Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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olm/olm-CC-MAIN-2017-22-sampling-ratio-0.16178770949
--- annotations_creators: - no-annotation language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: OLM May 2017 Common Crawl size_categories: - 10M<n<100M source_datasets: [] tags: - pretraining - language modelling - common crawl - web task_categories: [] task_ids: [] --- # Dataset Card for OLM May 2017 Common Crawl Cleaned and deduplicated pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from 16% of the May 2017 Common Crawl snapshot. Note: `last_modified_timestamp` was parsed from whatever a website returned in it's `Last-Modified` header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with `last_modified_timestamp`.
marup/GravesRVC200EpochsMW2
--- license: openrail ---
bigai/DiPlomat
--- language: - en license: cc-by-nc-sa-4.0 pretty_name: DiPlomat dataset_info: - config_name: CQA features: - name: text sequence: string - name: speaker sequence: string - name: gold_statement dtype: string - name: questions dtype: string - name: answer dtype: string splits: - name: train num_bytes: 19805284 num_examples: 15585 - name: validation num_bytes: 1959148 num_examples: 1559 - name: test num_bytes: 2967746 num_examples: 2338 download_size: 25566918 dataset_size: 24732178 - config_name: NLI_with_context features: - name: dialogue sequence: string - name: speaker sequence: string - name: human answer dtype: string splits: - name: train num_bytes: 2977929 num_examples: 2551 download_size: 3042193 dataset_size: 2977929 - config_name: NLI_without_context features: - name: text dtype: string - name: hypothesis dtype: string splits: - name: train num_bytes: 1095335 num_examples: 2551 download_size: 1146864 dataset_size: 1095335 - config_name: PIR_first features: - name: text sequence: string - name: speaker sequence: string - name: correct_turn_number sequence: int64 splits: - name: train num_bytes: 3442927 num_examples: 3341 - name: validation num_bytes: 161433 num_examples: 168 - name: test num_bytes: 687605 num_examples: 668 download_size: 4366468 dataset_size: 4291965 - config_name: PIR_second features: - name: text sequence: string - name: speaker sequence: string - name: correct_turn_number dtype: int64 - name: label dtype: int64 - name: choice sequence: string splits: - name: train num_bytes: 9263111 num_examples: 5188 - name: validation num_bytes: 399924 num_examples: 244 - name: test num_bytes: 1890798 num_examples: 1062 download_size: 11740508 dataset_size: 11553833 --- # DiPlomat <!-- Provide a quick summary of the dataset. --> Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations and is essential for the development of communicative social agents. In this paper, we introduce a novel challenge, **DiPlomat**, aiming at benchmarking machines’ capabilities on pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative expressions (e.g. metaphor, sarcasm) as individual tasks, **DiPlomat** provides a cohesive framework towards general pragmatic understanding. ## Dataset Details The **DiPlomat** dataset owns 4,177 data and covers a vocabulary of 48,900 words. More than that, human-annotated answers reach an amount of 6,494, hold a vocabulary size of 20,000, and cover 5 types of reasoning. Along with the dataset, we propose two tasks: **Pragmatic Identification and Reasoning (PIR)** and **Conversational Question Answering (CQA)**. Furthermore, we provide the data that we use for **zero-NLI**. - **Language(s) (NLP):** [English] - **License:** [CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)](https://creativecommons.org/licenses/by-nc-sa/4.0/) ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** [link](https://github.com/diplomat-dataset/diplomat) - **Paper:** [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030) - **Website:** [link](https://diplomat-dataset.github.io) ## 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. --> | Field | Task| | --- | --- | | ``PIR_first`` | Pragmatic Identification and Reasoning Subtask1| | ``PIR_second`` | Pragmatic Identification and Reasoning Subtask2| | ``CQA`` | Conversational Question Answering| | ``NLI_with_context`` | Zero-Shot NLI with context| | ``NLI_without_context`` | Zero-Shot NLI without context| **NOTE:** If you'd like to test on the whole PIR task, please don't change the order of PIR Subtask 1's and Subtask 2's test file's data, as both of them are deliberately arranged as the same order. ## Dataset Creation ### Source Data We leverage the data of [INTERVIEW dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts) collected by Majumder et al as our source. <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> ### Annotating Process <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> #### Step I. Automatic Selection: The extensive size of the source dataset introduces redundancy, and thus requires automatic measures to alleviate the burden of human annotation. Therefore, we employ algorithms and models to perform an initial filtering process. #### Step II. Fine-grained Annotation: We leverage Amazon Mechanical Turk to conduct detailed annotations of pragmatic turns within our dialogues. Workers participating in the annotation task are instructed to select all turns that exhibit a divergence between their literal meaning and their intended meaning. Due to the subjective nature of pragmatic reasoning, we request the workers to provide confidence scores along with reasons for their choices. #### Step III. Human Refinement: In this process, tasks for workers are formulated as multiple-choice questions. Previously collected human-annotated reasons are transformed into choices, utilizing a template format: [turn {turn_id}: {reason}]. In addition, to mitigate the impact of careless workers, we introduce a distractor choice for each gold choice. ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> ``` @inproceedings{li2023diplomat, title={DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning}, author={Hengli Li and Song-Chun Zhu and Zilong Zheng}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023} } ``` ## Dataset Card Contact If there is any problem with the dataset, please email [lihengli@stu.pku.edu.cn](mailto: 2000017754@stu.pku.edu.cn).
muhammadravi251001/indonesian-nli-and-qa
--- license: mit ---
mask-distilled-one-sec-cv12/chunk_96
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1248125580 num_examples: 245115 download_size: 1273303637 dataset_size: 1248125580 --- # Dataset Card for "chunk_96" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Babypotatotang/logo-captioning-blip
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 166009745.001 num_examples: 12911 - name: test num_bytes: 41712829.864 num_examples: 3228 download_size: 208992324 dataset_size: 207722574.86499998 --- # Dataset Card for "logo-captioning-blip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Tverous/claim-amr-test
--- dataset_info: features: - name: uid dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: reason dtype: string - name: claim_cleaned_amr dtype: string splits: - name: train num_bytes: 2143 num_examples: 2 download_size: 11051 dataset_size: 2143 --- # Dataset Card for "claim-amr-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/find_second_sent_train_50_eval_10
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 133699 num_examples: 110 - name: validation num_bytes: 9071 num_examples: 10 download_size: 80138 dataset_size: 142770 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "find_second_sent_train_50_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HPGomes/MarioVoice
--- license: openrail ---
adamjweintraut/eli5_precomputed_best
--- dataset_info: features: - name: index dtype: int64 - name: q_id dtype: string - name: question dtype: string - name: best_answer dtype: string - name: all_answers sequence: string - name: num_answers dtype: int64 - name: top_answers sequence: string - name: num_top_answers dtype: int64 - name: context dtype: string - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 5071864050.742018 num_examples: 183333 - name: test num_bytes: 633993380.6289911 num_examples: 22917 - name: validation num_bytes: 633993380.6289911 num_examples: 22917 download_size: 3932220410 dataset_size: 6339850812.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
bigscience-data/roots_indic-hi_wikisource
--- language: hi license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-hi_wikisource # wikisource_filtered - Dataset uid: `wikisource_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 2.6306 % of total - 12.7884 % of fr - 19.8886 % of indic-bn - 20.9966 % of indic-ta - 2.3478 % of ar - 4.7068 % of indic-hi - 18.0998 % of indic-te - 1.7155 % of es - 19.4800 % of indic-kn - 9.1737 % of indic-ml - 17.1771 % of indic-mr - 17.1870 % of indic-gu - 70.3687 % of indic-as - 1.0165 % of pt - 7.8642 % of indic-pa - 1.3501 % of vi - 4.9411 % of indic-or - 0.5307 % of ca - 2.3593 % of id - 1.5928 % of eu ### BigScience processing steps #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ar - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-kn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - remove_wiki_mojibake - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-as - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-pa - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-or - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: ca - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs
jorgeortizfuentes/mc4_es_cl
--- dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: train num_bytes: 33474281363.716232 num_examples: 8698099 - name: validation num_bytes: 33072762.075400714 num_examples: 8582 download_size: 15903346209 dataset_size: 33507354125.791634 --- # Dataset Card for "mc4_es_cl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Circularmachines/batch_indexing_machine_230529_002
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 156369319.0 num_examples: 720 download_size: 156379644 dataset_size: 156369319.0 --- # Dataset Card for "batch_indexing_machine_230529_002" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/autotrain-data-test-news-class
Invalid username or password.
legacy107/newsqa-retrieved-ce-chunk-100
--- 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: context dtype: string - name: question dtype: string - name: answers sequence: string - name: key dtype: string - name: labels list: - name: end sequence: int64 - name: start sequence: int64 - name: document_id dtype: int64 - name: retrieved_context dtype: string splits: - name: train num_bytes: 506360128 num_examples: 69960 - name: validation num_bytes: 31115876 num_examples: 4200 - name: test num_bytes: 30314274 num_examples: 4212 download_size: 80627687 dataset_size: 567790278 --- # Dataset Card for "newsqa-retrieved-ce-chunk-100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_pankajmathur__model_007
--- pretty_name: Evaluation run of pankajmathur/model_007 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [pankajmathur/model_007](https://huggingface.co/pankajmathur/model_007) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 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_pankajmathur__model_007\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-10-09T02:03:09.335068](https://huggingface.co/datasets/open-llm-leaderboard/details_pankajmathur__model_007/blob/main/results_2023-10-09T02-03-09.335068.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\": 0.6901502879968988,\n\ \ \"acc_stderr\": 0.031344534847114004,\n \"acc_norm\": 0.6939037892141556,\n\ \ \"acc_norm_stderr\": 0.03131458982120537,\n \"mc1\": 0.44920440636474906,\n\ \ \"mc1_stderr\": 0.01741294198611531,\n \"mc2\": 0.6312306236860621,\n\ \ \"mc2_stderr\": 0.014945471343395618\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6749146757679181,\n \"acc_stderr\": 0.01368814730972912,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393441\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6908982274447322,\n\ \ \"acc_stderr\": 0.004611787665905346,\n \"acc_norm\": 0.8765186217884884,\n\ \ \"acc_norm_stderr\": 0.003283165867631372\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n\ \ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7320754716981132,\n \"acc_stderr\": 0.027257260322494845,\n\ \ \"acc_norm\": 0.7320754716981132,\n \"acc_norm_stderr\": 0.027257260322494845\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8125,\n\ \ \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n\ \ \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\ \ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\ \ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745657,\n\ \ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745657\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.046570472605949625,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.046570472605949625\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.0407032901370707,\n\ \ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.0407032901370707\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.455026455026455,\n \"acc_stderr\": 0.025646928361049398,\n \"\ acc_norm\": 0.455026455026455,\n \"acc_norm_stderr\": 0.025646928361049398\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8129032258064516,\n \"acc_stderr\": 0.022185710092252252,\n \"\ acc_norm\": 0.8129032258064516,\n \"acc_norm_stderr\": 0.022185710092252252\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5369458128078818,\n \"acc_stderr\": 0.035083705204426656,\n \"\ acc_norm\": 0.5369458128078818,\n \"acc_norm_stderr\": 0.035083705204426656\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821678,\n \"\ acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821678\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078894,\n\ \ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078894\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7102564102564103,\n \"acc_stderr\": 0.023000628243687968,\n\ \ \"acc_norm\": 0.7102564102564103,\n \"acc_norm_stderr\": 0.023000628243687968\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7436974789915967,\n \"acc_stderr\": 0.02835962087053395,\n \ \ \"acc_norm\": 0.7436974789915967,\n \"acc_norm_stderr\": 0.02835962087053395\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\ acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8899082568807339,\n \"acc_stderr\": 0.0134199390186812,\n \"acc_norm\"\ : 0.8899082568807339,\n \"acc_norm_stderr\": 0.0134199390186812\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.03398110890294636,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.03398110890294636\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658925,\n\ \ \"acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658925\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\ \ \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.7982062780269058,\n\ \ \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445815,\n \"\ acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445815\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822582,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822582\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8748403575989783,\n\ \ \"acc_stderr\": 0.011832954239305724,\n \"acc_norm\": 0.8748403575989783,\n\ \ \"acc_norm_stderr\": 0.011832954239305724\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7630057803468208,\n \"acc_stderr\": 0.02289408248992599,\n\ \ \"acc_norm\": 0.7630057803468208,\n \"acc_norm_stderr\": 0.02289408248992599\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5497206703910614,\n\ \ \"acc_stderr\": 0.016639615236845817,\n \"acc_norm\": 0.5497206703910614,\n\ \ \"acc_norm_stderr\": 0.016639615236845817\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.02555316999182651,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.02555316999182651\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7684887459807074,\n\ \ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\ \ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.02202136610022019,\n\ \ \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.02202136610022019\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5460992907801419,\n \"acc_stderr\": 0.029700453247291477,\n \ \ \"acc_norm\": 0.5460992907801419,\n \"acc_norm_stderr\": 0.029700453247291477\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.545632333767927,\n\ \ \"acc_stderr\": 0.012716941720734818,\n \"acc_norm\": 0.545632333767927,\n\ \ \"acc_norm_stderr\": 0.012716941720734818\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.026917481224377197,\n\ \ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.026917481224377197\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.75,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.7363636363636363,\n \"acc_stderr\": 0.04220224692971987,\n\ \ \"acc_norm\": 0.7363636363636363,\n \"acc_norm_stderr\": 0.04220224692971987\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7918367346938775,\n\ \ \"acc_stderr\": 0.025991117672813296,\n \"acc_norm\": 0.7918367346938775,\n\ \ \"acc_norm_stderr\": 0.025991117672813296\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.8557213930348259,\n \"acc_stderr\": 0.02484575321230604,\n\ \ \"acc_norm\": 0.8557213930348259,\n \"acc_norm_stderr\": 0.02484575321230604\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \"acc_norm\": 0.86,\n\ \ \"acc_norm_stderr\": 0.03487350880197769\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n\ \ \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8654970760233918,\n\ \ \"acc_stderr\": 0.026168221344662297,\n \"acc_norm\": 0.8654970760233918,\n\ \ \"acc_norm_stderr\": 0.026168221344662297\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.44920440636474906,\n \"mc1_stderr\": 0.01741294198611531,\n\ \ \"mc2\": 0.6312306236860621,\n \"mc2_stderr\": 0.014945471343395618\n\ \ }\n}\n```" repo_url: https://huggingface.co/pankajmathur/model_007 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_10_09T02_03_09.335068 path: - '**/details_harness|arc:challenge|25_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hellaswag|10_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-09T02-03-09.335068.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-management|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T02-03-09.335068.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_09T02_03_09.335068 path: - '**/details_harness|truthfulqa:mc|0_2023-10-09T02-03-09.335068.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-09T02-03-09.335068.parquet' - config_name: results data_files: - split: 2023_10_09T02_03_09.335068 path: - results_2023-10-09T02-03-09.335068.parquet - split: latest path: - results_2023-10-09T02-03-09.335068.parquet --- # Dataset Card for Evaluation run of pankajmathur/model_007 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/pankajmathur/model_007 - **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 [pankajmathur/model_007](https://huggingface.co/pankajmathur/model_007) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_pankajmathur__model_007", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-10-09T02:03:09.335068](https://huggingface.co/datasets/open-llm-leaderboard/details_pankajmathur__model_007/blob/main/results_2023-10-09T02-03-09.335068.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": 0.6901502879968988, "acc_stderr": 0.031344534847114004, "acc_norm": 0.6939037892141556, "acc_norm_stderr": 0.03131458982120537, "mc1": 0.44920440636474906, "mc1_stderr": 0.01741294198611531, "mc2": 0.6312306236860621, "mc2_stderr": 0.014945471343395618 }, "harness|arc:challenge|25": { "acc": 0.6749146757679181, "acc_stderr": 0.01368814730972912, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.013250012579393441 }, "harness|hellaswag|10": { "acc": 0.6908982274447322, "acc_stderr": 0.004611787665905346, "acc_norm": 0.8765186217884884, "acc_norm_stderr": 0.003283165867631372 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8157894736842105, "acc_stderr": 0.0315469804508223, "acc_norm": 0.8157894736842105, "acc_norm_stderr": 0.0315469804508223 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7320754716981132, "acc_stderr": 0.027257260322494845, "acc_norm": 0.7320754716981132, "acc_norm_stderr": 0.027257260322494845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6680851063829787, "acc_stderr": 0.030783736757745657, "acc_norm": 0.6680851063829787, "acc_norm_stderr": 0.030783736757745657 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.046570472605949625, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.046570472605949625 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6068965517241379, "acc_stderr": 0.0407032901370707, "acc_norm": 0.6068965517241379, "acc_norm_stderr": 0.0407032901370707 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.455026455026455, "acc_stderr": 0.025646928361049398, "acc_norm": 0.455026455026455, "acc_norm_stderr": 0.025646928361049398 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677173, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677173 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8129032258064516, "acc_stderr": 0.022185710092252252, "acc_norm": 0.8129032258064516, "acc_norm_stderr": 0.022185710092252252 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5369458128078818, "acc_stderr": 0.035083705204426656, "acc_norm": 0.5369458128078818, "acc_norm_stderr": 0.035083705204426656 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706467, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706467 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821678, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821678 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9326424870466321, "acc_stderr": 0.018088393839078894, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.018088393839078894 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7102564102564103, "acc_stderr": 0.023000628243687968, "acc_norm": 0.7102564102564103, "acc_norm_stderr": 0.023000628243687968 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7436974789915967, "acc_stderr": 0.02835962087053395, "acc_norm": 0.7436974789915967, "acc_norm_stderr": 0.02835962087053395 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4768211920529801, "acc_stderr": 0.04078093859163083, "acc_norm": 0.4768211920529801, "acc_norm_stderr": 0.04078093859163083 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8899082568807339, "acc_stderr": 0.0134199390186812, "acc_norm": 0.8899082568807339, "acc_norm_stderr": 0.0134199390186812 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658925, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658925 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8734177215189873, "acc_stderr": 0.021644195727955173, "acc_norm": 0.8734177215189873, "acc_norm_stderr": 0.021644195727955173 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7982062780269058, "acc_stderr": 0.026936111912802273, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.026936111912802273 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8677685950413223, "acc_stderr": 0.030922788320445815, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.030922788320445815 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742179, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742179 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822582, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822582 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8748403575989783, "acc_stderr": 0.011832954239305724, "acc_norm": 0.8748403575989783, "acc_norm_stderr": 0.011832954239305724 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7630057803468208, "acc_stderr": 0.02289408248992599, "acc_norm": 0.7630057803468208, "acc_norm_stderr": 0.02289408248992599 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5497206703910614, "acc_stderr": 0.016639615236845817, "acc_norm": 0.5497206703910614, "acc_norm_stderr": 0.016639615236845817 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.02555316999182651, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.02555316999182651 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7684887459807074, "acc_stderr": 0.023956532766639133, "acc_norm": 0.7684887459807074, "acc_norm_stderr": 0.023956532766639133 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8055555555555556, "acc_stderr": 0.02202136610022019, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.02202136610022019 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5460992907801419, "acc_stderr": 0.029700453247291477, "acc_norm": 0.5460992907801419, "acc_norm_stderr": 0.029700453247291477 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.545632333767927, "acc_stderr": 0.012716941720734818, "acc_norm": 0.545632333767927, "acc_norm_stderr": 0.012716941720734818 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7316176470588235, "acc_stderr": 0.026917481224377197, "acc_norm": 0.7316176470588235, "acc_norm_stderr": 0.026917481224377197 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.75, "acc_stderr": 0.01751781884501444, "acc_norm": 0.75, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7918367346938775, "acc_stderr": 0.025991117672813296, "acc_norm": 0.7918367346938775, "acc_norm_stderr": 0.025991117672813296 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8557213930348259, "acc_stderr": 0.02484575321230604, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.02484575321230604 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness|truthfulqa:mc|0": { "mc1": 0.44920440636474906, "mc1_stderr": 0.01741294198611531, "mc2": 0.6312306236860621, "mc2_stderr": 0.014945471343395618 } } ``` ### 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]
PGT/artificial-unbalanced-500K
--- dataset_info: features: - name: edge_index sequence: sequence: int64 - name: y sequence: int64 - name: num_nodes dtype: int64 splits: - name: train num_bytes: 2712963616 num_examples: 499986 download_size: 398809184 dataset_size: 2712963616 --- # Dataset Card for "artificial-unbalanced-500Kb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Theon1130/ROCO_for_training
--- license: apache-2.0 ---
fshala/8samples
--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 13794666.0 num_examples: 8 download_size: 13733548 dataset_size: 13794666.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
bigscience-data/roots_indic-ml_ted_talks_iwslt
--- language: ml license: cc-by-nc-nd-4.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-ml_ted_talks_iwslt # WIT Ted Talks - Dataset uid: `ted_talks_iwslt` ### Description The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform. ### Homepage https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md ### Licensing - open license - cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks! ### Speaker Locations - Southern Europe - Italy ### Sizes - 0.0305 % of total - 0.0736 % of ar - 0.2002 % of pt - 0.0128 % of zh - 0.2236 % of vi - 0.0330 % of fr - 0.0545 % of es - 0.0122 % of en - 0.3704 % of id - 0.0373 % of indic-hi - 0.0330 % of indic-ta - 0.1393 % of indic-mr - 0.0305 % of ca - 0.1179 % of indic-ur - 0.0147 % of indic-bn - 0.0240 % of indic-ml - 0.0244 % of indic-te - 0.0503 % of indic-gu - 0.0211 % of indic-kn - 0.0274 % of eu - 0.0023 % of indic-as - 0.0001 % of indic-pa ### BigScience processing steps #### Filters applied to: ar - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: pt - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: zh - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: vi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: fr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: es - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: en - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ca - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-ur - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-kn - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-as - dedup_document - filter_remove_empty_docs #### Filters applied to: indic-pa - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300
lansinuote/gen.5.flower.book
--- dataset_info: features: - name: image dtype: image - name: cls dtype: int64 splits: - name: train num_bytes: 105735918.0 num_examples: 2000 download_size: 0 dataset_size: 105735918.0 --- # Dataset Card for "gen.5.flower.book" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-computer_security-verbal-neg-prepend
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 41847 num_examples: 100 download_size: 28432 dataset_size: 41847 --- # Dataset Card for "mmlu-computer_security-verbal-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_sst2_negative_concord
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 6956 num_examples: 48 - name: test num_bytes: 12384 num_examples: 84 - name: train num_bytes: 165604 num_examples: 1366 download_size: 95983 dataset_size: 184944 --- # Dataset Card for "MULTI_VALUE_sst2_negative_concord" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joshwe/storiesdas2
--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 45208540 num_examples: 11005 download_size: 7958523 dataset_size: 45208540 configs: - config_name: default data_files: - split: train path: data/train-* ---
AdapterOcean/med_alpaca_standardized_cluster_18_alpaca
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3182419 num_examples: 5915 download_size: 1319748 dataset_size: 3182419 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_18_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Janekmanek/Aje
--- license: agpl-3.0 ---
deboramachadoandrade/sft_dataset_rlaif
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: text dtype: string splits: - name: train num_bytes: 8091 num_examples: 5 download_size: 16567 dataset_size: 8091 configs: - config_name: default data_files: - split: train path: data/train-* ---
AWeirdDev/confucius
--- dataset_info: features: - name: chapter dtype: string - name: content dtype: string - name: translation dtype: string splits: - name: train num_bytes: 163063 num_examples: 503 download_size: 100365 dataset_size: 163063 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - translation - text-generation language: - zh tags: - chinese - analect - confucius - language pretty_name: Confucius Analects size_categories: - n<1K --- # confucius Confucius for real. See all [analects](https://ctext.org/analects). ```python from datasets import load_dataset dataset = load_dataset("AWeirdDev/confucius") ``` ## Format ```python { "chapter": "學而", # Chapter name "content": "子曰：「學而時習之，不亦說乎？…", # Content "translation": "孔子說：「經常學習，不也喜悅嗎？…" # Translated (zh-TW) } ``` <div align="center"> <figure> <img src="https://cdn-uploads.huggingface.co/production/uploads/640739e3a5e2ff2832ead08b/Ks3e4yFB5v9jN4F19rQ6v.png" alt="Confucius lore" width="300" /> <figcaption>Confucius, confused.</figcaption> </figure> </div>
ikezoe/alpha-test
--- license: afl-3.0 ---
CyberHarem/leberecht_maass_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of leberecht_maass/レーベレヒト・マース/Z1 (Azur Lane) This is the dataset of leberecht_maass/レーベレヒト・マース/Z1 (Azur Lane), containing 88 images and their tags. The core tags of this character are `purple_eyes, hair_between_eyes, bangs, hat, white_hair, beret, long_hair, side_ponytail, black_headwear`, 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 | 88 | 97.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leberecht_maass_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 88 | 59.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leberecht_maass_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 204 | 126.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leberecht_maass_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 88 | 88.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leberecht_maass_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 204 | 173.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leberecht_maass_azurlane/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/leberecht_maass_azurlane', 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 | 6 | ![](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, bare_shoulders, black_jacket, black_necktie, black_shorts, collared_shirt, looking_at_viewer, sleeveless_shirt, solo, white_shirt, blush, iron_cross, long_sleeves, midriff, navel, open_mouth, short_shorts, sitting, white_gloves, :d, black_socks, kneehighs, sidelocks, armband, black_sleeves, braid, detached_sleeves, off_shoulder, shadow, simple_background, white_belt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_jacket | black_necktie | black_shorts | collared_shirt | looking_at_viewer | sleeveless_shirt | solo | white_shirt | blush | iron_cross | long_sleeves | midriff | navel | open_mouth | short_shorts | sitting | white_gloves | :d | black_socks | kneehighs | sidelocks | armband | black_sleeves | braid | detached_sleeves | off_shoulder | shadow | simple_background | white_belt | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:----------------|:---------------|:-----------------|:--------------------|:-------------------|:-------|:--------------|:--------|:-------------|:---------------|:----------|:--------|:-------------|:---------------|:----------|:---------------|:-----|:--------------|:------------|:------------|:----------|:----------------|:--------|:-------------------|:---------------|:---------|:--------------------|:-------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
fathyshalab/massive_social-de
--- dataset_info: features: - name: id dtype: string - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 28548 num_examples: 391 - name: validation num_bytes: 4886 num_examples: 68 - name: test num_bytes: 7331 num_examples: 106 download_size: 25046 dataset_size: 40765 --- # Dataset Card for "massive_social-de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_TheBloke__GPlatty-30B-SuperHOT-8K-fp16
--- pretty_name: Evaluation run of TheBloke/GPlatty-30B-SuperHOT-8K-fp16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/GPlatty-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/GPlatty-30B-SuperHOT-8K-fp16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 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_TheBloke__GPlatty-30B-SuperHOT-8K-fp16\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-08-01T15:51:23.628970](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__GPlatty-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-01T15%3A51%3A23.628970.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\": 0.24941704039386783,\n\ \ \"acc_stderr\": 0.0314384194357432,\n \"acc_norm\": 0.2512238671780757,\n\ \ \"acc_norm_stderr\": 0.03145763914734606,\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.46272712607124966,\n\ \ \"mc2_stderr\": 0.016702158477967525\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.22696245733788395,\n \"acc_stderr\": 0.012240491536132868,\n\ \ \"acc_norm\": 0.2832764505119454,\n \"acc_norm_stderr\": 0.013167478735134576\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.28450507866958774,\n\ \ \"acc_stderr\": 0.004502563079349398,\n \"acc_norm\": 0.33479386576379205,\n\ \ \"acc_norm_stderr\": 0.0047095388649163105\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2518518518518518,\n\ \ \"acc_stderr\": 0.037498507091740206,\n \"acc_norm\": 0.2518518518518518,\n\ \ \"acc_norm_stderr\": 0.037498507091740206\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n\ \ \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\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\": 0.02575755989310675,\n\ \ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.02575755989310675\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\ : 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808777,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808777\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.2851063829787234,\n \"acc_stderr\": 0.029513196625539355,\n\ \ \"acc_norm\": 0.2851063829787234,\n \"acc_norm_stderr\": 0.029513196625539355\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.03835153954399421,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.03835153954399421\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.037245636197746325,\n\ \ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.037245636197746325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.21428571428571427,\n \"acc_stderr\": 0.02113285918275444,\n \"\ acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.02113285918275444\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.2645161290322581,\n \"acc_stderr\": 0.025091892378859275,\n \"\ acc_norm\": 0.2645161290322581,\n \"acc_norm_stderr\": 0.025091892378859275\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.22167487684729065,\n \"acc_stderr\": 0.029225575892489624,\n \"\ acc_norm\": 0.22167487684729065,\n \"acc_norm_stderr\": 0.029225575892489624\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\"\ : 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.21717171717171718,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.21717171717171718,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.18134715025906736,\n \"acc_stderr\": 0.02780703236068609,\n\ \ \"acc_norm\": 0.18134715025906736,\n \"acc_norm_stderr\": 0.02780703236068609\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132354,\n\ \ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132354\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2037037037037037,\n \"acc_stderr\": 0.024556172219141265,\n \ \ \"acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.024556172219141265\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.22268907563025211,\n \"acc_stderr\": 0.027025433498882385,\n\ \ \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.027025433498882385\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.19205298013245034,\n \"acc_stderr\": 0.032162984205936135,\n \"\ acc_norm\": 0.19205298013245034,\n \"acc_norm_stderr\": 0.032162984205936135\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.22568807339449543,\n \"acc_stderr\": 0.017923087667803053,\n \"\ acc_norm\": 0.22568807339449543,\n \"acc_norm_stderr\": 0.017923087667803053\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.26851851851851855,\n \"acc_stderr\": 0.030225226160012397,\n \"\ acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.030225226160012397\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\": 0.2869198312236287,\n \"acc_stderr\": 0.029443773022594693,\n \ \ \"acc_norm\": 0.2869198312236287,\n \"acc_norm_stderr\": 0.029443773022594693\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2914798206278027,\n\ \ \"acc_stderr\": 0.030500283176545902,\n \"acc_norm\": 0.2914798206278027,\n\ \ \"acc_norm_stderr\": 0.030500283176545902\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847835,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847835\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.26851851851851855,\n\ \ \"acc_stderr\": 0.04284467968052191,\n \"acc_norm\": 0.26851851851851855,\n\ \ \"acc_norm_stderr\": 0.04284467968052191\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.03487825168497892,\n\ \ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.03487825168497892\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2863247863247863,\n\ \ \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.2863247863247863,\n\ \ \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3065134099616858,\n\ \ \"acc_stderr\": 0.01648695289304151,\n \"acc_norm\": 0.3065134099616858,\n\ \ \"acc_norm_stderr\": 0.01648695289304151\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.23121387283236994,\n \"acc_stderr\": 0.022698657167855716,\n\ \ \"acc_norm\": 0.23121387283236994,\n \"acc_norm_stderr\": 0.022698657167855716\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\ \ \"acc_stderr\": 0.014400296429225629,\n \"acc_norm\": 0.24581005586592178,\n\ \ \"acc_norm_stderr\": 0.014400296429225629\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.3104575163398693,\n \"acc_stderr\": 0.026493033225145894,\n\ \ \"acc_norm\": 0.3104575163398693,\n \"acc_norm_stderr\": 0.026493033225145894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.27009646302250806,\n\ \ \"acc_stderr\": 0.025218040373410612,\n \"acc_norm\": 0.27009646302250806,\n\ \ \"acc_norm_stderr\": 0.025218040373410612\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2345679012345679,\n \"acc_stderr\": 0.023576881744005716,\n\ \ \"acc_norm\": 0.2345679012345679,\n \"acc_norm_stderr\": 0.023576881744005716\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.25177304964539005,\n \"acc_stderr\": 0.025892151156709405,\n \ \ \"acc_norm\": 0.25177304964539005,\n \"acc_norm_stderr\": 0.025892151156709405\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.26597131681877445,\n\ \ \"acc_stderr\": 0.011285033165551274,\n \"acc_norm\": 0.26597131681877445,\n\ \ \"acc_norm_stderr\": 0.011285033165551274\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.02315746830855934,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.02315746830855934\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.27450980392156865,\n \"acc_stderr\": 0.018054027458815198,\n \ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.018054027458815198\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.03831305140884601,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.03831305140884601\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.20816326530612245,\n \"acc_stderr\": 0.025991117672813292,\n\ \ \"acc_norm\": 0.20816326530612245,\n \"acc_norm_stderr\": 0.025991117672813292\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.26865671641791045,\n\ \ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.26865671641791045,\n\ \ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.29239766081871343,\n \"acc_stderr\": 0.034886477134579215,\n\ \ \"acc_norm\": 0.29239766081871343,\n \"acc_norm_stderr\": 0.034886477134579215\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.46272712607124966,\n\ \ \"mc2_stderr\": 0.016702158477967525\n }\n}\n```" repo_url: https://huggingface.co/TheBloke/GPlatty-30B-SuperHOT-8K-fp16 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_08_01T15_51_23.628970 path: - '**/details_harness|arc:challenge|25_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hellaswag|10_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-01T15:51:23.628970.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-management|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T15:51:23.628970.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_01T15_51_23.628970 path: - '**/details_harness|truthfulqa:mc|0_2023-08-01T15:51:23.628970.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-01T15:51:23.628970.parquet' - config_name: results data_files: - split: 2023_08_01T15_51_23.628970 path: - results_2023-08-01T15:51:23.628970.parquet - split: latest path: - results_2023-08-01T15:51:23.628970.parquet --- # Dataset Card for Evaluation run of TheBloke/GPlatty-30B-SuperHOT-8K-fp16 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/GPlatty-30B-SuperHOT-8K-fp16 - **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 [TheBloke/GPlatty-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/GPlatty-30B-SuperHOT-8K-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_TheBloke__GPlatty-30B-SuperHOT-8K-fp16", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-08-01T15:51:23.628970](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__GPlatty-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-01T15%3A51%3A23.628970.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": 0.24941704039386783, "acc_stderr": 0.0314384194357432, "acc_norm": 0.2512238671780757, "acc_norm_stderr": 0.03145763914734606, "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.46272712607124966, "mc2_stderr": 0.016702158477967525 }, "harness|arc:challenge|25": { "acc": 0.22696245733788395, "acc_stderr": 0.012240491536132868, "acc_norm": 0.2832764505119454, "acc_norm_stderr": 0.013167478735134576 }, "harness|hellaswag|10": { "acc": 0.28450507866958774, "acc_stderr": 0.004502563079349398, "acc_norm": 0.33479386576379205, "acc_norm_stderr": 0.0047095388649163105 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2518518518518518, "acc_stderr": 0.037498507091740206, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.037498507091740206 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19736842105263158, "acc_stderr": 0.03238981601699397, "acc_norm": 0.19736842105263158, "acc_norm_stderr": 0.03238981601699397 }, "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": 0.02575755989310675, "acc_norm": 0.22641509433962265, "acc_norm_stderr": 0.02575755989310675 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2361111111111111, "acc_stderr": 0.03551446610810826, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.19, "acc_stderr": 0.039427724440366234, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808777, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808777 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2851063829787234, "acc_stderr": 0.029513196625539355, "acc_norm": 0.2851063829787234, "acc_norm_stderr": 0.029513196625539355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03835153954399421, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03835153954399421 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.27586206896551724, "acc_stderr": 0.037245636197746325, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.037245636197746325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.21428571428571427, "acc_stderr": 0.02113285918275444, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.02113285918275444 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2645161290322581, "acc_stderr": 0.025091892378859275, "acc_norm": 0.2645161290322581, "acc_norm_stderr": 0.025091892378859275 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.22167487684729065, "acc_stderr": 0.029225575892489624, "acc_norm": 0.22167487684729065, "acc_norm_stderr": 0.029225575892489624 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21717171717171718, "acc_stderr": 0.029376616484945633, "acc_norm": 0.21717171717171718, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.18134715025906736, "acc_stderr": 0.02780703236068609, "acc_norm": 0.18134715025906736, "acc_norm_stderr": 0.02780703236068609 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2717948717948718, "acc_stderr": 0.022556551010132354, "acc_norm": 0.2717948717948718, "acc_norm_stderr": 0.022556551010132354 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2037037037037037, "acc_stderr": 0.024556172219141265, "acc_norm": 0.2037037037037037, "acc_norm_stderr": 0.024556172219141265 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.22268907563025211, "acc_stderr": 0.027025433498882385, "acc_norm": 0.22268907563025211, "acc_norm_stderr": 0.027025433498882385 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.19205298013245034, "acc_stderr": 0.032162984205936135, "acc_norm": 0.19205298013245034, "acc_norm_stderr": 0.032162984205936135 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22568807339449543, "acc_stderr": 0.017923087667803053, "acc_norm": 0.22568807339449543, "acc_norm_stderr": 0.017923087667803053 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.26851851851851855, "acc_stderr": 0.030225226160012397, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.030225226160012397 }, "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": 0.2869198312236287, "acc_stderr": 0.029443773022594693, "acc_norm": 0.2869198312236287, "acc_norm_stderr": 0.029443773022594693 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.2914798206278027, "acc_stderr": 0.030500283176545902, "acc_norm": 0.2914798206278027, "acc_norm_stderr": 0.030500283176545902 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847835, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847835 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04065578140908705, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04065578140908705 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.26851851851851855, "acc_stderr": 0.04284467968052191, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.04284467968052191 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.26993865030674846, "acc_stderr": 0.03487825168497892, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.03487825168497892 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness|hendrycksTest-management|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2863247863247863, "acc_stderr": 0.029614323690456648, "acc_norm": 0.2863247863247863, "acc_norm_stderr": 0.029614323690456648 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.3065134099616858, "acc_stderr": 0.01648695289304151, "acc_norm": 0.3065134099616858, "acc_norm_stderr": 0.01648695289304151 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.23121387283236994, "acc_stderr": 0.022698657167855716, "acc_norm": 0.23121387283236994, "acc_norm_stderr": 0.022698657167855716 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225629, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225629 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.3104575163398693, "acc_stderr": 0.026493033225145894, "acc_norm": 0.3104575163398693, "acc_norm_stderr": 0.026493033225145894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.27009646302250806, "acc_stderr": 0.025218040373410612, "acc_norm": 0.27009646302250806, "acc_norm_stderr": 0.025218040373410612 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2345679012345679, "acc_stderr": 0.023576881744005716, "acc_norm": 0.2345679012345679, "acc_norm_stderr": 0.023576881744005716 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.25177304964539005, "acc_stderr": 0.025892151156709405, "acc_norm": 0.25177304964539005, "acc_norm_stderr": 0.025892151156709405 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.26597131681877445, "acc_stderr": 0.011285033165551274, "acc_norm": 0.26597131681877445, "acc_norm_stderr": 0.011285033165551274 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.17647058823529413, "acc_stderr": 0.02315746830855934, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.02315746830855934 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.27450980392156865, "acc_stderr": 0.018054027458815198, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.018054027458815198 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2, "acc_stderr": 0.03831305140884601, "acc_norm": 0.2, "acc_norm_stderr": 0.03831305140884601 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.20816326530612245, "acc_stderr": 0.025991117672813292, "acc_norm": 0.20816326530612245, "acc_norm_stderr": 0.025991117672813292 }, "harness|hendrycksTest-sociology|5": { "acc": 0.26865671641791045, "acc_stderr": 0.03134328358208954, "acc_norm": 0.26865671641791045, "acc_norm_stderr": 0.03134328358208954 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.29239766081871343, "acc_stderr": 0.034886477134579215, "acc_norm": 0.29239766081871343, "acc_norm_stderr": 0.034886477134579215 }, "harness|truthfulqa:mc|0": { "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.46272712607124966, "mc2_stderr": 0.016702158477967525 } } ``` ### 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]
BangumiBase/mondaijitachigaisekaikarakurusoudesuyo
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Mondaiji-tachi Ga Isekai Kara Kuru Sou Desu Yo? This is the image base of bangumi Mondaiji-tachi ga Isekai kara Kuru Sou desu yo?, we detected 19 characters, 1545 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 61 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 26 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 37 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 55 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 79 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 175 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 151 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 20 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 41 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 19 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 9 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 132 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 68 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 21 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 221 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 35 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 13 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 22 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | noise | 360 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
yuvalkirstain/beautiful_interesting_spectacular_photo_model_30000_with_generated_captions
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: pclean dtype: float64 - name: generated_caption dtype: string splits: - name: train num_bytes: 120069364.0 num_examples: 228 download_size: 120060100 dataset_size: 120069364.0 --- # Dataset Card for "beautiful_interesting_spectacular_photo_model_30000_with_generated_captions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-a06941db-151a-4b58-9601-c0f67c181a54-6665
--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: binary_classification model: autoevaluate/binary-classification metrics: ['matthews_correlation'] dataset_name: glue dataset_config: sst2 dataset_split: validation col_mapping: text: sentence target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Binary Text Classification * Model: autoevaluate/binary-classification * Dataset: glue * Config: sst2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
CarperAI/pickapic_v1_no_images_training_sfw
--- license: mit --- ### Dataset Information This is an SFW sanitized prompt only version of the [PickAPic dataset](https://huggingface.co/datasets/yuvalkirstain/pickapic_v1), with 335,000 prompts and image URLs. ### Citation Information If you find this work useful, please cite: ```bibtex @inproceedings{Kirstain2023PickaPicAO, title={Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation}, author={Yuval Kirstain and Adam Polyak and Uriel Singer and Shahbuland Matiana and Joe Penna and Omer Levy}, year={2023} } ``` ### LICENSE MIT License Copyright (c) 2021 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
BangumiBase/zetsuennotempest
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Zetsuen No Tempest This is the image base of bangumi Zetsuen no Tempest, we detected 16 characters, 2070 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 75 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 405 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 19 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 435 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 15 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 35 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 40 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 124 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 10 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 28 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 402 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 88 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 55 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 40 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 175 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | noise | 124 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
Murple/mmcrsc
--- annotations_creators: - expert-generated language: - zh language_creators: - crowdsourced license: - cc-by-nc-nd-4.0 multilinguality: - monolingual pretty_name: MAGICDATA_Mandarin_Chinese_Read_Speech_Corpus size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for MMCRSC ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MAGICDATA Mandarin Chinese Read Speech Corpus](https://openslr.org/68/) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use. The contents and the corresponding descriptions of the corpus include: The corpus contains 755 hours of speech data, which is mostly mobile recorded data. 1080 speakers from different accent areas in China are invited to participate in the recording. The sentence transcription accuracy is higher than 98%. Recordings are conducted in a quiet indoor environment. The database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2. Detail information such as speech data coding and speaker information is preserved in the metadata file. The domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc. Segmented transcripts are also provided. The corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use. The corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via business@magicdatatech.com for more details. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages zh-CN ## Dataset Structure ### Data Instances ```json { 'file': '14_3466_20170826171404.wav', 'audio': { 'path': '14_3466_20170826171404.wav', 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 16000 }, 'text': '请搜索我附近的超市', 'speaker_id': 143466, 'id': '14_3466_20170826171404.wav' } ``` ### Data Fields - file: A path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. ### 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 Please cite the corpus as "Magic Data Technology Co., Ltd., "http://www.imagicdatatech.com/index.php/home/dataopensource/data_info/id/101", 05/2019".
thegovind/llamav2-instruct-miyagi
--- license: openrail task_categories: - conversational language: - en tags: - finance --- # 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]
mse5357/amazon-shoe-reviews
--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16847665.2 num_examples: 90000 - name: test num_bytes: 1871962.8 num_examples: 10000 download_size: 11141108 dataset_size: 18719628.0 --- # Dataset Card for "amazon-shoe-reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora_mlp-65B-HF
--- pretty_name: Evaluation run of TheBloke/gpt4-alpaca-lora_mlp-65B-HF dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/gpt4-alpaca-lora_mlp-65B-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora_mlp-65B-HF)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora_mlp-65B-HF\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-23T07:45:08.272902](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora_mlp-65B-HF/blob/main/results_2023-10-23T07-45-08.272902.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\": 0.015625,\n \ \ \"em_stderr\": 0.0012700767094662763,\n \"f1\": 0.09636115771812082,\n\ \ \"f1_stderr\": 0.0019819425315034905,\n \"acc\": 0.5447099133363212,\n\ \ \"acc_stderr\": 0.011752408531897077\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.015625,\n \"em_stderr\": 0.0012700767094662763,\n \ \ \"f1\": 0.09636115771812082,\n \"f1_stderr\": 0.0019819425315034905\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.28278999241849884,\n \ \ \"acc_stderr\": 0.01240502041787362\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.011099796645920533\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheBloke/gpt4-alpaca-lora_mlp-65B-HF 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_25T19_53_38.948593 path: - '**/details_harness|arc:challenge|25_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-25T19:53:38.948593.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_23T07_45_08.272902 path: - '**/details_harness|drop|3_2023-10-23T07-45-08.272902.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-23T07-45-08.272902.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_23T07_45_08.272902 path: - '**/details_harness|gsm8k|5_2023-10-23T07-45-08.272902.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-23T07-45-08.272902.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hellaswag|10_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-25T19:53:38.948593.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-management|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T19:53:38.948593.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_25T19_53_38.948593 path: - '**/details_harness|truthfulqa:mc|0_2023-07-25T19:53:38.948593.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-25T19:53:38.948593.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_23T07_45_08.272902 path: - '**/details_harness|winogrande|5_2023-10-23T07-45-08.272902.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-23T07-45-08.272902.parquet' - config_name: results data_files: - split: 2023_07_25T19_53_38.948593 path: - results_2023-07-25T19:53:38.948593.parquet - split: 2023_10_23T07_45_08.272902 path: - results_2023-10-23T07-45-08.272902.parquet - split: latest path: - results_2023-10-23T07-45-08.272902.parquet --- # Dataset Card for Evaluation run of TheBloke/gpt4-alpaca-lora_mlp-65B-HF ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/gpt4-alpaca-lora_mlp-65B-HF - **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 [TheBloke/gpt4-alpaca-lora_mlp-65B-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora_mlp-65B-HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora_mlp-65B-HF", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T07:45:08.272902](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora_mlp-65B-HF/blob/main/results_2023-10-23T07-45-08.272902.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": 0.015625, "em_stderr": 0.0012700767094662763, "f1": 0.09636115771812082, "f1_stderr": 0.0019819425315034905, "acc": 0.5447099133363212, "acc_stderr": 0.011752408531897077 }, "harness|drop|3": { "em": 0.015625, "em_stderr": 0.0012700767094662763, "f1": 0.09636115771812082, "f1_stderr": 0.0019819425315034905 }, "harness|gsm8k|5": { "acc": 0.28278999241849884, "acc_stderr": 0.01240502041787362 }, "harness|winogrande|5": { "acc": 0.8066298342541437, "acc_stderr": 0.011099796645920533 } } ``` ### 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]
Ehraim/PlSequentialLearner
--- license: apache-2.0 ---
freshpearYoon/vr_train_free_9
--- 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: 7229833194 num_examples: 10000 download_size: 1168147629 dataset_size: 7229833194 configs: - config_name: default data_files: - split: train path: data/train-* ---
Yorai/detect-waste
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': metals_and_plastic '1': other '2': non_recyclable '3': glass '4': paper '5': bio '6': unknown splits: - name: train num_bytes: 14799255261.307 num_examples: 3647 - name: test num_bytes: 3009820376.0 num_examples: 915 download_size: 3002391644 dataset_size: 17809075637.307 language: - en tags: - climate pretty_name: detect-waste size_categories: - 1K<n<10K --- # Dataset Card for detect-waste ## Dataset Description - **Homepage: https://github.com/wimlds-trojmiasto/detect-waste** ### Dataset Summary AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments. You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment. Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled. The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good. ### Supported Tasks and Leaderboards Object Detection ### Languages English ### Data Fields https://github.com/wimlds-trojmiasto/detect-waste/tree/main/annotations ## Dataset Creation The images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed ### Personal and Sensitive Information **BEWARE** This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/ ## Considerations for Using the Data ### Licensing Information https://raw.githubusercontent.com/wimlds-trojmiasto/detect-waste/main/LICENSE
CyberHarem/stechkin_girlsfrontline
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of stechkin/スチェッキン/斯捷奇金 (Girls' Frontline) This is the dataset of stechkin/スチェッキン/斯捷奇金 (Girls' Frontline), containing 33 images and their tags. The core tags of this character are `blue_eyes, long_hair, black_hair, bangs, breasts, very_long_hair, ahoge, hat, medium_breasts, white_headwear`, 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 | 33 | 42.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stechkin_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 33 | 23.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stechkin_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 82 | 49.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stechkin_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 33 | 36.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stechkin_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 82 | 68.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stechkin_girlsfrontline/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/stechkin_girlsfrontline', 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 | 33 | ![](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, looking_at_viewer, solo, blush, gun, simple_background, holding, dress, open_mouth, white_background, full_body, official_alternate_costume, smile, bare_shoulders, elbow_gloves, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | blush | gun | simple_background | holding | dress | open_mouth | white_background | full_body | official_alternate_costume | smile | bare_shoulders | elbow_gloves | thighhighs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:------|:--------------------|:----------|:--------|:-------------|:-------------------|:------------|:-----------------------------|:--------|:-----------------|:---------------|:-------------| | 0 | 33 | ![](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 |
anonymouse03052002/kan-ds-mini
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string - name: text_length dtype: int64 splits: - name: train num_bytes: 49353.04 num_examples: 88 - name: validation num_bytes: 5608.3 num_examples: 10 download_size: 0 dataset_size: 54961.340000000004 --- # Dataset Card for "kan-ds-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ovelozz/nicdalord
--- license: openrail ---
AlekseyKorshuk/davinci-pairwise-all
--- dataset_info: features: - name: chosen dtype: string - name: rejected sequence: string - name: prompt dtype: string - name: api_prompt dtype: string splits: - name: train num_bytes: 1866932601 num_examples: 47954 download_size: 820468098 dataset_size: 1866932601 --- # Dataset Card for "davinci-pairwise-all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mdeputy/timelapse_embryonic_lung
--- dataset_info: features: - name: ground truth mask sequence: sequence: sequence: float32 - name: normalized brightfield image sequence: sequence: sequence: float32 splits: - name: timelapse_embryonic_lung num_bytes: 755220840 num_examples: 30 download_size: 35007127 dataset_size: 755220840 configs: - config_name: default data_files: - split: timelapse_embryonic_lung path: data/timelapse_embryonic_lung-* ---
TinyPixel/tiny-codes
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3766347321 num_examples: 1632309 download_size: 856000484 dataset_size: 3766347321 --- # Dataset Card for "tiny-codes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nielsr/realworldqa
--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 678377348.0 num_examples: 765 download_size: 678335845 dataset_size: 678377348.0 configs: - config_name: default data_files: - split: train path: data/train-* --- Dataset from here: https://x.ai/blog/grok-1.5v Script used to push the dataset: ```python import json from datasets import load_dataset # load annotations with open('realworldqa/annotations.json') as f: data = json.load(f) # create metadata for the dataset metadata = [{"file_name": i["image"], "question": i["question"], "answer": i["answer"]} for i in data] # write metadata to json lines file with open('realworldqa/images/metadata.jsonl', 'w') as outfile: for entry in metadata: json.dump(entry, outfile) outfile.write('\n') # load image dataset dataset = load_dataset("imagefolder", data_dir="realworldqa/images") # push to hub! dataset.push_to_hub("xai-org/realworldqa") ``` Based on the guide here: https://huggingface.co/docs/datasets/en/image_dataset#object-detection
tianyang/repo_dedup_sep2023
--- dataset_info: features: - name: repo_name dtype: string - name: language dtype: string - name: created_at dtype: timestamp[ns] - name: license dtype: string - name: description dtype: string - name: stars dtype: int64 - name: forks dtype: int64 - name: url dtype: string - name: repo_code list: - name: code dtype: string - name: path dtype: string - name: repo_name dtype: string - name: size dtype: int64 splits: - name: train num_bytes: 219555370 num_examples: 1474 download_size: 71458940 dataset_size: 219555370 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "repo_dedup_sep2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jamessyx/PathCap
--- license: cc-by-nc-2.0 extra_ated_heading: "Access PathCap on Hugging Face" extra_gated_prompt: "Requests will be processed in 1 business days." extra_gated_fields: Country: country Affiliation: text Specific date: date_picker I want to use this dataset for: type: select options: - Research - Education - label: Other value: other I agree to use this dataset for non-commercial use ONLY: checkbox I agree to give appropriate cite for the source data: checkbox --- This is the official Hugging Face repo for **PathCap** dataset. ## Citation ``` @article{sun2023pathasst, title={Pathasst: Redefining pathology through generative foundation ai assistant for pathology}, author={Sun, Yuxuan and Zhu, Chenglu and Zheng, Sunyi and Zhang, Kai and Shui, Zhongyi and Yu, Xiaoxuan and Zhao, Yizhi and Li, Honglin and Zhang, Yunlong and Zhao, Ruojia and others}, journal={arXiv preprint arXiv:2305.15072}, year={2023} } ```
alpayariyak/prm800k
--- language: - en --- [From OpenAI](https://github.com/openai/prm800k) # PRM800K: A Process Supervision Dataset - [Blog Post](https://openai.com/research/improving-mathematical-reasoning-with-process-supervision) This repository accompanies the paper [Let's Verify Step by Step](https://openai.com/research/improving-mathematical-reasoning-with-process-supervision) and presents the PRM800K dataset introduced there. PRM800K is a process supervision dataset containing 800,000 step-level correctness labels for model-generated solutions to problems from the [MATH](https://github.com/hendrycks/math) dataset. More information on PRM800K and the project can be found in the paper. We are releasing the raw labels as well as the instructions we gave labelers during phase 1 and phase 2 of the project. Example labels can be seen in the image below. <p align="center"> <img src="https://github.com/openai/prm800k/blob/main/prm800k/img/interface.png?raw=true" height="300"/> </p> ## Data The data contains our labels formatted as newline-delimited lists of `json` data. Each line represents 1 full solution sample and can contain many step-level labels. Here is one annotated line: ```javascript { // UUID representing a particular labeler. "labeler": "340d89bc-f5b7-45e9-b272-909ba68ee363", // The timestamp this trajectory was submitted. "timestamp": "2023-01-22T04:34:27.052924", // In phase 2, we split our data collection into generations, using our best // PRM so far to pick which solutions to score in the next generation. // In phase 1, this value should always be null. "generation": 9, // In each generation, we reserve some solutions for quality control. We serve // these solutions to every labeler, and check that they agree with our // gold labels. "is_quality_control_question": false, // generation -1 was reserved for a set of 30 questions we served every // labeler in order to screen for base task performance. "is_initial_screening_question": false, // Metadata about the question this solution is a response to. "question": { // Text of the MATH problem being solved. "problem": "What is the greatest common factor of $20 !$ and $200,\\!000$? (Reminder: If $n$ is a positive integer, then $n!$ stands for the product $1\\cdot 2\\cdot 3\\cdot \\cdots \\cdot (n-1)\\cdot n$.)", // Ground truth solution from the MATH dataset. "ground_truth_solution": "The prime factorization of $200,000$ is $2^6 \\cdot 5^5$. Then count the number of factors of $2$ and $5$ in $20!$. Since there are $10$ even numbers, there are more than $6$ factors of $2$. There are $4$ factors of $5$. So the greatest common factor is $2^6 \\cdot 5^4=\\boxed{40,\\!000}$.", // Ground truth answer. "ground_truth_answer": "40,\\!000", // The full steps of the model-generated solution. This is only set for // phase 2 where we pre-generated all solutions that we labeled. "pre_generated_steps": [ "I want to find the largest positive integer that divides both $20 !$ and $200,\\!000$ evenly.", "One way to do this is to factor both numbers into prime factors and look for the common ones.", "I know that $200,\\!000 = 2^5\\cdot 10^4 = 2^9\\cdot 5^4$.", "To find the prime factorization of $20 !$, I can use the fact that it is the product of all the positive integers from $1$ to $20$.", "For each prime number $p$ between $1$ and $20$, I can count how many multiples of $p$ are in that range.", "For example, there are $10$ multiples of $2$ between $1$ and $20$, namely $2, 4, 6, \\dots, 20$.", "But there are also $5$ multiples of $4$, which is $2^2$, and $2$ multiples of $8$, which is $2^3$, and $1$ multiple of $16$, which is $2^4$.", "So, the total power of $2$ in $20 !$ is $10 + 5 + 2 + 1 = 18$.", "Similarly, there are $4$ multiples of $5$, namely $5, 10, 15, 20$, so the power of $5$ in $20 !$ is $4$.", "There are $6$ multiples of $3$, namely $3, 6, 9, \\dots, 18$, but there are also $2$ multiples of $9$, which is $3^2$, so the power of $3$ in $20 !$ is $6 + 2 = 8$.", "There are $2$ multiples of $7$, namely $7$ and $14$, so the power of $7$ in $20 !$ is $2$.", "There are $1$ multiple of each of the other prime numbers $11, 13, 17$, and $19$, so the powers of those primes in $20 !$ are $1$ each.", "Therefore, the prime factorization of $20 !$ is $2^{18}\\cdot 3^8\\cdot 5^4\\cdot 7^2\\cdot 11\\cdot 13\\cdot 17\\cdot 19$.", "To find the greatest common factor of $20 !$ and $200,\\!000$, I need to take the lowest power of each common prime factor.", "The only common prime factors are $2$ and $5$, and the lowest powers are $9$ and $4$, respectively.", "So, the greatest common factor is $2^9\\cdot 5^4 = 512\\cdot 625 = 320,\\!000$.\n\n# Answer\n\n320,000" ], // The answer given as the end of the pre-generated solution. We can see // this solution is incorrect. "pre_generated_answer": "320,000", // The score given by our PRM to this solution. This one isn't rated very // highly! "pre_generated_verifier_score": 0.010779580529581414 }, // The human data we collected for this solution, containing correctness // labels for each step of the solution. "label": { "steps": [ // Each object here represents labels for one step of the solution. { // Each step will contain one or more completions. These are candidate // steps the model output at this step of the trajectory. In phase 1, // we frequently collect labels on alternative steps, while in phase 2 // we only collect labels on alternative steps after the first mistake, // so most completions lists are singletons. "completions": [ { // Text of the step. "text": "I want to find the largest positive integer that divides both $20 !$ and $200,\\!000$ evenly.", // The rating the labeler gave to this step. Can be -1, 0, or +1. // This is a 0 because it isn't incorrect, but it does not make // any progress. "rating": 0, // The labeler can flag steps that they don't know how to label. // This is rarely used. "flagged": null } ], // In phase 1, if all completions were rated -1, we allowed labelers to // write their own +1 step. This is null for all steps in phase 2. "human_completion": null, // The index of the completion "chosen" at this step, or null if the // human_completion was used. You can reconstruct the solution // trajectory like: // [ // step["human_completion"] if step["chosen_completion"] is None // else step["completions"][step["chosen_completion"]]["text"] // for step in labeled_solution["label"]["steps"] // ] "chosen_completion": 0 }, { "completions": [ { "text": "One way to do this is to factor both numbers into prime factors and look for the common ones.", "rating": 0, "flagged": null } ], "human_completion": null, "chosen_completion": 0 }, { // Some steps contain multiple alternative completions, and each one // gets a rating. "completions": [ { "text": "I know that $200,\\!000 = 2^5\\cdot 10^4 = 2^9\\cdot 5^4$.", "rating": -1, "flagged": null }, { "text": "To factor $20 !$, I can use the fact that every factorial is a multiple of every number less than or equal to it.", "rating": 0, "flagged": false }, { "text": "I can use a factor tree to find the prime factors of $200,\\!000$: $200,\\!000 = 2^5\\cdot 10^4 = 2^5\\cdot 2^4\\cdot 5^4 = 2^9\\cdot 5^4$.", "rating": -1, "flagged": false }, { "text": "I can use a factor tree to find the prime factors of $200,\\!000$.", "rating": 0, "flagged": false }, { "text": "To factor $20 !$, I can use the fact that any factorial is divisible by all the primes less than or equal to the input.", "rating": 0, "flagged": false } ], "human_completion": null, "chosen_completion": null } ], // Total time in milliseconds spent on labeling this solution. "total_time": 278270, // Final result of labeling this solution. Will be one of: // - "found_error": In phase 2 we stop labeling a solution after the // first error is found. // - "solution": We reached a step that concluded in the correct answer // to the problem. // - "bad_problem": The labeler reported the problem as broken. // - "give_up": The labeler was stuck (the problem was taking too long, // or the instructions were unclear) and moved onto the // next problem. "finish_reason": "found_error" } } ``` ## Citation Please use the below BibTeX entry to cite this dataset: COMING SOON
nayohan/030_web
--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 11864271386 num_examples: 5532953 download_size: 6779466232 dataset_size: 11864271386 configs: - config_name: default data_files: - split: train path: data/train-* ---
Zarxrax/ModernAnimation1080_v2
--- license: wtfpl configs: - config_name: sample data_files: - split: sample path: "sample.zip" --- - This dataset, designed for training Single Image Super Resolution models, includes 2200 full size frames taken from Blu-rays of modern animation across a variety of different art styles. Efforts have been taken to try to ensure that all sources were produced at native 1080p or better (or perhaps, it would be better to say "Full HD"). - The only processing that has been done was a conversion to RGB, and for letterboxed sources, cropping. Sol Levante was produced at 4k, and has been downscaled to 1080p to match the other sources. - All frames are at a size of 1920x1080, with the exception of Wolfwalkers and Wish, which have had their letterboxes cropped off. - The files are named according to the source, making it easy to exclude any sources you don't want. It's designed to be modular, just use what you want. - You may wish to do further processing such as generating tiles prior to training. You are also expected to generate any LR images yourself. - All images have been saved as lossless webp to conserve space. My tests show that webp typically decodes just slightly slower than png, so there should not be any significant performance difference if you train from these directly. Of course, feel free to convert to png if you like. - 23 additional validation images have also been included, one from each source. - To download, click on "Files and Versions" and download the file "ModernAnimation1080_v2.zip"
Cmarti24/downsampled_upsampled
--- dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation sequence: string - name: annotation_agent dtype: string - name: vectors dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata struct: - name: split dtype: string - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 15655201.915976798 num_examples: 37289 - name: test num_bytes: 6709792.084023202 num_examples: 15982 download_size: 12515554 dataset_size: 22364994.0 --- # Dataset Card for "downsampled_upsampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sijuade/ImageNette
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 'tench' '1': 'springer' '2': 'casette_player' '3': 'chain_saw' '4': 'church' '5': 'French_horn' '6': 'garbage_truck' '7': 'gas_pump' '8': 'gold_ball' '9': 'parachute' splits: - name: train num_bytes: 1154087527.273 num_examples: 9469 - name: validation num_bytes: 494394874.125 num_examples: 4425 download_size: 1629620479 dataset_size: 1648482401.398 ---
simonveitner/MetaMathQA-40K-Filtered
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 10337682 num_examples: 15259 download_size: 4926366 dataset_size: 10337682 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-d42d3c12-7815011
--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: dfsj/xlm-roberta-base-finetuned-panx-de metrics: [] dataset_name: xtreme dataset_config: PAN-X.de dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: dfsj/xlm-roberta-base-finetuned-panx-de * Dataset: xtreme To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
rishabhjain16/myst_pf_ot50
--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 8509570768.06 num_examples: 19332 - name: test num_bytes: 1447570290.631 num_examples: 3317 download_size: 8974808612 dataset_size: 9957141058.691 --- # Dataset Card for "myst_pf_ot50" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anzorq/hf-spaces-descriptions-embeddings
--- license: mit dataset_info: features: - name: id dtype: string - name: description dtype: string - name: embedding sequence: float64 splits: - name: train num_bytes: 94758018 num_examples: 29718 download_size: 78891306 dataset_size: 94758018 --- # Hugging Face Spaces Descriptions and Embeddings Dataset I parsed all the available public 🤗 spaces as of May 22, 2023, generated concise descriptions of their functionality, and created embeddings for them. The descriptions were generated using various LLMs from each space's app file (README.md -> app_file). The embeddings were created using the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) SentenceTransformer model. The dataset comprises approximately 30,000 spaces that meet specific criteria: having more than 40 lines of code and over 1000 characters in the app file. The descriptions provide an overview of the spaces and their features. ## Dataset Details - **Name**: HF Spaces Descriptions and Embeddings - **Creator**: [anzorq](https://huggingface.co/anzorq) - **License**: MIT ## Dataset Usage You can use this dataset for various natural language processing (NLP) tasks such as semantic search, clustering, etc. ## Loading the Dataset You can load the dataset using the datasets library: ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("anzorq/hf-spaces-descriptions-embeddings") # Access the different splits train_split = dataset['train'] valid_split = dataset['valid'] test_split = dataset['test'] ``` ## Semantic Search Example Performing a semantic search using the dataset's embeddings: ```python import torch from sentence_transformers import SentenceTransformer from datasets import load_dataset import numpy as np # Load the dataset dataset = load_dataset("anzorq/hf-spaces-descriptions-embeddings") # Load the SentenceTransformer model model = SentenceTransformer('all-MiniLM-L6-v2') # Example query query = "Removing background from images" # Encode the query query_embedding = model.encode([query], convert_to_tensor=True) # Get the space descriptions and embeddings descriptions = dataset['train']['description'] embeddings = np.array(dataset['train']['embedding']) # Calculate cosine similarity cosine_scores = torch.nn.functional.cosine_similarity(query_embedding, torch.tensor(embeddings)) # Sort the results top_k = torch.topk(cosine_scores, k=5) # Print the top-k results print("Query:", query) for idx in top_k.indices[0]: print("Space ID:", dataset['train']['id'][idx]) print("Description:", descriptions[idx]) print("Score:", cosine_scores[idx].item()) ``` ## License This dataset is distributed under the [MIT License](https://opensource.org/licenses/MIT).
sh110495/compressed_arc
--- dataset_info: features: - name: id sequence: string - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels dtype: int64 - name: candidate_length sequence: int64 splits: - name: test num_bytes: 8012734 num_examples: 1172 download_size: 1491232 dataset_size: 8012734 configs: - config_name: default data_files: - split: test path: data/test-* ---
msj19/static_10000_2
--- license: apache-2.0 ---
mask-distilled-one-sec-cv12/chunk_238
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1339053424 num_examples: 262972 download_size: 1363072631 dataset_size: 1339053424 --- # Dataset Card for "chunk_238" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aniketr/hpdv2-test-no-embeds
--- dataset_info: features: - name: user_id dtype: int64 - name: prompt dtype: string - name: image_left_id dtype: string - name: image_right_id dtype: string - name: pref dtype: int64 - name: ranking_id dtype: string splits: - name: train num_bytes: 20770830 num_examples: 153000 download_size: 1465804 dataset_size: 20770830 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image language: - en size_categories: - 100K<n<1M --- Constructing a preference dataset from the test split of [HPDv2](https://huggingface.co/datasets/ymhao/HPDv2). Each row in the dataset is a unique ranking of preference on a single image pair and contains 1. `user_id`: unique identifier of the annotator 2. `prompt`: text prompt used to generate image candidates 3. `image_left_id`: id of left image in preference pair 4. `image_right_id`: id of right image in preference pair 5. `pref`: image preferred by annotator. 1 for left image, -1 for right image 6. `ranking_id`: unique identifier for the ranking pair
bigbio/minimayosrs
--- language: - en bigbio_language: - English license: cc0-1.0 multilinguality: monolingual bigbio_license_shortname: CC0_1p0 pretty_name: MiniMayoSRS homepage: https://conservancy.umn.edu/handle/11299/196265 bigbio_pubmed: False bigbio_public: True bigbio_tasks: - SEMANTIC_SIMILARITY --- # Dataset Card for MiniMayoSRS ## Dataset Description - **Homepage:** https://conservancy.umn.edu/handle/11299/196265 - **Pubmed:** False - **Public:** True - **Tasks:** STS MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was achieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78. ## Citation Information ``` @article{pedersen2007measures, title={Measures of semantic similarity and relatedness in the biomedical domain}, author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G}, journal={Journal of biomedical informatics}, volume={40}, number={3}, pages={288--299}, year={2007}, publisher={Elsevier} } ```
ShenaoZ/0.0001_idpo_same_3itersn_dataset
--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: is_better dtype: bool splits: - name: train_prefs_1 num_bytes: 168081294 num_examples: 20378 - name: test_prefs_1 num_bytes: 16410846 num_examples: 2000 - name: train_prefs_2 num_bytes: 172983228 num_examples: 20378 - name: test_prefs_2 num_bytes: 16854087 num_examples: 2000 download_size: 206699429 dataset_size: 374329455 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* - split: train_prefs_2 path: data/train_prefs_2-* - split: test_prefs_2 path: data/test_prefs_2-* --- # Dataset Card for "0.0001_idpo_same_3itersn_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mber/subset_squadv2_format_date_only_train
--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 2651123.3716349658 num_examples: 2852 download_size: 4253248 dataset_size: 2651123.3716349658 --- # Dataset Card for "subset_squadv2_format_date_only_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HEMASENTHIL/NEWDEMO
--- dataset_info: features: - name: English Sentences dtype: string - name: Thanglish sentences dtype: string - name: Text dtype: string splits: - name: train num_bytes: 4761.428571428572 num_examples: 5 - name: test num_bytes: 1904.5714285714287 num_examples: 2 download_size: 19197 dataset_size: 6666.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
yardeny/mlm_test_set_context_len_64
--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 506880 num_examples: 1280 download_size: 0 dataset_size: 506880 --- # Dataset Card for "loss_landscape_test_set_context_len_64" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
InfoCert/faces
--- license: apache-2.0 ---
gguichard/wsd_fr_wngt_semcor_translated_aligned_all_v1
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: tokens sequence: string - name: wn_sens sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 919383947.317851 num_examples: 925349 - name: test num_bytes: 9286746.682149062 num_examples: 9347 download_size: 218724215 dataset_size: 928670694.0 --- # Dataset Card for "wsd_fr_wngt_semcor_translated_aligned_all_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TenzinGayche/t5_predata_nocs
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: file_name dtype: audio: sampling_rate: 16000 - name: uni dtype: string - name: wylie dtype: string - name: url dtype: string - name: dept dtype: string - name: timestamp dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 16669393849.934 num_examples: 276501 - name: test num_bytes: 836705600.272 num_examples: 14564 download_size: 42185941593 dataset_size: 17506099450.206 --- # Dataset Card for "t5_predata_nocs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_sst2_his_he
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 6515 num_examples: 42 - name: test num_bytes: 17181 num_examples: 102 - name: train num_bytes: 202559 num_examples: 1660 download_size: 111675 dataset_size: 226255 --- # Dataset Card for "MULTI_VALUE_sst2_his_he" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Locutusque/InstructMix
--- dataset: name: InstructiveMix tagline: A Combined Dataset of Diverse Instructional Content description: > InstructiveMix is a comprehensive dataset that brings together various instructional content from different domains. It combines instructions for tasks, code, poems, math, essays, medical texts, and more. With a diverse range of instructional data, this dataset is suitable for a wide range of natural language processing (NLP) tasks and research. license: CC-BY-SA-4.0 dataset_creation: '2023-08-02T00:00:00.000Z' dataset_version: 1.0.0 authors: - name: Locutusque email: locutusque.airshipcraft@gmail.com task_categories: - text-generation - conversational - question-answering language: - en --- **Dataset Summary:** InstructMix is a comprehensive combined dataset that offers diverse instructional content for a range of tasks. It includes data from various sources, such as code instructions, poems, essays, medical texts, and more. This dataset is designed to support natural language processing (NLP) research, model training, and evaluation across different domains. **Dataset Contents:** The dataset contains a collection of instructional data with corresponding inputs and outputs. Each entry has an "Input" field that contains the instructional content, and an "Output" field that represents the corresponding response or completion. Here is a list of the datasets used: - Locutusque/ColumnedChatCombined - TokenBender/code_instructions_120k_alpaca_style - Open-Orca/OpenOrca - vicgalle/alpaca-gpt4 - ChristophSchuhmann/essays-with-instructions - checkai/instruction-poems - pubmed_qa - BI55/MedText - nampdn-ai/tiny-codes - TIGER-Lab/MathInstruct - garage-bAInd/Open-Platypus It contains two of the following columns: - Input (string) - Output (string) These should hopefully be self-explanatory **Dataset Composition:** - Number of samples: 7570315 - Languages: English **Use Cases:** The InstructiveMix dataset is suitable for various NLP tasks, including text generation, text completion, translation, summarization, and more. It can be used to train and evaluate language models, code generation models, and other NLP-based applications. **Dataset Creation:** The InstructiveMix dataset was created by combining multiple existing datasets with instructional content and adding metadata to facilitate seamless integration. The content spans a diverse set of domains and was sourced from reputable datasets and public sources. **Acknowledgements:** I would like to acknowledge the original creators of the datasets used to construct InstructiveMix. Their contributions have enabled the creation of this valuable resource for the NLP community. **Contact:** For any questions or inquiries related to the InstructiveMix dataset, please contact me at [locutusque.airshipcraft@gmail.com]. ---
folkopinion/government-interpellation-qa-swedish
--- task_categories: - table-question-answering language: - sv tags: - government - politics - interpellation pretty_name: Riksdagen Interpellation Debate QA size_categories: - 1K<n<10K ---
CyberHarem/frost_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of frost/Frost/霜华 (Arknights) This is the dataset of frost/Frost/霜华 (Arknights), containing 32 images and their tags. The core tags of this character are `black_hair, hat, short_hair, breasts, large_breasts, black_headwear, blue_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 | 32 | 37.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/frost_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 32 | 32.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/frost_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 72 | 58.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/frost_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/frost_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, beanie, jacket, solo, looking_at_viewer, black_gloves, long_sleeves, fur_trim, pants, pouch, closed_mouth, knee_pads, outdoors, snow, tactical_clothes, holding_gun, holster, military, mole, rifle | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | beanie | jacket | solo | looking_at_viewer | black_gloves | long_sleeves | fur_trim | pants | pouch | closed_mouth | knee_pads | outdoors | snow | tactical_clothes | holding_gun | holster | military | mole | rifle | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------|:-------|:--------------------|:---------------|:---------------|:-----------|:--------|:--------|:---------------|:------------|:-----------|:-------|:-------------------|:--------------|:----------|:-----------|:-------|:--------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
sh0416/humanextension
--- license: mit --- Related github repository: https://github.com/sh0416/humanextension
christinacdl/binary_hate_speech
--- license: apache-2.0 task_categories: - text-classification language: - en tags: - code size_categories: - 10K<n<100K ---
heliosprime/twitter_dataset_1712932789
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 14444 num_examples: 35 download_size: 10368 dataset_size: 14444 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712932789" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
frluquba/adv-ele
--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 293295 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
moritzrippe/231130_submit_button
--- license: other license_name: moritzrippe license_link: LICENSE ---
AlanYky/flan-phi-2-800k
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task dtype: string splits: - name: train num_bytes: 1228291142 num_examples: 720000 - name: validation num_bytes: 137023175 num_examples: 80000 download_size: 765837799 dataset_size: 1365314317 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
philschmid/tgi_benchmark_sharegpt
--- license: apache-2.0 ---
Vinnyyw/Maitesongs
--- license: openrail ---
Seanxh/twitter_dataset_1713201760
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 115388 num_examples: 270 download_size: 44665 dataset_size: 115388 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru) This is the dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 128 images and their tags. The core tags of this character are `ponytail, long_hair, blue_hair, mole, mole_under_eye, purple_eyes, scrunchie`, 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 | 128 | 69.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 128 | 58.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 265 | 117.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 128 | 69.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 265 | 135.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/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/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru', 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 | 5 | ![](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, bartender, solo, closed_eyes, grey_hair, vest, formal | | 1 | 20 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, anime_coloring, white_shirt, open_mouth, looking_at_viewer, smile | | 2 | 30 | ![](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, sobu_high_school_uniform, solo, blazer, shirt, black_jacket | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bartender | solo | closed_eyes | grey_hair | vest | formal | anime_coloring | white_shirt | open_mouth | looking_at_viewer | smile | sobu_high_school_uniform | blazer | shirt | black_jacket | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------|:--------------|:------------|:-------|:---------|:-----------------|:--------------|:-------------|:--------------------|:--------|:---------------------------|:---------|:--------|:---------------| | 0 | 5 | ![](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 | | | | | | | | | | | 1 | 20 | ![](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 | | | | | | 2 | 30 | ![](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 |
roa7n/patched_test_p_40_f_UCH_m1_predictions
--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 - name: m1_preds dtype: float32 splits: - name: train num_bytes: 50671867 num_examples: 114052 download_size: 4695593 dataset_size: 50671867 --- # Dataset Card for "patched_test_p_40_f_UCH_m1_predictions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_sst2_invariant_tag_fronted_isnt
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: train num_bytes: 963 num_examples: 9 download_size: 2847 dataset_size: 963 --- # Dataset Card for "MULTI_VALUE_sst2_invariant_tag_fronted_isnt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Codec-SUPERB/SNIPS
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: audio dtype: audio - name: id dtype: string splits: - name: train num_bytes: 19197922646.0 num_examples: 209344 - name: test num_bytes: 1035368762.0 num_examples: 11200 - name: valid num_bytes: 1047359800.0 num_examples: 11200 download_size: 21173943484 dataset_size: 21280651208.0 --- # Dataset Card for "SNIPS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__feed-sen_en_-7dbe88-2245971653
--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-66b metrics: [] dataset_name: futin/feed dataset_config: sen_en_ dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-66b * Dataset: futin/feed * Config: sen_en_ * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
Vctr-dev/tagesspiegel-article-metadata
--- license: mit ---
zhengxuanzenwu/fair_glue_stsb
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float32 - name: idx dtype: int32 splits: - name: train num_bytes: 754791 num_examples: 5749 - name: validation num_bytes: 108032.0 num_examples: 750 - name: test num_bytes: 216064 num_examples: 1500 download_size: 735298 dataset_size: 1078887.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
otalorajuand/data_house_museum
--- configs: - config_name: data_museum data_files: "data_museum.csv" - config_name: data_santuario data_files: "data_santuario.csv" ---
heloisy/cosmos_qa_ptbr
--- pretty_name: CosmosQA Português language: - pt license: - cc-by-4.0 source_datasets: - cosmos_qa size_categories: - 10K<n<100K dataset_info: features: - name: contexto dtype: string - name: pergunta dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: Label dtype: string - name: id dtype: string splits: - name: train num_examples: 25262 - name: validation num_examples: 2985 - name: test num_examples: 6963 task_categories: - multiple-choice --- # Cosmos QA Português Este dataset é uma tradução para português do Cosmos QA, que originalmente é na língua inglesa. A tradução foi feita automaticamente usando o GPT-3.5-turbo, logo pode ter erros que não foram notados numa análise superficial. Se atente ao uso. # Dataset Card for cosmos_qa ## Licensing Information The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ## Source Data Citation INformation ``` @inproceedings{huang-etal-2019-cosmos, title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning", author = "Huang, Lifu and Le Bras, Ronan and Bhagavatula, Chandra and Choi, Yejin", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1243", doi = "10.18653/v1/D19-1243", pages = "2391--2401", } ```
Circularmachines/batch_indexing_machine_230529_001
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 157818200.0 num_examples: 720 download_size: 157829992 dataset_size: 157818200.0 --- # Dataset Card for "batch_indexing_machine_230529_001" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rishiraj/samvaad-hi-v1
--- language: - en - hi license: apache-2.0 task_categories: - text-generation dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 421602696 num_examples: 91281 - name: test num_bytes: 46377710 num_examples: 10143 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- 100k high-quality conversations in English, Hindi, and Hinglish curated exclusively with an Indic context.
vwxyzjn/summarize_from_feedback_oai_preprocessing_pythia-160m_169
--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: response0 dtype: string - name: response0_token sequence: int64 - name: response0_token_len dtype: int64 - name: response1 dtype: string - name: response1_token sequence: int64 - name: response1_token_len dtype: int64 - name: response0_policy dtype: string - name: response1_policy dtype: string - name: policies dtype: string - name: query_response0 dtype: string - name: query_response0_token sequence: int64 - name: query_response0_token_len dtype: int64 - name: query_response1 dtype: string - name: query_response1_token sequence: int64 - name: query_response1_token_len dtype: int64 splits: - name: train num_bytes: 2210564467 num_examples: 92858 - name: validation num_bytes: 2054238499 num_examples: 86086 download_size: 271347530 dataset_size: 4264802966 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_pythia-160m_169" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vtuber-plan/quantdata-10k
--- license: openrail ---