davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
lukebarousse/data_jobs	--- license: apache-2.0 ---
Nooha/job_categorization_dataset	--- dataset_info: features: - name: title dtype: string - name: category dtype: string splits: - name: train num_bytes: 435029 num_examples: 6539 download_size: 164148 dataset_size: 435029 configs: - config_name: default data_files: - split: train path: data/train-* ---
heliosprime/twitter_dataset_1713032076	--- 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: 12085 num_examples: 28 download_size: 9998 dataset_size: 12085 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713032076" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kilt_tasks	--- annotations_creators: - crowdsourced - found - machine-generated language_creators: - crowdsourced - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended\|natural_questions - extended\|other-aidayago - extended\|other-fever - extended\|other-hotpotqa - extended\|other-trex - extended\|other-triviaqa - extended\|other-wizardsofwikipedia - extended\|other-wned-cweb - extended\|other-wned-wiki - extended\|other-zero-shot-re - original task_categories: - fill-mask - question-answering - text-classification - text-generation - text-retrieval - text2text-generation task_ids: - abstractive-qa - dialogue-modeling - document-retrieval - entity-linking-retrieval - extractive-qa - fact-checking - fact-checking-retrieval - open-domain-abstractive-qa - open-domain-qa - slot-filling paperswithcode_id: kilt pretty_name: KILT config_names: - aidayago2 - cweb - eli5 - fever - hotpotqa - nq - structured_zeroshot - trex - triviaqa_support_only - wned - wow dataset_info: - config_name: aidayago2 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 68943890 num_examples: 18395 - name: validation num_bytes: 20743172 num_examples: 4784 - name: test num_bytes: 14210587 num_examples: 4463 download_size: 13419920 dataset_size: 103897649 - config_name: cweb features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 89819252 num_examples: 5599 - name: test num_bytes: 99208393 num_examples: 5543 download_size: 32809813 dataset_size: 189027645 - config_name: eli5 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 525554458 num_examples: 272634 - name: validation num_bytes: 13860033 num_examples: 1507 - name: test num_bytes: 107092 num_examples: 600 download_size: 329302944 dataset_size: 539521583 - config_name: fever features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 23937486 num_examples: 104966 - name: validation num_bytes: 3167751 num_examples: 10444 - name: test num_bytes: 1040116 num_examples: 10100 download_size: 11571038 dataset_size: 28145353 - config_name: hotpotqa features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 33595295 num_examples: 88869 - name: validation num_bytes: 2371262 num_examples: 5600 - name: test num_bytes: 887204 num_examples: 5569 download_size: 17914796 dataset_size: 36853761 - config_name: nq features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 30385368 num_examples: 87372 - name: validation num_bytes: 6190373 num_examples: 2837 - name: test num_bytes: 333162 num_examples: 1444 download_size: 16535475 dataset_size: 36908903 - config_name: structured_zeroshot features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 47165561 num_examples: 147909 - name: validation num_bytes: 1612123 num_examples: 3724 - name: test num_bytes: 1140265 num_examples: 4966 download_size: 21038900 dataset_size: 49917949 - config_name: trex features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 1190183022 num_examples: 2284168 - name: validation num_bytes: 2573444 num_examples: 5000 - name: test num_bytes: 757470 num_examples: 5000 download_size: 546671157 dataset_size: 1193513936 - config_name: triviaqa_support_only features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 72021515 num_examples: 61844 - name: validation num_bytes: 6824398 num_examples: 5359 - name: test num_bytes: 340692 num_examples: 6586 download_size: 31946196 dataset_size: 79186605 - config_name: wned features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 12659518 num_examples: 3396 - name: test num_bytes: 13080824 num_examples: 3376 download_size: 3608615 dataset_size: 25740342 - config_name: wow features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 41870938 num_examples: 63734 - name: validation num_bytes: 2021752 num_examples: 3054 - name: test num_bytes: 1339546 num_examples: 2944 download_size: 25441975 dataset_size: 45232236 configs: - config_name: aidayago2 data_files: - split: train path: aidayago2/train-* - split: validation path: aidayago2/validation-* - split: test path: aidayago2/test-* - config_name: cweb data_files: - split: validation path: cweb/validation-* - split: test path: cweb/test-* - config_name: eli5 data_files: - split: train path: eli5/train-* - split: validation path: eli5/validation-* - split: test path: eli5/test-* - config_name: fever data_files: - split: train path: fever/train-* - split: validation path: fever/validation-* - split: test path: fever/test-* - config_name: hotpotqa data_files: - split: train path: hotpotqa/train-* - split: validation path: hotpotqa/validation-* - split: test path: hotpotqa/test-* - config_name: nq data_files: - split: train path: nq/train-* - split: validation path: nq/validation-* - split: test path: nq/test-* default: true - config_name: structured_zeroshot data_files: - split: train path: structured_zeroshot/train-* - split: validation path: structured_zeroshot/validation-* - split: test path: structured_zeroshot/test-* - config_name: trex data_files: - split: train path: trex/train-* - split: validation path: trex/validation-* - split: test path: trex/test-* - config_name: triviaqa_support_only data_files: - split: train path: triviaqa_support_only/train-* - split: validation path: triviaqa_support_only/validation-* - split: test path: triviaqa_support_only/test-* - config_name: wned data_files: - split: validation path: wned/validation-* - split: test path: wned/test-* - config_name: wow data_files: - split: train path: wow/train-* - split: validation path: wow/validation-* - split: test path: wow/test-* --- # Dataset Card for KILT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://ai.facebook.com/tools/kilt/ - Repository: https://github.com/facebookresearch/KILT - Paper: https://arxiv.org/abs/2009.02252 - Leaderboard: https://eval.ai/web/challenges/challenge-page/689/leaderboard/ - Point of Contact: [Needs More Information] ### Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: - Fact-checking - Entity linking - Slot filling - Open domain QA - Dialog generation All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ```python from datasets import load_dataset # Get the pre-processed Wikipedia knowledge source for kild kilt_wiki = load_dataset("kilt_wikipedia") # Get the KILT task datasets kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only") # Most tasks in KILT already have all required data, but KILT-TriviaQA # only provides the question IDs, not the questions themselves. # Thankfully, we can get the original TriviaQA data with: trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # The KILT IDs can then be mapped to the TriviaQA questions with: triviaqa_map = {} def add_missing_data(x, trivia_qa_subset, triviaqa_map): i = triviaqa_map[x['id']] x['input'] = trivia_qa_subset[i]['question'] x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value'] return x for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])]) kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map) kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map)) ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure ### Data Instances An example of open-domain QA from the Natural Questions `nq` configuration looks as follows: ``` {'id': '-5004457603684974952', 'input': 'who is playing the halftime show at super bowl 2016', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'Coldplay', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 186, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 178, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Beyoncé', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 224, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 217, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Bruno Mars', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 239, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 229, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 0.9657992720603943, 'end_character': 341, 'end_paragraph_id': 1, 'meta': {'annotation_id': '2430977867500315580', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': 'NONE'}, 'section': 'Section::::Abstract.', 'start_character': 0, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': -1.0, 'end_character': -1, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.', "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars", "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': -1, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}]} ``` ### Data Fields Examples from all configurations have the following features: - `input`: a `string` feature representing the query. - `output`: a `list` of features each containing information for an answer, made up of: - `answer`: a `string` feature representing a possible answer. - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by: - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from. - `section`: a `string` feature, the title of the section in Wikipedia article. - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article. - `start_character`: a `int32` feature. - `start_paragraph_id`: a `int32` feature. - `end_character`: a `int32` feature. - `end_paragraph_id`: a `int32` feature. ### Data Splits The configurations have the following splits: \| \| Train \| Validation \| Test \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| triviaqa \| 61844 \| 5359 \| 6586 \| \| fever \| 104966 \| 10444 \| 10100 \| \| aidayago2 \| 18395 \| 4784 \| 4463 \| \| wned \| \| 3396 \| 3376 \| \| cweb \| \| 5599 \| 5543 \| \| trex \| 2284168 \| 5000 \| 5000 \| \| structured_zeroshot \| 147909 \| 3724 \| 4966 \| \| nq \| 87372 \| 2837 \| 1444 \| \| hotpotqa \| 88869 \| 5600 \| 5569 \| \| eli5 \| 272634 \| 1507 \| 600 \| \| wow \| 94577 \| 3058 \| 2944 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{kilt_tasks, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick S. H. Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vladimir Karpukhin and Jean Maillard and Vassilis Plachouras and Tim Rockt{\"{a}}schel and Sebastian Riedel}, editor = {Kristina Toutanova and Anna Rumshisky and Luke Zettlemoyer and Dilek Hakkani{-}T{\"{u}}r and Iz Beltagy and Steven Bethard and Ryan Cotterell and Tanmoy Chakraborty and Yichao Zhou}, title = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021, Online, June 6-11, 2021}, pages = {2523--2544}, publisher = {Association for Computational Linguistics}, year = {2021}, url = {https://www.aclweb.org/anthology/2021.naacl-main.200/} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.
hpprc/jawiki-books	--- dataset_info: config_name: '2024-04-01' features: - name: id dtype: int64 - name: title dtype: string - name: text dtype: string - name: paragraphs list: - name: paragraph_id dtype: int64 - name: tag dtype: string - name: text dtype: string - name: title dtype: string - name: abstract dtype: string - name: wikitext dtype: string - name: date_created dtype: string - name: date_modified dtype: string - name: templates sequence: string - name: url dtype: string splits: - name: train num_bytes: 172222533.07899398 num_examples: 13490 download_size: 74095911 dataset_size: 172222533.07899398 configs: - config_name: '2024-04-01' data_files: - split: train path: 2024-04-01/train-* license: cc-by-sa-3.0 language: - ja ---
TuringsSolutions/FoolTheGANDataset	--- license: other license_name: onlyuseifyoucontactmefirstlicense license_link: LICENSE ---
najju/sign-psl-13b-new	--- dataset_info: features: - name: Text dtype: string - name: Gloss dtype: string splits: - name: train num_bytes: 347110 num_examples: 5446 download_size: 215108 dataset_size: 347110 configs: - config_name: default data_files: - split: train path: data/train-* ---
Jingmiao/PUZZLEQA	--- language: - en license: apache-2.0 --- ### Acknowledgements The PUZZLEQA is scraped from [NPR Sunday Puzzle Official Website](https://www.npr.org/series/4473090/sunday-puzzle) and [NPR Puzzle Synopsis](https://groups.google.com/g/nprpuzzle), made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle. The authors of the dataset cleaned the data and made some multiple choice based on the question and answers. ### Creation The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 1. Read the fr_big_exp.tsv.tsv file 2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\ 4. identify the correct answer for the given question as the "gold" answer. Recent.tsv is the dataset based on the NPR PUZZLE in 2023. # Citation @inproceedings{zhao2023solving, title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, author={Jingmiao Zhao and Carolyn Jane Anderson}, year={2023}, eprint={2306.12255}, archivePrefix={arXiv}, primaryClass={cs.CL} }
awettig/Pile-Gutenberg-0.5B-6K-opt	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 6500959920 num_examples: 81380 - name: test num_bytes: 64945692 num_examples: 813 download_size: 1706776857 dataset_size: 6565905612 --- # Dataset Card for "Pile-Gutenberg-0.5B-6K-opt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': banded '1': blotchy '2': braided '3': bubbly '4': bumpy '5': chequered '6': cobwebbed '7': cracked '8': crosshatched '9': crystalline '10': dotted '11': fibrous '12': flecked '13': freckled '14': frilly '15': gauzy '16': grid '17': grooved '18': honeycombed '19': interlaced '20': knitted '21': lacelike '22': lined '23': marbled '24': matted '25': meshed '26': paisley '27': perforated '28': pitted '29': pleated '30': polka-dotted '31': porous '32': potholed '33': scaly '34': smeared '35': spiralled '36': sprinkled '37': stained '38': stratified '39': striped '40': studded '41': swirly '42': veined '43': waffled '44': woven '45': wrinkled '46': zigzagged - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: LLM_Description_opt175b_downstream_tasks_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: clip_tag_ViT_L_14_specific dtype: string - name: clip_tags_ViT_L_14_ensemble_specific dtype: string - name: clip_tags_ViT_L_14_simple_specific dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: Attributes_ViT_L_14_text_davinci_003 sequence: string - name: Attributes_ViT_L_14_text_davinci_003_dtd sequence: string - name: Attributes_ViT_L_14_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_with_openai_classes sequence: string - name: clip_tags_ViT_L_14_wo_openai_classes sequence: string - name: clip_tags_ViT_B_16_simple_specific dtype: string - name: clip_tags_ViT_B_16_ensemble_specific dtype: string - name: clip_tags_ViT_B_32_simple_specific dtype: string - name: clip_tags_ViT_B_32_ensemble_specific dtype: string - name: Attributes_ViT_B_16_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_LAION_ViT_H_14_2B_simple_specific dtype: string - name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific dtype: string splits: - name: test num_bytes: 184279525.4 num_examples: 1880 download_size: 180002375 dataset_size: 184279525.4 --- # Dataset Card for "DTD_parition1_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
erichilarysmithsr/dovichousesimulation	--- license: ncsa ---
CyberHarem/akari_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of akari/鰐渕アカリ/明里 (Blue Archive) This is the dataset of akari/鰐渕アカリ/明里 (Blue Archive), containing 152 images and their tags. The core tags of this character are `blonde_hair, long_hair, horns, halo, breasts, ahoge, large_breasts, blue_eyes, braid, hair_between_eyes, demon_horns, symbol-shaped_pupils`, 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 \| 152 \| 208.67 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 152 \| 180.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 376 \| 369.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/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/akari_bluearchive', 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 \| 26 \| ![](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, black_gloves, solo, long_sleeves, smile, garter_straps, looking_at_viewer, simple_background, blush, open_mouth, black_thighhighs, white_background, neck_ribbon, red_ribbon \| \| 1 \| 34 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, looking_at_viewer, black_kimono, smile, black_flower, hair_flower, solo, black_gloves, obi, long_sleeves, simple_background, wide_sleeves, collarbone, open_mouth, white_background, black_horns, cleavage, heart-shaped_pupils, floral_print, official_alternate_costume, upper_body \| \| 2 \| 9 \| ![](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, looking_at_viewer, bare_shoulders, navel, black_bikini, cleavage, smile, solo, very_long_hair, blush, simple_background, closed_mouth, collarbone, heart-shaped_pupils, stomach, string_bikini, white_background, cowboy_shot, holding_food \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| solo \| long_sleeves \| smile \| garter_straps \| looking_at_viewer \| simple_background \| blush \| open_mouth \| black_thighhighs \| white_background \| neck_ribbon \| red_ribbon \| black_kimono \| black_flower \| hair_flower \| obi \| wide_sleeves \| collarbone \| black_horns \| cleavage \| heart-shaped_pupils \| floral_print \| official_alternate_costume \| upper_body \| bare_shoulders \| navel \| black_bikini \| very_long_hair \| closed_mouth \| stomach \| string_bikini \| cowboy_shot \| holding_food \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:---------------\|:--------\|:----------------\|:--------------------\|:--------------------\|:--------\|:-------------\|:-------------------\|:-------------------\|:--------------\|:-------------\|:---------------\|:---------------\|:--------------\|:------\|:---------------\|:-------------\|:--------------\|:-----------\|:----------------------\|:---------------\|:-----------------------------\|:-------------\|:-----------------\|:--------\|:---------------\|:-----------------\|:---------------\|:----------\|:----------------\|:--------------\|:---------------\| \| 0 \| 26 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 34 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| \| X \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| X \| \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a1a25b97	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1337 dataset_size: 182 --- # Dataset Card for "a1a25b97" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shuwang127/patch_sum_db	--- license: cc-by-4.0 task_categories: - summarization language: - en tags: - code pretty_name: pard_4k size_categories: - 1K<n<10K --- # A dataset for security patch summarization task. Currently, the dataset is under data curation phase; thus, `patch_sum_db` is not ready for public release.
niv-al/sq-babi_nli_basic-coreference	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: class_label: names: '0': not-entailed '1': entailed splits: - name: train num_bytes: 209225 num_examples: 1000 - name: validation num_bytes: 29532 num_examples: 144 - name: test num_bytes: 30008 num_examples: 144 download_size: 48437 dataset_size: 268765 language: - sq --- # Dataset Card for "sq-babi_nli_basic-coreference" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deetsadi/musiccaps_mel_spectrograms	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 117739026.0 num_examples: 999 download_size: 115824318 dataset_size: 117739026.0 --- # Dataset Card for "musiccaps_mel_spectrograms" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bloyal/small-uniref30	--- license: cc-by-4.0 dataset_info: features: - name: id dtype: int64 - name: num dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1067207.070393368 num_examples: 4096 - name: test num_bytes: 167427.70557437633 num_examples: 640 - name: validation num_bytes: 169382.9274292743 num_examples: 640 download_size: 1368501 dataset_size: 1404017.7033970184 task_categories: - fill-mask size_categories: - 1K<n<10K ---
adamjweintraut/eli5_precomputed_top_slice	--- 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: 277523684 num_examples: 10000 - name: test num_bytes: 34508046 num_examples: 1250 - name: validation num_bytes: 34663133 num_examples: 1250 download_size: 214939984 dataset_size: 346694863 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
jahb57/gpt2_embeddings_BATCH_3	--- dataset_info: features: - name: sentence dtype: string - name: last_hidden_state sequence: sequence: float32 splits: - name: train num_bytes: 18590683446 num_examples: 100000 download_size: 18639362250 dataset_size: 18590683446 configs: - config_name: default data_files: - split: train path: data/train-* ---
bahidalgo/Me	--- license: afl-3.0 ---
AdapterOcean/med_alpaca_standardized_cluster_9_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2443204 num_examples: 8301 download_size: 838833 dataset_size: 2443204 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_9_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Pankaj202133/Pankaj	--- license: cc-by-nc-4.0 ---
jondurbin/airoboros-3.0	--- license: apache-2.0 --- ## Overview This dataset is a continuation of the airoboros datasets, with two main new contributions: * MathJSON - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations. * Anon-contributed RP dataset to enhance multi-turn coherency. Some of the MathJSON data was adapted from https://huggingface.co/datasets/meta-math/MetaMathQA ## Format The format is now in ShareGPT format, to better accomodate the OS ecosystem fine-tuning tooling. ## MathJSON As we all know, math is really difficult for LLMs, especially floating point operations, trig functions, factorials, and several-digit numbers. Instead of sending millions and millions of examples to the LLM for fine-tuning, I've created a new category `mathjson` which simply presents the solution to the problem as a JSON string that can be evaluated by traditional computation. The dataset has roughly 4k samples, as a proof of concept. This can be expanded fairly easily, now that the fine-tuned models have a basic understanding of how to represent the problems. For example: ``` Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST] ``` ``` The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1. Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr². Solution as MathJSON: <mathjson> [ "Multiply", "Pi", [ "Power", 17.2456, 2 ] ] </mathjson> ``` Simply extract the JSON string between the mathjson tags, and evaluate it with https://cortexjs.io/compute-engine/ or my ultra-hacky limited-functionality python version of it https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py
open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX	--- pretty_name: Evaluation run of nlpguy/AlloyIngotNeoX dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [nlpguy/AlloyIngotNeoX](https://huggingface.co/nlpguy/AlloyIngotNeoX) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-15T11:28:14.890311](https://huggingface.co/datasets/open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX/blob/main/results_2024-02-15T11-28-14.890311.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\": 0.6559618494796027,\n\ \ \"acc_stderr\": 0.03203002675451656,\n \"acc_norm\": 0.6554016452842437,\n\ \ \"acc_norm_stderr\": 0.03269903110679164,\n \"mc1\": 0.6034271725826194,\n\ \ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.7456883658583785,\n\ \ \"mc2_stderr\": 0.014353519946726465\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7158703071672355,\n \"acc_stderr\": 0.013179442447653886,\n\ \ \"acc_norm\": 0.7431740614334471,\n \"acc_norm_stderr\": 0.0127669237941168\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7193786098386775,\n\ \ \"acc_stderr\": 0.004483845735187827,\n \"acc_norm\": 0.8906592312288388,\n\ \ \"acc_norm_stderr\": 0.0031142850772280335\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404907,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404907\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7935483870967742,\n\ \ \"acc_stderr\": 0.023025899617188712,\n \"acc_norm\": 0.7935483870967742,\n\ \ \"acc_norm_stderr\": 0.023025899617188712\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \ \ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524565,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524565\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n\ \ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4480446927374302,\n\ \ \"acc_stderr\": 0.016631976628930595,\n \"acc_norm\": 0.4480446927374302,\n\ \ \"acc_norm_stderr\": 0.016631976628930595\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188936\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.012752858346533127,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.012752858346533127\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.6034271725826194,\n\ \ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.7456883658583785,\n\ \ \"mc2_stderr\": 0.014353519946726465\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8453038674033149,\n \"acc_stderr\": 0.010163172650433533\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6982562547384382,\n \ \ \"acc_stderr\": 0.012643544762873358\n }\n}\n```" repo_url: https://huggingface.co/nlpguy/AlloyIngotNeoX leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|arc:challenge\|25_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-15T11-28-14.890311.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|gsm8k\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hellaswag\|10_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T11-28-14.890311.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-15T11-28-14.890311.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '/details_harness\|winogrande\|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-15T11-28-14.890311.parquet' - config_name: results data_files: - split: 2024_02_15T11_28_14.890311 path: - results_2024-02-15T11-28-14.890311.parquet - split: latest path: - results_2024-02-15T11-28-14.890311.parquet --- # Dataset Card for Evaluation run of nlpguy/AlloyIngotNeoX <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [nlpguy/AlloyIngotNeoX](https://huggingface.co/nlpguy/AlloyIngotNeoX) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-15T11:28:14.890311](https://huggingface.co/datasets/open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX/blob/main/results_2024-02-15T11-28-14.890311.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": 0.6559618494796027, "acc_stderr": 0.03203002675451656, "acc_norm": 0.6554016452842437, "acc_norm_stderr": 0.03269903110679164, "mc1": 0.6034271725826194, "mc1_stderr": 0.017124930942023515, "mc2": 0.7456883658583785, "mc2_stderr": 0.014353519946726465 }, "harness\|arc:challenge\|25": { "acc": 0.7158703071672355, "acc_stderr": 0.013179442447653886, "acc_norm": 0.7431740614334471, "acc_norm_stderr": 0.0127669237941168 }, "harness\|hellaswag\|10": { "acc": 0.7193786098386775, "acc_stderr": 0.004483845735187827, "acc_norm": 0.8906592312288388, "acc_norm_stderr": 0.0031142850772280335 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404907, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404907 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188712, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188712 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.03374402644139403, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.03374402644139403 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524565, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524565 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601443, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601443 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7369942196531792, "acc_stderr": 0.023703099525258176, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.023703099525258176 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4480446927374302, "acc_stderr": 0.016631976628930595, "acc_norm": 0.4480446927374302, "acc_norm_stderr": 0.016631976628930595 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188936, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188936 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47392438070404175, "acc_stderr": 0.012752858346533127, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.012752858346533127 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.044612721759105085, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.044612721759105085 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.02812342933514278, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.02812342933514278 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.6034271725826194, "mc1_stderr": 0.017124930942023515, "mc2": 0.7456883658583785, "mc2_stderr": 0.014353519946726465 }, "harness\|winogrande\|5": { "acc": 0.8453038674033149, "acc_stderr": 0.010163172650433533 }, "harness\|gsm8k\|5": { "acc": 0.6982562547384382, "acc_stderr": 0.012643544762873358 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
sKushagra/hmm	--- license: openrail ---
yzhuang/autotree_automl_electricity_gosdt_l512_d3_sd2	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 5538400000 num_examples: 100000 - name: validation num_bytes: 553840000 num_examples: 10000 download_size: 1560789998 dataset_size: 6092240000 --- # Dataset Card for "autotree_automl_electricity_gosdt_l512_d3_sd2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alvations/c4p0-x1-ko-en	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 20460 num_examples: 18 download_size: 20224 dataset_size: 20460 configs: - config_name: default data_files: - split: train path: e8af6469d8d0c520/train-* ---
LasseRogers2111/test	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: start_time dtype: string - name: end_time dtype: string splits: - name: train num_bytes: 1273208.0 num_examples: 6 - name: validation num_bytes: 1229380.0 num_examples: 5 download_size: 2508853 dataset_size: 2502588.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
Sasini/Audio_NER_dataset_2	--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: text sequence: string - name: ner_tags sequence: int64 splits: - name: testtrue num_bytes: 106107756.6 num_examples: 3100 download_size: 104273091 dataset_size: 106107756.6 configs: - config_name: default data_files: - split: testtrue path: data/testtrue-* ---
GeneralAwareness/Various	--- license: cc-by-nc-sa-4.0 ---
sonny-dev/databricks-mini	--- license: apache-2.0 task_categories: - question-answering - text-classification - summarization language: - en ---
wndknd/german-law-bgb	--- license: mit task_categories: - text-generation language: - de --- The Bürgerliche Gesetzbuch divided by each paragraph for text-generation.
akash140500/failure13	--- license: apache-2.0 ---
senhorsapo/cream	--- license: openrail ---
SINAI/COAR	--- language: - es license: cc-by-nc-sa-4.0 --- # COAR ## Description The COAR (Corpus of Restaurant Opinions) dataset is designed for research in the field of document-level polarity classification and is focused on the hospitality domain (tourism-hospitality). The corpus consists of 2202 opinions extracted from TripAdvisor, which are categorized on a scale of five levels of opinion intensity (1 (negative) - 5 (positive)). The number of opinions per class is as follows: \| Rating \| 1 \| 2 \| 3 \| 4 \| 5 \| Total \| \| --- \|:---:\|:---:\|:---:\|:---:\|:---:\|:---: \| \| #Opinions \| 565 \| 246 \| 188 \| 333 \| 870 \| 2202 \| ## Citation If you use the corpus in your research, please cite: [Cross-Domain Sentiment Analysis Using Spanish Opinionated Words](https://link.springer.com/chapter/10.1007/978-3-319-07983-7_28). ``` @inproceedings{molina2014cross, title={Cross-domain sentiment analysis using Spanish opinionated words}, author={Molina-Gonz{\'a}lez, M Dolores and Mart{\'\i}nez-C{\'a}mara, Eugenio and Mart{\'\i}n-Valdivia, M Teresa and Urena-L{\'o}pez, L Alfonso}, booktitle={Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings 19}, pages={214--219}, year={2014}, organization={Springer} } ``` # COAR ## Descripción Corpus de opiniones de restaurantes destinado a la investigación en el ámbito de la clasificación de la polaridad a nivel de documento, y se circunscribe en el dominio de alojamiento hostelero (turismo-hostelera). El corpus está formado por 2202 opiniones extraídas de TripAdvisor, las cuales están catalogadas en una escala de cinco niveles de intensidad de opinión (1 (negativo) - 5 (positivo)). El número de opiniones por clase es: \| Puntuación \| 1 \| 2 \| 3 \| 4 \| 5 \| Total \| \| --- \|:---:\|:---:\|:---:\|:---:\|:---:\|:---: \| \| #Opiniones \| 565 \| 246 \| 188 \| 333 \| 870 \| 2202 \| # Cita Si utiliza el corpus en su investigación, por favor cite: [Cross-Domain Sentiment Analysis Using Spanish Opinionated Words](https://link.springer.com/chapter/10.1007/978-3-319-07983-7_28). ``` @inproceedings{molina2014cross, title={Cross-domain sentiment analysis using Spanish opinionated words}, author={Molina-Gonz{\'a}lez, M Dolores and Mart{\'\i}nez-C{\'a}mara, Eugenio and Mart{\'\i}n-Valdivia, M Teresa and Urena-L{\'o}pez, L Alfonso}, booktitle={Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings 19}, pages={214--219}, year={2014}, organization={Springer} } ```
yoshitomo-matsubara/srsd-feynman_easy_dummy	--- pretty_name: SRSD-Feynman (Easy w/ Dummy Variables) annotations_creators: - expert language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended task_categories: - tabular-regression task_ids: [] --- # Dataset Card for SRSD-Feynman (Easy set with Dummy Variables) ## 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: - Repository: https://github.com/omron-sinicx/srsd-benchmark - Paper: [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540) - Point of Contact: [Yoshitaka Ushiku](mailto:yoshitaka.ushiku@sinicx.com) ### Dataset Summary Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery. We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets. This is the *Easy set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas: [![Click here to open a PDF file](problem_table.png)](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_easy_dummy/resolve/main/problem_table.pdf) Dummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions. The following datasets contain 1 dummy variable: I.12.1, I.12.4, I.12.5, I.18.12, I.25.13, I.47.23 2 dummy variables: I.14.3, I.18.16, I.43.16, II.3.24, II.8.31, II.10.9, II.13.17, II.15.5, II.27.18, III.7.38, III.12.43 3 dummy variables: I.14.4, I.26.2, I.27.6, I.30.5, II.2.42, II.4.23, II.15.4, II.27.16, II.34.11, II.34.29b, II.38.3, II.38.14, III.15.27 More details of these datasets are provided in [the paper and its supplementary material](https://openreview.net/forum?id=qrUdrXsiXX). ### Supported Tasks and Leaderboards Symbolic Regression ## Dataset Structure ### Data Instances Tabular data + Ground-truth equation per equation Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables. Note that the number of variables (`num_variables`) varies from equation to equation. Ground-truth equation: pickled symbolic representation (equation with symbols in sympy) of the target function. ### Data Fields For each dataset, we have 1. train split (txt file, whitespace as a delimiter) 2. val split (txt file, whitespace as a delimiter) 3. test split (txt file, whitespace as a delimiter) 4. true equation (pickle file for sympy object) ### Data Splits - train: 8,000 samples per equation - val: 1,000 samples per equation - test: 1,000 samples per equation ## Dataset Creation ### Curation Rationale We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html). ### Annotations #### Annotation process We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database. First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants. Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation. In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen. Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes. Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly. In addition, variables that take a specific sign were set to be sampled within that range. #### Who are the annotators? The main annotators are - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) ### Personal and Sensitive Information N/A ## Considerations for Using the Data ### Social Impact of Dataset We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery. ### Discussion of Biases Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics. ### Other Known Limitations Some variables used in our datasets indicate some numbers (counts), which should be treated as integer. Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25}) ## Additional Information ### Dataset Curators The main curators are - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) ### Licensing Information Creative Commons Attribution 4.0 ### Citation Information [[OpenReview](https://openreview.net/forum?id=qrUdrXsiXX)] [[Video](https://www.youtube.com/watch?v=MmeOXuUUAW0)] [[Preprint](https://arxiv.org/abs/2206.10540)] ```bibtex @article{matsubara2024rethinking, title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery}, author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka}, journal={Journal of Data-centric Machine Learning Research}, year={2024}, url={https://openreview.net/forum?id=qrUdrXsiXX} } ``` ### Contributions Authors: - Yoshitomo Matsubara (@yoshitomo-matsubara) - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) - Yoshitaka Ushiku (@yushiku)
snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset	--- dataset_info: features: - name: prompt_id dtype: string - name: prompt 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: all_generated_responses sequence: string - name: all_rm_scores sequence: float64 splits: - name: train_iteration_1 num_bytes: 276734865 num_examples: 19766 - name: test_iteration_1 num_bytes: 13616124 num_examples: 994 - name: train_iteration_2 num_bytes: 313248218 num_examples: 19958 - name: test_iteration_2 num_bytes: 15553468 num_examples: 1000 - name: train_iteration_3 num_bytes: 379805458 num_examples: 19996 - name: test_iteration_3 num_bytes: 19111694 num_examples: 1000 download_size: 485703305 dataset_size: 1018069827 configs: - config_name: default data_files: - split: train_iteration_1 path: data/train_iteration_1-* - split: test_iteration_1 path: data/test_iteration_1-* - split: train_iteration_2 path: data/train_iteration_2-* - split: test_iteration_2 path: data/test_iteration_2-* - split: train_iteration_3 path: data/train_iteration_3-* - split: test_iteration_3 path: data/test_iteration_3-* license: apache-2.0 task_categories: - text-generation --- #### Dataset: This is the data used for training [Snorkel model](https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO) We use ONLY the prompts from [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized); no external LLM responses used. #### Methodology: 1. Generate 5 response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). 2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking. 3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses. 4. Use this LLM as the base model for the next iteration and use a different set of 20,000 prompts, repeating three times in total. Please see the model page for more details on the methodology. Columns: - prompt: the current prompt - chosen: the list of messages for the chosen response - rejected: the list of messages for the rejected response - all_generated_responses: The 5 generated responses - all_rm_scores: The 5 corresponding reward model scores Splits: - train/test_iteration_{n}: The dataset used at the n_th iteration. We did 3 iterations in total. Training recipe: This data is formatted to be compatible with the Hugging Face's [Zephyr recipe](https://github.com/huggingface/alignment-handbook/tree/main/recipes/zephyr-7b-beta). We executed the n_th DPO iteration using the "train/test_iteration_{n}".
RCC-MSU/collection3	--- annotations_creators: - other language: - ru language_creators: - found license: - other multilinguality: - monolingual pretty_name: Collection3 size_categories: - 10K<n<100K source_datasets: [] tags: [] task_categories: - token-classification task_ids: - named-entity-recognition dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: test num_bytes: 935298 num_examples: 1922 - name: train num_bytes: 4380588 num_examples: 9301 - name: validation num_bytes: 1020711 num_examples: 2153 download_size: 878777 dataset_size: 6336597 --- # Dataset Card for Collection3 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - Homepage: [Collection3 homepage](http://labinform.ru/pub/named_entities/index.htm) - Repository: [Needs More Information] - Paper: [Two-stage approach in Russian named entity recognition](https://ieeexplore.ieee.org/document/7584769) - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection [Persons-1000](http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000) originally containing 1000 news documents labeled only with names of persons. Additional labels were obtained using guidelines similar to MUC-7 with web-based tool [Brat](http://brat.nlplab.org/) for collaborative text annotation. Currently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations). Conversion to the IOB2 format and splitting into train, validation and test sets was done by [DeepPavlov team](http://files.deeppavlov.ai/deeppavlov_data/collection3_v2.tar.gz). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Russian ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { "id": "851", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0], "tokens": ['Главный', 'архитектор', 'программного', 'обеспечения', '(', 'ПО', ')', 'американского', 'высокотехнологичного', 'гиганта', 'Microsoft', 'Рэй', 'Оззи', 'покидает', 'компанию', '.'] } ``` ### Data Fields - id: a string feature. - tokens: a list of string features. - ner_tags: a list of classification labels (int). Full tagset with indices: ``` {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6} ``` ### Data Splits \|name\|train\|validation\|test\| \|---------\|----:\|---------:\|---:\| \|Collection3\|9301\|2153\|1922\| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner, author={Mozharova, Valerie and Loukachevitch, Natalia}, booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)}, title={Two-stage approach in Russian named entity recognition}, year={2016}, pages={1-6}, doi={10.1109/FRUCT.2016.7584769}} ```
Falah/family_lifestyle_photography	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1039539 num_examples: 10000 download_size: 22749 dataset_size: 1039539 --- # Dataset Card for "family_lifestyle_photography" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bdebayan/my_dataset	--- license: bsd task_categories: - question-answering language: - en size_categories: - n<1K --- # 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 [English] ## Dataset Structure CSV ### 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]
dcml0714/Heros	--- license: apache-2.0 size_categories: - n<1K --- HEROS is a dataset used to compare the sentence cosine similarity among sentences with high lexical overlapping but differ in their semantics. Please refer to the paper, "Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS" for more details of how the dataset is constructed and the comparison of different sentence encoders. The dataset `heros.tsv` consists of 6 columns: `Original`, `Synonym`, `Antonym`, `Negation`, `Random`, `Typo`, `Negation`. The first column, `Original` are the sentences from GoEmotion dataset, and sentences in the other columns are constructed by replacing some words in the original sentences based on different rules, making up different subsets in HEROS. Different subsets in HEROS capture various aspects of semantics. Comparing the average cosine similarity between minimal pairs in Synonym and Antonym allows one to understand whether replacing a word with an antonym is more dissimilar to the original semantics than replacing a word with a synonym. The average cosine similarity between minimal pairs in Negation can tell us how negation affects sentence embedding similarity. Typos are realistic and happen every day. While humans can infer the original word from a typo and get the original meaning of the sentence, it will be interesting to see how the typos affect the sentences' similarity with the original sentences. The Random MLM subset can tell us how similar the sentence embedding can be when two sentences are semantically different but with high lexical overlaps. By comparing the performance of different SEs on different subsets in HEROS, we can further understand the trait of different SEs.
shujatoor/test_dataset-meaningful	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 30177 num_examples: 129 download_size: 16560 dataset_size: 30177 configs: - config_name: default data_files: - split: train path: data/train-* ---
emozilla/proofpile-test-tokenized	--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tokenized_len dtype: int64 splits: - name: test num_bytes: 1644067664 num_examples: 46251 download_size: 552973486 dataset_size: 1644067664 --- # Dataset Card for "proofpile-test-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/allen_m_sumner_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane) This is the dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane), containing 41 images and their tags. The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, hair_between_eyes, twintails, hair_ornament, medium_breasts, very_long_hair, bow, large_breasts, animal_ears, blue_hair`, 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 \| 41 \| 67.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 41 \| 35.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 104 \| 76.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 41 \| 57.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 104 \| 113.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_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/allen_m_sumner_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 \| 13 \| ![](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, double_bun, off_shoulder, official_alternate_costume, playboy_bunny, rabbit_ears, solo, black_jacket, black_leotard, long_sleeves, looking_at_viewer, open_jacket, fake_animal_ears, smile, hair_bow, underboob_cutout, braided_bun, brown_pantyhose, sitting, ass, tongue_out, bodystocking, closed_mouth, simple_background, sleeves_past_wrists, black_footwear, blush, shoes, white_background \| \| 1 \| 18 \| ![](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) \| looking_at_viewer, underboob_cutout, 1girl, solo, bare_shoulders, two-tone_leotard, off_shoulder, open_coat, black_leotard, open_mouth, skindentation, black_coat, blush, groin, long_sleeves, thigh_strap, badge, cowboy_shot, frilled_leotard, standing, sidelocks, :d, armpits, ass_visible_through_thighs, white_leotard \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| double_bun \| off_shoulder \| official_alternate_costume \| playboy_bunny \| rabbit_ears \| solo \| black_jacket \| black_leotard \| long_sleeves \| looking_at_viewer \| open_jacket \| fake_animal_ears \| smile \| hair_bow \| underboob_cutout \| braided_bun \| brown_pantyhose \| sitting \| ass \| tongue_out \| bodystocking \| closed_mouth \| simple_background \| sleeves_past_wrists \| black_footwear \| blush \| shoes \| white_background \| two-tone_leotard \| open_coat \| open_mouth \| skindentation \| black_coat \| groin \| thigh_strap \| badge \| cowboy_shot \| frilled_leotard \| standing \| sidelocks \| :d \| armpits \| ass_visible_through_thighs \| white_leotard \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-------------\|:---------------\|:-----------------------------\|:----------------\|:--------------\|:-------\|:---------------\|:----------------\|:---------------\|:--------------------\|:--------------\|:-------------------\|:--------\|:-----------\|:-------------------\|:--------------\|:------------------\|:----------\|:------\|:-------------\|:---------------\|:---------------\|:--------------------\|:----------------------\|:-----------------\|:--------\|:--------\|:-------------------\|:-------------------\|:------------\|:-------------\|:----------------\|:-------------\|:--------\|:--------------\|:--------\|:--------------\|:------------------\|:-----------\|:------------\|:-----\|:----------\|:-----------------------------\|:----------------\| \| 0 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 18 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| X \| \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875716	--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: csebuetnlp/mT5_multilingual_XLSum metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: csebuetnlp/mT5_multilingual_XLSum * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model.
AIBrainBox/guanaco-llama2-1k	--- license: apache-2.0 ---
iamnguyen/cdnc_law_eval	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: citation dtype: string - name: content dtype: string - name: question dtype: string splits: - name: train num_bytes: 153441 num_examples: 100 download_size: 71332 dataset_size: 153441 --- # Dataset Card for "cdnc_law_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KshitizPandya/GenzTranscribe-hi	--- task_categories: - automatic-speech-recognition language: - hi pretty_name: GenzTranscribe-hi size_categories: - 10K<n<100K viewer: True ---
chirunder/text_message_transliteration_1k	--- dataset_info: features: - name: text dtype: string - name: translations struct: - name: chinese dtype: string - name: hindi dtype: string - name: russian dtype: string - name: transliteration struct: - name: chinese dtype: string - name: hindi dtype: string - name: russian dtype: string splits: - name: train num_bytes: 609895 num_examples: 1000 download_size: 361488 dataset_size: 609895 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "text_message_transliteration_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FaalSa/f5	--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 79710 num_examples: 1 - name: validation num_bytes: 80190 num_examples: 1 - name: test num_bytes: 80670 num_examples: 1 download_size: 69957 dataset_size: 240570 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
presencesw/Llama_data_bad	--- dataset_info: features: - name: topic dtype: string - name: Evidence dtype: string - name: predict dtype: string - name: Label dtype: string - name: Claim dtype: string - name: eval dtype: int64 splits: - name: train num_bytes: 24278461 num_examples: 5781 download_size: 5443107 dataset_size: 24278461 configs: - config_name: default data_files: - split: train path: data/train-* ---
mpasila/DarkViperAU-QA-ChatML	--- language: - en --- This is unfinished, only meant for testing.
kaleemWaheed/twitter_dataset_1712982744	--- 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: 153449 num_examples: 409 download_size: 47466 dataset_size: 153449 configs: - config_name: default data_files: - split: train path: data/train-* ---
communityai/ise-uiuc___Magicoder-Evol-Instruct-110K	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 485762091.0 num_examples: 106878 download_size: 222135894 dataset_size: 485762091.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
robji/MYSD	--- license: unknown ---
dmrau/cqadupstack-unix	--- configs: - config_name: default data_files: - split: queries path: data/queries-* - split: corpus path: data/corpus-* dataset_info: features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: queries num_bytes: 72357 num_examples: 1072 - name: corpus num_bytes: 46102756 num_examples: 47382 download_size: 24571026 dataset_size: 46175113 --- # Dataset Card for "cqadupstack-unix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test	--- pretty_name: Evaluation run of Lazycuber/L2-7b-Orca-WVG-Test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Lazycuber/L2-7b-Orca-WVG-Test](https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test)\ \ 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_Lazycuber__L2-7b-Orca-WVG-Test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T20:44:34.027885](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test/blob/main/results_2023-10-26T20-44-34.027885.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\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.00048005108166191996,\n \"f1\": 0.07443687080536941,\n\ \ \"f1_stderr\": 0.0016342523738966323,\n \"acc\": 0.4119262338489193,\n\ \ \"acc_stderr\": 0.009880953290999535\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166191996,\n\ \ \"f1\": 0.07443687080536941,\n \"f1_stderr\": 0.0016342523738966323\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0803639120545868,\n \ \ \"acc_stderr\": 0.007488258573239077\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.01227364800875999\n\ \ }\n}\n```" repo_url: https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|arc:challenge\|25_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-10T15-39-37.735727.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T20_44_34.027885 path: - '/details_harness\|drop\|3_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-26T20-44-34.027885.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T20_44_34.027885 path: - '/details_harness\|gsm8k\|5_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-26T20-44-34.027885.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hellaswag\|10_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-10T15-39-37.735727.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_10T15_39_37.735727 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-10T15-39-37.735727.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T20_44_34.027885 path: - '/details_harness\|winogrande\|5_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-26T20-44-34.027885.parquet' - config_name: results data_files: - split: 2023_10_10T15_39_37.735727 path: - results_2023-10-10T15-39-37.735727.parquet - split: 2023_10_26T20_44_34.027885 path: - results_2023-10-26T20-44-34.027885.parquet - split: latest path: - results_2023-10-26T20-44-34.027885.parquet --- # Dataset Card for Evaluation run of Lazycuber/L2-7b-Orca-WVG-Test ## Dataset Description - Homepage: - Repository: https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test - 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 [Lazycuber/L2-7b-Orca-WVG-Test](https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test) 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_Lazycuber__L2-7b-Orca-WVG-Test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T20:44:34.027885](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test/blob/main/results_2023-10-26T20-44-34.027885.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": 0.002202181208053691, "em_stderr": 0.00048005108166191996, "f1": 0.07443687080536941, "f1_stderr": 0.0016342523738966323, "acc": 0.4119262338489193, "acc_stderr": 0.009880953290999535 }, "harness\|drop\|3": { "em": 0.002202181208053691, "em_stderr": 0.00048005108166191996, "f1": 0.07443687080536941, "f1_stderr": 0.0016342523738966323 }, "harness\|gsm8k\|5": { "acc": 0.0803639120545868, "acc_stderr": 0.007488258573239077 }, "harness\|winogrande\|5": { "acc": 0.7434885556432518, "acc_stderr": 0.01227364800875999 } } ``` ### 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]
open-source-metrics/optimum-dependents	--- license: apache-2.0 pretty_name: optimum metrics tags: - github-stars dataset_info: features: - name: name dtype: string - name: stars dtype: int64 - name: forks dtype: int64 splits: - name: package num_bytes: 555 num_examples: 13 - name: repository num_bytes: 3790 num_examples: 81 download_size: 6617 dataset_size: 4345 --- # optimum metrics This dataset contains metrics about the huggingface/optimum package. Number of repositories in the dataset: 19 Number of packages in the dataset: 6 ## Package dependents This contains the data available in the [used-by](https://github.com/huggingface/optimum/network/dependents) tab on GitHub. ### Package & Repository star count This section shows the package and repository star count, individually. Package \| Repository :-------------------------:\|:-------------------------: ![optimum-dependent package star count](./optimum-dependents/resolve/main/optimum-dependent_package_star_count.png) \| ![optimum-dependent repository star count](./optimum-dependents/resolve/main/optimum-dependent_repository_star_count.png) There are 0 packages that have more than 1000 stars. There are 0 repositories that have more than 1000 stars. The top 10 in each category are the following: Package [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24 [bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 10 Repository [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288 [marqo-ai/marqo](https://github.com/marqo-ai/marqo): 265 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114 [graphcore/tutorials](https://github.com/graphcore/tutorials): 65 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24 [philschmid/optimum-static-quantization](https://github.com/philschmid/optimum-static-quantization): 20 [philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 15 [girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 15 ### Package & Repository fork count This section shows the package and repository fork count, individually. Package \| Repository :-------------------------:\|:-------------------------: ![optimum-dependent package forks count](./optimum-dependents/resolve/main/optimum-dependent_package_forks_count.png) \| ![optimum-dependent repository forks count](./optimum-dependents/resolve/main/optimum-dependent_repository_forks_count.png) There are 0 packages that have more than 200 forks. There are 0 repositories that have more than 200 forks. The top 10 in each category are the following: Package [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3 [bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 2 Repository [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82 [graphcore/tutorials](https://github.com/graphcore/tutorials): 33 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18 [girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 14 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10 [marqo-ai/marqo](https://github.com/marqo-ai/marqo): 6 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6 [whatofit/LevelWordWithFreq](https://github.com/whatofit/LevelWordWithFreq): 5 [philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 3 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3
merve/my_notes	--- license: apache-2.0 --- ## My Notes 📓 This repository contains my lecture notes from graduate school on following topics 👇🏼 - Data Science: 8 cheatsheets - Machine Learning (follows [Tom Mitchell's book](http://www.cs.cmu.edu/~tom/mlbook.html)): 25 pages of notes - Statistics: 9 cheatsheets - Deep Learning: 12 cheatsheets, will upload more - Image Processing (follows [digital image processing book](https://www.amazon.fr/Digital-Image-Processing-Rafael-Gonzalez/dp/013168728X)): 21 cheatsheets - Data Structures and Algorithms (follows [this book by Goodrich](https://www.wiley.com/en-us/Data+Structures+and+Algorithms+in+Python-p-9781118549582)): 26 cheatsheets ✨ Some notes ✨ - Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews. - There might be very small Turkish notes in few of the pages, you can ignore them. - I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned! - It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository. Updates 🎉 - I uploaded hierarchical clustering and improved version of K-means. - I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under `Compiled PDF`s. - I uploaded Hugging Face cheatsheets.
open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp	--- pretty_name: Evaluation run of chasedreaminf/Dream-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chasedreaminf/Dream-7B-slerp](https://huggingface.co/chasedreaminf/Dream-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-16T20:05:27.586603](https://huggingface.co/datasets/open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp/blob/main/results_2024-02-16T20-05-27.586603.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\": 0.6507213038118388,\n\ \ \"acc_stderr\": 0.03214268781825063,\n \"acc_norm\": 0.6503644948036131,\n\ \ \"acc_norm_stderr\": 0.03280953874341137,\n \"mc1\": 0.4724602203182375,\n\ \ \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6184735043628918,\n\ \ \"mc2_stderr\": 0.015107906651203224\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6663822525597269,\n \"acc_stderr\": 0.013778687054176538,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6787492531368253,\n\ \ \"acc_stderr\": 0.004660025270817022,\n \"acc_norm\": 0.8634734116709819,\n\ \ \"acc_norm_stderr\": 0.0034264517445078474\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n\ \ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440679,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"\ acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\ : 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\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\"\ : 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\ acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.0364129708131373,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.0364129708131373\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.02126271940040697,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.02126271940040697\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.01358661921990334,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.01358661921990334\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.42905027932960893,\n\ \ \"acc_stderr\": 0.016553287863116037,\n \"acc_norm\": 0.42905027932960893,\n\ \ \"acc_norm_stderr\": 0.016553287863116037\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \ \ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4576271186440678,\n\ \ \"acc_stderr\": 0.012724296550980188,\n \"acc_norm\": 0.4576271186440678,\n\ \ \"acc_norm_stderr\": 0.012724296550980188\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.02619392354445412,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.02619392354445412\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4724602203182375,\n\ \ \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6184735043628918,\n\ \ \"mc2_stderr\": 0.015107906651203224\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8034727703235991,\n \"acc_stderr\": 0.011168120593569562\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7225170583775588,\n \ \ \"acc_stderr\": 0.01233344758104754\n }\n}\n```" repo_url: https://huggingface.co/chasedreaminf/Dream-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|arc:challenge\|25_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-16T20-05-27.586603.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|gsm8k\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hellaswag\|10_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-16T20-05-27.586603.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-16T20-05-27.586603.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '/details_harness\|winogrande\|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-16T20-05-27.586603.parquet' - config_name: results data_files: - split: 2024_02_16T20_05_27.586603 path: - results_2024-02-16T20-05-27.586603.parquet - split: latest path: - results_2024-02-16T20-05-27.586603.parquet --- # Dataset Card for Evaluation run of chasedreaminf/Dream-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [chasedreaminf/Dream-7B-slerp](https://huggingface.co/chasedreaminf/Dream-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T20:05:27.586603](https://huggingface.co/datasets/open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp/blob/main/results_2024-02-16T20-05-27.586603.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": 0.6507213038118388, "acc_stderr": 0.03214268781825063, "acc_norm": 0.6503644948036131, "acc_norm_stderr": 0.03280953874341137, "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6184735043628918, "mc2_stderr": 0.015107906651203224 }, "harness\|arc:challenge\|25": { "acc": 0.6663822525597269, "acc_stderr": 0.013778687054176538, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.013572657703084948 }, "harness\|hellaswag\|10": { "acc": 0.6787492531368253, "acc_stderr": 0.004660025270817022, "acc_norm": 0.8634734116709819, "acc_norm_stderr": 0.0034264517445078474 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.032081157507886836, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.032081157507886836 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440679, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440679 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7677419354838709, "acc_stderr": 0.024022256130308235, "acc_norm": 0.7677419354838709, "acc_norm_stderr": 0.024022256130308235 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { 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"acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.027599174300640766, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640766 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7932489451476793, "acc_stderr": 0.0263616516683891, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.0263616516683891 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.0364129708131373, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.0364129708131373 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.02126271940040697, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.02126271940040697 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8250319284802043, "acc_stderr": 0.01358661921990334, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.01358661921990334 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7485549132947977, "acc_stderr": 0.02335736578587403, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.02335736578587403 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.42905027932960893, "acc_stderr": 0.016553287863116037, "acc_norm": 0.42905027932960893, "acc_norm_stderr": 0.016553287863116037 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4576271186440678, "acc_stderr": 0.012724296550980188, "acc_norm": 0.4576271186440678, "acc_norm_stderr": 0.012724296550980188 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396556, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396556 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.02619392354445412, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.02619392354445412 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6184735043628918, "mc2_stderr": 0.015107906651203224 }, "harness\|winogrande\|5": { "acc": 0.8034727703235991, "acc_stderr": 0.011168120593569562 }, "harness\|gsm8k\|5": { "acc": 0.7225170583775588, "acc_stderr": 0.01233344758104754 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
zolak/twitter_dataset_50_1713181945	--- 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: 374526 num_examples: 897 download_size: 184190 dataset_size: 374526 configs: - config_name: default data_files: - split: train path: data/train-* ---
zolak/twitter_dataset_80_1713203438	--- 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: 4036256 num_examples: 10039 download_size: 2034440 dataset_size: 4036256 configs: - config_name: default data_files: - split: train path: data/train-* ---
learnanything/feedback-series	--- license: other ---
HuggingFaceM4/enwiki-v2_valid	Invalid username or password.
louam123/darja-tounsi1	--- task_categories: - translation language: - ar - en tags: - darja - tunisian pretty_name: 'darja-tounsi-louam ' size_categories: - 1K<n<10K ---
baptistecolle/sam-controlnet-3	--- dataset_info: features: - name: image dtype: image - name: filepath dtype: string - name: sentids sequence: int64 - name: filename dtype: string - name: imgid dtype: int64 - name: split dtype: string - name: sentences struct: - name: imgid dtype: int64 - name: raw dtype: string - name: sentid dtype: int64 - name: tokens sequence: string - name: cocoid dtype: int64 - name: masks sequence: sequence: sequence: bool - name: scores sequence: float32 splits: - name: train num_bytes: 2086529.0 num_examples: 1 download_size: 150908 dataset_size: 2086529.0 --- # Dataset Card for "sam-controlnet-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rthatha/GLDv2-All-Caption-Filename	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 109018225.0 num_examples: 203 download_size: 108837389 dataset_size: 109018225.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
hlillemark/flores200_devtest_mt5-600m-flores200-baseline	--- dataset_info: features: - name: id dtype: int32 - name: source_lang dtype: string - name: target_lang dtype: string - name: source dtype: string - name: target dtype: string - name: prediction dtype: string - name: chrf_unreduced dtype: string splits: - name: devtest num_bytes: 734237740 num_examples: 1000000 download_size: 514219403 dataset_size: 734237740 --- # Dataset Card for "flores200_devtest_mt5-600m-flores200-baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
marup/kotonoha-sisters-rvc-50epochs	--- license: openrail ---
Nexdata/20_People_Chinese_Mandarin_Multi_emotional_Synthesis_Corpus	--- license: cc-by-nc-nd-4.0 --- ## Description 20 People - Chinese Mandarin Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker, covering different ages and genders. seven emotional texts, are all from novels and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/dataset/1213?source=Huggingface ## Format 48,000Hz, 24bit, uncompressed wav, mono channel ## Recording environment professional recording studio ## Recording content seven emotions (happiness, anger, sadness, surprise, fear, disgust, neutral) ；texts are all from novels ## Speaker 20 persons, different age groups and genders ## Device microphone ## Language Mandarin ## Annotation word and pinyin transcription, prosodic boundary annotation ## Application scenarios speech synthesis ## The amount of data The amount of data for per person is 140 minutes, each emotion is 20 minutes # Licensing Information Commercial License
codedog-lee/llama2-tut	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---
showery/my_dataset	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 641917.0 num_examples: 22 download_size: 0 dataset_size: 641917.0 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kirim9001/Tryin	--- license: other ---
mach-12/ecommerce-ner-conll-2003-data	--- license: mit dataset_info: features: - name: tokens sequence: string - name: pos_tag sequence: int64 - name: chunk_tag sequence: int64 - name: ner_tag sequence: int64 - name: sentiment dtype: string splits: - name: train num_bytes: 13886708.0 num_examples: 31520 - name: test num_bytes: 3471677.0 num_examples: 7880 download_size: 1323190 dataset_size: 17358385.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
pwc-india/fabric_dataset	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 41259319.0 num_examples: 20 download_size: 41261924 dataset_size: 41259319.0 --- # Dataset Card for "fabric_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/fc019c9e	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1340 dataset_size: 182 --- # Dataset Card for "fc019c9e" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LucasThil/miniwob_snippets	--- dataset_info: features: - name: episodes dtype: string - name: refs dtype: int64 - name: click dtype: int64 - name: dblclick dtype: int64 - name: keydown dtype: int64 - name: keypress dtype: int64 - name: keyup dtype: int64 - name: mousedown dtype: int64 - name: mouseup dtype: int64 - name: scroll dtype: int64 splits: - name: train num_bytes: 674906155 num_examples: 587268 - name: test num_bytes: 84528980 num_examples: 73420 - name: validate num_bytes: 84695448 num_examples: 73318 download_size: 140471741 dataset_size: 844130583 --- # Dataset Card for "miniwob_snippets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rishikesh/mini-sarcasm-data	--- license: mit ---
Wulichao/single_cell_RNA_seq	--- license: mit ---
arthurmluz/wikilingua_data-wiki_temario_results	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 23695646 num_examples: 8165 download_size: 14096829 dataset_size: 23695646 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "wikilingua_data-wiki_temario_results" rouge= {'rouge1': 0.32186631280345346, 'rouge2': 0.11643663933189041, 'rougeL': 0.22613896268852546, 'rougeLsum': 0.22613896268852546} bert= {'precision': 0.7288013449272931, 'recall': 0.7668859778849245, 'f1': 0.7463569133178047} mover = 0.6063795152381846
njoeljack/hai-babi	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1465597.0 num_examples: 50 download_size: 1465887 dataset_size: 1465597.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
yiyic/clirmatrix_query_doc	--- dataset_info: features: - name: text dtype: string - name: __index_level_0__ dtype: string splits: - name: de_en_multi8_test1 num_bytes: 1342125 num_examples: 2000 - name: de_fr_multi8_test1 num_bytes: 1346729 num_examples: 2000 - name: de_es_multi8_test1 num_bytes: 1346235 num_examples: 2000 - name: en_de_multi8_test1 num_bytes: 1155356 num_examples: 2000 - name: en_fr_multi8_test1 num_bytes: 1158530 num_examples: 2000 - name: en_es_multi8_test1 num_bytes: 1158036 num_examples: 2000 - name: es_en_multi8_test1 num_bytes: 1127237 num_examples: 2000 - name: es_fr_multi8_test1 num_bytes: 1131841 num_examples: 2000 - name: es_de_multi8_test1 num_bytes: 1128667 num_examples: 2000 - name: fr_en_multi8_test1 num_bytes: 1168520 num_examples: 2000 - name: fr_de_multi8_test1 num_bytes: 1169950 num_examples: 2000 - name: fr_es_multi8_test1 num_bytes: 1172630 num_examples: 2000 download_size: 8905953 dataset_size: 14405856 configs: - config_name: default data_files: - split: de_en_multi8_test1 path: data/de_en_multi8_test1-* - split: de_fr_multi8_test1 path: data/de_fr_multi8_test1-* - split: de_es_multi8_test1 path: data/de_es_multi8_test1-* - split: en_de_multi8_test1 path: data/en_de_multi8_test1-* - split: en_fr_multi8_test1 path: data/en_fr_multi8_test1-* - split: en_es_multi8_test1 path: data/en_es_multi8_test1-* - split: es_en_multi8_test1 path: data/es_en_multi8_test1-* - split: es_fr_multi8_test1 path: data/es_fr_multi8_test1-* - split: es_de_multi8_test1 path: data/es_de_multi8_test1-* - split: fr_en_multi8_test1 path: data/fr_en_multi8_test1-* - split: fr_de_multi8_test1 path: data/fr_de_multi8_test1-* - split: fr_es_multi8_test1 path: data/fr_es_multi8_test1-* ---
JuneKo/book_sf_ch	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1889814.0 num_examples: 39 download_size: 1847651 dataset_size: 1889814.0 --- # Dataset Card for "book_sf_ch" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tashkent_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tashkent/タシュケント/塔什干 (Azur Lane) This is the dataset of tashkent/タシュケント/塔什干 (Azur Lane), containing 400 images and their tags. The core tags of this character are `long_hair, purple_hair, blue_eyes, very_long_hair, bangs, animal_ears, ribbon, hair_ribbon, fake_animal_ears, hair_ornament`, 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 \| 400 \| 686.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 400 \| 330.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1039 \| 759.67 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 400 \| 577.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1039 \| 1.15 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/tashkent_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/tashkent_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 \| 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, blush, brown_sweater, fur_trim, looking_at_viewer, solo, thigh_boots, white_coat, white_thighhighs, winter_clothes, full_body, open_mouth, simple_background, sleeves_past_fingers, very_long_sleeves, white_background, white_footwear, dress, pom_pom_(clothes), smile, standing, sitting, torpedo_tubes \| \| 1 \| 16 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, dress, long_sleeves, looking_at_viewer, solo, white_coat, sleeves_past_fingers, white_background, white_thighhighs, winter_clothes, brown_sweater, open_mouth, simple_background, ahoge, fur-trimmed_sleeves, smile \| \| 2 \| 12 \| ![](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, hair_bow, looking_at_viewer, solo, bare_shoulders, fur_trim, blush, paw_gloves, plaid, purple_skirt, white_thighhighs, white_background, open_mouth, off_shoulder, simple_background \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, looking_at_viewer, solo, blush, navel, nipples, nude, small_breasts, white_thighhighs, pussy, ass, collar, ponytail, tongue_out \| \| 4 \| 55 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, white_shirt, blush, ponytail, solo, white_thighhighs, black_shorts, looking_at_viewer, suspender_shorts, chain, short_shorts, collar, bound_arms, open_mouth, sitting, cuffs, necktie \| \| 5 \| 22 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, blush, solo, white_shirt, looking_at_viewer, long_sleeves, off_shoulder, naked_shirt, collared_shirt, sitting, collarbone, barefoot, dress_shirt, bare_shoulders, official_alternate_costume, ahoge, closed_mouth, small_breasts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| brown_sweater \| fur_trim \| looking_at_viewer \| solo \| thigh_boots \| white_coat \| white_thighhighs \| winter_clothes \| full_body \| open_mouth \| simple_background \| sleeves_past_fingers \| very_long_sleeves \| white_background \| white_footwear \| dress \| pom_pom_(clothes) \| smile \| standing \| sitting \| torpedo_tubes \| long_sleeves \| ahoge \| fur-trimmed_sleeves \| hair_bow \| bare_shoulders \| paw_gloves \| plaid \| purple_skirt \| off_shoulder \| navel \| nipples \| nude \| small_breasts \| pussy \| ass \| collar \| ponytail \| tongue_out \| white_shirt \| black_shorts \| suspender_shorts \| chain \| short_shorts \| bound_arms \| cuffs \| necktie \| naked_shirt \| collared_shirt \| collarbone \| barefoot \| dress_shirt \| official_alternate_costume \| closed_mouth \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:----------------\|:-----------\|:--------------------\|:-------\|:--------------\|:-------------\|:-------------------\|:-----------------\|:------------\|:-------------\|:--------------------\|:-----------------------\|:--------------------\|:-------------------\|:-----------------\|:--------\|:--------------------\|:--------\|:-----------\|:----------\|:----------------\|:---------------\|:--------\|:----------------------\|:-----------\|:-----------------\|:-------------\|:--------\|:---------------\|:---------------\|:--------\|:----------\|:-------\|:----------------\|:--------\|:------\|:---------\|:-----------\|:-------------\|:--------------\|:---------------\|:-------------------\|:--------\|:---------------\|:-------------\|:--------\|:----------\|:--------------\|:-----------------\|:-------------\|:-----------\|:--------------\|:-----------------------------\|:---------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 16 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| \| X \| \| X \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 12 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 55 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| X \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| 5 \| 22 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \|
tyson0420/stackexchange-4dpo-filby-ai-keywords	--- license: cc-by-4.0 ---
davanstrien/fuego-20230428-014702-04e0f5	--- tags: - fuego fuego: id: 20230428-014702-04e0f5 status: running script: scratchpad.py requirements_file: requirements.txt space_id: davanstrien/fuego-20230428-014702-04e0f5 space_hardware: cpu-basic ---
liuyanchen1015/MULTI_VALUE_mrpc_negative_inversion	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: train num_bytes: 3645 num_examples: 13 - name: validation num_bytes: 294 num_examples: 1 download_size: 10298 dataset_size: 3939 --- # Dataset Card for "MULTI_VALUE_mrpc_negative_inversion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lionelchg/dolly-information-extraction	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: text dtype: string splits: - name: train num_bytes: 4656769.3625498 num_examples: 1430 - name: test num_bytes: 247492.6374501992 num_examples: 76 download_size: 2854506 dataset_size: 4904262.0 --- # Dataset Card for "dolly-information-extraction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1713199899	--- 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: 20766 num_examples: 57 download_size: 19065 dataset_size: 20766 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713199899" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Isamu136/oxford_pets_with_l14_emb	--- dataset_info: features: - name: path dtype: string - name: label dtype: string - name: dog dtype: bool - name: image dtype: image - name: embeddings sequence: float32 splits: - name: train num_bytes: 257189204.25 num_examples: 7390 download_size: 261518905 dataset_size: 257189204.25 --- # Dataset Card for "oxford_pets_with_l14_emb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b	--- pretty_name: Evaluation run of logicker/SkkuDataScienceGlobal-10.7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [logicker/SkkuDataScienceGlobal-10.7b](https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T13:43:44.357190](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b/blob/main/results_2024-01-04T13-43-44.357190.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\": 0.6670223395120712,\n\ \ \"acc_stderr\": 0.031613933491978684,\n \"acc_norm\": 0.6677265646501975,\n\ \ \"acc_norm_stderr\": 0.03225832144808963,\n \"mc1\": 0.5716034271725826,\n\ \ \"mc1_stderr\": 0.017323088597314747,\n \"mc2\": 0.7192487275907953,\n\ \ \"mc2_stderr\": 0.015001105564856102\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6851535836177475,\n \"acc_stderr\": 0.01357265770308495,\n\ \ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266125\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7131049591714798,\n\ \ \"acc_stderr\": 0.004513877465062106,\n \"acc_norm\": 0.8840868352917746,\n\ \ \"acc_norm_stderr\": 0.003194665266078602\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n\ \ \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.040131241954243856,\n\ \ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.040131241954243856\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\ acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267822,\n \"\ acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267822\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8481012658227848,\n \"acc_stderr\": 0.023363878096632446,\n \ \ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.023363878096632446\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8045977011494253,\n\ \ \"acc_stderr\": 0.014179171373424383,\n \"acc_norm\": 0.8045977011494253,\n\ \ \"acc_norm_stderr\": 0.014179171373424383\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123563,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123563\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.394413407821229,\n\ \ \"acc_stderr\": 0.01634538676210397,\n \"acc_norm\": 0.394413407821229,\n\ \ \"acc_norm_stderr\": 0.01634538676210397\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0227797190887334,\n\ \ \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0227797190887334\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4954367666232073,\n\ \ \"acc_stderr\": 0.012769704263117522,\n \"acc_norm\": 0.4954367666232073,\n\ \ \"acc_norm_stderr\": 0.012769704263117522\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103128,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103128\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5716034271725826,\n\ \ \"mc1_stderr\": 0.017323088597314747,\n \"mc2\": 0.7192487275907953,\n\ \ \"mc2_stderr\": 0.015001105564856102\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781093\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6573161485974223,\n \ \ \"acc_stderr\": 0.013073030230827915\n }\n}\n```" repo_url: https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|arc:challenge\|25_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-04T13-43-44.357190.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|gsm8k\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hellaswag\|10_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-43-44.357190.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T13-43-44.357190.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '/details_harness\|winogrande\|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-04T13-43-44.357190.parquet' - config_name: results data_files: - split: 2024_01_04T13_43_44.357190 path: - results_2024-01-04T13-43-44.357190.parquet - split: latest path: - results_2024-01-04T13-43-44.357190.parquet --- # Dataset Card for Evaluation run of logicker/SkkuDataScienceGlobal-10.7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [logicker/SkkuDataScienceGlobal-10.7b](https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T13:43:44.357190](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b/blob/main/results_2024-01-04T13-43-44.357190.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": 0.6670223395120712, "acc_stderr": 0.031613933491978684, "acc_norm": 0.6677265646501975, "acc_norm_stderr": 0.03225832144808963, "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314747, "mc2": 0.7192487275907953, "mc2_stderr": 0.015001105564856102 }, "harness\|arc:challenge\|25": { "acc": 0.6851535836177475, "acc_stderr": 0.01357265770308495, "acc_norm": 0.712457337883959, "acc_norm_stderr": 0.013226719056266125 }, "harness\|hellaswag\|10": { "acc": 0.7131049591714798, "acc_stderr": 0.004513877465062106, "acc_norm": 0.8840868352917746, "acc_norm_stderr": 0.003194665266078602 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.756578947368421, "acc_stderr": 0.034923496688842384, "acc_norm": 0.756578947368421, "acc_norm_stderr": 0.034923496688842384 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956913, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.625531914893617, "acc_stderr": 0.03163910665367291, "acc_norm": 0.625531914893617, "acc_norm_stderr": 0.03163910665367291 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6344827586206897, "acc_stderr": 0.040131241954243856, "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.040131241954243856 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4947089947089947, "acc_stderr": 0.02574986828855657, "acc_norm": 0.4947089947089947, "acc_norm_stderr": 0.02574986828855657 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8161290322580645, "acc_stderr": 0.022037217340267822, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.022037217340267822 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656209, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656209 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7184873949579832, "acc_stderr": 0.02921354941437217, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.02921354941437217 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.033622774366080424, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.033622774366080424 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156862, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156862 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8481012658227848, "acc_stderr": 0.023363878096632446, "acc_norm": 0.8481012658227848, "acc_norm_stderr": 0.023363878096632446 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8543689320388349, "acc_stderr": 0.03492606476623791, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.03492606476623791 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.0230866350868414, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.0230866350868414 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8045977011494253, "acc_stderr": 0.014179171373424383, "acc_norm": 0.8045977011494253, "acc_norm_stderr": 0.014179171373424383 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7601156069364162, "acc_stderr": 0.022989592543123563, "acc_norm": 0.7601156069364162, "acc_norm_stderr": 0.022989592543123563 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.394413407821229, "acc_stderr": 0.01634538676210397, "acc_norm": 0.394413407821229, "acc_norm_stderr": 0.01634538676210397 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.729903536977492, "acc_stderr": 0.02521804037341062, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341062 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0227797190887334, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0227797190887334 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4954367666232073, "acc_stderr": 0.012769704263117522, "acc_norm": 0.4954367666232073, "acc_norm_stderr": 0.012769704263117522 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7389705882352942, "acc_stderr": 0.026679252270103128, "acc_norm": 0.7389705882352942, "acc_norm_stderr": 0.026679252270103128 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6813725490196079, "acc_stderr": 0.018850084696468712, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.018850084696468712 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142783, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142783 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03188578017686398, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03188578017686398 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314747, "mc2": 0.7192487275907953, "mc2_stderr": 0.015001105564856102 }, "harness\|winogrande\|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781093 }, "harness\|gsm8k\|5": { "acc": 0.6573161485974223, "acc_stderr": 0.013073030230827915 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
freshpearYoon/vr_train_free_66	--- 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: 6222416946 num_examples: 10000 download_size: 935984914 dataset_size: 6222416946 configs: - config_name: default data_files: - split: train path: data/train-* ---
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-25000	--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 6316415857 num_examples: 1000 download_size: 1243432299 dataset_size: 6316415857 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-d42d3c12-7815008	--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: PdF/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: PdF/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.
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84812225.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 86719164.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90542888.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 94354856.125 num_examples: 5647 download_size: 333860025 dataset_size: 356429133.5 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NepaliAI/Nepali-HealthChat	--- license: apache-2.0 task_categories: - question-answering language: - ne tags: - medical - health ---
paullatham1/headlines-train-balanced	--- dataset_info: features: - name: data dtype: string - name: is_sarcastic dtype: int64 splits: - name: train num_bytes: 1440540 num_examples: 19632 download_size: 969261 dataset_size: 1440540 configs: - config_name: default data_files: - split: train path: data/train-* ---
ismaeltorres00/Dataset_de_prueba	--- size_categories: - 1K<n<10K --- Extraído de <https://github.com/anthony-wang/BestPractices/tree/master/data>. Campos: * Formula (`string`) * T (`float64`): Temperatura (K) * CP (`float64`): Capacidad calorífica (J/mol K)
Nexdata/96_Hours_Japanese_Children_Real_world_Casual_Conversation_and_Monologue_speech_dataset	--- license: cc-by-nc-nd-4.0 --- ## Description Japanese(Japan) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks.rnQuality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied. For more details, please refer to the link: https://www.nexdata.ai/dataset/1328?source=Huggingface ## Format 16kHz, 16 bit, wav, mono channel ## Age 12 years old and younger children ## Content category including interview, self-meida,variety show, etc. ## Recording environment Low background noise ## Country Japan(JPN) ## Language(Region) Code ja-JP ## Language Japanese ## Features of annotation Transcription text, timestamp, speaker ID, gender, noise ## Accuracy Word Accuracy Rate (WAR) 98% # Licensing Information Commercial License
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667118	--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_v5 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-13b_eval metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_v5 dataset_config: mathemakitten--winobias_antistereotype_test_v5 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: inverse-scaling/opt-13b_eval * Dataset: mathemakitten/winobias_antistereotype_test_v5 * Config: mathemakitten--winobias_antistereotype_test_v5 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

lukebarousse/data_jobs

--- license: apache-2.0 ---

Nooha/job_categorization_dataset

--- dataset_info: features: - name: title dtype: string - name: category dtype: string splits: - name: train num_bytes: 435029 num_examples: 6539 download_size: 164148 dataset_size: 435029 configs: - config_name: default data_files: - split: train path: data/train-* ---

heliosprime/twitter_dataset_1713032076

--- 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: 12085 num_examples: 28 download_size: 9998 dataset_size: 12085 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713032076" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kilt_tasks

--- annotations_creators: - crowdsourced - found - machine-generated language_creators: - crowdsourced - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended|natural_questions - extended|other-aidayago - extended|other-fever - extended|other-hotpotqa - extended|other-trex - extended|other-triviaqa - extended|other-wizardsofwikipedia - extended|other-wned-cweb - extended|other-wned-wiki - extended|other-zero-shot-re - original task_categories: - fill-mask - question-answering - text-classification - text-generation - text-retrieval - text2text-generation task_ids: - abstractive-qa - dialogue-modeling - document-retrieval - entity-linking-retrieval - extractive-qa - fact-checking - fact-checking-retrieval - open-domain-abstractive-qa - open-domain-qa - slot-filling paperswithcode_id: kilt pretty_name: KILT config_names: - aidayago2 - cweb - eli5 - fever - hotpotqa - nq - structured_zeroshot - trex - triviaqa_support_only - wned - wow dataset_info: - config_name: aidayago2 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 68943890 num_examples: 18395 - name: validation num_bytes: 20743172 num_examples: 4784 - name: test num_bytes: 14210587 num_examples: 4463 download_size: 13419920 dataset_size: 103897649 - config_name: cweb features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 89819252 num_examples: 5599 - name: test num_bytes: 99208393 num_examples: 5543 download_size: 32809813 dataset_size: 189027645 - config_name: eli5 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 525554458 num_examples: 272634 - name: validation num_bytes: 13860033 num_examples: 1507 - name: test num_bytes: 107092 num_examples: 600 download_size: 329302944 dataset_size: 539521583 - config_name: fever features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 23937486 num_examples: 104966 - name: validation num_bytes: 3167751 num_examples: 10444 - name: test num_bytes: 1040116 num_examples: 10100 download_size: 11571038 dataset_size: 28145353 - config_name: hotpotqa features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 33595295 num_examples: 88869 - name: validation num_bytes: 2371262 num_examples: 5600 - name: test num_bytes: 887204 num_examples: 5569 download_size: 17914796 dataset_size: 36853761 - config_name: nq features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 30385368 num_examples: 87372 - name: validation num_bytes: 6190373 num_examples: 2837 - name: test num_bytes: 333162 num_examples: 1444 download_size: 16535475 dataset_size: 36908903 - config_name: structured_zeroshot features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 47165561 num_examples: 147909 - name: validation num_bytes: 1612123 num_examples: 3724 - name: test num_bytes: 1140265 num_examples: 4966 download_size: 21038900 dataset_size: 49917949 - config_name: trex features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 1190183022 num_examples: 2284168 - name: validation num_bytes: 2573444 num_examples: 5000 - name: test num_bytes: 757470 num_examples: 5000 download_size: 546671157 dataset_size: 1193513936 - config_name: triviaqa_support_only features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 72021515 num_examples: 61844 - name: validation num_bytes: 6824398 num_examples: 5359 - name: test num_bytes: 340692 num_examples: 6586 download_size: 31946196 dataset_size: 79186605 - config_name: wned features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 12659518 num_examples: 3396 - name: test num_bytes: 13080824 num_examples: 3376 download_size: 3608615 dataset_size: 25740342 - config_name: wow features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 41870938 num_examples: 63734 - name: validation num_bytes: 2021752 num_examples: 3054 - name: test num_bytes: 1339546 num_examples: 2944 download_size: 25441975 dataset_size: 45232236 configs: - config_name: aidayago2 data_files: - split: train path: aidayago2/train-* - split: validation path: aidayago2/validation-* - split: test path: aidayago2/test-* - config_name: cweb data_files: - split: validation path: cweb/validation-* - split: test path: cweb/test-* - config_name: eli5 data_files: - split: train path: eli5/train-* - split: validation path: eli5/validation-* - split: test path: eli5/test-* - config_name: fever data_files: - split: train path: fever/train-* - split: validation path: fever/validation-* - split: test path: fever/test-* - config_name: hotpotqa data_files: - split: train path: hotpotqa/train-* - split: validation path: hotpotqa/validation-* - split: test path: hotpotqa/test-* - config_name: nq data_files: - split: train path: nq/train-* - split: validation path: nq/validation-* - split: test path: nq/test-* default: true - config_name: structured_zeroshot data_files: - split: train path: structured_zeroshot/train-* - split: validation path: structured_zeroshot/validation-* - split: test path: structured_zeroshot/test-* - config_name: trex data_files: - split: train path: trex/train-* - split: validation path: trex/validation-* - split: test path: trex/test-* - config_name: triviaqa_support_only data_files: - split: train path: triviaqa_support_only/train-* - split: validation path: triviaqa_support_only/validation-* - split: test path: triviaqa_support_only/test-* - config_name: wned data_files: - split: validation path: wned/validation-* - split: test path: wned/test-* - config_name: wow data_files: - split: train path: wow/train-* - split: validation path: wow/validation-* - split: test path: wow/test-* --- # Dataset Card for KILT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ai.facebook.com/tools/kilt/ - **Repository:** https://github.com/facebookresearch/KILT - **Paper:** https://arxiv.org/abs/2009.02252 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/ - **Point of Contact:** [Needs More Information] ### Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: - Fact-checking - Entity linking - Slot filling - Open domain QA - Dialog generation All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ```python from datasets import load_dataset # Get the pre-processed Wikipedia knowledge source for kild kilt_wiki = load_dataset("kilt_wikipedia") # Get the KILT task datasets kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only") # Most tasks in KILT already have all required data, but KILT-TriviaQA # only provides the question IDs, not the questions themselves. # Thankfully, we can get the original TriviaQA data with: trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # The KILT IDs can then be mapped to the TriviaQA questions with: triviaqa_map = {} def add_missing_data(x, trivia_qa_subset, triviaqa_map): i = triviaqa_map[x['id']] x['input'] = trivia_qa_subset[i]['question'] x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value'] return x for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])]) kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map) kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map)) ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure ### Data Instances An example of open-domain QA from the Natural Questions `nq` configuration looks as follows: ``` {'id': '-5004457603684974952', 'input': 'who is playing the halftime show at super bowl 2016', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'Coldplay', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 186, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 178, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Beyoncé', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 224, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 217, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Bruno Mars', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 239, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 229, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 0.9657992720603943, 'end_character': 341, 'end_paragraph_id': 1, 'meta': {'annotation_id': '2430977867500315580', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': 'NONE'}, 'section': 'Section::::Abstract.', 'start_character': 0, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': -1.0, 'end_character': -1, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.', "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars", "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': -1, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}]} ``` ### Data Fields Examples from all configurations have the following features: - `input`: a `string` feature representing the query. - `output`: a `list` of features each containing information for an answer, made up of: - `answer`: a `string` feature representing a possible answer. - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by: - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from. - `section`: a `string` feature, the title of the section in Wikipedia article. - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article. - `start_character`: a `int32` feature. - `start_paragraph_id`: a `int32` feature. - `end_character`: a `int32` feature. - `end_paragraph_id`: a `int32` feature. ### Data Splits The configurations have the following splits: | | Train | Validation | Test | | ----------- | ----------- | ----------- | ----------- | | triviaqa | 61844 | 5359 | 6586 | | fever | 104966 | 10444 | 10100 | | aidayago2 | 18395 | 4784 | 4463 | | wned | | 3396 | 3376 | | cweb | | 5599 | 5543 | | trex | 2284168 | 5000 | 5000 | | structured_zeroshot | 147909 | 3724 | 4966 | | nq | 87372 | 2837 | 1444 | | hotpotqa | 88869 | 5600 | 5569 | | eli5 | 272634 | 1507 | 600 | | wow | 94577 | 3058 | 2944 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{kilt_tasks, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick S. H. Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vladimir Karpukhin and Jean Maillard and Vassilis Plachouras and Tim Rockt{\"{a}}schel and Sebastian Riedel}, editor = {Kristina Toutanova and Anna Rumshisky and Luke Zettlemoyer and Dilek Hakkani{-}T{\"{u}}r and Iz Beltagy and Steven Bethard and Ryan Cotterell and Tanmoy Chakraborty and Yichao Zhou}, title = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021, Online, June 6-11, 2021}, pages = {2523--2544}, publisher = {Association for Computational Linguistics}, year = {2021}, url = {https://www.aclweb.org/anthology/2021.naacl-main.200/} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.

hpprc/jawiki-books

--- dataset_info: config_name: '2024-04-01' features: - name: id dtype: int64 - name: title dtype: string - name: text dtype: string - name: paragraphs list: - name: paragraph_id dtype: int64 - name: tag dtype: string - name: text dtype: string - name: title dtype: string - name: abstract dtype: string - name: wikitext dtype: string - name: date_created dtype: string - name: date_modified dtype: string - name: templates sequence: string - name: url dtype: string splits: - name: train num_bytes: 172222533.07899398 num_examples: 13490 download_size: 74095911 dataset_size: 172222533.07899398 configs: - config_name: '2024-04-01' data_files: - split: train path: 2024-04-01/train-* license: cc-by-sa-3.0 language: - ja ---

TuringsSolutions/FoolTheGANDataset

--- license: other license_name: onlyuseifyoucontactmefirstlicense license_link: LICENSE ---

najju/sign-psl-13b-new

--- dataset_info: features: - name: Text dtype: string - name: Gloss dtype: string splits: - name: train num_bytes: 347110 num_examples: 5446 download_size: 215108 dataset_size: 347110 configs: - config_name: default data_files: - split: train path: data/train-* ---

Jingmiao/PUZZLEQA

--- language: - en license: apache-2.0 --- ### Acknowledgements The PUZZLEQA is scraped from [NPR Sunday Puzzle Official Website](https://www.npr.org/series/4473090/sunday-puzzle) and [NPR Puzzle Synopsis](https://groups.google.com/g/nprpuzzle), made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle. The authors of the dataset cleaned the data and made some multiple choice based on the question and answers. ### Creation The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 1. Read the fr_big_exp.tsv.tsv file 2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\ 4. identify the correct answer for the given question as the "gold" answer. Recent.tsv is the dataset based on the NPR PUZZLE in 2023. # Citation @inproceedings{zhao2023solving, title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, author={Jingmiao Zhao and Carolyn Jane Anderson}, year={2023}, eprint={2306.12255}, archivePrefix={arXiv}, primaryClass={cs.CL} }

awettig/Pile-Gutenberg-0.5B-6K-opt

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 6500959920 num_examples: 81380 - name: test num_bytes: 64945692 num_examples: 813 download_size: 1706776857 dataset_size: 6565905612 --- # Dataset Card for "Pile-Gutenberg-0.5B-6K-opt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Multimodal-Fatima/DTD_parition1_test

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': banded '1': blotchy '2': braided '3': bubbly '4': bumpy '5': chequered '6': cobwebbed '7': cracked '8': crosshatched '9': crystalline '10': dotted '11': fibrous '12': flecked '13': freckled '14': frilly '15': gauzy '16': grid '17': grooved '18': honeycombed '19': interlaced '20': knitted '21': lacelike '22': lined '23': marbled '24': matted '25': meshed '26': paisley '27': perforated '28': pitted '29': pleated '30': polka-dotted '31': porous '32': potholed '33': scaly '34': smeared '35': spiralled '36': sprinkled '37': stained '38': stratified '39': striped '40': studded '41': swirly '42': veined '43': waffled '44': woven '45': wrinkled '46': zigzagged - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: LLM_Description_opt175b_downstream_tasks_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: clip_tag_ViT_L_14_specific dtype: string - name: clip_tags_ViT_L_14_ensemble_specific dtype: string - name: clip_tags_ViT_L_14_simple_specific dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: Attributes_ViT_L_14_text_davinci_003 sequence: string - name: Attributes_ViT_L_14_text_davinci_003_dtd sequence: string - name: Attributes_ViT_L_14_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_with_openai_classes sequence: string - name: clip_tags_ViT_L_14_wo_openai_classes sequence: string - name: clip_tags_ViT_B_16_simple_specific dtype: string - name: clip_tags_ViT_B_16_ensemble_specific dtype: string - name: clip_tags_ViT_B_32_simple_specific dtype: string - name: clip_tags_ViT_B_32_ensemble_specific dtype: string - name: Attributes_ViT_B_16_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_LAION_ViT_H_14_2B_simple_specific dtype: string - name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific dtype: string splits: - name: test num_bytes: 184279525.4 num_examples: 1880 download_size: 180002375 dataset_size: 184279525.4 --- # Dataset Card for "DTD_parition1_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

erichilarysmithsr/dovichousesimulation

--- license: ncsa ---

CyberHarem/akari_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of akari/鰐渕アカリ/明里 (Blue Archive) This is the dataset of akari/鰐渕アカリ/明里 (Blue Archive), containing 152 images and their tags. The core tags of this character are `blonde_hair, long_hair, horns, halo, breasts, ahoge, large_breasts, blue_eyes, braid, hair_between_eyes, demon_horns, symbol-shaped_pupils`, 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 | 152 | 208.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 152 | 180.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 376 | 369.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akari_bluearchive/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/akari_bluearchive', 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 | 26 | ![](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, black_gloves, solo, long_sleeves, smile, garter_straps, looking_at_viewer, simple_background, blush, open_mouth, black_thighhighs, white_background, neck_ribbon, red_ribbon | | 1 | 34 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, looking_at_viewer, black_kimono, smile, black_flower, hair_flower, solo, black_gloves, obi, long_sleeves, simple_background, wide_sleeves, collarbone, open_mouth, white_background, black_horns, cleavage, heart-shaped_pupils, floral_print, official_alternate_costume, upper_body | | 2 | 9 | ![](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, looking_at_viewer, bare_shoulders, navel, black_bikini, cleavage, smile, solo, very_long_hair, blush, simple_background, closed_mouth, collarbone, heart-shaped_pupils, stomach, string_bikini, white_background, cowboy_shot, holding_food | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | solo | long_sleeves | smile | garter_straps | looking_at_viewer | simple_background | blush | open_mouth | black_thighhighs | white_background | neck_ribbon | red_ribbon | black_kimono | black_flower | hair_flower | obi | wide_sleeves | collarbone | black_horns | cleavage | heart-shaped_pupils | floral_print | official_alternate_costume | upper_body | bare_shoulders | navel | black_bikini | very_long_hair | closed_mouth | stomach | string_bikini | cowboy_shot | holding_food | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:---------------|:--------|:----------------|:--------------------|:--------------------|:--------|:-------------|:-------------------|:-------------------|:--------------|:-------------|:---------------|:---------------|:--------------|:------|:---------------|:-------------|:--------------|:-----------|:----------------------|:---------------|:-----------------------------|:-------------|:-----------------|:--------|:---------------|:-----------------|:---------------|:----------|:----------------|:--------------|:---------------| | 0 | 26 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 1 | 34 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | X | | X | X | X | | | X | | | | | | | | X | | X | X | | | | X | X | X | X | X | X | X | X | X |

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a1a25b97

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1337 dataset_size: 182 --- # Dataset Card for "a1a25b97" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shuwang127/patch_sum_db

--- license: cc-by-4.0 task_categories: - summarization language: - en tags: - code pretty_name: pard_4k size_categories: - 1K<n<10K --- # A dataset for security patch summarization task. Currently, the dataset is under data curation phase; thus, `patch_sum_db` is not ready for public release.

niv-al/sq-babi_nli_basic-coreference

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: class_label: names: '0': not-entailed '1': entailed splits: - name: train num_bytes: 209225 num_examples: 1000 - name: validation num_bytes: 29532 num_examples: 144 - name: test num_bytes: 30008 num_examples: 144 download_size: 48437 dataset_size: 268765 language: - sq --- # Dataset Card for "sq-babi_nli_basic-coreference" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

deetsadi/musiccaps_mel_spectrograms

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 117739026.0 num_examples: 999 download_size: 115824318 dataset_size: 117739026.0 --- # Dataset Card for "musiccaps_mel_spectrograms" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bloyal/small-uniref30

--- license: cc-by-4.0 dataset_info: features: - name: id dtype: int64 - name: num dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1067207.070393368 num_examples: 4096 - name: test num_bytes: 167427.70557437633 num_examples: 640 - name: validation num_bytes: 169382.9274292743 num_examples: 640 download_size: 1368501 dataset_size: 1404017.7033970184 task_categories: - fill-mask size_categories: - 1K<n<10K ---

adamjweintraut/eli5_precomputed_top_slice

--- 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: 277523684 num_examples: 10000 - name: test num_bytes: 34508046 num_examples: 1250 - name: validation num_bytes: 34663133 num_examples: 1250 download_size: 214939984 dataset_size: 346694863 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

jahb57/gpt2_embeddings_BATCH_3

--- dataset_info: features: - name: sentence dtype: string - name: last_hidden_state sequence: sequence: float32 splits: - name: train num_bytes: 18590683446 num_examples: 100000 download_size: 18639362250 dataset_size: 18590683446 configs: - config_name: default data_files: - split: train path: data/train-* ---

bahidalgo/Me

--- license: afl-3.0 ---

AdapterOcean/med_alpaca_standardized_cluster_9_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2443204 num_examples: 8301 download_size: 838833 dataset_size: 2443204 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_9_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Pankaj202133/Pankaj

--- license: cc-by-nc-4.0 ---

jondurbin/airoboros-3.0

--- license: apache-2.0 --- ## Overview This dataset is a continuation of the airoboros datasets, with two main new contributions: * MathJSON - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations. * Anon-contributed RP dataset to enhance multi-turn coherency. Some of the MathJSON data was adapted from https://huggingface.co/datasets/meta-math/MetaMathQA ## Format The format is now in ShareGPT format, to better accomodate the OS ecosystem fine-tuning tooling. ## MathJSON As we all know, math is really difficult for LLMs, especially floating point operations, trig functions, factorials, and several-digit numbers. Instead of sending millions and millions of examples to the LLM for fine-tuning, I've created a new category `mathjson` which simply presents the solution to the problem as a JSON string that can be evaluated by traditional computation. The dataset has roughly 4k samples, as a proof of concept. This can be expanded fairly easily, now that the fine-tuned models have a basic understanding of how to represent the problems. For example: ``` Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST] ``` ``` The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1. Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr². Solution as MathJSON: <mathjson> [ "Multiply", "Pi", [ "Power", 17.2456, 2 ] ] </mathjson> ``` Simply extract the JSON string between the mathjson tags, and evaluate it with https://cortexjs.io/compute-engine/ or my ultra-hacky limited-functionality python version of it https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py

open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX

--- pretty_name: Evaluation run of nlpguy/AlloyIngotNeoX dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [nlpguy/AlloyIngotNeoX](https://huggingface.co/nlpguy/AlloyIngotNeoX) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-15T11:28:14.890311](https://huggingface.co/datasets/open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX/blob/main/results_2024-02-15T11-28-14.890311.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\": 0.6559618494796027,\n\ \ \"acc_stderr\": 0.03203002675451656,\n \"acc_norm\": 0.6554016452842437,\n\ \ \"acc_norm_stderr\": 0.03269903110679164,\n \"mc1\": 0.6034271725826194,\n\ \ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.7456883658583785,\n\ \ \"mc2_stderr\": 0.014353519946726465\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7158703071672355,\n \"acc_stderr\": 0.013179442447653886,\n\ \ \"acc_norm\": 0.7431740614334471,\n \"acc_norm_stderr\": 0.0127669237941168\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7193786098386775,\n\ \ \"acc_stderr\": 0.004483845735187827,\n \"acc_norm\": 0.8906592312288388,\n\ \ \"acc_norm_stderr\": 0.0031142850772280335\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404907,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404907\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n\ \ \"acc_stderr\": 0.023025899617188712,\n \"acc_norm\": 0.7935483870967742,\n\ \ \"acc_norm_stderr\": 0.023025899617188712\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \ \ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524565,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524565\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n\ \ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4480446927374302,\n\ \ \"acc_stderr\": 0.016631976628930595,\n \"acc_norm\": 0.4480446927374302,\n\ \ \"acc_norm_stderr\": 0.016631976628930595\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188936\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.012752858346533127,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.012752858346533127\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6034271725826194,\n\ \ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.7456883658583785,\n\ \ \"mc2_stderr\": 0.014353519946726465\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8453038674033149,\n \"acc_stderr\": 0.010163172650433533\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6982562547384382,\n \ \ \"acc_stderr\": 0.012643544762873358\n }\n}\n```" repo_url: https://huggingface.co/nlpguy/AlloyIngotNeoX leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|arc:challenge|25_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-15T11-28-14.890311.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|gsm8k|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hellaswag|10_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-15T11-28-14.890311.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-management|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-15T11-28-14.890311.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|truthfulqa:mc|0_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-15T11-28-14.890311.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_15T11_28_14.890311 path: - '**/details_harness|winogrande|5_2024-02-15T11-28-14.890311.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-15T11-28-14.890311.parquet' - config_name: results data_files: - split: 2024_02_15T11_28_14.890311 path: - results_2024-02-15T11-28-14.890311.parquet - split: latest path: - results_2024-02-15T11-28-14.890311.parquet --- # Dataset Card for Evaluation run of nlpguy/AlloyIngotNeoX  Dataset automatically created during the evaluation run of model [nlpguy/AlloyIngotNeoX](https://huggingface.co/nlpguy/AlloyIngotNeoX) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-15T11:28:14.890311](https://huggingface.co/datasets/open-llm-leaderboard/details_nlpguy__AlloyIngotNeoX/blob/main/results_2024-02-15T11-28-14.890311.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": 0.6559618494796027, "acc_stderr": 0.03203002675451656, "acc_norm": 0.6554016452842437, "acc_norm_stderr": 0.03269903110679164, "mc1": 0.6034271725826194, "mc1_stderr": 0.017124930942023515, "mc2": 0.7456883658583785, "mc2_stderr": 0.014353519946726465 }, "harness|arc:challenge|25": { "acc": 0.7158703071672355, "acc_stderr": 0.013179442447653886, "acc_norm": 0.7431740614334471, "acc_norm_stderr": 0.0127669237941168 }, "harness|hellaswag|10": { "acc": 0.7193786098386775, "acc_stderr": 0.004483845735187827, "acc_norm": 0.8906592312288388, "acc_norm_stderr": 0.0031142850772280335 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404907, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404907 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188712, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188712 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 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0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47392438070404175, "acc_stderr": 0.012752858346533127, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.012752858346533127 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.044612721759105085, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.044612721759105085 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.02812342933514278, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.02812342933514278 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.6034271725826194, "mc1_stderr": 0.017124930942023515, "mc2": 0.7456883658583785, "mc2_stderr": 0.014353519946726465 }, "harness|winogrande|5": { "acc": 0.8453038674033149, "acc_stderr": 0.010163172650433533 }, "harness|gsm8k|5": { "acc": 0.6982562547384382, "acc_stderr": 0.012643544762873358 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

sKushagra/hmm

--- license: openrail ---

yzhuang/autotree_automl_electricity_gosdt_l512_d3_sd2

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 5538400000 num_examples: 100000 - name: validation num_bytes: 553840000 num_examples: 10000 download_size: 1560789998 dataset_size: 6092240000 --- # Dataset Card for "autotree_automl_electricity_gosdt_l512_d3_sd2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alvations/c4p0-x1-ko-en

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: timestamp[us] - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 20460 num_examples: 18 download_size: 20224 dataset_size: 20460 configs: - config_name: default data_files: - split: train path: e8af6469d8d0c520/train-* ---

LasseRogers2111/test

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: start_time dtype: string - name: end_time dtype: string splits: - name: train num_bytes: 1273208.0 num_examples: 6 - name: validation num_bytes: 1229380.0 num_examples: 5 download_size: 2508853 dataset_size: 2502588.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Sasini/Audio_NER_dataset_2

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: text sequence: string - name: ner_tags sequence: int64 splits: - name: testtrue num_bytes: 106107756.6 num_examples: 3100 download_size: 104273091 dataset_size: 106107756.6 configs: - config_name: default data_files: - split: testtrue path: data/testtrue-* ---

GeneralAwareness/Various

--- license: cc-by-nc-sa-4.0 ---

sonny-dev/databricks-mini

--- license: apache-2.0 task_categories: - question-answering - text-classification - summarization language: - en ---

wndknd/german-law-bgb

--- license: mit task_categories: - text-generation language: - de --- The Bürgerliche Gesetzbuch divided by each paragraph for text-generation.

akash140500/failure13

--- license: apache-2.0 ---

senhorsapo/cream

--- license: openrail ---

SINAI/COAR

--- language: - es license: cc-by-nc-sa-4.0 --- # COAR ## Description The COAR (Corpus of Restaurant Opinions) dataset is designed for research in the field of document-level polarity classification and is focused on the hospitality domain (tourism-hospitality). The corpus consists of 2202 opinions extracted from TripAdvisor, which are categorized on a scale of five levels of opinion intensity (1 (negative) - 5 (positive)). The number of opinions per class is as follows: | Rating | 1 | 2 | 3 | 4 | 5 | Total | | --- |:---:|:---:|:---:|:---:|:---:|:---: | | #Opinions | 565 | 246 | 188 | 333 | 870 | 2202 | ## Citation If you use the corpus in your research, please cite: [Cross-Domain Sentiment Analysis Using Spanish Opinionated Words](https://link.springer.com/chapter/10.1007/978-3-319-07983-7_28). ``` @inproceedings{molina2014cross, title={Cross-domain sentiment analysis using Spanish opinionated words}, author={Molina-Gonz{\'a}lez, M Dolores and Mart{\'\i}nez-C{\'a}mara, Eugenio and Mart{\'\i}n-Valdivia, M Teresa and Urena-L{\'o}pez, L Alfonso}, booktitle={Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings 19}, pages={214--219}, year={2014}, organization={Springer} } ``` # COAR ## Descripción Corpus de opiniones de restaurantes destinado a la investigación en el ámbito de la clasificación de la polaridad a nivel de documento, y se circunscribe en el dominio de alojamiento hostelero (turismo-hostelera). El corpus está formado por 2202 opiniones extraídas de TripAdvisor, las cuales están catalogadas en una escala de cinco niveles de intensidad de opinión (1 (negativo) - 5 (positivo)). El número de opiniones por clase es: | Puntuación | 1 | 2 | 3 | 4 | 5 | Total | | --- |:---:|:---:|:---:|:---:|:---:|:---: | | #Opiniones | 565 | 246 | 188 | 333 | 870 | 2202 | # Cita Si utiliza el corpus en su investigación, por favor cite: [Cross-Domain Sentiment Analysis Using Spanish Opinionated Words](https://link.springer.com/chapter/10.1007/978-3-319-07983-7_28). ``` @inproceedings{molina2014cross, title={Cross-domain sentiment analysis using Spanish opinionated words}, author={Molina-Gonz{\'a}lez, M Dolores and Mart{\'\i}nez-C{\'a}mara, Eugenio and Mart{\'\i}n-Valdivia, M Teresa and Urena-L{\'o}pez, L Alfonso}, booktitle={Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings 19}, pages={214--219}, year={2014}, organization={Springer} } ```

yoshitomo-matsubara/srsd-feynman_easy_dummy

--- pretty_name: SRSD-Feynman (Easy w/ Dummy Variables) annotations_creators: - expert language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended task_categories: - tabular-regression task_ids: [] --- # Dataset Card for SRSD-Feynman (Easy set with Dummy Variables) ## 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:** - **Repository:** https://github.com/omron-sinicx/srsd-benchmark - **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540) - **Point of Contact:** [Yoshitaka Ushiku](mailto:yoshitaka.ushiku@sinicx.com) ### Dataset Summary Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery. We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets. This is the ***Easy set with dummy variables*** of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas: [![Click here to open a PDF file](problem_table.png)](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_easy_dummy/resolve/main/problem_table.pdf) Dummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions. The following datasets contain **1 dummy variable**: I.12.1, I.12.4, I.12.5, I.18.12, I.25.13, I.47.23 **2 dummy variables**: I.14.3, I.18.16, I.43.16, II.3.24, II.8.31, II.10.9, II.13.17, II.15.5, II.27.18, III.7.38, III.12.43 **3 dummy variables**: I.14.4, I.26.2, I.27.6, I.30.5, II.2.42, II.4.23, II.15.4, II.27.16, II.34.11, II.34.29b, II.38.3, II.38.14, III.15.27 More details of these datasets are provided in [the paper and its supplementary material](https://openreview.net/forum?id=qrUdrXsiXX). ### Supported Tasks and Leaderboards Symbolic Regression ## Dataset Structure ### Data Instances Tabular data + Ground-truth equation per equation Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables. Note that the number of variables (`num_variables`) varies from equation to equation. Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function. ### Data Fields For each dataset, we have 1. train split (txt file, whitespace as a delimiter) 2. val split (txt file, whitespace as a delimiter) 3. test split (txt file, whitespace as a delimiter) 4. true equation (pickle file for sympy object) ### Data Splits - train: 8,000 samples per equation - val: 1,000 samples per equation - test: 1,000 samples per equation ## Dataset Creation ### Curation Rationale We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html). ### Annotations #### Annotation process We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database. First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants. Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation. In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen. Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes. Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly. In addition, variables that take a specific sign were set to be sampled within that range. #### Who are the annotators? The main annotators are - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) ### Personal and Sensitive Information N/A ## Considerations for Using the Data ### Social Impact of Dataset We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery. ### Discussion of Biases Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics. ### Other Known Limitations Some variables used in our datasets indicate some numbers (counts), which should be treated as integer. Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25}) ## Additional Information ### Dataset Curators The main curators are - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) ### Licensing Information Creative Commons Attribution 4.0 ### Citation Information [[OpenReview](https://openreview.net/forum?id=qrUdrXsiXX)] [[Video](https://www.youtube.com/watch?v=MmeOXuUUAW0)] [[Preprint](https://arxiv.org/abs/2206.10540)] ```bibtex @article{matsubara2024rethinking, title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery}, author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka}, journal={Journal of Data-centric Machine Learning Research}, year={2024}, url={https://openreview.net/forum?id=qrUdrXsiXX} } ``` ### Contributions Authors: - Yoshitomo Matsubara (@yoshitomo-matsubara) - Naoya Chiba (@nchiba) - Ryo Igarashi (@rigarash) - Yoshitaka Ushiku (@yushiku)

snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset

--- dataset_info: features: - name: prompt_id dtype: string - name: prompt 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: all_generated_responses sequence: string - name: all_rm_scores sequence: float64 splits: - name: train_iteration_1 num_bytes: 276734865 num_examples: 19766 - name: test_iteration_1 num_bytes: 13616124 num_examples: 994 - name: train_iteration_2 num_bytes: 313248218 num_examples: 19958 - name: test_iteration_2 num_bytes: 15553468 num_examples: 1000 - name: train_iteration_3 num_bytes: 379805458 num_examples: 19996 - name: test_iteration_3 num_bytes: 19111694 num_examples: 1000 download_size: 485703305 dataset_size: 1018069827 configs: - config_name: default data_files: - split: train_iteration_1 path: data/train_iteration_1-* - split: test_iteration_1 path: data/test_iteration_1-* - split: train_iteration_2 path: data/train_iteration_2-* - split: test_iteration_2 path: data/test_iteration_2-* - split: train_iteration_3 path: data/train_iteration_3-* - split: test_iteration_3 path: data/test_iteration_3-* license: apache-2.0 task_categories: - text-generation --- #### Dataset: This is the data used for training [Snorkel model](https://huggingface.co/snorkelai/Snorkel-Mistral-PairRM-DPO) We use ONLY the prompts from [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized); **no external LLM responses used**. #### Methodology: 1. Generate 5 response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). 2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking. 3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses. 4. Use this LLM as the base model for the next iteration and use a different set of 20,000 prompts, repeating three times in total. Please see the model page for more details on the methodology. Columns: - prompt: the current prompt - chosen: the list of messages for the chosen response - rejected: the list of messages for the rejected response - all_generated_responses: The 5 generated responses - all_rm_scores: The 5 corresponding reward model scores Splits: - train/test_iteration_{n}: The dataset used at the n_th iteration. We did 3 iterations in total. **Training recipe**: This data is formatted to be compatible with the Hugging Face's [Zephyr recipe](https://github.com/huggingface/alignment-handbook/tree/main/recipes/zephyr-7b-beta). We executed the n_th DPO iteration using the "train/test_iteration_{n}".

RCC-MSU/collection3

--- annotations_creators: - other language: - ru language_creators: - found license: - other multilinguality: - monolingual pretty_name: Collection3 size_categories: - 10K<n<100K source_datasets: [] tags: [] task_categories: - token-classification task_ids: - named-entity-recognition dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: test num_bytes: 935298 num_examples: 1922 - name: train num_bytes: 4380588 num_examples: 9301 - name: validation num_bytes: 1020711 num_examples: 2153 download_size: 878777 dataset_size: 6336597 --- # Dataset Card for Collection3 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Collection3 homepage](http://labinform.ru/pub/named_entities/index.htm) - **Repository:** [Needs More Information] - **Paper:** [Two-stage approach in Russian named entity recognition](https://ieeexplore.ieee.org/document/7584769) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection [Persons-1000](http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000) originally containing 1000 news documents labeled only with names of persons. Additional labels were obtained using guidelines similar to MUC-7 with web-based tool [Brat](http://brat.nlplab.org/) for collaborative text annotation. Currently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations). Conversion to the IOB2 format and splitting into train, validation and test sets was done by [DeepPavlov team](http://files.deeppavlov.ai/deeppavlov_data/collection3_v2.tar.gz). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Russian ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { "id": "851", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0], "tokens": ['Главный', 'архитектор', 'программного', 'обеспечения', '(', 'ПО', ')', 'американского', 'высокотехнологичного', 'гиганта', 'Microsoft', 'Рэй', 'Оззи', 'покидает', 'компанию', '.'] } ``` ### Data Fields - id: a string feature. - tokens: a list of string features. - ner_tags: a list of classification labels (int). Full tagset with indices: ``` {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6} ``` ### Data Splits |name|train|validation|test| |---------|----:|---------:|---:| |Collection3|9301|2153|1922| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner, author={Mozharova, Valerie and Loukachevitch, Natalia}, booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)}, title={Two-stage approach in Russian named entity recognition}, year={2016}, pages={1-6}, doi={10.1109/FRUCT.2016.7584769}} ```

Falah/family_lifestyle_photography

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1039539 num_examples: 10000 download_size: 22749 dataset_size: 1039539 --- # Dataset Card for "family_lifestyle_photography" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bdebayan/my_dataset

--- license: bsd task_categories: - question-answering language: - en size_categories: - n<1K --- # 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 [English] ## Dataset Structure CSV ### 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]

dcml0714/Heros

--- license: apache-2.0 size_categories: - n<1K --- HEROS is a dataset used to compare the sentence cosine similarity among sentences with high lexical overlapping but differ in their semantics. Please refer to the paper, "Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS" for more details of how the dataset is constructed and the comparison of different sentence encoders. The dataset `heros.tsv` consists of 6 columns: `Original`, `Synonym`, `Antonym`, `Negation`, `Random`, `Typo`, `Negation`. The first column, `Original` are the sentences from GoEmotion dataset, and sentences in the other columns are constructed by replacing some words in the original sentences based on different rules, making up different subsets in HEROS. Different subsets in HEROS capture various aspects of semantics. Comparing the average cosine similarity between minimal pairs in Synonym and Antonym allows one to understand whether replacing a word with an antonym is more dissimilar to the original semantics than replacing a word with a synonym. The average cosine similarity between minimal pairs in Negation can tell us how negation affects sentence embedding similarity. Typos are realistic and happen every day. While humans can infer the original word from a typo and get the original meaning of the sentence, it will be interesting to see how the typos affect the sentences' similarity with the original sentences. The Random MLM subset can tell us how similar the sentence embedding can be when two sentences are semantically different but with high lexical overlaps. By comparing the performance of different SEs on different subsets in HEROS, we can further understand the trait of different SEs.

shujatoor/test_dataset-meaningful

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 30177 num_examples: 129 download_size: 16560 dataset_size: 30177 configs: - config_name: default data_files: - split: train path: data/train-* ---

emozilla/proofpile-test-tokenized

--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tokenized_len dtype: int64 splits: - name: test num_bytes: 1644067664 num_examples: 46251 download_size: 552973486 dataset_size: 1644067664 --- # Dataset Card for "proofpile-test-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/allen_m_sumner_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane) This is the dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane), containing 41 images and their tags. The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, hair_between_eyes, twintails, hair_ornament, medium_breasts, very_long_hair, bow, large_breasts, animal_ears, blue_hair`, 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 | 41 | 67.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 41 | 35.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 104 | 76.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 41 | 57.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 104 | 113.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_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/allen_m_sumner_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 | 13 | ![](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, double_bun, off_shoulder, official_alternate_costume, playboy_bunny, rabbit_ears, solo, black_jacket, black_leotard, long_sleeves, looking_at_viewer, open_jacket, fake_animal_ears, smile, hair_bow, underboob_cutout, braided_bun, brown_pantyhose, sitting, ass, tongue_out, bodystocking, closed_mouth, simple_background, sleeves_past_wrists, black_footwear, blush, shoes, white_background | | 1 | 18 | ![](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) | looking_at_viewer, underboob_cutout, 1girl, solo, bare_shoulders, two-tone_leotard, off_shoulder, open_coat, black_leotard, open_mouth, skindentation, black_coat, blush, groin, long_sleeves, thigh_strap, badge, cowboy_shot, frilled_leotard, standing, sidelocks, :d, armpits, ass_visible_through_thighs, white_leotard | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | double_bun | off_shoulder | official_alternate_costume | playboy_bunny | rabbit_ears | solo | black_jacket | black_leotard | long_sleeves | looking_at_viewer | open_jacket | fake_animal_ears | smile | hair_bow | underboob_cutout | braided_bun | brown_pantyhose | sitting | ass | tongue_out | bodystocking | closed_mouth | simple_background | sleeves_past_wrists | black_footwear | blush | shoes | white_background | two-tone_leotard | open_coat | open_mouth | skindentation | black_coat | groin | thigh_strap | badge | cowboy_shot | frilled_leotard | standing | sidelocks | :d | armpits | ass_visible_through_thighs | white_leotard | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------|:---------------|:-----------------------------|:----------------|:--------------|:-------|:---------------|:----------------|:---------------|:--------------------|:--------------|:-------------------|:--------|:-----------|:-------------------|:--------------|:------------------|:----------|:------|:-------------|:---------------|:---------------|:--------------------|:----------------------|:-----------------|:--------|:--------|:-------------------|:-------------------|:------------|:-------------|:----------------|:-------------|:--------|:--------------|:--------|:--------------|:------------------|:-----------|:------------|:-----|:----------|:-----------------------------|:----------------| | 0 | 13 | ![](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 | | | | | | | | | | | | | | | | | | 1 | 18 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | | | | X | | X | X | X | | | | | X | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875716

--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: csebuetnlp/mT5_multilingual_XLSum metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: csebuetnlp/mT5_multilingual_XLSum * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model.

AIBrainBox/guanaco-llama2-1k

--- license: apache-2.0 ---

iamnguyen/cdnc_law_eval

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: citation dtype: string - name: content dtype: string - name: question dtype: string splits: - name: train num_bytes: 153441 num_examples: 100 download_size: 71332 dataset_size: 153441 --- # Dataset Card for "cdnc_law_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

KshitizPandya/GenzTranscribe-hi

--- task_categories: - automatic-speech-recognition language: - hi pretty_name: GenzTranscribe-hi size_categories: - 10K<n<100K viewer: True ---

chirunder/text_message_transliteration_1k

--- dataset_info: features: - name: text dtype: string - name: translations struct: - name: chinese dtype: string - name: hindi dtype: string - name: russian dtype: string - name: transliteration struct: - name: chinese dtype: string - name: hindi dtype: string - name: russian dtype: string splits: - name: train num_bytes: 609895 num_examples: 1000 download_size: 361488 dataset_size: 609895 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "text_message_transliteration_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

FaalSa/f5

--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 79710 num_examples: 1 - name: validation num_bytes: 80190 num_examples: 1 - name: test num_bytes: 80670 num_examples: 1 download_size: 69957 dataset_size: 240570 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

presencesw/Llama_data_bad

--- dataset_info: features: - name: topic dtype: string - name: Evidence dtype: string - name: predict dtype: string - name: Label dtype: string - name: Claim dtype: string - name: eval dtype: int64 splits: - name: train num_bytes: 24278461 num_examples: 5781 download_size: 5443107 dataset_size: 24278461 configs: - config_name: default data_files: - split: train path: data/train-* ---

mpasila/DarkViperAU-QA-ChatML

--- language: - en --- This is unfinished, only meant for testing.

kaleemWaheed/twitter_dataset_1712982744

--- 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: 153449 num_examples: 409 download_size: 47466 dataset_size: 153449 configs: - config_name: default data_files: - split: train path: data/train-* ---

communityai/ise-uiuc___Magicoder-Evol-Instruct-110K

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 485762091.0 num_examples: 106878 download_size: 222135894 dataset_size: 485762091.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

robji/MYSD

--- license: unknown ---

dmrau/cqadupstack-unix

--- configs: - config_name: default data_files: - split: queries path: data/queries-* - split: corpus path: data/corpus-* dataset_info: features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: queries num_bytes: 72357 num_examples: 1072 - name: corpus num_bytes: 46102756 num_examples: 47382 download_size: 24571026 dataset_size: 46175113 --- # Dataset Card for "cqadupstack-unix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test

--- pretty_name: Evaluation run of Lazycuber/L2-7b-Orca-WVG-Test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Lazycuber/L2-7b-Orca-WVG-Test](https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test)\ \ 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_Lazycuber__L2-7b-Orca-WVG-Test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-26T20:44:34.027885](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test/blob/main/results_2023-10-26T20-44-34.027885.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\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.00048005108166191996,\n \"f1\": 0.07443687080536941,\n\ \ \"f1_stderr\": 0.0016342523738966323,\n \"acc\": 0.4119262338489193,\n\ \ \"acc_stderr\": 0.009880953290999535\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166191996,\n\ \ \"f1\": 0.07443687080536941,\n \"f1_stderr\": 0.0016342523738966323\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \ \ \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.01227364800875999\n\ \ }\n}\n```" repo_url: https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|arc:challenge|25_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-10T15-39-37.735727.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T20_44_34.027885 path: - '**/details_harness|drop|3_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-26T20-44-34.027885.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T20_44_34.027885 path: - '**/details_harness|gsm8k|5_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-26T20-44-34.027885.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hellaswag|10_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T15-39-37.735727.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T15-39-37.735727.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_10T15_39_37.735727 path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T15-39-37.735727.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T15-39-37.735727.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T20_44_34.027885 path: - '**/details_harness|winogrande|5_2023-10-26T20-44-34.027885.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-26T20-44-34.027885.parquet' - config_name: results data_files: - split: 2023_10_10T15_39_37.735727 path: - results_2023-10-10T15-39-37.735727.parquet - split: 2023_10_26T20_44_34.027885 path: - results_2023-10-26T20-44-34.027885.parquet - split: latest path: - results_2023-10-26T20-44-34.027885.parquet --- # Dataset Card for Evaluation run of Lazycuber/L2-7b-Orca-WVG-Test ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test - **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 [Lazycuber/L2-7b-Orca-WVG-Test](https://huggingface.co/Lazycuber/L2-7b-Orca-WVG-Test) 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_Lazycuber__L2-7b-Orca-WVG-Test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-26T20:44:34.027885](https://huggingface.co/datasets/open-llm-leaderboard/details_Lazycuber__L2-7b-Orca-WVG-Test/blob/main/results_2023-10-26T20-44-34.027885.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": 0.002202181208053691, "em_stderr": 0.00048005108166191996, "f1": 0.07443687080536941, "f1_stderr": 0.0016342523738966323, "acc": 0.4119262338489193, "acc_stderr": 0.009880953290999535 }, "harness|drop|3": { "em": 0.002202181208053691, "em_stderr": 0.00048005108166191996, "f1": 0.07443687080536941, "f1_stderr": 0.0016342523738966323 }, "harness|gsm8k|5": { "acc": 0.0803639120545868, "acc_stderr": 0.007488258573239077 }, "harness|winogrande|5": { "acc": 0.7434885556432518, "acc_stderr": 0.01227364800875999 } } ``` ### 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]

open-source-metrics/optimum-dependents

--- license: apache-2.0 pretty_name: optimum metrics tags: - github-stars dataset_info: features: - name: name dtype: string - name: stars dtype: int64 - name: forks dtype: int64 splits: - name: package num_bytes: 555 num_examples: 13 - name: repository num_bytes: 3790 num_examples: 81 download_size: 6617 dataset_size: 4345 --- # optimum metrics This dataset contains metrics about the huggingface/optimum package. Number of repositories in the dataset: 19 Number of packages in the dataset: 6 ## Package dependents This contains the data available in the [used-by](https://github.com/huggingface/optimum/network/dependents) tab on GitHub. ### Package & Repository star count This section shows the package and repository star count, individually. Package | Repository :-------------------------:|:-------------------------: ![optimum-dependent package star count](./optimum-dependents/resolve/main/optimum-dependent_package_star_count.png) | ![optimum-dependent repository star count](./optimum-dependents/resolve/main/optimum-dependent_repository_star_count.png) There are 0 packages that have more than 1000 stars. There are 0 repositories that have more than 1000 stars. The top 10 in each category are the following: *Package* [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24 [bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 10 *Repository* [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288 [marqo-ai/marqo](https://github.com/marqo-ai/marqo): 265 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114 [graphcore/tutorials](https://github.com/graphcore/tutorials): 65 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24 [philschmid/optimum-static-quantization](https://github.com/philschmid/optimum-static-quantization): 20 [philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 15 [girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 15 ### Package & Repository fork count This section shows the package and repository fork count, individually. Package | Repository :-------------------------:|:-------------------------: ![optimum-dependent package forks count](./optimum-dependents/resolve/main/optimum-dependent_package_forks_count.png) | ![optimum-dependent repository forks count](./optimum-dependents/resolve/main/optimum-dependent_repository_forks_count.png) There are 0 packages that have more than 200 forks. There are 0 repositories that have more than 200 forks. The top 10 in each category are the following: *Package* [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3 [bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 2 *Repository* [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82 [graphcore/tutorials](https://github.com/graphcore/tutorials): 33 [huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18 [girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 14 [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10 [marqo-ai/marqo](https://github.com/marqo-ai/marqo): 6 [AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6 [whatofit/LevelWordWithFreq](https://github.com/whatofit/LevelWordWithFreq): 5 [philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 3 [huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3

merve/my_notes

--- license: apache-2.0 --- ## My Notes 📓 This repository contains my lecture notes from graduate school on following topics 👇🏼 - Data Science: 8 cheatsheets - Machine Learning (follows [Tom Mitchell's book](http://www.cs.cmu.edu/~tom/mlbook.html)): 25 pages of notes - Statistics: 9 cheatsheets - Deep Learning: 12 cheatsheets, will upload more - Image Processing (follows [digital image processing book](https://www.amazon.fr/Digital-Image-Processing-Rafael-Gonzalez/dp/013168728X)): 21 cheatsheets - Data Structures and Algorithms (follows [this book by Goodrich](https://www.wiley.com/en-us/Data+Structures+and+Algorithms+in+Python-p-9781118549582)): 26 cheatsheets ✨ *Some notes* ✨ - Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews. - There might be very small Turkish notes in few of the pages, you can ignore them. - I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned! - It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository. *Updates* 🎉 - I uploaded hierarchical clustering and improved version of K-means. - I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under `Compiled PDF`s. - I uploaded Hugging Face cheatsheets.

open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp

--- pretty_name: Evaluation run of chasedreaminf/Dream-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chasedreaminf/Dream-7B-slerp](https://huggingface.co/chasedreaminf/Dream-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-16T20:05:27.586603](https://huggingface.co/datasets/open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp/blob/main/results_2024-02-16T20-05-27.586603.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\": 0.6507213038118388,\n\ \ \"acc_stderr\": 0.03214268781825063,\n \"acc_norm\": 0.6503644948036131,\n\ \ \"acc_norm_stderr\": 0.03280953874341137,\n \"mc1\": 0.4724602203182375,\n\ \ \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6184735043628918,\n\ \ \"mc2_stderr\": 0.015107906651203224\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6663822525597269,\n \"acc_stderr\": 0.013778687054176538,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6787492531368253,\n\ \ \"acc_stderr\": 0.004660025270817022,\n \"acc_norm\": 0.8634734116709819,\n\ \ \"acc_norm_stderr\": 0.0034264517445078474\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n\ \ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440679,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"\ acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\ : 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\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\"\ : 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\ acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.0364129708131373,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.0364129708131373\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.02126271940040697,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.02126271940040697\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.01358661921990334,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.01358661921990334\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42905027932960893,\n\ \ \"acc_stderr\": 0.016553287863116037,\n \"acc_norm\": 0.42905027932960893,\n\ \ \"acc_norm_stderr\": 0.016553287863116037\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \ \ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4576271186440678,\n\ \ \"acc_stderr\": 0.012724296550980188,\n \"acc_norm\": 0.4576271186440678,\n\ \ \"acc_norm_stderr\": 0.012724296550980188\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.02619392354445412,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.02619392354445412\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4724602203182375,\n\ \ \"mc1_stderr\": 0.017476930190712187,\n \"mc2\": 0.6184735043628918,\n\ \ \"mc2_stderr\": 0.015107906651203224\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8034727703235991,\n \"acc_stderr\": 0.011168120593569562\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7225170583775588,\n \ \ \"acc_stderr\": 0.01233344758104754\n }\n}\n```" repo_url: https://huggingface.co/chasedreaminf/Dream-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|arc:challenge|25_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-16T20-05-27.586603.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|gsm8k|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hellaswag|10_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-16T20-05-27.586603.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-management|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T20-05-27.586603.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|truthfulqa:mc|0_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-16T20-05-27.586603.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_16T20_05_27.586603 path: - '**/details_harness|winogrande|5_2024-02-16T20-05-27.586603.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-16T20-05-27.586603.parquet' - config_name: results data_files: - split: 2024_02_16T20_05_27.586603 path: - results_2024-02-16T20-05-27.586603.parquet - split: latest path: - results_2024-02-16T20-05-27.586603.parquet --- # Dataset Card for Evaluation run of chasedreaminf/Dream-7B-slerp  Dataset automatically created during the evaluation run of model [chasedreaminf/Dream-7B-slerp](https://huggingface.co/chasedreaminf/Dream-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T20:05:27.586603](https://huggingface.co/datasets/open-llm-leaderboard/details_chasedreaminf__Dream-7B-slerp/blob/main/results_2024-02-16T20-05-27.586603.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": 0.6507213038118388, "acc_stderr": 0.03214268781825063, "acc_norm": 0.6503644948036131, "acc_norm_stderr": 0.03280953874341137, "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6184735043628918, "mc2_stderr": 0.015107906651203224 }, "harness|arc:challenge|25": { "acc": 0.6663822525597269, "acc_stderr": 0.013778687054176538, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.013572657703084948 }, "harness|hellaswag|10": { "acc": 0.6787492531368253, "acc_stderr": 0.004660025270817022, "acc_norm": 0.8634734116709819, "acc_norm_stderr": 0.0034264517445078474 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5957446808510638, "acc_stderr": 0.032081157507886836, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.032081157507886836 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440679, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440679 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7677419354838709, "acc_stderr": 0.024022256130308235, "acc_norm": 0.7677419354838709, "acc_norm_stderr": 0.024022256130308235 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374308, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374308 }, "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": 0.8088235294117647, "acc_stderr": 0.027599174300640766, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640766 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7932489451476793, "acc_stderr": 0.0263616516683891, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.0263616516683891 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.0364129708131373, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.0364129708131373 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.02126271940040697, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.02126271940040697 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8250319284802043, "acc_stderr": 0.01358661921990334, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.01358661921990334 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7485549132947977, "acc_stderr": 0.02335736578587403, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.02335736578587403 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.42905027932960893, "acc_stderr": 0.016553287863116037, "acc_norm": 0.42905027932960893, "acc_norm_stderr": 0.016553287863116037 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4576271186440678, "acc_stderr": 0.012724296550980188, "acc_norm": 0.4576271186440678, "acc_norm_stderr": 0.012724296550980188 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396556, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396556 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.02619392354445412, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.02619392354445412 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.4724602203182375, "mc1_stderr": 0.017476930190712187, "mc2": 0.6184735043628918, "mc2_stderr": 0.015107906651203224 }, "harness|winogrande|5": { "acc": 0.8034727703235991, "acc_stderr": 0.011168120593569562 }, "harness|gsm8k|5": { "acc": 0.7225170583775588, "acc_stderr": 0.01233344758104754 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

zolak/twitter_dataset_50_1713181945

--- 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: 374526 num_examples: 897 download_size: 184190 dataset_size: 374526 configs: - config_name: default data_files: - split: train path: data/train-* ---

zolak/twitter_dataset_80_1713203438

--- 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: 4036256 num_examples: 10039 download_size: 2034440 dataset_size: 4036256 configs: - config_name: default data_files: - split: train path: data/train-* ---

learnanything/feedback-series

--- license: other ---

HuggingFaceM4/enwiki-v2_valid

Invalid username or password.

louam123/darja-tounsi1

--- task_categories: - translation language: - ar - en tags: - darja - tunisian pretty_name: 'darja-tounsi-louam ' size_categories: - 1K<n<10K ---

baptistecolle/sam-controlnet-3

--- dataset_info: features: - name: image dtype: image - name: filepath dtype: string - name: sentids sequence: int64 - name: filename dtype: string - name: imgid dtype: int64 - name: split dtype: string - name: sentences struct: - name: imgid dtype: int64 - name: raw dtype: string - name: sentid dtype: int64 - name: tokens sequence: string - name: cocoid dtype: int64 - name: masks sequence: sequence: sequence: bool - name: scores sequence: float32 splits: - name: train num_bytes: 2086529.0 num_examples: 1 download_size: 150908 dataset_size: 2086529.0 --- # Dataset Card for "sam-controlnet-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rthatha/GLDv2-All-Caption-Filename

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 109018225.0 num_examples: 203 download_size: 108837389 dataset_size: 109018225.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

hlillemark/flores200_devtest_mt5-600m-flores200-baseline

--- dataset_info: features: - name: id dtype: int32 - name: source_lang dtype: string - name: target_lang dtype: string - name: source dtype: string - name: target dtype: string - name: prediction dtype: string - name: chrf_unreduced dtype: string splits: - name: devtest num_bytes: 734237740 num_examples: 1000000 download_size: 514219403 dataset_size: 734237740 --- # Dataset Card for "flores200_devtest_mt5-600m-flores200-baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

marup/kotonoha-sisters-rvc-50epochs

--- license: openrail ---

Nexdata/20_People_Chinese_Mandarin_Multi_emotional_Synthesis_Corpus

--- license: cc-by-nc-nd-4.0 --- ## Description 20 People - Chinese Mandarin Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker, covering different ages and genders. seven emotional texts, are all from novels and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/dataset/1213?source=Huggingface ## Format 48,000Hz, 24bit, uncompressed wav, mono channel ## Recording environment professional recording studio ## Recording content seven emotions (happiness, anger, sadness, surprise, fear, disgust, neutral) ；texts are all from novels ## Speaker 20 persons, different age groups and genders ## Device microphone ## Language Mandarin ## Annotation word and pinyin transcription, prosodic boundary annotation ## Application scenarios speech synthesis ## The amount of data The amount of data for per person is 140 minutes, each emotion is 20 minutes # Licensing Information Commercial License

codedog-lee/llama2-tut

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---

showery/my_dataset

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 641917.0 num_examples: 22 download_size: 0 dataset_size: 641917.0 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kirim9001/Tryin

--- license: other ---

mach-12/ecommerce-ner-conll-2003-data

--- license: mit dataset_info: features: - name: tokens sequence: string - name: pos_tag sequence: int64 - name: chunk_tag sequence: int64 - name: ner_tag sequence: int64 - name: sentiment dtype: string splits: - name: train num_bytes: 13886708.0 num_examples: 31520 - name: test num_bytes: 3471677.0 num_examples: 7880 download_size: 1323190 dataset_size: 17358385.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

pwc-india/fabric_dataset

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 41259319.0 num_examples: 20 download_size: 41261924 dataset_size: 41259319.0 --- # Dataset Card for "fabric_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/fc019c9e

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1340 dataset_size: 182 --- # Dataset Card for "fc019c9e" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

LucasThil/miniwob_snippets

--- dataset_info: features: - name: episodes dtype: string - name: refs dtype: int64 - name: click dtype: int64 - name: dblclick dtype: int64 - name: keydown dtype: int64 - name: keypress dtype: int64 - name: keyup dtype: int64 - name: mousedown dtype: int64 - name: mouseup dtype: int64 - name: scroll dtype: int64 splits: - name: train num_bytes: 674906155 num_examples: 587268 - name: test num_bytes: 84528980 num_examples: 73420 - name: validate num_bytes: 84695448 num_examples: 73318 download_size: 140471741 dataset_size: 844130583 --- # Dataset Card for "miniwob_snippets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rishikesh/mini-sarcasm-data

--- license: mit ---

Wulichao/single_cell_RNA_seq

--- license: mit ---

arthurmluz/wikilingua_data-wiki_temario_results

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 23695646 num_examples: 8165 download_size: 14096829 dataset_size: 23695646 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "wikilingua_data-wiki_temario_results" rouge= {'rouge1': 0.32186631280345346, 'rouge2': 0.11643663933189041, 'rougeL': 0.22613896268852546, 'rougeLsum': 0.22613896268852546} bert= {'precision': 0.7288013449272931, 'recall': 0.7668859778849245, 'f1': 0.7463569133178047} mover = 0.6063795152381846

njoeljack/hai-babi

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1465597.0 num_examples: 50 download_size: 1465887 dataset_size: 1465597.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

yiyic/clirmatrix_query_doc

--- dataset_info: features: - name: text dtype: string - name: __index_level_0__ dtype: string splits: - name: de_en_multi8_test1 num_bytes: 1342125 num_examples: 2000 - name: de_fr_multi8_test1 num_bytes: 1346729 num_examples: 2000 - name: de_es_multi8_test1 num_bytes: 1346235 num_examples: 2000 - name: en_de_multi8_test1 num_bytes: 1155356 num_examples: 2000 - name: en_fr_multi8_test1 num_bytes: 1158530 num_examples: 2000 - name: en_es_multi8_test1 num_bytes: 1158036 num_examples: 2000 - name: es_en_multi8_test1 num_bytes: 1127237 num_examples: 2000 - name: es_fr_multi8_test1 num_bytes: 1131841 num_examples: 2000 - name: es_de_multi8_test1 num_bytes: 1128667 num_examples: 2000 - name: fr_en_multi8_test1 num_bytes: 1168520 num_examples: 2000 - name: fr_de_multi8_test1 num_bytes: 1169950 num_examples: 2000 - name: fr_es_multi8_test1 num_bytes: 1172630 num_examples: 2000 download_size: 8905953 dataset_size: 14405856 configs: - config_name: default data_files: - split: de_en_multi8_test1 path: data/de_en_multi8_test1-* - split: de_fr_multi8_test1 path: data/de_fr_multi8_test1-* - split: de_es_multi8_test1 path: data/de_es_multi8_test1-* - split: en_de_multi8_test1 path: data/en_de_multi8_test1-* - split: en_fr_multi8_test1 path: data/en_fr_multi8_test1-* - split: en_es_multi8_test1 path: data/en_es_multi8_test1-* - split: es_en_multi8_test1 path: data/es_en_multi8_test1-* - split: es_fr_multi8_test1 path: data/es_fr_multi8_test1-* - split: es_de_multi8_test1 path: data/es_de_multi8_test1-* - split: fr_en_multi8_test1 path: data/fr_en_multi8_test1-* - split: fr_de_multi8_test1 path: data/fr_de_multi8_test1-* - split: fr_es_multi8_test1 path: data/fr_es_multi8_test1-* ---

JuneKo/book_sf_ch

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1889814.0 num_examples: 39 download_size: 1847651 dataset_size: 1889814.0 --- # Dataset Card for "book_sf_ch" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tashkent_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tashkent/タシュケント/塔什干 (Azur Lane) This is the dataset of tashkent/タシュケント/塔什干 (Azur Lane), containing 400 images and their tags. The core tags of this character are `long_hair, purple_hair, blue_eyes, very_long_hair, bangs, animal_ears, ribbon, hair_ribbon, fake_animal_ears, hair_ornament`, 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 | 400 | 686.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 400 | 330.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1039 | 759.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 400 | 577.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1039 | 1.15 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tashkent_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/tashkent_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 | 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, blush, brown_sweater, fur_trim, looking_at_viewer, solo, thigh_boots, white_coat, white_thighhighs, winter_clothes, full_body, open_mouth, simple_background, sleeves_past_fingers, very_long_sleeves, white_background, white_footwear, dress, pom_pom_(clothes), smile, standing, sitting, torpedo_tubes | | 1 | 16 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, dress, long_sleeves, looking_at_viewer, solo, white_coat, sleeves_past_fingers, white_background, white_thighhighs, winter_clothes, brown_sweater, open_mouth, simple_background, ahoge, fur-trimmed_sleeves, smile | | 2 | 12 | ![](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, hair_bow, looking_at_viewer, solo, bare_shoulders, fur_trim, blush, paw_gloves, plaid, purple_skirt, white_thighhighs, white_background, open_mouth, off_shoulder, simple_background | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, solo, blush, navel, nipples, nude, small_breasts, white_thighhighs, pussy, ass, collar, ponytail, tongue_out | | 4 | 55 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, white_shirt, blush, ponytail, solo, white_thighhighs, black_shorts, looking_at_viewer, suspender_shorts, chain, short_shorts, collar, bound_arms, open_mouth, sitting, cuffs, necktie | | 5 | 22 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, blush, solo, white_shirt, looking_at_viewer, long_sleeves, off_shoulder, naked_shirt, collared_shirt, sitting, collarbone, barefoot, dress_shirt, bare_shoulders, official_alternate_costume, ahoge, closed_mouth, small_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | brown_sweater | fur_trim | looking_at_viewer | solo | thigh_boots | white_coat | white_thighhighs | winter_clothes | full_body | open_mouth | simple_background | sleeves_past_fingers | very_long_sleeves | white_background | white_footwear | dress | pom_pom_(clothes) | smile | standing | sitting | torpedo_tubes | long_sleeves | ahoge | fur-trimmed_sleeves | hair_bow | bare_shoulders | paw_gloves | plaid | purple_skirt | off_shoulder | navel | nipples | nude | small_breasts | pussy | ass | collar | ponytail | tongue_out | white_shirt | black_shorts | suspender_shorts | chain | short_shorts | bound_arms | cuffs | necktie | naked_shirt | collared_shirt | collarbone | barefoot | dress_shirt | official_alternate_costume | closed_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------------|:-----------|:--------------------|:-------|:--------------|:-------------|:-------------------|:-----------------|:------------|:-------------|:--------------------|:-----------------------|:--------------------|:-------------------|:-----------------|:--------|:--------------------|:--------|:-----------|:----------|:----------------|:---------------|:--------|:----------------------|:-----------|:-----------------|:-------------|:--------|:---------------|:---------------|:--------|:----------|:-------|:----------------|:--------|:------|:---------|:-----------|:-------------|:--------------|:---------------|:-------------------|:--------|:---------------|:-------------|:--------|:----------|:--------------|:-----------------|:-------------|:-----------|:--------------|:-----------------------------|:---------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 16 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | | X | X | X | | X | X | X | | X | | X | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | X | | | X | | | X | X | | | X | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 4 | 55 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | X | X | | | X | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | 5 | 22 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | | X | X | | | | | | | | | | | | | | | | X | | X | X | | | X | | | | X | | | | X | | | | | | X | | | | | | | | X | X | X | X | X | X | X |

tyson0420/stackexchange-4dpo-filby-ai-keywords

--- license: cc-by-4.0 ---

davanstrien/fuego-20230428-014702-04e0f5

--- tags: - fuego fuego: id: 20230428-014702-04e0f5 status: running script: scratchpad.py requirements_file: requirements.txt space_id: davanstrien/fuego-20230428-014702-04e0f5 space_hardware: cpu-basic ---

liuyanchen1015/MULTI_VALUE_mrpc_negative_inversion

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: train num_bytes: 3645 num_examples: 13 - name: validation num_bytes: 294 num_examples: 1 download_size: 10298 dataset_size: 3939 --- # Dataset Card for "MULTI_VALUE_mrpc_negative_inversion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lionelchg/dolly-information-extraction

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: text dtype: string splits: - name: train num_bytes: 4656769.3625498 num_examples: 1430 - name: test num_bytes: 247492.6374501992 num_examples: 76 download_size: 2854506 dataset_size: 4904262.0 --- # Dataset Card for "dolly-information-extraction" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

heliosprime/twitter_dataset_1713199899

--- 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: 20766 num_examples: 57 download_size: 19065 dataset_size: 20766 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713199899" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Isamu136/oxford_pets_with_l14_emb

--- dataset_info: features: - name: path dtype: string - name: label dtype: string - name: dog dtype: bool - name: image dtype: image - name: embeddings sequence: float32 splits: - name: train num_bytes: 257189204.25 num_examples: 7390 download_size: 261518905 dataset_size: 257189204.25 --- # Dataset Card for "oxford_pets_with_l14_emb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b

--- pretty_name: Evaluation run of logicker/SkkuDataScienceGlobal-10.7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [logicker/SkkuDataScienceGlobal-10.7b](https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T13:43:44.357190](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b/blob/main/results_2024-01-04T13-43-44.357190.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\": 0.6670223395120712,\n\ \ \"acc_stderr\": 0.031613933491978684,\n \"acc_norm\": 0.6677265646501975,\n\ \ \"acc_norm_stderr\": 0.03225832144808963,\n \"mc1\": 0.5716034271725826,\n\ \ \"mc1_stderr\": 0.017323088597314747,\n \"mc2\": 0.7192487275907953,\n\ \ \"mc2_stderr\": 0.015001105564856102\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6851535836177475,\n \"acc_stderr\": 0.01357265770308495,\n\ \ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266125\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7131049591714798,\n\ \ \"acc_stderr\": 0.004513877465062106,\n \"acc_norm\": 0.8840868352917746,\n\ \ \"acc_norm_stderr\": 0.003194665266078602\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n\ \ \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.040131241954243856,\n\ \ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.040131241954243856\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\ acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267822,\n \"\ acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267822\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8481012658227848,\n \"acc_stderr\": 0.023363878096632446,\n \ \ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.023363878096632446\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8045977011494253,\n\ \ \"acc_stderr\": 0.014179171373424383,\n \"acc_norm\": 0.8045977011494253,\n\ \ \"acc_norm_stderr\": 0.014179171373424383\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123563,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123563\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.394413407821229,\n\ \ \"acc_stderr\": 0.01634538676210397,\n \"acc_norm\": 0.394413407821229,\n\ \ \"acc_norm_stderr\": 0.01634538676210397\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0227797190887334,\n\ \ \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0227797190887334\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4954367666232073,\n\ \ \"acc_stderr\": 0.012769704263117522,\n \"acc_norm\": 0.4954367666232073,\n\ \ \"acc_norm_stderr\": 0.012769704263117522\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103128,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103128\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5716034271725826,\n\ \ \"mc1_stderr\": 0.017323088597314747,\n \"mc2\": 0.7192487275907953,\n\ \ \"mc2_stderr\": 0.015001105564856102\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781093\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6573161485974223,\n \ \ \"acc_stderr\": 0.013073030230827915\n }\n}\n```" repo_url: https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|arc:challenge|25_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-04T13-43-44.357190.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|gsm8k|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hellaswag|10_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-43-44.357190.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-43-44.357190.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T13-43-44.357190.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T13_43_44.357190 path: - '**/details_harness|winogrande|5_2024-01-04T13-43-44.357190.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-04T13-43-44.357190.parquet' - config_name: results data_files: - split: 2024_01_04T13_43_44.357190 path: - results_2024-01-04T13-43-44.357190.parquet - split: latest path: - results_2024-01-04T13-43-44.357190.parquet --- # Dataset Card for Evaluation run of logicker/SkkuDataScienceGlobal-10.7b  Dataset automatically created during the evaluation run of model [logicker/SkkuDataScienceGlobal-10.7b](https://huggingface.co/logicker/SkkuDataScienceGlobal-10.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T13:43:44.357190](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDataScienceGlobal-10.7b/blob/main/results_2024-01-04T13-43-44.357190.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": 0.6670223395120712, "acc_stderr": 0.031613933491978684, "acc_norm": 0.6677265646501975, "acc_norm_stderr": 0.03225832144808963, "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314747, "mc2": 0.7192487275907953, "mc2_stderr": 0.015001105564856102 }, "harness|arc:challenge|25": { "acc": 0.6851535836177475, "acc_stderr": 0.01357265770308495, "acc_norm": 0.712457337883959, "acc_norm_stderr": 0.013226719056266125 }, "harness|hellaswag|10": { "acc": 0.7131049591714798, "acc_stderr": 0.004513877465062106, "acc_norm": 0.8840868352917746, "acc_norm_stderr": 0.003194665266078602 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.756578947368421, "acc_stderr": 0.034923496688842384, "acc_norm": 0.756578947368421, "acc_norm_stderr": 0.034923496688842384 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956913, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956913 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.625531914893617, "acc_stderr": 0.03163910665367291, "acc_norm": 0.625531914893617, "acc_norm_stderr": 0.03163910665367291 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6344827586206897, "acc_stderr": 0.040131241954243856, "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.040131241954243856 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4947089947089947, "acc_stderr": 0.02574986828855657, "acc_norm": 0.4947089947089947, "acc_norm_stderr": 0.02574986828855657 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8161290322580645, "acc_stderr": 0.022037217340267822, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.022037217340267822 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656209, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656209 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7184873949579832, "acc_stderr": 0.02921354941437217, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.02921354941437217 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5833333333333334, "acc_stderr": 0.033622774366080424, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.033622774366080424 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156862, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156862 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8481012658227848, "acc_stderr": 0.023363878096632446, "acc_norm": 0.8481012658227848, "acc_norm_stderr": 0.023363878096632446 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.8543689320388349, "acc_stderr": 0.03492606476623791, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.03492606476623791 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.0230866350868414, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.0230866350868414 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8045977011494253, "acc_stderr": 0.014179171373424383, "acc_norm": 0.8045977011494253, "acc_norm_stderr": 0.014179171373424383 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7601156069364162, "acc_stderr": 0.022989592543123563, "acc_norm": 0.7601156069364162, "acc_norm_stderr": 0.022989592543123563 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.394413407821229, "acc_stderr": 0.01634538676210397, "acc_norm": 0.394413407821229, "acc_norm_stderr": 0.01634538676210397 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.729903536977492, "acc_stderr": 0.02521804037341062, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341062 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0227797190887334, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0227797190887334 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4954367666232073, "acc_stderr": 0.012769704263117522, "acc_norm": 0.4954367666232073, "acc_norm_stderr": 0.012769704263117522 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7389705882352942, "acc_stderr": 0.026679252270103128, "acc_norm": 0.7389705882352942, "acc_norm_stderr": 0.026679252270103128 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6813725490196079, "acc_stderr": 0.018850084696468712, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.018850084696468712 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142783, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142783 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03188578017686398, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03188578017686398 }, "harness|truthfulqa:mc|0": { "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314747, "mc2": 0.7192487275907953, "mc2_stderr": 0.015001105564856102 }, "harness|winogrande|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781093 }, "harness|gsm8k|5": { "acc": 0.6573161485974223, "acc_stderr": 0.013073030230827915 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

freshpearYoon/vr_train_free_66

--- 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: 6222416946 num_examples: 10000 download_size: 935984914 dataset_size: 6222416946 configs: - config_name: default data_files: - split: train path: data/train-* ---

kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-25000

--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 6316415857 num_examples: 1000 download_size: 1243432299 dataset_size: 6316415857 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-staging-eval-project-d42d3c12-7815008

--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: PdF/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: PdF/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.

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84812225.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 86719164.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90542888.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 94354856.125 num_examples: 5647 download_size: 333860025 dataset_size: 356429133.5 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NepaliAI/Nepali-HealthChat

--- license: apache-2.0 task_categories: - question-answering language: - ne tags: - medical - health ---

paullatham1/headlines-train-balanced

--- dataset_info: features: - name: data dtype: string - name: is_sarcastic dtype: int64 splits: - name: train num_bytes: 1440540 num_examples: 19632 download_size: 969261 dataset_size: 1440540 configs: - config_name: default data_files: - split: train path: data/train-* ---

ismaeltorres00/Dataset_de_prueba

--- size_categories: - 1K<n<10K --- Extraído de <https://github.com/anthony-wang/BestPractices/tree/master/data>. Campos: * Formula (`string`) * T (`float64`): Temperatura (K) * CP (`float64`): Capacidad calorífica (J/mol K)

Nexdata/96_Hours_Japanese_Children_Real_world_Casual_Conversation_and_Monologue_speech_dataset

--- license: cc-by-nc-nd-4.0 --- ## Description Japanese(Japan) Children Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live, lecture, variety show and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, age, accent and other attributes. Our dataset was collected from extensive and diversify speakers(12 years old and younger children), geographicly speaking, enhancing model performance in real and complex tasks.rnQuality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied. For more details, please refer to the link: https://www.nexdata.ai/dataset/1328?source=Huggingface ## Format 16kHz, 16 bit, wav, mono channel ## Age 12 years old and younger children ## Content category including interview, self-meida,variety show, etc. ## Recording environment Low background noise ## Country Japan(JPN) ## Language(Region) Code ja-JP ## Language Japanese ## Features of annotation Transcription text, timestamp, speaker ID, gender, noise ## Accuracy Word Accuracy Rate (WAR) 98% # Licensing Information Commercial License

autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667118

--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_v5 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-13b_eval metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_v5 dataset_config: mathemakitten--winobias_antistereotype_test_v5 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: inverse-scaling/opt-13b_eval * Dataset: mathemakitten/winobias_antistereotype_test_v5 * Config: mathemakitten--winobias_antistereotype_test_v5 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.