets/airbusgeo/airbus-wind-turbines-patches) ## Citation Information [Airbus Wind Turbine Patches](https://www.kaggle.com/datasets/airbusgeo/airbus-wind-turbines-patches) ``` @misc{kaggle_awtp, author = {Airbus DS GEO S.A.}, title = {Airbus Wind Turbine Patches}, howpublished = {\url{https://www.kaggle.com/datasets/airbusgeo/airbus-wind-turbines-patches}}, year = {2021}, version = {1.0} } ```
lshowway/wikipedia.reorder.sov.de
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2385745587 num_examples: 1137317 download_size: 1068439913 dataset_size: 2385745587 --- # Dataset Card for "wikipedia.reorder.sov.de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NamCyan/thevault-docstringstyle
--- dataset_info: features: - name: hexsha dtype: string - name: repo dtype: string - name: path dtype: string - name: license sequence: string - name: language dtype: string - name: identifier dtype: string - name: return_type dtype: string - name: original_string dtype: string - name: original_docstring dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: code dtype: string - name: code_tokens sequence: string - name: short_docstring dtype: string - name: short_docstring_tokens sequence: string - name: comment sequence: string - name: parameters list: - name: param dtype: string - name: type dtype: string - name: docstring_params struct: - name: returns list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: raises list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: outlier_params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: others list: - name: identifier dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: instruction dtype: string splits: - name: train num_bytes: 6545943535 num_examples: 1261519 download_size: 1969238091 dataset_size: 6545943535 --- # Dataset Card for "thevault-docstringstyle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BelleGroup/train_2M_CN
--- license: gpl-3.0 task_categories: - text2text-generation language: - zh size_categories: - 1M<n<10M --- ## 内容 包含约200万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。 ## 样例 ``` { "instruction": "将以下三个句子组合成一个有意义的段落。\n狗是人类最好的朋友。它们非常聪明，可以进行各种活动。如果你喜欢散步，狗可以成为你一起散步的伙伴。", "input": "", "output": "狗是人类最好的朋友，它们非常聪明，可以进行各种活动。如果你喜欢散步，狗可以成为你一起散步的伙伴。出门散步是一种良好的锻炼方式，而有狗的陪伴会让散步变得更有趣，并且有狗在身边也能给你带来安全感。所以，拥有一只狗作为你的伙伴，可以帮助你变得更加积极主动和健康。" } ``` ### 字段： ``` instruction: 指令 input: 输入（本数据集均为空） output: 输出 ``` ## 使用限制 仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。 本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
ibivibiv/alpaca_lamini2
--- dataset_info: features: - name: output dtype: string - name: instruction dtype: string - name: input dtype: string splits: - name: train num_bytes: 56166594 num_examples: 129280 download_size: 36317206 dataset_size: 56166594 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_shitshow123__mistral7b_sft_dpo
--- pretty_name: Evaluation run of shitshow123/mistral7b_sft_dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [shitshow123/mistral7b_sft_dpo](https://huggingface.co/shitshow123/mistral7b_sft_dpo)\ \ 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_shitshow123__mistral7b_sft_dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-11T07:28:54.566656](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__mistral7b_sft_dpo/blob/main/results_2024-01-11T07-28-54.566656.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\": 0.24115205900155798,\n\ \ \"acc_stderr\": 0.030240327476101683,\n \"acc_norm\": 0.24138243110295876,\n\ \ \"acc_norm_stderr\": 0.031046885606606598,\n \"mc1\": 0.2350061199510404,\n\ \ \"mc1_stderr\": 0.014843061507731608,\n \"mc2\": 0.4967512296032591,\n\ \ \"mc2_stderr\": 0.016399783558395026\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.21075085324232082,\n \"acc_stderr\": 0.011918271754852184,\n\ \ \"acc_norm\": 0.27559726962457337,\n \"acc_norm_stderr\": 0.013057169655761838\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25692093208524197,\n\ \ \"acc_stderr\": 0.004360424536145123,\n \"acc_norm\": 0.255327623979287,\n\ \ \"acc_norm_stderr\": 0.004351540603988566\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.31851851851851853,\n\ \ \"acc_stderr\": 0.040247784019771096,\n \"acc_norm\": 0.31851851851851853,\n\ \ \"acc_norm_stderr\": 0.040247784019771096\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.26973684210526316,\n \"acc_stderr\": 0.03611780560284898,\n\ \ \"acc_norm\": 0.26973684210526316,\n \"acc_norm_stderr\": 0.03611780560284898\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.22641509433962265,\n \"acc_stderr\": 0.025757559893106748,\n\ \ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.025757559893106748\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\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.24,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.2023121387283237,\n\ \ \"acc_stderr\": 0.030631145539198816,\n \"acc_norm\": 0.2023121387283237,\n\ \ \"acc_norm_stderr\": 0.030631145539198816\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179962,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179962\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349424,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349424\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\ \ \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n\ \ \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.30344827586206896,\n \"acc_stderr\": 0.038312260488503336,\n\ \ \"acc_norm\": 0.30344827586206896,\n \"acc_norm_stderr\": 0.038312260488503336\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2275132275132275,\n \"acc_stderr\": 0.021591269407823792,\n \"\ acc_norm\": 0.2275132275132275,\n \"acc_norm_stderr\": 0.021591269407823792\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\ \ \"acc_stderr\": 0.03852273364924318,\n \"acc_norm\": 0.24603174603174602,\n\ \ \"acc_norm_stderr\": 0.03852273364924318\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.022755204959542936,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.022755204959542936\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.030712730070982592,\n\ \ \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.030712730070982592\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\"\ : 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.02962022787479048,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02962022787479048\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.26424870466321243,\n \"acc_stderr\": 0.03182155050916648,\n\ \ \"acc_norm\": 0.26424870466321243,\n \"acc_norm_stderr\": 0.03182155050916648\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2282051282051282,\n \"acc_stderr\": 0.02127839386358628,\n \ \ \"acc_norm\": 0.2282051282051282,\n \"acc_norm_stderr\": 0.02127839386358628\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.21851851851851853,\n \"acc_stderr\": 0.02519575225182379,\n \ \ \"acc_norm\": 0.21851851851851853,\n \"acc_norm_stderr\": 0.02519575225182379\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.025859164122051463,\n\ \ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.025859164122051463\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008937,\n \"\ acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008937\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1908256880733945,\n \"acc_stderr\": 0.016847676400091105,\n \"\ acc_norm\": 0.1908256880733945,\n \"acc_norm_stderr\": 0.016847676400091105\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.18518518518518517,\n \"acc_stderr\": 0.026491914727355157,\n \"\ acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.026491914727355157\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.24509803921568626,\n \"acc_stderr\": 0.03019028245350195,\n \"\ acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.03019028245350195\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.29957805907172996,\n \"acc_stderr\": 0.029818024749753095,\n \ \ \"acc_norm\": 0.29957805907172996,\n \"acc_norm_stderr\": 0.029818024749753095\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2062780269058296,\n\ \ \"acc_stderr\": 0.027157150479563824,\n \"acc_norm\": 0.2062780269058296,\n\ \ \"acc_norm_stderr\": 0.027157150479563824\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\":\ \ 0.19008264462809918,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.19008264462809918,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.17592592592592593,\n\ \ \"acc_stderr\": 0.03680918141673881,\n \"acc_norm\": 0.17592592592592593,\n\ \ \"acc_norm_stderr\": 0.03680918141673881\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\ \ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\ \ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.21359223300970873,\n \"acc_stderr\": 0.04058042015646033,\n\ \ \"acc_norm\": 0.21359223300970873,\n \"acc_norm_stderr\": 0.04058042015646033\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.029343114798094476,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.029343114798094476\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.25798212005108556,\n\ \ \"acc_stderr\": 0.01564583018834895,\n \"acc_norm\": 0.25798212005108556,\n\ \ \"acc_norm_stderr\": 0.01564583018834895\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2861271676300578,\n \"acc_stderr\": 0.02433214677913413,\n\ \ \"acc_norm\": 0.2861271676300578,\n \"acc_norm_stderr\": 0.02433214677913413\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.014422292204808843,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808843\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.02440439492808787,\n\ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.02440439492808787\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2958199356913183,\n\ \ \"acc_stderr\": 0.025922371788818798,\n \"acc_norm\": 0.2958199356913183,\n\ \ \"acc_norm_stderr\": 0.025922371788818798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023132376234543332,\n\ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023132376234543332\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24113475177304963,\n \"acc_stderr\": 0.025518731049537762,\n \ \ \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.025518731049537762\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24902216427640156,\n\ \ \"acc_stderr\": 0.01104489226404077,\n \"acc_norm\": 0.24902216427640156,\n\ \ \"acc_norm_stderr\": 0.01104489226404077\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.16544117647058823,\n \"acc_stderr\": 0.022571771025494763,\n\ \ \"acc_norm\": 0.16544117647058823,\n \"acc_norm_stderr\": 0.022571771025494763\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25163398692810457,\n \"acc_stderr\": 0.01755581809132226,\n \ \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.01755581809132226\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.18181818181818182,\n\ \ \"acc_stderr\": 0.03694284335337801,\n \"acc_norm\": 0.18181818181818182,\n\ \ \"acc_norm_stderr\": 0.03694284335337801\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.19183673469387755,\n \"acc_stderr\": 0.0252069631542254,\n\ \ \"acc_norm\": 0.19183673469387755,\n \"acc_norm_stderr\": 0.0252069631542254\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\ \ \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.22885572139303484,\n\ \ \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21686746987951808,\n\ \ \"acc_stderr\": 0.03208284450356365,\n \"acc_norm\": 0.21686746987951808,\n\ \ \"acc_norm_stderr\": 0.03208284450356365\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.03508771929824565,\n\ \ \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.03508771929824565\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2350061199510404,\n\ \ \"mc1_stderr\": 0.014843061507731608,\n \"mc2\": 0.4967512296032591,\n\ \ \"mc2_stderr\": 0.016399783558395026\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.531965272296764,\n \"acc_stderr\": 0.014023739221166384\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/shitshow123/mistral7b_sft_dpo 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_11T07_28_54.566656 path: - '**/details_harness|arc:challenge|25_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-11T07-28-54.566656.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|gsm8k|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hellaswag|10_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-11T07-28-54.566656.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-management|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-11T07-28-54.566656.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|truthfulqa:mc|0_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-11T07-28-54.566656.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_11T07_28_54.566656 path: - '**/details_harness|winogrande|5_2024-01-11T07-28-54.566656.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-11T07-28-54.566656.parquet' - config_name: results data_files: - split: 2024_01_11T07_28_54.566656 path: - results_2024-01-11T07-28-54.566656.parquet - split: latest path: - results_2024-01-11T07-28-54.566656.parquet --- # Dataset Card for Evaluation run of shitshow123/mistral7b_sft_dpo <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [shitshow123/mistral7b_sft_dpo](https://huggingface.co/shitshow123/mistral7b_sft_dpo) 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_shitshow123__mistral7b_sft_dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-11T07:28:54.566656](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__mistral7b_sft_dpo/blob/main/results_2024-01-11T07-28-54.566656.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": 0.24115205900155798, "acc_stderr": 0.030240327476101683, "acc_norm": 0.24138243110295876, "acc_norm_stderr": 0.031046885606606598, "mc1": 0.2350061199510404, "mc1_stderr": 0.014843061507731608, "mc2": 0.4967512296032591, "mc2_stderr": 0.016399783558395026 }, "harness|arc:challenge|25": { "acc": 0.21075085324232082, "acc_stderr": 0.011918271754852184, "acc_norm": 0.27559726962457337, "acc_norm_stderr": 0.013057169655761838 }, "harness|hellaswag|10": { "acc": 0.25692093208524197, "acc_stderr": 0.004360424536145123, "acc_norm": 0.255327623979287, "acc_norm_stderr": 0.004351540603988566 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.31851851851851853, "acc_stderr": 0.040247784019771096, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.040247784019771096 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.26973684210526316, "acc_stderr": 0.03611780560284898, "acc_norm": 0.26973684210526316, "acc_norm_stderr": 0.03611780560284898 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.22641509433962265, "acc_stderr": 0.025757559893106748, "acc_norm": 0.22641509433962265, "acc_norm_stderr": 0.025757559893106748 }, "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": 0.17, "acc_stderr": 0.0377525168068637, "acc_norm": 0.17, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2023121387283237, "acc_stderr": 0.030631145539198816, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.030631145539198816 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179962, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179962 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349424, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349424 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2719298245614035, "acc_stderr": 0.04185774424022056, "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.30344827586206896, "acc_stderr": 0.038312260488503336, "acc_norm": 0.30344827586206896, "acc_norm_stderr": 0.038312260488503336 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2275132275132275, "acc_stderr": 0.021591269407823792, "acc_norm": 0.2275132275132275, "acc_norm_stderr": 0.021591269407823792 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.03852273364924318, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.03852273364924318 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.03861229196653694, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2, "acc_stderr": 0.022755204959542936, "acc_norm": 0.2, "acc_norm_stderr": 0.022755204959542936 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2561576354679803, "acc_stderr": 0.030712730070982592, "acc_norm": 0.2561576354679803, "acc_norm_stderr": 0.030712730070982592 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21212121212121213, "acc_stderr": 0.031922715695483, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02962022787479048, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02962022787479048 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.26424870466321243, "acc_stderr": 0.03182155050916648, "acc_norm": 0.26424870466321243, "acc_norm_stderr": 0.03182155050916648 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2282051282051282, "acc_stderr": 0.02127839386358628, "acc_norm": 0.2282051282051282, "acc_norm_stderr": 0.02127839386358628 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.21851851851851853, "acc_stderr": 0.02519575225182379, "acc_norm": 0.21851851851851853, "acc_norm_stderr": 0.02519575225182379 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.19747899159663865, "acc_stderr": 0.025859164122051463, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.025859164122051463 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2251655629139073, "acc_stderr": 0.03410435282008937, "acc_norm": 0.2251655629139073, "acc_norm_stderr": 0.03410435282008937 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.1908256880733945, "acc_stderr": 0.016847676400091105, "acc_norm": 0.1908256880733945, "acc_norm_stderr": 0.016847676400091105 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.18518518518518517, "acc_stderr": 0.026491914727355157, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.026491914727355157 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.24509803921568626, "acc_stderr": 0.03019028245350195, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.03019028245350195 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.29957805907172996, "acc_stderr": 0.029818024749753095, "acc_norm": 0.29957805907172996, "acc_norm_stderr": 0.029818024749753095 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.2062780269058296, "acc_stderr": 0.027157150479563824, "acc_norm": 0.2062780269058296, "acc_norm_stderr": 0.027157150479563824 }, "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": 0.19008264462809918, "acc_stderr": 0.03581796951709282, "acc_norm": 0.19008264462809918, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.17592592592592593, "acc_stderr": 0.03680918141673881, "acc_norm": 0.17592592592592593, "acc_norm_stderr": 0.03680918141673881 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.25766871165644173, "acc_stderr": 0.03436150827846917, "acc_norm": 0.25766871165644173, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3392857142857143, "acc_stderr": 0.04493949068613539, "acc_norm": 0.3392857142857143, "acc_norm_stderr": 0.04493949068613539 }, "harness|hendrycksTest-management|5": { "acc": 0.21359223300970873, "acc_stderr": 0.04058042015646033, "acc_norm": 0.21359223300970873, "acc_norm_stderr": 0.04058042015646033 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2777777777777778, "acc_stderr": 0.029343114798094476, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.029343114798094476 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.25798212005108556, "acc_stderr": 0.01564583018834895, "acc_norm": 0.25798212005108556, "acc_norm_stderr": 0.01564583018834895 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2861271676300578, "acc_stderr": 0.02433214677913413, "acc_norm": 0.2861271676300578, "acc_norm_stderr": 0.02433214677913413 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808843, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808843 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.238562091503268, "acc_stderr": 0.02440439492808787, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.02440439492808787 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2958199356913183, "acc_stderr": 0.025922371788818798, "acc_norm": 0.2958199356913183, "acc_norm_stderr": 0.025922371788818798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2222222222222222, "acc_stderr": 0.023132376234543332, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.023132376234543332 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24113475177304963, "acc_stderr": 0.025518731049537762, "acc_norm": 0.24113475177304963, "acc_norm_stderr": 0.025518731049537762 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24902216427640156, "acc_stderr": 0.01104489226404077, "acc_norm": 0.24902216427640156, "acc_norm_stderr": 0.01104489226404077 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.16544117647058823, "acc_stderr": 0.022571771025494763, "acc_norm": 0.16544117647058823, "acc_norm_stderr": 0.022571771025494763 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25163398692810457, "acc_stderr": 0.01755581809132226, "acc_norm": 0.25163398692810457, "acc_norm_stderr": 0.01755581809132226 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.18181818181818182, "acc_stderr": 0.03694284335337801, "acc_norm": 0.18181818181818182, "acc_norm_stderr": 0.03694284335337801 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.19183673469387755, "acc_stderr": 0.0252069631542254, "acc_norm": 0.19183673469387755, "acc_norm_stderr": 0.0252069631542254 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772436, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772436 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-virology|5": { "acc": 0.21686746987951808, "acc_stderr": 0.03208284450356365, "acc_norm": 0.21686746987951808, "acc_norm_stderr": 0.03208284450356365 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2982456140350877, "acc_stderr": 0.03508771929824565, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.03508771929824565 }, "harness|truthfulqa:mc|0": { "mc1": 0.2350061199510404, "mc1_stderr": 0.014843061507731608, "mc2": 0.4967512296032591, "mc2_stderr": 0.016399783558395026 }, "harness|winogrande|5": { "acc": 0.531965272296764, "acc_stderr": 0.014023739221166384 }, "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]
kheopss/kheops_prompts_datasets
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 775661 num_examples: 500 download_size: 347088 dataset_size: 775661 configs: - config_name: default data_files: - split: train path: data/train-* ---
fewfsgrf/20231013test
--- license: other license_name: other license_link: LICENSE ---
indra-inc/signature_genuine_forged_combined_cedar_beng_hindi
--- dataset_info: features: - name: Label dtype: int64 - name: Anchor_Signature_Image dtype: image - name: Pos_Neg_Signature_Image dtype: image splits: - name: train num_bytes: 107817711375.17 num_examples: 214074 - name: valid num_bytes: 23059117558.956 num_examples: 45873 - name: test num_bytes: 23236559366.574 num_examples: 45873 download_size: 18518085261 dataset_size: 154113388300.7 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
apcl/so13m
--- license: bigscience-openrail-m task_categories: - text-generation pretty_name: SO13M size_categories: - 10M<n<100M --- # so13m so13m is a dataset containing 13m discussion threads from StackOverflow. The origin of the data is the StackExchange data dump from between January 2014 and December 2022. The threads cover a multitude of topics. This dataset serves as a natural language and (often) accompanying code in the domain of software engineering. Its inclusion could help downstream tasks depending on generating or understanding natural language. --- ## so13m file list - so13m.pkl -- a pickle file that is a dictionary for stackoverflow's posts with key = post id and value = stackoverflow post - so13m.json.gz -- a compressed version of json file that is a dicrionary for stackoverflow's posts with key = post id and value = stackoverflow post - stackoverflow_txtfiles.pkl -- a pickle file that is a list of id of stackoverflow's post - train.bin; val.bin -- bin files for traning and fine-tuning models --- ## so13m dataset details We provide the size of our dataset in the following table: | Config | Value | | ------- | ------- | |number of tokens | 10,495,518,108| |number of Stack Overflow Posts | 13,071,148| |megabytes after processing |16,695 | We tokenize our data using scripts provided in our [github repository](https://github.com/apcl-research/jam/blob/main/data/jam_so13m/prepare_stackoverflow.py).
Pablao0948/Chiro2
--- license: openrail ---
atmallen/conj_neg_companies_azaria_mitchell
--- dataset_info: features: - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 74376.0 num_examples: 440 - name: test num_bytes: 18594.0 num_examples: 110 download_size: 30307 dataset_size: 92970.0 --- # Dataset Card for "conj_neg_companies_azaria_mitchell" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sam-mosaic/wizard_vicuna_unfiltered_chatml
--- language: en dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 208337670.38355604 num_examples: 87708 - name: test num_bytes: 712606.6164439596 num_examples: 300 download_size: 101987390 dataset_size: 209050277.0 --- # Dataset Card for "wizard_vicuna_unfiltered_chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyrealqian/WG2022_EN_Feb8
--- license: mit --- The 2022 Winter Olympics Twitter data
Divyan-shu-Singh/sample2
--- dataset_info: features: - name: Instruction dtype: string - name: Output dtype: string splits: - name: train num_bytes: 621675 num_examples: 2000 download_size: 22057 dataset_size: 621675 configs: - config_name: default data_files: - split: train path: data/train-* ---
batalovme/oge_prob
--- dataset_info: features: - name: text dtype: string - name: solution dtype: string - name: answer dtype: string splits: - name: train num_bytes: 48579.0 num_examples: 61 - name: validation num_bytes: 48579.0 num_examples: 61 download_size: 38527 dataset_size: 97158.0 --- # Dataset Card for "oge_prob" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Shaharb/us-social-security-medicare-FAQs-test
--- license: mit ---
Suchinthana/databricks-dolly-15k-tamil
--- license: cc-by-sa-3.0 dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string splits: - name: train num_bytes: 35396494 num_examples: 15012 download_size: 12881336 dataset_size: 35396494 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - ta size_categories: - 10K<n<100K ---
tyzhu/random_letter_same_length_find_passage_train10_eval20_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: 13924 num_examples: 40 - name: validation num_bytes: 7230 num_examples: 20 download_size: 19631 dataset_size: 21154 --- # Dataset Card for "random_letter_same_length_find_passage_train10_eval20_num" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
guillaumeai/gia-ds-daliwill-210123-v02_new-135ik
--- license: mit ---
MartinKu/wikipedia_stage2_coverage_20230316
--- dataset_info: features: - name: text dtype: string - name: S_V_position sequence: int64 - name: O_C_position sequence: int64 - name: start_point_list sequence: int64 splits: - name: train num_bytes: 58992077079 num_examples: 1054477 download_size: 18229138004 dataset_size: 58992077079 --- # Dataset Card for "wikipedia_stage2_coverage_20230316" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
master-frog/frog
--- license: apache-2.0 ---
RikeshSilwal/swos
--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 458181626.25 num_examples: 3726 download_size: 456319554 dataset_size: 458181626.25 configs: - config_name: default data_files: - split: train path: data/train-* ---
pablouribe/speech2text_robustness_en
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: language dtype: string - name: accent dtype: string - name: sentence dtype: string splits: - name: train num_bytes: 17359459.0 num_examples: 30 download_size: 14749982 dataset_size: 17359459.0 --- # Dataset Card for "speech2text_robustness_en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Trix4felix/Felix_the_Cat
--- license: odbl task_categories: - image-feature-extraction tags: - art size_categories: - 100K<n<1M ---