Datasets:
davanstrien
/
dataset_cards_with_long_context_embeddins

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ymoslem/Law-StackExchange
2023-08-20T17:25:54.000Z
[ "task_categories:question-answering", "task_categories:text-classification", "task_categories:sentence-similarity", "size_categories:10K<n<100K", "language:en", "license:cc-by-sa-4.0", "legal", "region:us" ]
ymoslem
null
null
7
17
2023-08-20T16:54:45
--- license: cc-by-sa-4.0 task_categories: - question-answering - text-classification - sentence-similarity language: - en tags: - legal pretty_name: Law Stack Exchange Questions and Answers size_categories: - 10K<n<100K --- All StackExchange legal questions and their answers from the Law site, up to 14 August 2023. The repository includes a notebook for the process using the official StackExchange API.
407
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zxvix/pubmed_subset_new
2023-08-23T09:04:37.000Z
[ "region:us" ]
zxvix
null
null
0
17
2023-08-23T08:08:51
--- dataset_info: features: - name: MedlineCitation struct: - name: PMID dtype: int32 - name: DateCompleted struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: NumberOfReferences dtype: int32 - name: DateRevised struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: Article struct: - name: Abstract struct: - name: AbstractText dtype: string - name: ArticleTitle dtype: string - name: AuthorList struct: - name: Author sequence: - name: LastName dtype: string - name: ForeName dtype: string - name: Initials dtype: string - name: CollectiveName dtype: string - name: Language dtype: string - name: GrantList struct: - name: Grant sequence: - name: GrantID dtype: string - name: Agency dtype: string - name: Country dtype: string - name: PublicationTypeList struct: - name: PublicationType sequence: string - name: MedlineJournalInfo struct: - name: Country dtype: string - name: ChemicalList struct: - name: Chemical sequence: - name: RegistryNumber dtype: string - name: NameOfSubstance dtype: string - name: CitationSubset dtype: string - name: MeshHeadingList struct: - name: MeshHeading sequence: - name: DescriptorName dtype: string - name: QualifierName dtype: string - name: PubmedData struct: - name: ArticleIdList sequence: - name: ArticleId sequence: string - name: PublicationStatus dtype: string - name: History struct: - name: PubMedPubDate sequence: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: ReferenceList sequence: - name: Citation dtype: string - name: CitationId dtype: int32 - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 3033204166.457245 num_examples: 1000000 - name: test num_bytes: 3033204.166457245 num_examples: 1000 download_size: 1638343655 dataset_size: 3036237370.623702 --- # Dataset Card for "pubmed_subset_new" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
2,841
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TaylorAI/pubmed_commercial
2023-08-26T07:32:30.000Z
[ "region:us" ]
TaylorAI
null
null
11
17
2023-08-23T19:00:38
Entry not found
15
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vikp/evol_instruct_code_filtered_39k
2023-08-29T17:35:13.000Z
[ "region:us" ]
vikp
null
null
3
17
2023-08-29T14:35:42
--- dataset_info: features: - name: output dtype: string - name: instruction dtype: string - name: quality_prob dtype: float64 - name: learning_prob dtype: float64 splits: - name: train num_bytes: 56854896.038860105 num_examples: 39078 download_size: 37822990 dataset_size: 56854896.038860105 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "evol_instruct_code_filtered_38k" Filtered version of `nickrosh/Evol-Instruct-Code-80k-v1`, with manual filtering, and automatic filtering based on quality and learning value classifiers.
632
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StudentLLM/Sampled_Orca_GPT4
2023-08-31T02:58:44.000Z
[ "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]
StudentLLM
null
null
0
17
2023-08-30T06:57:21
--- language: - en size_categories: - 10K<n<100K license: mit --- # Stratify Sampled Dataset of Open-Orca 🐬 This dataset is a stratified sampled dataset of Open-Orca's GPT-4 answered dataset(1M-GPT4-Augmented.parquet) [[Link](https://huggingface.co/datasets/Open-Orca/OpenOrca)] For sampling the dataset stratify, `train_test_split` of scikit-learn library was used. The specific setup of sampling is as follows: - split_size: 0.05 - shuffle: True - stratify: `'id'` of Open-Orca dataset
491
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Falah/photography_prompts
2023-09-10T12:53:20.000Z
[ "region:us" ]
Falah
null
null
1
17
2023-09-10T12:53:18
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 36884997 num_examples: 100000 download_size: 5112133 dataset_size: 36884997 --- # Dataset Card for "photography_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
368
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msinankhan1/India_Tax_FAQs
2023-09-14T12:12:26.000Z
[ "region:us" ]
msinankhan1
null
null
0
17
2023-09-12T07:22:30
Entry not found
15
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open-llm-leaderboard/details_TigerResearch__tigerbot-70b-base
2023-10-24T09:25:34.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
0
17
2023-09-13T01:25:28
--- pretty_name: Evaluation run of TigerResearch/tigerbot-70b-base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TigerResearch/tigerbot-70b-base](https://huggingface.co/TigerResearch/tigerbot-70b-base)\ \ 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_TigerResearch__tigerbot-70b-base\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T09:25:20.725516](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-base/blob/main/results_2023-10-24T09-25-20.725516.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\": 0.4872063758389262,\n\ \ \"em_stderr\": 0.005118791512925044,\n \"f1\": 0.5244914010067125,\n\ \ \"f1_stderr\": 0.004935563924712029,\n \"acc\": 0.5897264974960701,\n\ \ \"acc_stderr\": 0.012277506705422794\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.4872063758389262,\n \"em_stderr\": 0.005118791512925044,\n\ \ \"f1\": 0.5244914010067125,\n \"f1_stderr\": 0.004935563924712029\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3775587566338135,\n \ \ \"acc_stderr\": 0.013353150666358539\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.011201862744487047\n\ \ }\n}\n```" repo_url: https://huggingface.co/TigerResearch/tigerbot-70b-base 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_09_13T01_25_14.196261 path: - '**/details_harness|arc:challenge|25_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-13T01-25-14.196261.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T09_25_20.725516 path: - '**/details_harness|drop|3_2023-10-24T09-25-20.725516.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-24T09-25-20.725516.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T09_25_20.725516 path: - '**/details_harness|gsm8k|5_2023-10-24T09-25-20.725516.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-24T09-25-20.725516.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hellaswag|10_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T01-25-14.196261.parquet' - 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'**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T01-25-14.196261.parquet' - 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'**/details_harness|hendrycksTest-econometrics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T01-25-14.196261.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T01-25-14.196261.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T01_25_14.196261 path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T01-25-14.196261.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T01-25-14.196261.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T09_25_20.725516 path: - '**/details_harness|winogrande|5_2023-10-24T09-25-20.725516.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-24T09-25-20.725516.parquet' - config_name: results data_files: - split: 2023_09_13T01_25_14.196261 path: - results_2023-09-13T01-25-14.196261.parquet - split: 2023_10_24T09_25_20.725516 path: - results_2023-10-24T09-25-20.725516.parquet - split: latest path: - results_2023-10-24T09-25-20.725516.parquet --- # Dataset Card for Evaluation run of TigerResearch/tigerbot-70b-base ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TigerResearch/tigerbot-70b-base - **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 [TigerResearch/tigerbot-70b-base](https://huggingface.co/TigerResearch/tigerbot-70b-base) 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_TigerResearch__tigerbot-70b-base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T09:25:20.725516](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-base/blob/main/results_2023-10-24T09-25-20.725516.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": 0.4872063758389262, "em_stderr": 0.005118791512925044, "f1": 0.5244914010067125, "f1_stderr": 0.004935563924712029, "acc": 0.5897264974960701, "acc_stderr": 0.012277506705422794 }, "harness|drop|3": { "em": 0.4872063758389262, "em_stderr": 0.005118791512925044, "f1": 0.5244914010067125, "f1_stderr": 0.004935563924712029 }, "harness|gsm8k|5": { "acc": 0.3775587566338135, "acc_stderr": 0.013353150666358539 }, "harness|winogrande|5": { "acc": 0.8018942383583267, "acc_stderr": 0.011201862744487047 } } ``` ### 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]
38,674
[ [ -0.031280517578125, -0.043212890625, 0.0120697021484375, 0.014892578125, -0.016082763671875, 0.0113983154296875, -0.0272216796875, -0.01059722900390625, 0.03253173828125, 0.0423583984375, -0.05059814453125, -0.0670166015625, -0.0391845703125, 0.0152740478515...
Nacholmo/coco-pattern
2023-09-16T05:43:17.000Z
[ "region:us" ]
Nacholmo
null
null
0
17
2023-09-16T04:10:25
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: filepath dtype: string - name: sentids list: int32 - name: filename dtype: string - name: imgid dtype: int32 - name: split dtype: string - name: sentences_tokens list: list: string - name: sentences_raw list: string - name: sentences_sentid list: int32 - name: cocoid dtype: int32 - name: id dtype: int64 - name: conditioning_image dtype: image splits: - name: train num_bytes: 14068039590.25 num_examples: 113287 download_size: 14013924288 dataset_size: 14068039590.25 --- # Dataset Card for "coco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
877
[ [ -0.0401611328125, -0.0269012451171875, 0.00255584716796875, 0.035369873046875, -0.01496124267578125, 0.0188751220703125, 0.00988006591796875, -0.0289764404296875, 0.06524658203125, 0.035919189453125, -0.056304931640625, -0.05914306640625, -0.04449462890625, ...
infinityofspace/python_codestyles-random-1k
2023-10-18T20:42:59.000Z
[ "size_categories:100K<n<1M", "license:mit", "python", "code-style", "random", "doi:10.57967/hf/1232", "region:us" ]
infinityofspace
null
null
0
17
2023-09-17T18:24:57
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: code dtype: string - name: code_codestyle dtype: int64 - name: style_context dtype: string - name: style_context_codestyle dtype: int64 - name: label dtype: int64 splits: - name: train num_bytes: 3604934957 num_examples: 308000 - name: test num_bytes: 645620388 num_examples: 56400 download_size: 671035436 dataset_size: 4250555345 license: mit tags: - python - code-style - random size_categories: - 100K<n<1M --- # Dataset Card for "python_codestyles-random-1k" This dataset contains negative and positive examples with python code of compliance with a code style. A positive example represents compliance with the code style (label is 1). Each example is composed of two components, the first component consists of a code that either conforms to the code style or violates it and the second component corresponding to an example code that already conforms to a code style. In total, the dataset contains `1.000` completely different code styles. The code styles differ in at least one codestyle rule, which is called a `random` codestyle dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between groups. In addition, both groups contain completely different underlying codes. The examples contain source code from the following repositories: | repository | tag or commit | |:-----------------------------------------------------------------------:|:----------------------------------------:| | [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 | | [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 | | [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 | | [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 | | [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 | You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json). The code styles in the file are split by training and test group and the index corresponds to the class for the columns `code_codestyle` and `style_context_codestyle` in the dataset. There are 364.400 samples in total and 182.200 positive and 182.200 negative samples.
2,745
[ [ -0.0447998046875, -0.03277587890625, -0.0106353759765625, 0.0308990478515625, -0.012451171875, -0.0154876708984375, -0.0133209228515625, -0.01409912109375, 0.03887939453125, 0.0263214111328125, -0.053955078125, -0.0440673828125, -0.0289764404296875, 0.023300...
legacy107/sentence_transformer_wikipedia_chunked
2023-09-19T04:00:50.000Z
[ "region:us" ]
legacy107
null
null
0
17
2023-09-18T08:27:13
--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answer_start dtype: int64 - name: answer dtype: string - name: article dtype: string - name: chunked_article sequence: string splits: - name: train num_bytes: 3734770114 num_examples: 27742 - name: test num_bytes: 408448904 num_examples: 3468 - name: validation num_bytes: 564192755 num_examples: 3458 download_size: 717817867 dataset_size: 4707411773 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for "qa_wikipedia_sentence_transformer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
953
[ [ -0.0369873046875, -0.0230865478515625, 0.0191192626953125, 0.0099639892578125, -0.0087738037109375, -0.01439666748046875, 0.00814056396484375, 0.0001316070556640625, 0.04833984375, 0.031524658203125, -0.050872802734375, -0.040557861328125, -0.033935546875, -...
dim/databricks_dolly_15k_ru
2023-09-20T15:51:37.000Z
[ "region:us" ]
dim
null
null
0
17
2023-09-20T15:51:24
--- dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string splits: - name: train num_bytes: 22121608 num_examples: 14914 download_size: 11365356 dataset_size: 22121608 --- # Dataset Card for "databricks_dolly_15k_ru" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
486
[ [ -0.02276611328125, -0.0214080810546875, -0.002872467041015625, 0.040283203125, -0.019134521484375, 0.005435943603515625, 0.042236328125, 0.0012607574462890625, 0.0518798828125, 0.0238037109375, -0.06805419921875, -0.045379638671875, -0.036376953125, -0.00255...
infCapital/vnnews-corpus
2023-09-22T00:10:16.000Z
[ "region:us" ]
infCapital
null
null
1
17
2023-09-21T17:43:50
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
tyzhu/eval_tag_nq_test_v0.5
2023-09-25T06:07:50.000Z
[ "region:us" ]
tyzhu
null
null
0
17
2023-09-25T06:07:43
--- dataset_info: features: - name: question dtype: string - name: title dtype: string - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: id dtype: string splits: - name: train num_bytes: 1972 num_examples: 10 - name: validation num_bytes: 787384 num_examples: 3610 download_size: 488101 dataset_size: 789356 --- # Dataset Card for "eval_tag_nq_test_v0.5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
680
[ [ -0.04742431640625, -0.020599365234375, -0.003551483154296875, 0.005035400390625, -0.012939453125, 0.0135650634765625, 0.030517578125, -0.00644683837890625, 0.052398681640625, 0.0309295654296875, -0.04791259765625, -0.051605224609375, -0.01074981689453125, 0....
MattCoddity/dockerNLcommands
2023-10-06T08:35:01.000Z
[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]
MattCoddity
null
null
2
17
2023-09-27T04:21:12
--- license: apache-2.0 task_categories: - question-answering language: - en size_categories: - 10K<n<100K --- # Natural Language to Docker Command Dataset This dataset is designed to translate natural language instructions into Docker commands. It contains mappings of textual phrases to corresponding Docker commands, aiding in the development of models capable of understanding and translating user requests into executable Docker instructions. ## Dataset Format Each entry in the dataset consists of a JSON object with the following keys: - `input`: The natural language phrase. - `instruction`: A static field indicating the task to translate the phrase into a Docker command. - `output`: The corresponding Docker command. ### Example Entry ```json { "input": "Can you show me the digests of all the available Docker images?", "instruction": "translate this sentence in docker command", "output": "docker images --digests" } ``` ## Usage This dataset can be utilized to train and evaluate models for a variety of applications including, but not limited to, Natural Language Processing (NLP), Command Line Interface (CLI) automation, and educational tools for Docker. ## Commands coverage - docker ps - docker images - docker stop - docker kill - docker login ## Contributing We welcome contributions to improve this dataset. Please feel free to open a Pull Request or an Issue to discuss potential improvements, bug fixes, or other changes.
1,463
[ [ -0.05291748046875, -0.047149658203125, 0.0335693359375, 0.0227813720703125, -0.03399658203125, 0.0042724609375, -0.004589080810546875, -0.0031280517578125, 0.0027923583984375, 0.08026123046875, -0.0518798828125, -0.069580078125, -0.03436279296875, 0.01538848...
lowem1/mimic_radiology_ocr
2023-09-27T15:47:13.000Z
[ "region:us" ]
lowem1
null
null
0
17
2023-09-27T15:47:09
--- dataset_info: features: - name: tag dtype: string - name: ocr_data dtype: string - name: text dtype: string splits: - name: train num_bytes: 2270338 num_examples: 1000 download_size: 1178315 dataset_size: 2270338 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "mimic_radiology_ocr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
516
[ [ -0.022796630859375, -0.00682830810546875, 0.0308074951171875, -0.018524169921875, -0.003887176513671875, 0.0011892318725585938, 0.0260009765625, -0.033416748046875, 0.056427001953125, 0.03363037109375, -0.043426513671875, -0.04522705078125, -0.04058837890625, ...
jhuang14/Labeled_Data
2023-09-28T08:32:36.000Z
[ "region:us" ]
jhuang14
null
null
0
17
2023-09-28T08:32:09
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': airplane '1': bustruck '2': other '3': rail splits: - name: train num_bytes: 1652124.1515151516 num_examples: 92 - name: test num_bytes: 718314.8484848485 num_examples: 40 download_size: 2372957 dataset_size: 2370439.0 --- # Dataset Card for "Labeled_Data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
708
[ [ -0.039276123046875, -0.025146484375, 0.01020050048828125, 0.0211181640625, -0.010223388671875, -0.00010776519775390625, 0.0149688720703125, -0.0218048095703125, 0.0552978515625, 0.039276123046875, -0.049835205078125, -0.06683349609375, -0.046722412109375, -0...
ashiyakatuka11/corpus1_dataset
2023-10-03T12:01:15.000Z
[ "region:us" ]
ashiyakatuka11
null
null
0
17
2023-09-28T10:08:14
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Session_ID dtype: int64 - name: 'Speaker ' dtype: string - name: UserID dtype: string - name: prev_Utterance dtype: string - name: Utterance dtype: string - name: prevUtt_TAG dtype: string - name: TAG dtype: string - name: new_TAG dtype: string - name: new_TAG_name dtype: string - name: labels dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 826401 num_examples: 4964 - name: test num_bytes: 207557 num_examples: 1241 download_size: 426039 dataset_size: 1033958 --- # Dataset Card for "corpus1_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
927
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ashiyakatuka11/corpus2_dataset
2023-10-03T12:01:21.000Z
[ "region:us" ]
ashiyakatuka11
null
null
0
17
2023-09-28T10:08:18
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Corpus Utterance #' dtype: int64 - name: 'Session Utterance #' dtype: string - name: Time dtype: string - name: User dtype: string - name: Utterance dtype: string - name: TAG dtype: string - name: Session ID dtype: string - name: new_TAG dtype: string - name: new_TAG_name dtype: string - name: labels dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 327599 num_examples: 2720 - name: test num_bytes: 81553 num_examples: 681 download_size: 165842 dataset_size: 409152 --- # Dataset Card for "corpus2_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
932
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piyush23111991/amazonProductData
2023-10-13T04:50:11.000Z
[ "region:us" ]
piyush23111991
null
null
0
17
2023-10-02T20:25:57
Entry not found
15
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ashiyakatuka11/en_es_combo_dataset
2023-10-03T12:19:15.000Z
[ "region:us" ]
ashiyakatuka11
null
null
0
17
2023-10-03T12:19:12
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Session_ID dtype: float64 - name: 'Speaker ' dtype: string - name: UserID dtype: string - name: prev_Utterance dtype: string - name: Utterance dtype: string - name: prevUtt_TAG dtype: string - name: TAG dtype: string - name: new_TAG dtype: string - name: new_TAG_name dtype: string - name: labels dtype: int64 - name: 'Corpus Utterance #' dtype: float64 - name: 'Session Utterance #' dtype: string - name: Time dtype: string - name: User dtype: string - name: Session ID dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1348026 num_examples: 7684 - name: test num_bytes: 337648 num_examples: 1922 download_size: 595953 dataset_size: 1685674 --- # Dataset Card for "en_es_combo_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,139
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Hack90/ncbi_genbank_part_0
2023-10-04T19:45:14.000Z
[ "region:us" ]
Hack90
null
null
0
17
2023-10-04T18:59:55
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: sequence dtype: string - name: name dtype: string - name: description dtype: string - name: features dtype: int64 - name: seq_length dtype: int64 splits: - name: train num_bytes: 257341428 num_examples: 156 download_size: 118952731 dataset_size: 257341428 --- # Dataset Card for "ncbi_genbank_part_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
634
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jayashri710/llama2-cricketdata
2023-10-06T09:50:46.000Z
[ "region:us" ]
jayashri710
null
null
0
17
2023-10-05T13:30:28
Entry not found
15
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PericlesSavio/contratacao4
2023-10-06T14:42:45.000Z
[ "region:us" ]
PericlesSavio
null
null
0
17
2023-10-06T14:41:26
Entry not found
15
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towhid/aesir-test
2023-10-06T20:29:56.000Z
[ "region:us" ]
towhid
null
null
0
17
2023-10-06T20:29:29
Entry not found
15
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JordanTallon/political_bias
2023-10-07T21:43:25.000Z
[ "region:us" ]
JordanTallon
null
null
0
17
2023-10-07T21:42:19
Entry not found
15
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Mizukiluke/ureader-instruction-1.0
2023-10-13T19:17:19.000Z
[ "region:us" ]
Mizukiluke
null
null
0
17
2023-10-09T02:07:28
Entry not found
15
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joheras/spanish-suicide-intent
2023-10-10T14:20:03.000Z
[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:es", "license:cc-by-4.0", "region:us" ]
joheras
null
null
0
17
2023-10-10T12:34:26
--- license: cc-by-4.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: Text dtype: string - name: Label dtype: int64 - name: dataset dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 31442785 num_examples: 136136 - name: val num_bytes: 3542897 num_examples: 15131 - name: test num_bytes: 8671755 num_examples: 37820 download_size: 17952583 dataset_size: 43657437 task_categories: - text-classification language: - es size_categories: - 100K<n<1M --- ## Dataset Summary The dataset consists of comments from several sources translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal. # Dataset Structure The dataset has 175010 rows (77223 considered as Suicidal Ideation/Behavior and 97787 considered Not Suicidal). ## Dataset fields * `Text`: User comment. * `Label`: 1 if suicidal ideation/behavior; 0 if not suicidal comment. * `Dataset`: Source of the comment # Dataset Creation * 112385 (84485 non suicidal, 27905 suicidal) from the [Suicide Watch dataset](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch/). * 46894 (46894 suicidal) from the [TwitterSuicidalAnalysis](https://github.com/IE-NITK/TwitterSuicidalAnalysis). * 9919 (9183 non suicidal, 736 suicidal) from the corpus genereated in [Hackaton Somos NLP](https://huggingface.co/datasets/hackathon-somos-nlp-2023/suicide-comments-es) * 8744 (4802 non suicidal, 3942 suicidal) from the paper [An Attention-based hybrid architecture with explainability for depressive social media text detection in Bangla](https://github.com/NM001007/An-Attention-based-Hybrid-Suicide-Ideation-Detection) * 7084 (3559 non suicidal, 3525 suicidal) from the paper [Supervised Learning for Suicidal Ideation Detection in Online User Content](https://github.com/TabbieD/NLP-Sentiment-Analysis) * 1972 (1540 non suicidal, 432 suicidal) from the paper [Detection of Suicidal Intent in Spanish Language Social Networks using Machine Learning](https://github.com/kvvaldez/spanish_suicide/blob/master/dataset/suicidio_notacion.csv) * 1769 (1122 non suicidal, 647 suicidal) from the corpus [Suicidal Tweet Detection](https://www.kaggle.com/datasets/aunanya875/suicidal-tweet-detection-dataset/data) * 316 (204 non suicidal, 112 suicidal) from the paper [Data Mining Approach to the Detection of Suicide in Social Media: A Case Study of Singapore](https://github.com/shingkid/data-mining-suicide-sg/tree/master) # Considerations for Using the Data ## Social Impact of Dataset The dataset could contain some patterns to detect suicidal ideation/behavior. ## Discussion of Biases No measures have been taken to estimate the bias and toxicity embedded in the dataset. However, the most of the data is collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because [the Internet is used more by younger people](https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide). # Additional Information ## Team * [joheras](https://huggingface.co/joheras)
3,242
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W1lson/RMData
2023-10-11T05:39:01.000Z
[ "region:us" ]
W1lson
null
null
0
17
2023-10-11T05:38:59
--- dataset_info: features: - name: Source ID dtype: int64 - name: Primary Text dtype: string - name: Artifact Type dtype: string - name: Design Package dtype: string - name: Location dtype: string - name: Verification Method dtype: string - name: Validation Method dtype: string splits: - name: train num_bytes: 6326 num_examples: 35 download_size: 7719 dataset_size: 6326 --- # Dataset Card for "RMData" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
598
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datastax/entomology
2023-10-11T08:55:50.000Z
[ "size_categories:n<1K", "language:en", "license:apache-2.0", "region:us" ]
datastax
null
null
0
17
2023-10-11T08:03:34
--- license: apache-2.0 language: - en pretty_name: Fictional entomology size_categories: - n<1K --- 32 made-up insect descriptions with Latin name and order (well, there's a spider, too), as one would find in a field guide. These were created with ChatGPT 3.5 / ChatGPT 4 for the purpose of running example applications such as a "entomology field guide helper". It was chosen to use entirely fictional material to avoid inadvertently using the LLM's implicit knowledge from pretraining in the demos.
502
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shrutisingh/dataset_recommendation_mcq_mc
2023-10-12T17:15:59.000Z
[ "license:apache-2.0", "region:us" ]
shrutisingh
null
null
0
17
2023-10-12T17:02:16
--- license: apache-2.0 --- Task: MCQ with multiple correct answers. Dataset: Recommendation of datasets to validate a research question. This dataset is derived from the [DataFinder](https://aclanthology.org/2023.acl-long.573/) dataset. We curate the abstracts of each dataset from [PapersWithCode](https://paperswithcode.com/datasets). Given is a short `query` discussing a research question, and keyphrases relevant the query. The original training set of the DataFinder dataset has positive and negative candidates for each query, to train a contrastive model. We objective is to convert the dataset into a MCQ question-answering task with multiple correct answers. We also add the abstracts from the research papers introducing the datasets so that context can be provided to the models. To reproduce the construction of this dataset, please visit [https://github.com/shruti-singh/scidata_recommendation](https://github.com/shruti-singh/scidata_recommendation). Please note that the query instances in this dataset have no intersection with the [`dataset_recommendation_mcq_sc`](https://huggingface.co/datasets/shrutisingh/dataset_recommendation_mcq_sc) dataset. [`dataset_recommendation_mcq_sc`](https://huggingface.co/datasets/shrutisingh/dataset_recommendation_mcq_sc) is a variant of this MCQ question-answering task with only single correct answer.
1,378
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Eitanli/abstracts_cleaned
2023-10-14T11:37:43.000Z
[ "region:us" ]
Eitanli
null
null
0
17
2023-10-13T11:43:34
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: recall dtype: int64 - name: article_title dtype: string - name: topic dtype: string - name: abstract dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 137515873.22056717 num_examples: 79863 - name: test num_bytes: 17189699.389716417 num_examples: 9983 - name: valid num_bytes: 17189699.389716417 num_examples: 9983 download_size: 92795013 dataset_size: 171895272.0 --- # Dataset Card for "abstracts_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
844
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Konthee/pokemon
2023-10-14T04:42:21.000Z
[ "region:us" ]
Konthee
null
null
0
17
2023-10-13T17:06:50
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: th-input_ids sequence: int64 - name: th-attention_mask sequence: int64 splits: - name: train num_bytes: 496836 num_examples: 666 - name: val num_bytes: 124582 num_examples: 167 download_size: 32687 dataset_size: 621418 --- # Dataset Card for "pokemon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
666
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khalidalt/Ashaar_diac_1
2023-10-14T13:55:59.000Z
[ "region:us" ]
khalidalt
null
null
0
17
2023-10-14T13:48:44
--- dataset_info: features: - name: output dtype: string - name: instruction dtype: string - name: input dtype: string splits: - name: train num_bytes: 12159497 num_examples: 23481 download_size: 6059483 dataset_size: 12159497 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Ashaar_diac" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
518
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phanvancongthanh/pubchem_bioassay
2023-10-17T06:51:24.000Z
[ "region:us" ]
phanvancongthanh
null
null
0
17
2023-10-16T04:41:57
--- dataset_info: features: - name: PUBCHEM_CID dtype: float64 - name: PUBCHEM_EXT_DATASOURCE_SMILES dtype: string splits: - name: train num_bytes: 13266669373.336466 num_examples: 210186056 download_size: 6660630004 dataset_size: 13266669373.336466 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pubchem_bioassay" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
540
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HumanCompatibleAI/random-seals-HalfCheetah-v1
2023-10-17T05:38:15.000Z
[ "region:us" ]
HumanCompatibleAI
null
null
0
17
2023-10-17T05:37:48
--- dataset_info: features: - name: obs sequence: sequence: float64 - name: acts sequence: sequence: float32 - name: infos sequence: string - name: terminal dtype: bool - name: rews sequence: float32 splits: - name: train num_bytes: 109003139 num_examples: 100 download_size: 46825772 dataset_size: 109003139 --- # Dataset Card for "random-seals-HalfCheetah-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
554
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Back-up/flan-5k-sample
2023-10-17T12:07:55.000Z
[ "region:us" ]
Back-up
null
null
0
17
2023-10-17T12:07:46
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task dtype: string splits: - name: train num_bytes: 3596003.2 num_examples: 4000 - name: test num_bytes: 899000.8 num_examples: 1000 download_size: 2413137 dataset_size: 4495004.0 --- # Dataset Card for "flan-5k-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
617
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schhetri41/SSDataset
2023-10-18T07:16:01.000Z
[ "region:us" ]
schhetri41
null
null
0
17
2023-10-18T07:02:17
Entry not found
15
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goodcoffee/covidQA_eval
2023-10-19T11:56:42.000Z
[ "region:us" ]
goodcoffee
null
null
0
17
2023-10-18T21:42:53
--- dataset_info: features: - name: input_ids sequence: int64 - name: attention_mask sequence: int64 - name: answer dtype: string - name: start_positions dtype: int64 - name: end_positions dtype: int64 splits: - name: train num_bytes: 414807 num_examples: 50 download_size: 50631 dataset_size: 414807 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "covidQA_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
605
[ [ -0.03558349609375, -0.03228759765625, 0.002117156982421875, 0.016387939453125, -0.006366729736328125, 0.01174163818359375, 0.025054931640625, -0.0018777847290039062, 0.049163818359375, 0.0181884765625, -0.052459716796875, -0.054351806640625, -0.030364990234375, ...
cmu-mlsp/encodec_24khz-opt-125m-pretrained-ft-librispeech_asr-validation.clean-features
2023-10-24T12:45:07.000Z
[ "region:us" ]
cmu-mlsp
null
null
0
17
2023-10-20T16:25:40
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 24000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: audio_codes sequence: sequence: int64 splits: - name: validation.clean num_bytes: 955281891.125 num_examples: 2703 download_size: 914893005 dataset_size: 955281891.125 configs: - config_name: default data_files: - split: validation.clean path: data/validation.clean-* --- # Dataset Card for "encodec_24khz-opt-125m-pretrained-ft-librispeech_asr-validation.clean-features" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
820
[ [ -0.057037353515625, -0.00872802734375, -0.0038661956787109375, 0.011871337890625, -0.0273895263671875, 0.01198577880859375, -0.0099945068359375, -0.0166015625, 0.035430908203125, 0.038116455078125, -0.0667724609375, -0.044830322265625, -0.0306549072265625, -...
Claudiano/donut-invoices
2023-10-21T00:07:13.000Z
[ "region:us" ]
Claudiano
null
null
1
17
2023-10-21T00:07:12
--- dataset_info: features: - name: ground_truth dtype: string - name: image dtype: image splits: - name: test2 num_bytes: 99821.0 num_examples: 1 download_size: 103707 dataset_size: 99821.0 configs: - config_name: default data_files: - split: test2 path: data/test2-* --- # Dataset Card for "donut-invoices" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
479
[ [ -0.0104217529296875, -0.00476837158203125, 0.0153656005859375, 0.00270843505859375, -0.002044677734375, 0.01287841796875, 0.0160369873046875, -0.0052337646484375, 0.0556640625, 0.052398681640625, -0.0445556640625, -0.046875, -0.035888671875, -0.0293426513671...
qgyd2021/nxcloud_customer_service
2023-10-24T03:11:08.000Z
[ "task_categories:text-generation", "task_categories:feature-extraction", "task_categories:conversational", "size_categories:100M<n<1B", "language:zh", "region:us" ]
qgyd2021
null
@dataset{nxcloud_customer_service, author = {Xing Tian}, title = {nxcloud_customer_service}, month = sep, year = 2023, publisher = {Xing Tian}, version = {1.0}, }
0
17
2023-10-23T06:44:51
--- task_categories: - text-generation - feature-extraction - conversational language: - zh size_categories: - 100M<n<1B --- ## NXCloud Customer Service
154
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roupenminassian/vehicle-dataset
2023-10-23T09:40:06.000Z
[ "region:us" ]
roupenminassian
null
null
0
17
2023-10-23T09:39:23
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: image_id dtype: int64 - name: width dtype: int64 - name: height dtype: int64 - name: objects struct: - name: id sequence: int64 - name: area sequence: float64 - name: bbox sequence: sequence: float64 - name: category sequence: int64 splits: - name: train num_bytes: 74749784.0 num_examples: 618 download_size: 74708626 dataset_size: 74749784.0 --- # Dataset Card for "vehicle-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
762
[ [ -0.04937744140625, -0.007080078125, 0.0233917236328125, 0.0165863037109375, -0.0143585205078125, 0.00820159912109375, 0.022705078125, -0.0114898681640625, 0.041656494140625, 0.0205078125, -0.06640625, -0.0418701171875, -0.0276031494140625, -0.036712646484375...
Mihir1108/json_data
2023-10-23T13:02:52.000Z
[ "region:us" ]
Mihir1108
null
null
0
17
2023-10-23T13:02:25
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
kardosdrur/hestenet-qa
2023-10-23T14:16:16.000Z
[ "license:mit", "region:us" ]
kardosdrur
null
null
1
17
2023-10-23T13:37:15
--- license: mit dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1144206.5903728174 num_examples: 1695 - name: test num_bytes: 286220.40962718264 num_examples: 424 download_size: 936129 dataset_size: 1430427.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Hestenet Question-Answer The dataset is based on data from Hestenettet in the Danish Gigaword corpus. Question-answer pairs are purely extracted on the basis of heuristics, and have not been manually evaluated. The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project. The dataset is currently not production-ready. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
960
[ [ -0.04058837890625, -0.05169677734375, 0.0242767333984375, 0.00762939453125, 0.002361297607421875, -0.0006561279296875, -0.02313232421875, -0.0302276611328125, 0.02947998046875, 0.04791259765625, -0.0601806640625, -0.024383544921875, -0.04193115234375, 0.0098...
optech/fbz_chat
2023-10-24T04:37:22.000Z
[ "region:us" ]
optech
null
null
0
17
2023-10-24T04:36:47
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
gabrielmbmb/my-dataset
2023-10-24T09:27:36.000Z
[ "region:us" ]
gabrielmbmb
null
null
0
17
2023-10-24T09:27:34
--- dataset_info: features: - name: instruction dtype: string - name: generations sequence: string - name: score sequence: int64 - name: rationale sequence: string splits: - name: train num_bytes: 176800 num_examples: 50 download_size: 94403 dataset_size: 176800 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "my-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
559
[ [ -0.05706787109375, -0.01666259765625, 0.015167236328125, 0.01261138916015625, -0.001407623291015625, 0.003391265869140625, 0.0207977294921875, -0.00951385498046875, 0.07794189453125, 0.03778076171875, -0.06475830078125, -0.04388427734375, -0.037078857421875, ...
gayathrimanoj/dataset-llama-unix-extended
2023-10-24T14:43:50.000Z
[ "region:us" ]
gayathrimanoj
null
null
0
17
2023-10-24T14:43:26
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
Geonmo/gcc12m_caption_only
2023-10-25T08:40:33.000Z
[ "region:us" ]
Geonmo
null
null
0
17
2023-10-25T08:32:24
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1329443791 num_examples: 12423374 download_size: 943024335 dataset_size: 1329443791 --- # Dataset Card for "gcc12m_caption_only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
373
[ [ -0.0345458984375, -0.018951416015625, 0.02313232421875, 0.0222015380859375, -0.0372314453125, 0.01192474365234375, 0.002719879150390625, -0.00952911376953125, 0.057891845703125, 0.049652099609375, -0.06573486328125, -0.061981201171875, -0.050384521484375, -0...
HoangHa/Vie_alpaca
2023-10-26T09:44:26.000Z
[ "region:us" ]
HoangHa
null
null
0
17
2023-10-26T09:44:22
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 51907952 num_examples: 49999 download_size: 24606528 dataset_size: 51907952 --- # Dataset Card for "Vie_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
442
[ [ -0.048248291015625, -0.03411865234375, 0.002208709716796875, 0.01922607421875, -0.0214080810546875, -0.012359619140625, 0.04229736328125, -0.0146026611328125, 0.0828857421875, 0.052276611328125, -0.0467529296875, -0.053924560546875, -0.044921875, -0.03216552...
emi429/humansleepproject-small-individuals
2023-10-26T18:18:10.000Z
[ "region:us" ]
emi429
null
null
0
17
2023-10-26T14:31:15
--- dataset_info: features: - name: rr_intervals dtype: int64 - name: sleep_stage dtype: int64 - name: patient_id dtype: int64 splits: - name: test num_bytes: 12096 num_examples: 504 - name: train num_bytes: 49680 num_examples: 2070 download_size: 47116 dataset_size: 61776 --- # Dataset Card for "humansleepproject-small-individuals" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
513
[ [ -0.034149169921875, -0.005870819091796875, 0.0173187255859375, 0.0200653076171875, -0.0056304931640625, 0.004215240478515625, 0.00959014892578125, -0.0217437744140625, 0.0704345703125, 0.0266571044921875, -0.057464599609375, -0.03948974609375, -0.026229858398437...
Kateway/Thursday
2023-10-26T18:42:34.000Z
[ "region:us" ]
Kateway
null
null
0
17
2023-10-26T18:36:22
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
DataScienceClubUVU/ServiceProjectFall2023
2023-10-29T02:27:16.000Z
[ "region:us" ]
DataScienceClubUVU
null
null
0
17
2023-10-26T20:16:29
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': d0 '1': d1 '2': d10 '3': d100 '4': d101 '5': d102 '6': d103 '7': d104 '8': d105 '9': d106 '10': d107 '11': d108 '12': d109 '13': d11 '14': d110 '15': d111 '16': d112 '17': d113 '18': d114 '19': d115 '20': d116 '21': d117 '22': d118 '23': d119 '24': d12 '25': d120 '26': d121 '27': d122 '28': d123 '29': d124 '30': d125 '31': d126 '32': d127 '33': d128 '34': d129 '35': d13 '36': d130 '37': d131 '38': d132 '39': d133 '40': d134 '41': d135 '42': d136 '43': d137 '44': d138 '45': d139 '46': d14 '47': d140 '48': d141 '49': d142 '50': d143 '51': d144 '52': d145 '53': d146 '54': d147 '55': d148 '56': d149 '57': d15 '58': d150 '59': d151 '60': d152 '61': d153 '62': d154 '63': d155 '64': d156 '65': d157 '66': d158 '67': d159 '68': d16 '69': d160 '70': d161 '71': d162 '72': d163 '73': d164 '74': d165 '75': d166 '76': d167 '77': d168 '78': d169 '79': d17 '80': d170 '81': d171 '82': d172 '83': d173 '84': d174 '85': d175 '86': d176 '87': d177 '88': d178 '89': d179 '90': d18 '91': d180 '92': d181 '93': d182 '94': d183 '95': d184 '96': d185 '97': d186 '98': d187 '99': d188 '100': d189 '101': d19 '102': d190 '103': d191 '104': d192 '105': d193 '106': d194 '107': d195 '108': d196 '109': d197 '110': d198 '111': d199 '112': d2 '113': d20 '114': d200 '115': d201 '116': d202 '117': d203 '118': d204 '119': d205 '120': d206 '121': d207 '122': d208 '123': d209 '124': d21 '125': d210 '126': d211 '127': d212 '128': d213 '129': d214 '130': d215 '131': d216 '132': d217 '133': d218 '134': d219 '135': d22 '136': d220 '137': d221 '138': d222 '139': d223 '140': d224 '141': d225 '142': d226 '143': d227 '144': d228 '145': d229 '146': d23 '147': d230 '148': d231 '149': d232 '150': d233 '151': d234 '152': d235 '153': d236 '154': d237 '155': d238 '156': d239 '157': d24 '158': d240 '159': d241 '160': d242 '161': d243 '162': d244 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21307658444.479 num_examples: 5976559 download_size: 19698451402 dataset_size: 21307658444.479 --- # Dataset Card for "ServiceProjectFall2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
22,314
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zelalt/content-papers-withprompt
2023-10-27T00:27:54.000Z
[ "region:us" ]
zelalt
null
null
0
17
2023-10-27T00:27:53
--- dataset_info: features: - name: id dtype: string - name: authors dtype: string - name: title dtype: string - name: abstract dtype: string - name: text dtype: string splits: - name: train num_bytes: 1283997 num_examples: 992 download_size: 797519 dataset_size: 1283997 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "content-papers-withprompt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
589
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Ioana23/codeparrot-ds-50k
2023-10-30T08:20:47.000Z
[ "region:us" ]
Ioana23
null
null
0
17
2023-10-30T08:19:20
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: repo_name dtype: string - name: path dtype: string - name: copies dtype: string - name: size dtype: string - name: content dtype: string - name: license dtype: string splits: - name: train num_bytes: 652784990.8524525 num_examples: 50000 - name: valid num_bytes: 6658657.886815172 num_examples: 500 download_size: 251530132 dataset_size: 659443648.7392677 --- # Dataset Card for "codeparrot-ds-50k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
757
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marziye-A/dataset-farma-test3
2023-11-01T10:15:26.000Z
[ "region:us" ]
marziye-A
null
null
0
17
2023-11-01T09:51:51
--- dataset_info: features: - name: audio dtype: audio - name: name dtype: string splits: - name: train num_bytes: 74308913.54 num_examples: 2005 download_size: 72537312 dataset_size: 74308913.54 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dataset-farma-test3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
489
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Shishir1807/test_drug
2023-11-02T07:01:49.000Z
[ "region:us" ]
Shishir1807
null
null
0
17
2023-11-02T07:01:32
Entry not found
15
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drAbreu/bc4chemd_ner
2022-10-25T10:02:51.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:GitHub", "language:en", "license:unknown", "region:us" ]
drAbreu
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
@article{Krallinger2015TheCC, title={The CHEMDNER corpus of chemicals and drugs and its annotation principles}, author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia}, journal={Journal of Cheminformatics}, year={2015}, volume={7}, pages={S2 - S2} }
1
16
2022-03-09T14:56:16
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - GitHub task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: bc4chemd pretty_name: bc4chemd_ner --- # Dataset Card for bc2gm_corpus ## 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:** [Github](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/) - **Repository:** [Github](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD) - **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards * Token Classification * Named Entity Recognition ### Languages - English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits ```python DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'ner_tags'], num_rows: 30683 }) validation: Dataset({ features: ['id', 'tokens', 'ner_tags'], num_rows: 30640 }) test: Dataset({ features: ['id', 'tokens', 'ner_tags'], num_rows: 26365 }) }) ``` ## Dataset Creation ### Curation Rationale The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] ### Annotations #### Annotation process We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. #### Who are the annotators? Expert chemistry literature curators ### Personal and Sensitive Information It does not contain this kind of information The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. ### Licensing Information Unknown ### Citation Information ```latex @article{Krallinger2015TheCC, title={The CHEMDNER corpus of chemicals and drugs and its annotation principles}, author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia}, journal={Journal of Cheminformatics}, year={2015}, volume={7}, pages={S2 - S2} } ``` ### Contributions Thanks to [@GamalC](https://github.com/GamalC) for uploading this dataset to GitHub.
5,465
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BlackSamorez/2ch_b_dialogues
2022-07-01T15:55:21.000Z
[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "region:us" ]
BlackSamorez
Dialogues build from 2ch.hk/b/ threads
@InProceedings{huggingface:dataset, title = {2ch b dialogues}, author={black_samorez}, year={2022} }
3
16
2022-06-05T13:05:55
--- annotations_creators: - no-annotation language_creators: - found language: - ru license: [] multilinguality: - monolingual pretty_name: Dialogues mined from 2ch/b/. size_categories: - 10K<n<100K source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation --- # Dataset Card for 2ch_b_dialogues ## 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:** https://github.com/BlackSamorez/ebanko - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Russian language dialogues mined from 2ch.hk/b/ ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Russian ## Dataset Structure ### Data Instances { "dialogue": ["Glad to hear!", "Fine, thank you!", "Hi, how are you?"] } ### Data Fields - dialogue: list of posts ordered last-to-first ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale Fun ### Source Data #### Initial Data Collection and Normalization In a thread graph only vertices with single parent were selected. Then non-overlapping threads of dialogues were build. #### Who are the source language producers? 2ch.hk/b/ users ### 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 Morally questionable data ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators blacks_samorez ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]
2,837
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relbert/analogy_questions
2023-05-16T20:24:12.000Z
[ "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:other", "region:us" ]
relbert
[Analogy Question](https://aclanthology.org/2021.acl-long.280/)
@inproceedings{ushio-etal-2021-bert, title = "{BERT} is to {NLP} what {A}lex{N}et is to {CV}: Can Pre-Trained Language Models Identify Analogies?", author = "Ushio, Asahi and Espinosa Anke, Luis and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.280", doi = "10.18653/v1/2021.acl-long.280", pages = "3609--3624", abstract = "Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as {``}eye is to seeing what ear is to hearing{''}, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.", }
2
16
2022-07-18T18:01:16
--- language: - en license: - other multilinguality: - monolingual size_categories: - n<1K pretty_name: Analogy Question --- # Dataset Card for "relbert/analogy_questions" ## Dataset Description - **Repository:** [RelBERT](https://github.com/asahi417/relbert) - **Paper:** [https://aclanthology.org/2021.acl-long.280/](https://aclanthology.org/2021.acl-long.280/) - **Dataset:** Analogy Questions ### Dataset Summary This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/). - original analogy questions | name | Size (valid/test) | Num of choice | Num of relation group | Original Reference | |-----------|------------------:|--------------:|----------------------:|:--------------------------------------------------------------------------:| | `u2` | 24/228 | 5,4,3 | 9 | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) | | `u4` | 48/432 | 5,4,3 | 5 | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) | | `google` | 50/500 | 4 | 2 | [Mikolov et al., (2013)](https://www.aclweb.org/anthology/N13-1090.pdf) | | `bats` | 199/1799 | 4 | 3 | [Gladkova et al., (2016)](https://www.aclweb.org/anthology/N18-2017.pdf) | - extra analogy questions | name | Size (valid/test) | Num of choice (valid/test) | Num of relation group (valid/test) | Original Reference | |:------------------------------------|:--------------------|:-----------------------------|:-------------------------------------|:-----------------------------------------------------------------------------------------------------------------------| | `semeval2012_relational_similarity` | 79/- | 3/- | 79/- | [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) | | `t_rex_relational_similarity` | 496/183 | 74/48 | 60/19 | [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity) | | `conceptnet_relational_similarity` | 1112/1192 | 19/17 | 18/16 | [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) | | `nell_relational_similarity` | 400/600 | 5/7 | 4/6 | [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity) | | `scan` | 178/1616 | 3,36,136,10,45,78,15,21,55,120,153,91,28/3,36,136,10,45,78,15,21,55,120,153,91,28 | 2/2 | [relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy) | ## Dataset Structure ### Data Instances An example of `test` looks as follows. ``` { "stem": ["raphael", "painter"], "answer": 2, "choice": [["andersen", "plato"], ["reading", "berkshire"], ["marx", "philosopher"], ["tolstoi", "edison"]] } ``` The `stem` is the query word pair, `choice` has word pair candidates, and `answer` indicates the index of correct candidate which starts from `0`. All data is lowercased except Google dataset. ### Citation Information ``` @inproceedings{ushio-etal-2021-bert-is, title ={{BERT} is to {NLP} what {A}lex{N}et is to {CV}: {C}an {P}re-{T}rained {L}anguage {M}odels {I}dentify {A}nalogies?}, author={Ushio, Asahi and Espinosa-Anke, Luis and Schockaert, Steven and Camacho-Collados, Jose}, booktitle={Proceedings of the {ACL}-{IJCNLP} 2021 Main Conference}, year={2021}, publisher={Association for Computational Linguistics} } ``` ### LICENSE The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
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nielsr/rvl_cdip_10_examples_per_class
2022-08-01T16:32:41.000Z
[ "region:us" ]
nielsr
null
null
0
16
2022-08-01T16:03:03
Entry not found
15
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Rifky/indonesian-hoax-news
2022-08-05T15:49:33.000Z
[ "region:us" ]
Rifky
null
null
1
16
2022-08-03T13:50:33
Entry not found
15
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PlanTL-GOB-ES/wnli-es
2022-11-18T12:03:25.000Z
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|glue", "language:es", "license:cc-by-4.0", "region:us" ]
PlanTL-GOB-ES
professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark. The Winograd NLI dataset presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).
ADD CITATION
2
16
2022-09-16T13:51:45
--- YAML tags: annotations_creators: - expert-generated language_creators: - found language: - es license: - cc-by-4.0 multilinguality: - monolingual pretty_name: wnli-es size_categories: - unknown source_datasets: - extended|glue task_categories: - text-classification task_ids: - natural-language-inference --- # WNLI-es ## 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 - **Website:** https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html - **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es) ### Dataset Summary "A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd." Source: [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html). The [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0). This dataset is a professional translation into Spanish of [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) as published in [GLUE Benchmark](https://gluebenchmark.com/tasks). Both the original dataset and this translation are licenced under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). ### Supported Tasks and Leaderboards Textual entailment, Text classification, Language Model. ### Languages * Spanish (es) ## Dataset Structure ### Data Instances Three tsv files. ### Data Fields - index - sentence 1: first sentence of the pair - sentence 2: second sentence of the pair - label: relation between the two sentences: * 0: the second sentence does not entail a correct interpretation of the first one (neutral) * 1: the second sentence entails a correct interpretation of the first one (entailment) ### Data Splits - wnli-train-es.csv: 636 sentence pairs - wnli-dev-es.csv: 72 sentence pairs - wnli-test-shuffled-es.csv: 147 sentence pairs ## Dataset Creation ### Curation Rationale We translated this dataset to contribute to the development of language models in Spanish. ### Source Data - [GLUE Benchmark site](https://gluebenchmark.com) #### Initial Data Collection and Normalization This is a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish, commissioned by [BSC TeMU](https://temu.bsc.es/) within the the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx). For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html). #### Who are the source language producers? For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html). ### Annotations #### Annotation process We comissioned a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish. #### Who are the annotators? Translation was commisioned to a professional translation agency. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset This dataset contributes to the development of language models in Spanish. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es). For further information, send an email to (plantl-gob-es@bsc.es). This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx). ### Licensing information This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License. Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) ### Contributions [N/A]
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kkotkar1/course-reviews
2022-10-04T00:50:55.000Z
[ "region:us" ]
kkotkar1
null
null
1
16
2022-09-30T21:04:25
Entry not found
15
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ghoumrassi/clothes_sample
2022-10-15T18:07:22.000Z
[ "region:us" ]
ghoumrassi
null
null
3
16
2022-10-15T15:50:15
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 20078406.0 num_examples: 990 download_size: 0 dataset_size: 20078406.0 --- # Dataset Card for "clothes_sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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crystina-z/mmarco
2023-02-07T14:21:54.000Z
[ "region:us" ]
crystina-z
mMARCO translated datasets
@misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, eprint={2108.13897}, archivePrefix={arXiv}, primaryClass={cs.CL} }
0
16
2022-11-09T00:48:48
Entry not found
15
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dreamproit/bill_summary_us
2023-10-17T04:16:57.000Z
[ "task_categories:summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "bills", "legal", "region:us" ]
dreamproit
null
null
4
16
2022-11-09T10:13:33
--- annotations_creators: - expert-generated language: - en language_creators: - expert-generated multilinguality: - monolingual pretty_name: bill_summary_us size_categories: - 100K<n<1M source_datasets: - original tags: - bills - legal task_categories: - summarization task_ids: [] configs: - config_name: default --- # Dataset Card for "bill_summary_us" ## 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:** [BillML](https://github.com/dreamproit/BillML) - **Repository:** [BillML](https://github.com/dreamproit/BillML) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary Dataset for summarization of summarization of US Congressional bills (bill_summary_us). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English ## Dataset Structure ### Data Instances #### default ### Data Fields - id: id of the bill in format(congress number + bill type + bill number + bill version). - congress: number of the congress. - bill_type: type of the bill. - bill_number: number of the bill. - bill_version: version of the bill. - sections: list of bill sections with section_id, text and header. - sections_length: number with lenght of the sections list. - text: bill text. - text_length: number of characters in the text. - summary: summary of the bill. - summary_length: number of characters in the summary. - title: official title of the bill. ### Data Splits train ## Dataset Creation ### Curation Rationale Bills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact. This dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts. As a result, this dataset collects bill and summary information; it provides text as a list of sections with the text and header. This could be used to create a summary of sections and then a summary of summaries. [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data [govinfo.gov](https://www.govinfo.gov/) #### Initial Data Collection and Normalization The data consists of the US congress bills that were collected from the [govinfo.gov](https://www.govinfo.gov/) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed] #### 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 [dreamproit.com](https://dreamproit.com/) ### Licensing Information Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information [More Information Needed] ### Contributions Thanks to [@aih](https://github.com/aih) [@BorodaUA](https://github.com/BorodaUA), [@alexbojko](https://github.com/alexbojko) for adding this dataset.
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bigbio/cantemist
2022-12-22T15:44:17.000Z
[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]
bigbio
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O). The original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. The training, development and test sets contain 501, 500 and 300 documents each, respectively. This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING. CANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. CANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes i.e. finding and normalizing tumor morphology mentions. CANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. For further information, please visit https://temu.bsc.es/cantemist or send an email to encargo-pln-life@bsc.es
@article{miranda2020named, title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.}, author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin}, journal={IberLEF@ SEPLN}, pages={303--323}, year={2020} }
0
16
2022-11-13T22:07:32
--- language: - es bigbio_language: - Spanish license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: CANTEMIST homepage: https://temu.bsc.es/cantemist/?p=4338 bigbio_pubmed: False bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - NAMED_ENTITY_DISAMBIGUATION - TEXT_CLASSIFICATION --- # Dataset Card for CANTEMIST ## Dataset Description - **Homepage:** https://temu.bsc.es/cantemist/?p=4338 - **Pubmed:** False - **Public:** True - **Tasks:** NER,NED,TXTCLASS Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O). The original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. The training, development and test sets contain 501, 500 and 300 documents each, respectively. This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING. CANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. CANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes i.e. finding and normalizing tumor morphology mentions. CANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. For further information, please visit https://temu.bsc.es/cantemist or send an email to encargo-pln-life@bsc.es ## Citation Information ``` @article{miranda2020named, title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.}, author={Miranda-Escalada, Antonio and Farr{'e}, Eul{\`a}lia and Krallinger, Martin}, journal={IberLEF@ SEPLN}, pages={303--323}, year={2020} } ```
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bigbio/genia_relation_corpus
2022-12-22T15:44:40.000Z
[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]
bigbio
The extraction of various relations stated to hold between biomolecular entities is one of the most frequently addressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein interactions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the state or properties of biomolecules are captured in the event annotation. The GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily) static relations, relations such as part-of that hold between entities without (necessarily) involving change.
@inproceedings{pyysalo-etal-2009-static, title = "Static Relations: a Piece in the Biomedical Information Extraction Puzzle", author = "Pyysalo, Sampo and Ohta, Tomoko and Kim, Jin-Dong and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the {B}io{NLP} 2009 Workshop", month = jun, year = "2009", address = "Boulder, Colorado", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W09-1301", pages = "1--9", } @article{article, author = {Ohta, Tomoko and Pyysalo, Sampo and Kim, Jin-Dong and Tsujii, Jun'ichi}, year = {2010}, month = {10}, pages = {917-28}, title = {A reevaluation of biomedical named entity - term relations}, volume = {8}, journal = {Journal of bioinformatics and computational biology}, doi = {10.1142/S0219720010005014} } @MISC{Hoehndorf_applyingontology, author = {Robert Hoehndorf and Axel-cyrille Ngonga Ngomo and Sampo Pyysalo and Tomoko Ohta and Anika Oellrich and Dietrich Rebholz-schuhmann}, title = {Applying ontology design patterns to the implementation of relations in GENIA}, year = {} }
1
16
2022-11-13T22:08:39
--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_shortname: GENIA_PROJECT_LICENSE pretty_name: GENIA Relation Corpus homepage: http://www.geniaproject.org/genia-corpus/relation-corpus bigbio_pubmed: True bigbio_public: True bigbio_tasks: - RELATION_EXTRACTION --- # Dataset Card for GENIA Relation Corpus ## Dataset Description - **Homepage:** http://www.geniaproject.org/genia-corpus/relation-corpus - **Pubmed:** True - **Public:** True - **Tasks:** RE The extraction of various relations stated to hold between biomolecular entities is one of the most frequently addressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein interactions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the state or properties of biomolecules are captured in the event annotation. The GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily) static relations, relations such as part-of that hold between entities without (necessarily) involving change. ## Citation Information ``` @inproceedings{pyysalo-etal-2009-static, title = "Static Relations: a Piece in the Biomedical Information Extraction Puzzle", author = "Pyysalo, Sampo and Ohta, Tomoko and Kim, Jin-Dong and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the {B}io{NLP} 2009 Workshop", month = jun, year = "2009", address = "Boulder, Colorado", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W09-1301", pages = "1--9", } @article{article, author = {Ohta, Tomoko and Pyysalo, Sampo and Kim, Jin-Dong and Tsujii, Jun'ichi}, year = {2010}, month = {10}, pages = {917-28}, title = {A reevaluation of biomedical named entity - term relations}, volume = {8}, journal = {Journal of bioinformatics and computational biology}, doi = {10.1142/S0219720010005014} } @MISC{Hoehndorf_applyingontology, author = {Robert Hoehndorf and Axel-cyrille Ngonga Ngomo and Sampo Pyysalo and Tomoko Ohta and Anika Oellrich and Dietrich Rebholz-schuhmann}, title = {Applying ontology design patterns to the implementation of relations in GENIA}, year = {} } ```
2,337
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bigbio/sciq
2022-12-22T15:46:48.000Z
[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-3.0", "region:us" ]
bigbio
The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For most questions, an additional paragraph with supporting evidence for the correct answer is provided.
@inproceedings{welbl-etal-2017-crowdsourcing, title = "Crowdsourcing Multiple Choice Science Questions", author = "Welbl, Johannes and Liu, Nelson F. and Gardner, Matt", booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W17-4413", doi = "10.18653/v1/W17-4413", pages = "94--106", }
1
16
2022-11-13T22:12:14
--- language: - en bigbio_language: - English license: cc-by-nc-3.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_NC_3p0 pretty_name: SciQ homepage: https://allenai.org/data/sciq bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for SciQ ## Dataset Description - **Homepage:** https://allenai.org/data/sciq - **Pubmed:** False - **Public:** True - **Tasks:** QA The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For most questions, an additional paragraph with supporting evidence for the correct answer is provided. ## Citation Information ``` @inproceedings{welbl-etal-2017-crowdsourcing, title = "Crowdsourcing Multiple Choice Science Questions", author = "Welbl, Johannes and Liu, Nelson F. and Gardner, Matt", booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W17-4413", doi = "10.18653/v1/W17-4413", pages = "94--106", } ```
1,280
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bigbio/spl_adr_200db
2022-12-22T15:46:56.000Z
[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]
bigbio
The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA).
@article{demner2018dataset, author = {Demner-Fushman, Dina and Shooshan, Sonya and Rodriguez, Laritza and Aronson, Alan and Lang, Francois and Rogers, Willie and Roberts, Kirk and Tonning, Joseph}, title = {A dataset of 200 structured product labels annotated for adverse drug reactions}, journal = {Scientific Data}, volume = {5}, year = {2018}, month = {01}, pages = {180001}, url = { https://www.researchgate.net/publication/322810855_A_dataset_of_200_structured_product_labels_annotated_for_adverse_drug_reactions }, doi = {10.1038/sdata.2018.1} }
2
16
2022-11-13T22:12:21
--- language: - en bigbio_language: - English license: cc0-1.0 multilinguality: monolingual bigbio_license_shortname: CC0_1p0 pretty_name: SPL ADR homepage: https://bionlp.nlm.nih.gov/tac2017adversereactions/ bigbio_pubmed: False bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - NAMED_ENTITY_DISAMBIGUATION - RELATION_EXTRACTION --- # Dataset Card for SPL ADR ## Dataset Description - **Homepage:** https://bionlp.nlm.nih.gov/tac2017adversereactions/ - **Pubmed:** False - **Public:** True - **Tasks:** NER,NED,RE The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). ## Citation Information ``` @article{demner2018dataset, author = {Demner-Fushman, Dina and Shooshan, Sonya and Rodriguez, Laritza and Aronson, Alan and Lang, Francois and Rogers, Willie and Roberts, Kirk and Tonning, Joseph}, title = {A dataset of 200 structured product labels annotated for adverse drug reactions}, journal = {Scientific Data}, volume = {5}, year = {2018}, month = {01}, pages = {180001}, url = { https://www.researchgate.net/publication/322810855_A_dataset_of_200_structured_product_labels_annotated_for_adverse_drug_reactions }, doi = {10.1038/sdata.2018.1} } ```
1,851
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cjvt/si_nli
2023-04-04T08:51:01.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:sl", "...
cjvt
SI-NLI (Slovene Natural Language Inference Dataset) contains 5,937 human-created Slovene sentence pairs (premise and hypothesis) that are manually labeled with the labels "entailment", "contradiction", and "neutral". The dataset was created using sentences that appear in the Slovenian reference corpus ccKres. Annotators were tasked to modify the hypothesis in a candidate pair in a way that reflects one of the labels. The dataset is balanced since the annotators created three modifications (entailment, contradiction, neutral) for each candidate sentence pair.
@misc{sinli, title = {Slovene Natural Language Inference Dataset {SI}-{NLI}}, author = {Klemen, Matej and {\v Z}agar, Ale{\v s} and {\v C}ibej, Jaka and Robnik-{\v S}ikonja, Marko}, url = {http://hdl.handle.net/11356/1707}, note = {Slovenian language resource repository {CLARIN}.{SI}}, year = {2022} }
0
16
2022-11-15T08:41:29
--- annotations_creators: - expert-generated language: - sl language_creators: - found - expert-generated license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: Slovene natural language inference dataset size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - text-classification task_ids: - multi-class-classification - natural-language-inference dataset_info: - config_name: default features: - name: pair_id dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: annotation1 dtype: string - name: annotator1_id dtype: string - name: annotation2 dtype: string - name: annotator2_id dtype: string - name: annotation3 dtype: string - name: annotator3_id dtype: string - name: annotation_final dtype: string - name: label dtype: string splits: - name: train num_bytes: 1352635 num_examples: 4392 - name: validation num_bytes: 164561 num_examples: 547 - name: test num_bytes: 246518 num_examples: 998 download_size: 410093 dataset_size: 1763714 - config_name: public features: - name: pair_id dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: annotation1 dtype: string - name: annotator1_id dtype: string - name: annotation2 dtype: string - name: annotator2_id dtype: string - name: annotation3 dtype: string - name: annotator3_id dtype: string - name: annotation_final dtype: string - name: label dtype: string splits: - name: train num_bytes: 1352591 num_examples: 4392 - name: validation num_bytes: 164517 num_examples: 547 - name: test num_bytes: 246474 num_examples: 998 download_size: 410093 dataset_size: 1763582 - config_name: private features: - name: pair_id dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: annotation1 dtype: string - name: annotator1_id dtype: string - name: annotation2 dtype: string - name: annotator2_id dtype: string - name: annotation3 dtype: string - name: annotator3_id dtype: string - name: annotation_final dtype: string - name: label dtype: string splits: - name: train - name: validation - name: test download_size: 0 dataset_size: 0 --- # Dataset Card for SI-NLI ### Dataset Summary SI-NLI (Slovene Natural Language Inference Dataset) contains 5,937 human-created Slovene sentence pairs (premise and hypothesis) that are manually labeled with the labels "entailment", "contradiction", and "neutral". We created the dataset using sentences that appear in the Slovenian reference corpus [ccKres](http://hdl.handle.net/11356/1034). Annotators were tasked to modify the hypothesis in a candidate pair in a way that reflects one of the labels. The dataset is balanced since the annotators created three modifications (entailment, contradiction, neutral) for each candidate sentence pair. The dataset is split into train, validation, and test sets, with sizes of 4,392, 547, and 998. Only the hypothesis and premise are given in the test set (i.e. no annotations) since SI-NLI is integrated into the Slovene evaluation framework [SloBENCH](https://slobench.cjvt.si/). If you use the dataset to train your models, please consider submitting the test set predictions to SloBENCH to get the evaluation score and see how it compares to others. If you have access to the private test set (with labels), you can load it instead of the public one via `datasets.load_dataset("cjvt/si_nli", "private", data_dir="<...>")`. ### Supported Tasks and Leaderboards Natural language inference. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset: ``` { 'pair_id': 'P0', 'premise': 'Vendar se je anglikanska večina v grofijah na severu otoka (Ulster) na plebiscitu odločila, da ostane v okviru Velike Britanije.', 'hypothesis': 'A na glasovanju o priključitvi ozemlja k Severni Irski so se prebivalci ulsterskih grofij, pretežno anglikanske veroizpovedi, izrekli o obstanku pod okriljem VB.', 'annotation1': 'entailment', 'annotator1_id': 'annotator_C', 'annotation2': 'entailment', 'annotator2_id': 'annotator_A', 'annotation3': '', 'annotator3_id': '', 'annotation_final': 'entailment', 'label': 'entailment' } ``` ### Data Fields - `pair_id`: string identifier of the pair (`""` in the test set), - `premise`: premise sentence, - `hypothesis`: hypothesis sentence, - `annotation1`: the first annotation (`""` if not available), - `annotator1_id`: anonymized identifier of the first annotator (`""` if not available), - `annotation2`: the second annotation (`""` if not available), - `annotator2_id`: anonymized identifier of the second annotator (`""` if not available), - `annotation3`: the third annotation (`""` if not available), - `annotator3_id`: anonymized identifier of the third annotator (`""` if not available), - `annotation_final`: aggregated annotation where it could be unanimously determined (`""` if not available or an unanimous agreement could not be reached), - `label`: aggregated annotation: either same as `annotation_final` (in case of agreement), same as `annotation1` (in case of disagreement), or `""` (in the test set). **Note that examples with disagreement are all put in the training set**. This aggregation is just the most simple possibility and the user may instead do something more advanced based on the individual annotations (e.g., learning with disagreement). \* A small number of examples did not go through the annotation process because they were constructed by the authors when writing the guidelines. The quality of these was therefore checked by the authors. Such examples do not have the individual annotations and the annotator IDs. ## Additional Information ### Dataset Curators Matej Klemen, Aleš Žagar, Jaka Čibej, Marko Robnik-Šikonja. ### Licensing Information CC BY-NC-SA 4.0. ### Citation Information ``` @misc{sinli, title = {Slovene Natural Language Inference Dataset {SI}-{NLI}}, author = {Klemen, Matej and {\v Z}agar, Ale{\v s} and {\v C}ibej, Jaka and Robnik-{\v S}ikonja, Marko}, url = {http://hdl.handle.net/11356/1707}, note = {Slovenian language resource repository {CLARIN}.{SI}}, year = {2022} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
6,567
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Norod78/RickAndMorty-HorizontalMirror-blip-captions
2022-11-15T14:38:40.000Z
[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]
Norod78
null
null
0
16
2022-11-15T14:31:28
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 161499799.0 num_examples: 530 download_size: 161488169 dataset_size: 161499799.0 pretty_name: 'Rick and Morty, Horizontal Mirror, BLIP captions' size_categories: - n<1K tags: [] task_categories: - text-to-image license: cc-by-nc-sa-4.0 annotations_creators: - machine-generated language: - en language_creators: - other multilinguality: - monolingual --- # Dataset Card for "RickAndMorty-HorizontalMirror-blip-captions"
580
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thennal/IMaSC
2022-12-08T17:21:02.000Z
[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ml", "license:cc-by-sa-4.0", "arxiv:2211.12796", ...
thennal
null
null
2
16
2022-11-17T05:16:00
--- annotations_creators: - expert-generated language: - ml language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: ICFOSS Malayalam Speech Corpus size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - text-to-speech - automatic-speech-recognition task_ids: [] --- # IMaSC: ICFOSS Malayalam Speech Corpus **IMaSC** is a Malayalam text and speech corpus made available by [ICFOSS](https://icfoss.in/) for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio. ## Dataset Description - **Paper:** [IMaSC — ICFOSS Malayalam Speech Corpus](https://arxiv.org/abs/2211.12796) - **Point of Contact:** [Thennal D K](mailto:thennal10@gmail.com) ## Dataset Structure The dataset consists of 34,473 instances with fields `text`, `speaker`, and `audio`. The audio is mono, sampled at 16kH. The transcription is normalized and only includes Malayalam characters and common punctuation. The table given below specifies how the 34,473 instances are split between the speakers, along with some basic speaker info: | Speaker | Gender | Age | Time (HH:MM:SS) | Sentences | | --- | --- | --- | --- | --- | | Joji | Male | 28 | 06:08:55 | 4,332 | | Sonia | Female | 43 | 05:22:39 | 4,294 | | Jijo | Male | 26 | 05:34:05 | 4,093 | | Greeshma | Female | 22 | 06:32:39 | 4,416 | | Anil | Male | 48 | 05:58:34 | 4,239 | | Vidhya | Female | 23 | 04:21:56 | 3,242 | | Sonu | Male | 25 | 06:04:43 | 4,219 | | Simla | Female | 24 | 09:34:21 | 5,638 | | **Total** | | | **49:37:54** | **34,473** | ### Data Instances An example instance is given below: ```json {'text': 'സർവ്വകലാശാല വൈസ് ചാൻസലർ ഡോ. ചന്ദ്രബാബുവിനും സംഭവം തലവേദനയാവുകയാണ്', 'speaker': 'Sonia', 'audio': {'path': None, 'array': array([ 0.00921631, 0.00930786, 0.00939941, ..., -0.00497437, -0.00497437, -0.00497437]), 'sampling_rate': 16000}} ``` ### Data Fields - **text** (str): Transcription of the audio file - **speaker** (str): The name of the speaker - **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio (always None) ### Data Splits We provide all the data in a single `train` split. The loaded dataset object thus looks like this: ```json DatasetDict({ train: Dataset({ features: ['text', 'speaker', 'audio'], num_rows: 34473 }) }) ``` ### Dataset Creation The text is sourced from [Malayalam Wikipedia](https://ml.wikipedia.org), and read by our speakers in studio conditions. Extensive error correction was conducted to provide a clean, accurate database. Further details are given in our paper, accessible at [https://arxiv.org/abs/2211.12796](https://arxiv.org/abs/2211.12796). ## Additional Information ### Licensing The corpus is made available under the [Creative Commons license (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation ``` @misc{gopinath2022imasc, title={IMaSC -- ICFOSS Malayalam Speech Corpus}, author={Deepa P Gopinath and Thennal D K and Vrinda V Nair and Swaraj K S and Sachin G}, year={2022}, eprint={2211.12796}, archivePrefix={arXiv}, primaryClass={cs.SD} } ```
3,343
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israfelsr/mm_tiny_imagenet
2022-12-16T11:19:54.000Z
[ "region:us" ]
israfelsr
null
null
1
16
2022-11-17T12:44:50
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': n01443537 '1': n01629819 '2': n01641577 '3': n01644900 '4': n01698640 '5': n01742172 '6': n01768244 '7': n01770393 '8': n01774384 '9': n01774750 '10': n01784675 '11': n01882714 '12': n01910747 '13': n01917289 '14': n01944390 '15': n01950731 '16': n01983481 '17': n01984695 '18': n02002724 '19': n02056570 '20': n02058221 '21': n02074367 '22': n02094433 '23': n02099601 '24': n02099712 '25': n02106662 '26': n02113799 '27': n02123045 '28': n02123394 '29': n02124075 '30': n02125311 '31': n02129165 '32': n02132136 '33': n02165456 '34': n02226429 '35': n02231487 '36': n02233338 '37': n02236044 '38': n02268443 '39': n02279972 '40': n02281406 '41': n02321529 '42': n02364673 '43': n02395406 '44': n02403003 '45': n02410509 '46': n02415577 '47': n02423022 '48': n02437312 '49': n02480495 '50': n02481823 '51': n02486410 '52': n02504458 '53': n02509815 '54': n02666347 '55': n02669723 '56': n02699494 '57': n02769748 '58': n02788148 '59': n02791270 '60': n02793495 '61': n02795169 '62': n02802426 '63': n02808440 '64': n02814533 '65': n02814860 '66': n02815834 '67': n02823428 '68': n02837789 '69': n02841315 '70': n02843684 '71': n02883205 '72': n02892201 '73': n02909870 '74': n02917067 '75': n02927161 '76': n02948072 '77': n02950826 '78': n02963159 '79': n02977058 '80': n02988304 '81': n03014705 '82': n03026506 '83': n03042490 '84': n03085013 '85': n03089624 '86': n03100240 '87': n03126707 '88': n03160309 '89': n03179701 '90': n03201208 '91': n03255030 '92': n03355925 '93': n03373237 '94': n03388043 '95': n03393912 '96': n03400231 '97': n03404251 '98': n03424325 '99': n03444034 '100': n03447447 '101': n03544143 '102': n03584254 '103': n03599486 '104': n03617480 '105': n03637318 '106': n03649909 '107': n03662601 '108': n03670208 '109': n03706229 '110': n03733131 '111': n03763968 '112': n03770439 '113': n03796401 '114': n03814639 '115': n03837869 '116': n03838899 '117': n03854065 '118': n03891332 '119': n03902125 '120': n03930313 '121': n03937543 '122': n03970156 '123': n03977966 '124': n03980874 '125': n03983396 '126': n03992509 '127': n04008634 '128': n04023962 '129': n04070727 '130': n04074963 '131': n04099969 '132': n04118538 '133': n04133789 '134': n04146614 '135': n04149813 '136': n04179913 '137': n04251144 '138': n04254777 '139': n04259630 '140': n04265275 '141': n04275548 '142': n04285008 '143': n04311004 '144': n04328186 '145': n04356056 '146': n04366367 '147': n04371430 '148': n04376876 '149': n04398044 '150': n04399382 '151': n04417672 '152': n04456115 '153': n04465666 '154': n04486054 '155': n04487081 '156': n04501370 '157': n04507155 '158': n04532106 '159': n04532670 '160': n04540053 '161': n04560804 '162': n04562935 '163': n04596742 '164': n04598010 '165': n06596364 '166': n07056680 '167': n07583066 '168': n07614500 '169': n07615774 '170': n07646821 '171': n07647870 '172': n07657664 '173': n07695742 '174': n07711569 '175': n07715103 '176': n07720875 '177': n07749582 '178': n07753592 '179': n07768694 '180': n07871810 '181': n07873807 '182': n07875152 '183': n07920052 '184': n07975909 '185': n08496334 '186': n08620881 '187': n08742578 '188': n09193705 '189': n09246464 '190': n09256479 '191': n09332890 '192': n09428293 '193': n12267677 '194': n12520864 '195': n13001041 '196': n13652335 '197': n13652994 '198': n13719102 '199': n14991210 - name: caption dtype: string - name: label_name dtype: string splits: - name: train num_bytes: 159978960.0 num_examples: 80000 - name: validation num_bytes: 40004701.0 num_examples: 20000 download_size: 149059401 dataset_size: 199983661.0 --- # Dataset Card for "mm_tiny_imagenet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
5,866
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graphs-datasets/AQSOL
2023-02-07T16:36:58.000Z
[ "task_categories:graph-ml", "license:mit", "arxiv:2003.00982", "region:us" ]
graphs-datasets
null
null
0
16
2022-12-08T11:54:55
--- license: mit task_categories: - graph-ml --- # Dataset Card for AQSOL ## 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) - [External Use](#external-use) - [PyGeometric](#pygeometric) - [Dataset Structure](#dataset-structure) - [Data Properties](#data-properties) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)** - **Paper:**: (see citation) ### Dataset Summary The AQSOL dataset comes "from the Benchmarking Graph Neural Networks paper based on AqSolDB, a standardized database of 9,982 molecular graphs with their aqueous solubility values, collected from 9 different data sources" (PyGeometric doc). ### Supported Tasks and Leaderboards `AQSOL` should be used for graph regression, on aqueous solubility. ## External Use ### PyGeometric To load in PyGeometric, do the following: ```python from datasets import load_dataset from torch_geometric.data import Data from torch_geometric.loader import DataLoader dataset_hf = load_dataset("graphs-datasets/<mydataset>") # For the train set (replace by valid or test as needed) dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] dataset_pg = DataLoader(dataset_pg_list) ``` ## Dataset Structure ### Data Properties | property | value | |---|---| | #graphs | 9,833 | | average #nodes | 17.6 | | average #edges | 35.8 | ### Data Fields Each row of a given file is a graph, with: - `node_feat` (list: #nodes x #node-features): nodes - `edge_index` (list: 2 x #edges): pairs of nodes constituting edges - `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features - `y` (list: #labels): contains the number of labels available to predict - `num_nodes` (int): number of nodes of the graph ### Data Splits This data is split. It comes from the PyGeometric version of the dataset. ## Additional Information ### Licensing Information The dataset has been released under MIT license. ### Citation Information ``` @article{DBLP:journals/corr/abs-2003-00982, author = {Vijay Prakash Dwivedi and Chaitanya K. Joshi and Thomas Laurent and Yoshua Bengio and Xavier Bresson}, title = {Benchmarking Graph Neural Networks}, journal = {CoRR}, volume = {abs/2003.00982}, year = {2020}, url = {https://arxiv.org/abs/2003.00982}, eprinttype = {arXiv}, eprint = {2003.00982}, timestamp = {Sat, 23 Jan 2021 01:14:30 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
3,054
[ [ -0.01537322998046875, -0.0199737548828125, 0.00936126708984375, 0.00403594970703125, 0.0016527175903320312, -0.00972747802734375, -0.0022258758544921875, -0.0211944580078125, 0.020660400390625, 0.013397216796875, -0.037872314453125, -0.0491943359375, -0.03179931...
Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
2022-12-29T03:19:16.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-sa-3.0", "region:us" ]
Jean-Baptiste
null
null
0
16
2022-12-24T03:49:34
--- language: - en dataset_info: splits: - name: test num_examples: 785 - name: train num_examples: 4446 annotations_creators: - expert-generated license: - cc-by-nc-sa-3.0 multilinguality: - monolingual pretty_name: financial_news_sentiment_mixte_with_phrasebank_75 size_categories: - 1K<n<10K tags: [] task_categories: - text-classification task_ids: - multi-class-classification - sentiment-classification --- # Dataset Card for "financial_news_sentiment_mixte_with_phrasebank_75" This is a customized version of the phrasebank dataset in which I kept only sentences validated by at least 75% annotators. In addition I added ~2000 articles of Canadian news where sentiment was validated manually. The dataset also include a column topic which contains one of the following value: * acquisition * other * quaterly financial release * appointment to new position * dividend * corporate update * drillings results * conference * share repurchase program * grant of stocks This was generated automatically using a zero-shot classification model and **was not** reviewed manually. ## References Original dataset is available here: [https://huggingface.co/datasets/financial_phrasebank]
1,207
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neulab/odex
2023-02-10T18:01:34.000Z
[ "task_categories:text2text-generation", "task_categories:text-generation", "size_categories:n<1K", "language:en", "language:es", "language:ja", "language:ru", "license:cc-by-sa-4.0", "region:us" ]
neulab
ODEX is an Open-Domain EXecution-based NL-to-Code generation data benchmark. It contains 945 samples with a total of 1,707 human-written test cases, covering intents in four different natural languages -- 439 in English, 90 in Spanish, 164 in Japanese, and 252 in Russian.
@article{wang2022execution, title={Execution-Based Evaluation for Open-Domain Code Generation}, author={Wang, Zhiruo and Zhou, Shuyan and Fried, Daniel and Neubig, Graham}, journal={arXiv preprint arXiv:2212.10481}, year={2022} }
6
16
2023-01-06T14:30:00
--- license: cc-by-sa-4.0 task_categories: - text2text-generation - text-generation language: - en - es - ja - ru size_categories: - n<1K --- __ODEX__ is an Open-Domain EXecution-based NL-to-Code generation data benchmark. It contains 945 samples with a total of 1,707 human-written test cases, covering intents in four different natural languages -- 439 in English, 90 in Spanish, 164 in Japanese, and 252 in Russian. You can load the dataset by specifying a subset from *en, es, ja, ru* (by default the english subset *en* is loaded): ```python from datasets import load_dataset ds = load_dataset("neulab/odex", "ja", split="test") ``` If you find our dataset useful, please cite the paper ``` @article{wang2022execution, title={Execution-Based Evaluation for Open-Domain Code Generation}, author={Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig}, journal={arXiv preprint arXiv:2212.10481}, year={2022} } ```
932
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Cohere/wikipedia-22-12-fr-embeddings
2023-03-22T16:53:41.000Z
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "multilinguality:multilingual", "language:fr", "license:apache-2.0", "region:us" ]
Cohere
null
null
4
16
2023-01-14T13:09:16
--- annotations_creators: - expert-generated language: - fr multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # Wikipedia (fr) embedded with cohere.ai `multilingual-22-12` encoder We encoded [Wikipedia (fr)](https://fr.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Further languages We provide embeddings of Wikipedia in many different languages: [ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings), You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Loading the dataset You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train", streaming=True) for doc in docs: docid = doc['id'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search A full search example: ```python #Run: pip install cohere datasets from datasets import load_dataset import torch import cohere co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com #Load at max 1000 documents + embeddings max_docs = 1000 docs_stream = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train", streaming=True) docs = [] doc_embeddings = [] for doc in docs_stream: docs.append(doc) doc_embeddings.append(doc['emb']) if len(docs) >= max_docs: break doc_embeddings = torch.tensor(doc_embeddings) query = 'Who founded Youtube' response = co.embed(texts=[query], model='multilingual-22-12') query_embedding = response.embeddings query_embedding = torch.tensor(query_embedding) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text'], "\n") ``` ## Performance You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)
3,845
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cyrilzhang/financial_phrasebank_split
2023-01-17T21:26:08.000Z
[ "region:us" ]
cyrilzhang
null
null
1
16
2023-01-17T21:26:00
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: class_label: names: 0: negative 1: neutral 2: positive splits: - name: train num_bytes: 611259.9339661576 num_examples: 4361 - name: test num_bytes: 67980.06603384235 num_examples: 485 download_size: 418548 dataset_size: 679240.0 --- # Dataset Card for "financial_phrasebank_split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
578
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csinva/fmri_language_responses
2023-02-12T22:46:10.000Z
[ "region:us" ]
csinva
null
null
1
16
2023-02-12T22:33:43
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
grosenthal/latin_english_translation
2023-07-17T21:59:06.000Z
[ "task_categories:translation", "size_categories:10K<n<100K", "language:la", "language:en", "license:mit", "doi:10.57967/hf/0903", "region:us" ]
grosenthal
null
null
4
16
2023-02-28T00:10:51
--- dataset_info: features: - name: id dtype: int64 - name: la dtype: string - name: en dtype: string - name: file dtype: string splits: - name: train num_bytes: 39252644 num_examples: 99343 - name: test num_bytes: 405056 num_examples: 1014 - name: valid num_bytes: 392886 num_examples: 1014 download_size: 25567350 dataset_size: 40050586 license: mit task_categories: - translation language: - la - en pretty_name: Latin to English Translation Pairs size_categories: - 10K<n<100K --- # Dataset Card for "latin_english_parallel" 101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation. For those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences. Each sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them. ![alt text](distribution.png)
1,113
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Zombely/wikisource-green
2023-03-18T11:50:26.000Z
[ "region:us" ]
Zombely
null
null
0
16
2023-03-15T02:03:19
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train_1 num_bytes: 15342818708.456 num_examples: 9816 - name: train_2 num_bytes: 13234327199.457 num_examples: 9997 - name: train_3 num_bytes: 8814747830.88 num_examples: 9935 - name: train_4 num_bytes: 10839226390.145 num_examples: 9995 - name: train_5 num_bytes: 12414635965.0 num_examples: 10000 - name: train_6 num_bytes: 5911580759.0 num_examples: 10000 - name: train_7 num_bytes: 11420080854.0 num_examples: 10000 - name: train_8 num_bytes: 18080629271.0 num_examples: 10000 - name: train_9 num_bytes: 11348011360.0 num_examples: 10000 - name: train_10 num_bytes: 14141957301.0 num_examples: 10000 - name: train_11 num_bytes: 9983910604.0 num_examples: 10000 - name: train_12 num_bytes: 13105253749.0 num_examples: 10000 - name: train_13 num_bytes: 15681320595.0 num_examples: 10000 - name: train_14 num_bytes: 14896725472.0 num_examples: 10000 - name: train_15 num_bytes: 11493364396.927 num_examples: 9987 - name: validation num_bytes: 4487934740.612 num_examples: 4077 download_size: 5330245163 dataset_size: 191196525196.477 --- # Dataset Card for "wikisource-green" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,495
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semeru/code-code-DefectDetection
2023-03-27T21:16:02.000Z
[ "license:mit", "region:us" ]
semeru
null
null
0
16
2023-03-22T03:30:09
--- license: mit Programminglanguage: "C" version: "N/A" Date: "Devign(Jun 2019 - paper release date)" Contaminated: "Very Likely" Size: "Standard Tokenizer" --- ### Dataset is imported from CodeXGLUE and pre-processed using their script. # Where to find in Semeru: The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru # CodeXGLUE -- Defect Detection ## Task Definition Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code. ### Dataset The dataset we use comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test. ### Data Format Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present For each file, each line in the uncompressed file represents one function. One row is illustrated below. - **func:** the source code - **target:** 0 or 1 (vulnerability or not) - **idx:** the index of example ### Data Statistics Data statistics of the dataset are shown in the below table: | | #Examples | | ----- | :-------: | | Train | 21,854 | | Dev | 2,732 | | Test | 2,732 | ## Reference <pre><code>@inproceedings{zhou2019devign, title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks}, author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang}, booktitle={Advances in Neural Information Processing Systems}, pages={10197--10207}, year={2019} }</code></pre>
2,035
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mstz/speeddating
2023-04-07T14:54:21.000Z
[ "task_categories:tabular-classification", "size_categories:1K<n<10K", "language:en", "speeddating", "tabular_classification", "binary_classification", "region:us" ]
mstz
null
null
0
16
2023-03-23T23:41:42
--- language: - en tags: - speeddating - tabular_classification - binary_classification pretty_name: Speed dating size_categories: - 1K<n<10K task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts - tabular-classification configs: - dating --- # Speed dating The [Speed dating dataset](https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536) from OpenML. # Configurations and tasks | **Configuration** | **Task** | Description | |-------------------|---------------------------|---------------------------------------------------------------| | dating | Binary classification | Will the two date? | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/speeddating")["train"] ``` # Features |**Features** |**Type** | |---------------------------------------------------|---------| |`is_dater_male` |`int8` | |`dater_age` |`int8` | |`dated_age` |`int8` | |`age_difference` |`int8` | |`dater_race` |`string` | |`dated_race` |`string` | |`are_same_race` |`int8` | |`same_race_importance_for_dater` |`float64`| |`same_religion_importance_for_dater` |`float64`| |`attractiveness_importance_for_dated` |`float64`| |`sincerity_importance_for_dated` |`float64`| |`intelligence_importance_for_dated` |`float64`| |`humor_importance_for_dated` |`float64`| |`ambition_importance_for_dated` |`float64`| |`shared_interests_importance_for_dated` |`float64`| |`attractiveness_score_of_dater_from_dated` |`float64`| |`sincerity_score_of_dater_from_dated` |`float64`| |`intelligence_score_of_dater_from_dated` |`float64`| |`humor_score_of_dater_from_dated` |`float64`| |`ambition_score_of_dater_from_dated` |`float64`| |`shared_interests_score_of_dater_from_dated` |`float64`| |`attractiveness_importance_for_dater` |`float64`| |`sincerity_importance_for_dater` |`float64`| |`intelligence_importance_for_dater` |`float64`| |`humor_importance_for_dater` |`float64`| |`ambition_importance_for_dater` |`float64`| |`shared_interests_importance_for_dater` |`float64`| |`self_reported_attractiveness_of_dater` |`float64`| |`self_reported_sincerity_of_dater` |`float64`| |`self_reported_intelligence_of_dater` |`float64`| |`self_reported_humor_of_dater` |`float64`| |`self_reported_ambition_of_dater` |`float64`| |`reported_attractiveness_of_dated_from_dater` |`float64`| |`reported_sincerity_of_dated_from_dater` |`float64`| |`reported_intelligence_of_dated_from_dater` |`float64`| |`reported_humor_of_dated_from_dater` |`float64`| |`reported_ambition_of_dated_from_dater` |`float64`| |`reported_shared_interests_of_dated_from_dater` |`float64`| |`dater_interest_in_sports` |`float64`| |`dater_interest_in_tvsports` |`float64`| |`dater_interest_in_exercise` |`float64`| |`dater_interest_in_dining` |`float64`| |`dater_interest_in_museums` |`float64`| |`dater_interest_in_art` |`float64`| |`dater_interest_in_hiking` |`float64`| |`dater_interest_in_gaming` |`float64`| |`dater_interest_in_clubbing` |`float64`| |`dater_interest_in_reading` |`float64`| |`dater_interest_in_tv` |`float64`| |`dater_interest_in_theater` |`float64`| |`dater_interest_in_movies` |`float64`| |`dater_interest_in_concerts` |`float64`| |`dater_interest_in_music` |`float64`| |`dater_interest_in_shopping` |`float64`| |`dater_interest_in_yoga` |`float64`| |`interests_correlation` |`float64`| |`expected_satisfaction_of_dater` |`float64`| |`expected_number_of_likes_of_dater_from_20_people` |`int8` | |`expected_number_of_dates_for_dater` |`int8` | |`dater_liked_dated` |`float64`| |`probability_dated_wants_to_date` |`float64`| |`already_met_before` |`int8` | |`dater_wants_to_date` |`int8` | |`dated_wants_to_date` |`int8` |
5,157
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pkyoyetera/luganda_english_dataset
2023-03-25T19:54:14.000Z
[ "task_categories:translation", "size_categories:10K<n<100K", "language:en", "language:lg", "license:apache-2.0", "region:us" ]
pkyoyetera
null
null
0
16
2023-03-25T06:34:10
--- dataset_info: features: - name: English dtype: string - name: Luganda dtype: string splits: - name: train num_bytes: 11844863.620338032 num_examples: 78238 download_size: 7020236 dataset_size: 11844863.620338032 license: apache-2.0 task_categories: - translation language: - en - lg size_categories: - 10K<n<100K --- # Dataset Card for "luganda_english_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Dataset might contain a few mistakes, espeecially on the one word translations. Indicators for verbs and nouns (v.i and n.i) may not have been completely filtered out properly.
709
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ipipan/maupqa
2023-09-18T07:28:41.000Z
[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:open-domain-qa", "task_ids:document-retrieval", "annotations_creators:found", "annotations_creators:machine-generated", "size_categories:1M<n<10M", "language:pl", "license:cc-by-sa-4.0", "arxiv:2305.05486", "arxiv:...
ipipan
MAUPQA is a collection of datasets for Polish Open-domain Question Answering.
@inproceedings{rybak-2023-maupqa, title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset", author = "Rybak, Piotr", booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bsnlp-1.2", pages = "11--16", abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.", }
2
16
2023-03-31T10:21:18
--- task_categories: - question-answering - text-retrieval task_ids: - open-domain-qa - document-retrieval language: - pl pretty_name: MAUPQA size_categories: - 1M<n<10M annotations_creators: - found - machine-generated license: cc-by-sa-4.0 --- # Dataset Card for MAUPQA Dataset ## Dataset Description - **Paper:** [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://arxiv.org/abs/2305.05486), [SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://arxiv.org/abs/2309.08469) - **Point of Contact:** [Piotr Rybak](mailto:piotr.cezary.rybak@gmail.com) ### Dataset Summary MAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs. ### Supported Tasks and Leaderboards - `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html). ### Languages The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL. ## Dataset Structure ### Data Instances The dataset consists of over 8 million question-passage pairs. For each instance, there is a `question`, a passage (`passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers). For a small subset of `question` there is also a list of possible `answers` formulated in a natural language, in a way a Polish speaker would answer the questions. ``` { 'question_id': 1, 'question': 'Na którym kontynencie leży państwo Gujana, panie Krzysztofie?', 'answers': "['W Ameryce Południowej']", 'passage_title': 'Gujana (ujednoznacznienie)', 'passage_text': 'Gujana (region) – region Ameryki Południowej Gujana – państwo w Ameryce Południowej Gujana Brytyjska – dawna kolonia brytyjska; obecnie państwo Gujana Gujana Francuska – departament zamorski Francji; dawniej kolonia francuska Gujana Holenderska – dawna kolonia holenderska; obecnie państwo Surinam', 'relevant': True, 'passage_source': 'crawling', 'subset': '1z10' } ``` ### Data Fields Question-passage pairs: - `question_id`: an integer id of the question - `question`: a string containing the question - `passage_title`: a string containing the title of the Wikipedia article - `passage_text`: a string containing the passage text as extracted by the human annotator - `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers) - `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair - `answers`: a string containing a list of possible short answers to the question - `passage_source`: a string containing the method of obtaining the passage. One of the following: - `manual-annotation`: the question-passage pair was manually annotated - `crawling`: the question-passage pairs were created by taking advantage of the specific structure of crawled website - `dataset-translation`: the dataset was created by machine-translating the English dataset - `generative-model`: the question was created by the generative model based on the given passage - `bm25-negatives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant - `bm25-positives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant - `subset`: a string containing the name of the dataset ### Data Splits MAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single `train` split. We recommend using the [PolQA](https://huggingface.co/datasets/ipipan/polqa) dataset for evaluation. Basic statistics of all 14 datasets: | dataset | # questions | # answers | # positive passages | # negative passages | |-------------------|------------:|----------:|--------------------:|--------------------:| | 1z10 | 22,835 | 21,415 | 22,014 | 139,471 | | czy-wiesz-v2 | 29,078 | - | 29,078 | 143,306 | | gpt3-cc | 10,146 | 10,146 | 10,177 | 89,203 | | gpt3.5-cc | 29,591 | 29,583 | 29,720 | 251,959 | | gpt3.5-wiki | 29,674 | 29,636 | 29,748 | 115,564 | | mkqa | 4,036 | 4,036 | 3,968 | 19,814 | | mqa | 172,768 | - | 178,131 | 1,249,659 | | msmarco | 389,987 | - | 416,763 | 3,006,996 | | multilingual-NLI | 100,752 | 64,900 | 68,096 | 743,857 | | nq | 135,781 | - | 139,976 | 797,436 | | poleval2021-pairs | 1,977 | - | 2,088 | 17,608 | | poquad | 56,588 | 46,157 | 46,187 | 299,865 | | templates | 15,993 | 14,504 | 15,993 | 45,228 | | wiki-def | 18,093 | 18,092 | 18,093 | 84,956 | | Total | 1,017,299 | 238,469 | 1,010,032 | 7,004,922 | ## Dataset Creation ### Curation Rationale Open-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish. ### Source Data #### Initial Data Collection and Normalization Below, we briefly describe each dataset. For a detailed description please refer to the [paper](https://arxiv.org/abs/2305.05486). * `1z10`: We transcribe 333 recordings of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages. * `czy-wiesz-v2`: We first crawl all questions from the [Did you know?](https://pl.wikipedia.org/wiki/Wikiprojekt:Czy_wiesz/archiwum) section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage. * `gpt3-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3 to generate a relevant question. * `gpt3.5-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3.5 to generate a relevant question. * `gpt3.5-wiki`: We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question. * `mkqa`: We clean the Polish subset of the [MKQA](https://huggingface.co/datasets/mkqa) dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones ("Who is the *current* president?*). We use the BM25 retriever and the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage. * `mqa`: We clean the Polish subset of the [MQA](https://huggingface.co/datasets/clips/mqa) dataset by removing artificially created questions like "What is the best hotel in *{city}*?" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions. * `msmarco`: We translate the [MS MARCO](https://huggingface.co/datasets/ms_marco) dataset into Polish using the machine translation model. * `multilingual-NLI`: We extract question-answer pairs from the Polish subset of the [multilingual-NLI](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) dataset. We create questions using the following template: "Czy *{premise}*?" (Eng. "Does *{premise}*?") and use hypotheses as passages. We consider `entailment` and `contradiction` labels as relevant and `neutral` as negative. * `nq`: We translate the [NQ](https://huggingface.co/datasets/natural_questions) dataset into Polish using the machine translation model. * `poleval2021-pairs`: We take [allegro/polish-question-passage-pairs](https://huggingface.co/datasets/allegro/polish-question-passage-pairs) without any changes. * `poquad`: We extract question-passages pairs from the training split of the [PoQuAD](https://huggingface.co/datasets/clarin-pl/poquad) dataset. * `templates`: We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. "Writers born in Poland") which allow generating questions like "Where was *{writer name}* born?". In total, we use 33 templates to generate questions. We use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage from the linked article. * `wiki-def`: We use [Wiktionary](https://www.wiktionary.org/) to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of "Monday" is "the first day of the week". Based on it, we generate the question "What is the name of *the first day of the week*?". Additionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to ensure that passages are not relevant. #### Who are the source language producers? The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models. ### Annotations #### Annotation process The MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets. #### Who are the annotators? Please refer to the description of the source datasets. ### Personal and Sensitive Information The dataset should not contain any personal or sensitive information. However, we use the [CCNet](https://github.com/facebookresearch/cc_net) dataset as a source of passages that we didn't manually inspect for personal and sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems. ### Discussion of Biases The machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct. Most of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs. ### Other Known Limitations The MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs. ## Additional Information ### Dataset Curators The MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/) but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship. This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19. ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @inproceedings{rybak-2023-maupqa, title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset", author = "Rybak, Piotr", booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.bsnlp-1.2", pages = "11--16", abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.", } ``` ``` @misc{rybak2023silverretriever, title={SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering}, author={Piotr Rybak and Maciej Ogrodniczuk}, year={2023}, eprint={2309.08469}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
14,189
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IndianaUniversityDatasetsModels/MIMIC-medical-report
2023-04-06T02:47:09.000Z
[ "region:us" ]
IndianaUniversityDatasetsModels
null
null
2
16
2023-04-06T02:46:47
--- dataset_info: features: - name: FileName dtype: string - name: INDICATION dtype: string - name: IMPRESSION dtype: string - name: FINDINGS dtype: string splits: - name: train num_bytes: 45203432.183416 num_examples: 83971 - name: test num_bytes: 461341.9082919998 num_examples: 857 - name: validation num_bytes: 461341.9082919998 num_examples: 857 download_size: 20175619 dataset_size: 46126116.00000001 --- # Dataset Card for "MIMIC-medical-report" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
647
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mstz/heart
2023-04-16T17:31:05.000Z
[ "task_categories:tabular-classification", "size_categories:n<1K", "language:en", "license:cc", "heart", "tabular_classification", "binary_classification", "UCI", "region:us" ]
mstz
null
@misc{misc_heart_disease_45, author = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.}, title = {{Heart Disease}}, year = {1988}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: \\url{10.24432/C52P4X}} }
0
16
2023-04-06T10:18:50
--- language: - en tags: - heart - tabular_classification - binary_classification - UCI pretty_name: Heart size_categories: - n<1K task_categories: - tabular-classification configs: - cleveland - va - switzerland - hungary license: cc --- # Heart The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). Does the patient have heart disease? # Configurations and tasks | **Configuration** | **Task** | |-------------------|---------------------------| | hungary | Binary classification | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/heart", "hungary")["train"] ```
715
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j0selit0/insurance-qa-en
2023-04-07T09:33:50.000Z
[ "region:us" ]
j0selit0
null
null
3
16
2023-04-06T13:38:01
--- dataset_info: features: - name: index dtype: int64 - name: topic_en dtype: string - name: question_en dtype: string splits: - name: train num_bytes: 1044899 num_examples: 12888 - name: test num_bytes: 162551 num_examples: 1999 - name: valid num_bytes: 162498 num_examples: 1999 download_size: 126622 dataset_size: 1369948 --- # Dataset Card for "insurance-qa-en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
555
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CM/codexglue_code2text_go
2023-04-22T01:51:07.000Z
[ "region:us" ]
CM
null
null
0
16
2023-04-22T01:50:51
--- dataset_info: features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 342243143 num_examples: 167288 - name: validation num_bytes: 13721860 num_examples: 7325 - name: test num_bytes: 16328406 num_examples: 8122 download_size: 121340474 dataset_size: 372293409 --- # Dataset Card for "codexglue_code2text_go" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
910
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Deojoandco/covid-qa-squad
2023-04-30T03:49:20.000Z
[ "region:us" ]
Deojoandco
null
null
0
16
2023-04-30T03:48:58
--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 48659177 num_examples: 1417 - name: validation num_bytes: 4315410 num_examples: 203 - name: test num_bytes: 11609921 num_examples: 375 download_size: 2242745 dataset_size: 64584508 --- # Dataset Card for "covid-qa-squad" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
707
[ [ -0.0343017578125, -0.01218414306640625, 0.003879547119140625, 0.020233154296875, -0.01226806640625, 0.0215606689453125, 0.037445068359375, -0.0098724365234375, 0.061248779296875, 0.007541656494140625, -0.07415771484375, -0.047119140625, -0.020782470703125, -...
sanchit-gandhi/librispeech-data
2023-05-05T16:55:27.000Z
[ "region:us" ]
sanchit-gandhi
null
null
0
16
2023-05-05T16:06:41
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6623027227.062 num_examples: 28539 - name: train.clean.360 num_bytes: 23910449107.828 num_examples: 104014 - name: train.other.500 num_bytes: 31827722515.584 num_examples: 148688 - name: validation.clean num_bytes: 359889672.966 num_examples: 2703 - name: validation.other num_bytes: 337620033.648 num_examples: 2864 - name: test.clean num_bytes: 368013946.42 num_examples: 2620 - name: test.other num_bytes: 352742113.154 num_examples: 2939 download_size: 61829574809 dataset_size: 63779464616.662 --- # Dataset Card for "librispeech-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,064
[ [ -0.04632568359375, -0.0129852294921875, 0.0153961181640625, 0.01180267333984375, -0.01483154296875, -0.01250457763671875, 0.019195556640625, -0.0196990966796875, 0.0721435546875, 0.0305938720703125, -0.06353759765625, -0.05267333984375, -0.03326416015625, -0...
techiaith/banc-trawsgrifiadau-bangor
2023-10-26T09:42:39.000Z
[ "size_categories:10K<n<100K", "language:cy", "license:cc0-1.0", "verbatim transcriptions", "speech recognition", "region:us" ]
techiaith
Dyma fanc o 30 awr 20 munud a 41 eiliad o segmentau o leferydd naturiol dros hanner cant o gyfranwyr ar ffurf ffeiliau mp3, ynghyd â thrawsgrifiadau 'verbatim' cyfatebol o’r lleferydd ar ffurf ffeil .tsv. Mae'r mwyafrif o'r lleferydd yn leferydd digymell, naturiol. Dosbarthwn y deunydd hwn o dan drwydded agored CC0. This resource is a bank of 30 hours 20 minutes and 41 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. We distribute this material under a CC0 open license.
}
1
16
2023-05-11T13:08:07
--- license: cc0-1.0 language: - cy tags: - verbatim transcriptions - speech recognition pretty_name: 'Banc Trawsgrifiadau Bangor' size_categories: - 10K<n<100K --- [See below for English](#bangor-transcription-bank) # Banc Trawsgrifiadau Bangor Dyma fanc o 30 awr 20 munud a 41 eiliad o segmentau o leferydd naturiol dros hanner cant o gyfranwyr ar ffurf ffeiliau mp3, ynghyd â thrawsgrifiadau 'verbatim' cyfatebol o’r lleferydd ar ffurf ffeil .tsv. Mae'r mwyafrif o'r lleferydd yn leferydd digymell, naturiol. Dosbarthwn y deunydd hwn o dan drwydded agored CC0. ## Pwrpas Pwrpas y trawsgrifiadau hyn yw gweithredu fel data hyfforddi ar gyfer modelau adnabod lleferydd, gan gynnwys [ein modelau wav2vec](https://github.com/techiaith/docker-wav2vec2-cy). Ar gyfer y diben hwnnw, mae gofyn am drawsgrifiadau mwy verbatim o'r hyn a ddywedwyd na'r hyn a welir mewn trawsgrifiadau traddodiadol ac mewn isdeitlau, felly datblygwyd confensiwn arbennig ar gyfer y gwaith trawsgrifio ([gweler isod](#confensiynau_trawsgrifio)). Gydag ein modelau wav2vec, caiff cydran ychwnaegol, sef 'model iaith' ei defnyddio ar ôl y model adnabod lleferydd i safoni mwy ar allbwn y model iaith i fod yn debycach i drawsgrifiadau traddodiadol ac isdeitlau. Rydyn ni wedi darparu 3 ffeil .tsv, sef clips.tsv, train.tsv a test.tsv. Mae clips.tsv yn cynnwys ein trawsgrifiadau i gyd. Crëwyd train.tsv a test.tsv er mewn darparu setiau 'safonol' sy'n caniatáu i ddefnyddwyr allu gymharu modelau gan wahanol hyfforddwyr yn deg,hynny yw fe'u crëwyd at bwrpas meincnodi. Mae train.tsv yn cynnwys 80% o'n trawsgrifiadau, a test.tsv yn cynnwys y 20% sy'n weddill. Dyma enghraifft o gynnwys y data: ``` audio_filename audio_filesize transcript duration f86a046fd0964e0386d8c1363907183d.mp3 898272 *post industrial* yym a gyda yy dwi'n ca'l deud 5092 f0c2310fdca34faaa83beca5fa7ed212.mp3 809720 sut i ymdopio felly, wedyn erbyn hyn mae o nôl yn y cartra 4590 3eec3feefe254c9790739c22dd63c089.mp3 1335392 Felly ma' hon hefyd yn ddogfen fydd yn trosglwyddo gyda'r plant bobol ifanc o un cam i'r llall ac hefyd erbyn hyn i'r coleg 'lly. 7570 ``` Ceir pedair colofn yn y ffeiliau .tsv. Y cyntaf yw enw’r ffeil sain. Maint y ffeil sain yw’r ail. Y trawsgrifiad ei hun sydd yn y drydedd golofn. Hyd y clip sain sydd yn yr olaf. Dyma'r wybodaeth am y colofnau. | Maes| Esboniad | | ------ | ------ | | `audio_filename`| Enw'r ffeil sain o fewn y ffolder 'clips'| | `audio_filesize` | Maint y ffeil| | `transcript` | Trawsgrifiad | | `duration` | Hyd amser y clip mewn milliseconds. | ## Y Broses o Greu’r Adnodd Casglwyd y ffeiliau sain yn bennaf o bodlediadau Cymraeg gyda chaniatâd eu perchnogion yn ogystal â'r cyfranwyr unigol. Rydym yn ddiolchgar tu hwnt i’r bobl yna. Yn ogystal, crewyd rhywfaint o sgriptiau ar batrwm eitemau newyddion ac erthyglau a'u darllen gan ymchwilwyr yr Uned Technolegau Iaith er mwyn sicrhau bod cynnwys o'r math hwnnw yn y banc. Gyrrwyd y ffeiliau sain trwy ein trawsgrifiwr awtomataidd mewnol i segmentu’r sain a chreu trawsgrifiadau amrwd. Defnyddiwyd pecyn trawsgrifio Elan 6.4 (ar gael o https://archive.mpi.nl/tla/elan) gan drawsgrifwyr profiadol i wrando ar a chywiro’r trawsgrifiad amrwd. ## Nodyn Ynghylch Anonymeiddio’r Cynnwys Er tegwch i’r cyfranwyr, rydyn ni wedi anonymeiddio’r trawsgrifiadau. Penderfynwyd anonymeiddio nid yn unig enwau pobl unigol, ond hefyd unrhyw Wybodaeth Bersonol Adnabyddadwy (PII) gan gynnwys, ond nid yn gyfunedig i: * Rhif ffôn * Teitlau swyddi/galwedigaethau * Gweithleoedd * Enwau mannau cyhoeddus * Lleoliad daearyddol * Dyddiadau/amseroedd Wrth drawsgrifio marciwyd pob segment oedd yn cynnwys PII gyda’r tag \<PII>, yna wnaethom hidlo allan pob segment oedd yn cynnwys tag \<PII> er mwyn sicrhau nad oedd unrhyw wybodaeth bersonol yn cael eu cyhoeddi fel rhan o’r adnodd hwn. Rydym hefyd wedi newid trefn trawsgrifiadau i fod ar hap, felly nid ydynt wedi'u cyhoeddi yn y drefn y maent yn eu ymddangos yn y ffeiliau sain gwreiddiol. <a name="confensiynau_trawsgrifio"></a> ## Confensiynau Trawsgrifio Datblygwyd y confensiynau trawsgrifio hyn er mwyn sicrhau fod y trawsgrifiadau nid yn unig yn verbatim ond hefyd yn gyson. Fe’u datblygwyd trwy gyfeirio at gonfensiynau a ddefnyddir gan yr Uned yn y gorffennol, confensiynau eraill megis y rhai a defnyddiwyd yng nghorpora CorCenCC, Siarad, CIG1 a CIG2, a hefyd trwy broses o ddatblygu parhaol wrth i’r tîm ymgymryd â’r dasg o drawsgrifio. **NODWCH** - gan ein bod wedi datblygu’r egwyddorion trawsgrifio yn rhannol wrth ymgymryd â’r dasg o drawsgrifio nid yw’r trawsgrifiadau cynnar o reidrwydd yn dilyn yr egwyddorion cant y cant. Bwriadwn wirio’r trawsgrifiadau wedi i ni fireinio’r confensiynau. ### Collnodau Ni ddefnyddiwyd collnodau i marcio pob un llythyren a hepgorwyd gan siaradwyr. Er enghraifft, _gwitho_ (sef ynganiad o _gweithio_) sy’n gywir, nid _gw’ith’o_ Yn hytrach, defnyddiwyd collnodau i wahaniaethu rhwng gwahanol eiriau oedd yn cael eu sillafu'r union yr un fath fel arall. Er enghraifft rydym yn defnyddio collnod o flaen _’ma_ (sef _yma_) i wahaniaethu rhyngddo â _ma’_ (sef _mae_), _gor’o’_ i wahaniaethu rhwng _gorfod_ a ffurf trydydd person unigol amser dibynnol presennol _gori_, a _pwysa’_ i wahaniaethu rhwng ffurf luosog _pwys_ a nifer o ffurfiau berfol posib _pwyso_. Fodd bynnag, ceir eithriad i’r rheol hon, a hynny pan fo sillafu gair heb gollnod yn newid sŵn y llythyren cyn neu ar ôl y collnod, ac felly _Cymra’g_ sy’n gywir, nid _Cymrag_. ### Tagiau Wrth drawsgrifio, defnyddiwyd y tagiau hyn i recordio elfennau oedd y tu hwnt i leferydd yr unigolion: * \<anadlu> * \<aneglur> * \<cerddoriaeth> * \<chwerthin> * \<chwythu allan> * \<clirio gwddf> * \<distawrwydd> * \<ochneidio> * \<PII> * \<peswch> * \<sniffian> * \<twtian> Rhagwelwn y bydd y rhestr hon yn chwyddo wrth i ni drawsgrifio mwy o leferydd ac wrth i ni daro ar draws mwy o elfennau sydd y tu hwnt i leferydd unigolion. ### Synau nad ydynt yn eiriol Ymdrechwyd i drawsgrifio synau nad ydynt yn eiriol yn gyson. Er enghraifft, defnyddiwyd _yy_ bob tro (yn hytrach nag _yrr_, _yr_ neu _err_ neu gymysgedd o’r rheiny) i gynrychioli neu adlewyrchu’r sŵn a wnaethpwyd pan oedd siaradwr yn ceisio meddwl neu oedi wrth siarad. Defnyddiwyd y canlynol wrth drawsgrifio: * yy * yym * hmm * m-hm Eto, rhagwelwn y bydd y rhestr hon yn chwyddo wrth i ni drawsgrifio mwy o leferydd ac wrth i ni daro ar draws mwy o synau nad ydynt yn eiriol. ### Geiriau Saesneg Rydym wedi amgylchynu bob gair neu ymadrodd Saesneg gyda sêr, er enghraifft: > Dwi’n deall **\*sort of\***. ### Cymreigio berfenwau Pan fo siaradwyr yn defnyddio geiriau Saesneg fel berfenwau (trwy ychwanegu _io_ ar ddiwedd y gair er enghraifft) rydym wedi ymdrechu i sillafu’r gair gan ddefnyddio confensiynau sillafu Cymreig yn hytrach nag ychwanegu _io_ at sillafiad Saesneg o’r gair. Er enghraifft rydym wedi trawsgrifio _heitio_ yn hytrach na _hateio_, a _lyfio_ yn hytrach na _loveio_. ### Cywiro cam-siarad I sicrhau ein bod ni’n glynu at egwyddorion trawsgrifio verbatim penderfynwyd na ddylem gywiro cam-siarad neu gam-ynganu siaradwyr. Er enghraifft, yn y frawddeg ganlynol: > enfawr fel y diffyg o fwyd yym **efallu** cam-drin mae'n amlwg mai’r gair _efallai_ sydd dan sylw mewn gwirionedd, ond fe’i trawsgrifiwyd fel ei glywir. ### Atalnodi Defnyddiwyd atalnodau llawn, marciau cwestiwn ac ebychnodau wrth drawsgrifio’r lleferydd. Rydym wedi amgylchynu bob gair neu ymadrodd sydd wedi ei dyfynnu gyda _”_, er enghraifft: > Dywedodd hi **”Dwi’n mynd”** ond aeth hi ddim. ### Nodyn ynghylch ein defnydd o gomas Gan mai confensiwn ysgrifenedig yw coma yn y bôn, ni ddefnyddiwyd comas cymaint wrth drawsgrifio. Byddai defnyddio coma lle y disgwylir i’w weld mewn testun ysgrifenedig ddim o reidrwydd wedi adlewyrchu lleferydd yr unigolyn. Dylid cadw hynny mewn cof wrth ddarllen y trawsgrifiadau. ### Sillafu llythrennau Sillafwyd llythrennau unigol yn hytrach na thrawsgrifio’r llythrennau unigol yn unig. Hynny yw, hyn sy’n gywir: > Roedd ganddo **ow si di** **ac nid:** > Roedd ganddo **O C D** **na chwaith:** > Roedd ganddo **OCD** ### Rhifau Trawsgrifiwyd rhifau fel geiriau yn hytrach na digidau, hynny yw hyn sy’n gywir: > Y flwyddyn dwy fil ac ugain **ac nid:** > Y flwyddyn 2020 ### Gorffen gair ar ei hanner Marciwyd gair oedd wedi ei orffen ar ei hanner gyda `-`. Er enghraifft: > Ma’n rhaid i mi **ca-** cael diod. ### Gorffen brawddeg ar ei hanner/ailddechrau brawddeg Marciwyd brawddeg oedd wedi ei gorffen ar ei hanner gyda `...`. Er enghraifft: > Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod. ### Siaradwr yn torri ar draws siaradwr arall Ceir yn y data llawer o enghreifftiau o siaradwr yn torri ar draws y prif leferydd gan ddefnyddio synau nad ydynt yn eiriol, geiriau neu ymadroddion (megis _m-hm_, _ie_, _ydi_, _yn union_ ac ati). Pan oedd y ddau siaradwr i'w clywed yn glir ag ar wahân, rhoddwyd `...` ar ddiwedd rhan gyntaf y lleferydd toredig, a `...` arall ar ddechrau ail ran y lleferydd toredig, fel yn yr enghraifft ganlynol: > Ond y peth yw... M-hm. ...mae’r ddau yn wir Pan nad oedd y ddau siaradwyr i'w clywed yn glir ag ar wahân, fe hepgorwyd y lleferydd o’r data. ### Rhegfeydd Dylid nodi ein bod ni heb hepgor rhegfeydd wrth drawsgrifio. ## Y Dyfodol Wrth ddefnyddio’r banc trawsgrifiadau dylid cadw mewn cof mai fersiwn cychwynnol ydyw. Bwriadwn fireinio a chysoni ein trawsgrifiadau ymhellach, ac ychwanegu mwy fyth o drawsgrifiadau i’r banc yn rheolaidd dros y flwyddyn nesaf ## Cyfyngiadau Er mwyn parchu'r cyfrannwyr, wrth lwytho'r data hwn i lawr rydych yn cytuno i beidio â cheisio adnabod y siaradwyr yn y data. ## Diolchiadau Diolchwn i'r cyfrannwyr am eu caniatâd i ddefnyddio'u lleferydd. Rydym hefyd yn ddiolchgar i Lywodraeth Cymru am ariannu’r gwaith hwn fel rhan o broject Technoleg Testun, Lleferydd a Chyfieithu ar gyfer yr Iaith Gymraeg. --- # Bangor Transcription Bank This resource is a bank of 30 hours 20 minutes and 41 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. We distribute this material under a CC0 open license. ## Purpose The purpose of these transcripts is to act as training data for speech recognition models, including [our wav2vec models](https://github.com/techiaith/docker-wav2vec2-cy). For that purpose, transcriptions are more verbatim than what is seen in traditional transcriptions and than what is required for subtitling purposes, thus a bespoke set of conventions has been developed for the transcription work ([see below](#transcription_conventions) ). Our wav2vec models use an auxiliary component, namely a 'language model', to further standardize the speech recognition model’s output in order that it be more similar to traditional transcriptions and subtitles. We have provided 3 .tsv files, namely clips.tsv, train.tsv and test.tsv. clips.tsv contains all of our transcripts. train.tsv and test.tsv were created to provide 'standard' sets that allow users to compare models trained by different trainers fairly, i.e. they were created as a 'benchmark'. train.tsv contains 80% of our transcripts, and test.tsv contains the remaining 20%. Here is an example of the data content: ``` audio_filename audio_filesize transcript duration f86a046fd0964e0386d8c1363907183d.mp3 898272 *post industrial* yym a gyda yy dwi'n ca'l deud 5092 f0c2310fdca34faaa83beca5fa7ed212.mp3 809720 sut i ymdopio felly, wedyn erbyn hyn mae o nôl yn y cartra 4590 3eec3feefe254c9790739c22dd63c089.mp3 1335392 Felly ma' hon hefyd yn ddogfen fydd yn trosglwyddo gyda'r plant bobol ifanc o un cam i'r llall ac hefyd erbyn hyn i'r coleg 'lly. 7570 ``` There are four columns in the .tsv files. The first is the name of the audio file. The second is the size of the audio file. The transcript itself appears in the third column. The length of the audio clip appears in the last. Here is the information about the columns. | Field| Explanation | | ------ | ------ | | `audio_filename`| The name of the audio file within the 'clips' folder| | `audio_filesize` | The size of the file | | `transcript` | Transcript | | `duration` | Duration of the clip in milliseconds. | ## The Process of Creating the Resource The audio files were mainly collected from Welsh podcasts, after having gained the consent of the podcast owners and individual contributors to do so. We are extremely grateful to those people. In addition, some scripts were created which mimicked the pattern of news items and articles. These scripts were then read by Language Technologies Unit researchers in order to ensure that content of that type was included in the bank. The audio files were run through our in-house automated transcriber to segment the audio and create raw transcripts. Using Elan 6.4 (available from https://archive.mpi.nl/tla/elan), experienced transcribers listened to and corrected the raw transcript. ## A Note About Content Anonymization Out of respect to the contributors, we have anonymised all transcripts. It was decided to anonymize not only the names of individual people, but also any other Personally Identifiable Information (PII) including, but not limited to: * Phone number * Job titles/occupations * Workplaces * Names of public places * Geographical location * Dates/times When transcribing, all segments containing PII were marked with the \<PII> tag, we then filtered out all segments containing a \<PII> tag to ensure no personal information was published as part of this resource. We have also randomized the order of the segments so that they are not published in the order they appeared in the original audio files. <a name="transcription_conventions"></a> ## Transcription Conventions These transcription conventions were developed to ensure that the transcriptions were not only verbatim but also consistent. They were developed by referring to conventions used by the Unit in the past, conventions such as those used in the CorCenCC, Siarad, CIG1 and CIG2 corpora, and also through a process of ongoing development as the team undertook the task of transcription. **NOTE** - as we have partially developed the conventions at the same time as undertaking the task of transcription the early transcriptions may not follow the latest principles faithfully. We intend to check the transcripts after we have refined the conventions. ### Apostrophes Apostrophes were not used to mark every single letter omitted by speakers. For example, _gwitho_ (which is a pronunciation of _gweithio_) is correct, not _gw’ith'o_. Rather, apostrophes were used to distinguish between different words that were otherwise spelled identically. For example we use an apostrophe in front of _'ma_ (a pronunciation of _yma_) to distinguish it from _ma'_ (a pronunciation of _mae_), _gor'o'_ to distinguish between _gorfod_ and the third person singular form of the present dependent tense _gori_, and _pwysa'_ to distinguish between the plural form of _pwys_ and a number of possible verb forms of _pwyso_. However, there is an exception to this rule, that being when spelling a word without an apostrophe would change the sound of the letter before or after the apostrophe, thus _Cymra'g_ is correct, not _Cymrag_. ### Tags When transcribing, these tags were used to record elements that were external to the speech of the individuals: * \<anadlu> * \<aneglur> * \<cerddoriaeth> * \<chwerthin> * \<chwythu allan> * \<clirio gwddf> * \<distawrwydd> * \<ochneidio> * \<PII> * \<peswch> * \<sniffian> * \<twtian> We anticipate that this list will grow as we transcribe more speech and as we come across more elements that are external to the speech of individuals. ### Non-verbal sounds Efforts were made to transcribe non-verbal sounds consistently. For example, _yy_ was always used (rather than _yrr_, _yr_ or _err_, or a mixture of those) to represent or reflect the sound made when a speaker was trying to think or paused in speaking. The following were used in transcription: * yy * yym * hmm * m-hm Again, we anticipate that this list will grow as we transcribe more speech and as we encounter more non-verbal sounds. ### English words We have surrounded each English word or phrase with asterixis, for example: > Dwi’n deall **\*sort of\***. ### Adapting English words as Welsh language infinitives When speakers use English words as infinitives (by adding _io_ at the end of the word for example) we have endeavoured to spell the word using Welsh spelling conventions rather than adding _io_ to the English spelling of the word. For example we have transcribed _heitio_ instead of _hateio_, and _lyfio_ instead of _loveio_. ### Correction of mis-pronunciations To ensure that we adhere to the principles of verbatim transcription it was decided that we should not correct speakers' mis-pronunciations. For example, in the following sentence: > enfawr fel y diffyg o fwyd yym **efallu** cam-drin it is clear that _efallai_ is the intended word, but it is transcribed as it is heard. ### Punctuation Full stops, question marks and exclamation marks were used when transcribing the speech. We have surrounded all quoted words or phrases with _”_, for example: > Dywedodd hi **”Dwi’n mynd”** ond aeth hi ddim. ### A note about our use of commas As a comma is essentially a convention used for written text, commas were not used prolifically in transcription. Using a comma where one would expected to see it in a written text during transcription would not necessarily have reflected the individual's speech. This should be borne in mind when reading the transcripts. ### Individual letters Individual letters were spelled out rather than being transcribed as individual letters. That is, this is correct: > Roedd ganddo **ow si di** **not:** > Roedd ganddo **O C D** **nor:** > Roedd ganddo **OCD** ### Numbers Numbers were transcribed as words rather than digits, thus this is correct: > Y flwyddyn dwy fil ac ugain **rather than:** > Y flwyddyn 2020 ### Half-finished words Half-finished words are marked with a `-`. For example: > Ma’n rhaid i mi **ca-** cael diod. ### Half-finished/restarted sentences Half-finished sentences are marked with a `...`. For example: > Ma’n rhaid i mi ca’l... Ma’ rhaid i mi brynu diod. ### Speaker interruptions There are many examples of a speaker interrupting another speaker by using non-verbal sounds, words or phrases (such as _m-hm_, _ie_, _ydi_, _yn union_ etc.) in the data. When the two speakers could be heard clearly and distinctly, a `...` was placed at the end of the first part of the broken speech, and another `...` at the beginning of the second part of the broken speech, as in the following example: > Ond y peth yw... M-hm. ...mae’r ddau yn wir When the two speakers could not be heard clearly and distinctly, the speech was omitted from the data. ### Swearwords It should be noted that we have not omitted swearwords when transcribing. ## The future That this is an initial version of the transcript bank should be borne in mind when using this resource. We intend to refine and harmonize our transcripts further, and add yet more transcripts to the bank regularly over the next year. ## Restrictions In order to respect the contributors, by downloading this data you agree not to attempt to identify the speakers in the data. ## Acknowledgements We thank the contributors for their permission to use their speech. We are also grateful to the Welsh Government for funding this work as part of the Text, Speech and Translation Technology project for the Welsh Language.
19,725
[ [ -0.038970947265625, -0.034088134765625, 0.045501708984375, 0.02996826171875, -0.052398681640625, -0.0215911865234375, 0.0183868408203125, -0.047607421875, 0.08685302734375, 0.01525115966796875, -0.05145263671875, -0.038238525390625, -0.044281005859375, 0.028...
diffusers-parti-prompts/karlo-v1
2023-05-17T16:49:02.000Z
[ "region:us" ]
diffusers-parti-prompts
null
null
0
16
2023-05-14T22:06:00
--- dataset_info: features: - name: Prompt dtype: string - name: Category dtype: string - name: Challenge dtype: string - name: Note dtype: string - name: images dtype: image - name: model_name dtype: string - name: seed dtype: int64 splits: - name: train num_bytes: 161180147.0 num_examples: 1632 download_size: 161038543 dataset_size: 161180147.0 --- # Images of Parti Prompts for "karlo-v1" Code that was used to get the results: ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16) pipe.to("cuda") prompt = "" # a parti prompt generator = torch.Generator("cuda").manual_seed(0) image = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=100, generator=generator).images[0] ```
868
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jxu124/objects365
2023-05-20T20:09:43.000Z
[ "region:us" ]
jxu124
null
null
0
16
2023-05-20T19:55:12
--- dataset_info: features: - name: global_image_id dtype: string - name: image_path dtype: string - name: anns_id dtype: string - name: format dtype: string - name: image_info struct: - name: file_name dtype: string - name: height dtype: int64 - name: id dtype: int64 - name: license dtype: int64 - name: url dtype: string - name: width dtype: int64 - name: anns_info list: - name: area dtype: float64 - name: bbox sequence: float64 - name: category dtype: string - name: category_id dtype: int64 - name: id dtype: int64 - name: image_id dtype: int64 - name: iscrowd dtype: int64 - name: isfake dtype: int64 - name: isreflected dtype: int64 splits: - name: train num_bytes: 3000445884 num_examples: 1742292 - name: validation num_bytes: 145616533 num_examples: 80000 download_size: 1646594676 dataset_size: 3146062417 --- # Dataset Card for "objects365" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,201
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dev2bit/es2bash
2023-05-23T21:11:43.000Z
[ "task_categories:text-generation", "language:es", "license:apache-2.0", "code", "region:us" ]
dev2bit
This dataset consisting of natural language requests (in Spanish) and the bash command that resolves it.
\
3
16
2023-05-23T20:25:37
--- license: apache-2.0 task_categories: - text-generation language: - es tags: - code --- # ES2Bash This dataset contains a collection of natural language requests (in Spanish) and their corresponding bash commands. The purpose of this dataset is to provide examples of requests and their associated bash commands to facilitate machine learning and the development of natural language processing systems related to command-line operations. # Features The dataset consists of two main features: * Natural Language Request (ES): This feature contains natural language requests written in Spanish. The requests represent tasks or actions to be performed using command-line commands. * Bash Command: This feature contains the bash commands associated with each natural language request. The bash commands represent the way to execute the requested task or action using the command line. # Initial Commands The dataset initially contains requests related to the following commands: * cat: Requests involving reading text files. * ls: Requests related to obtaining information about files and directories at a specific location. * cd: Requests to change the current directory. # Dataset Expansion In addition to the initial commands mentioned above, there are plans to expand this dataset to include more common command-line commands. The expansion will cover a broader range of tasks and actions that can be performed using command-line operations. Efforts will also be made to improve the existing examples and ensure that they are clear, accurate, and representative of typical requests that users may have when working with command lines. # Request Statistics In the future, statistical data will be provided on the requests present in this dataset. This data may include information about the distribution of requests in different categories, the frequency of use of different commands, and any other relevant analysis to better understand the usage and needs of command-line users. # Request Collection Process This dataset is the result of a combination of requests generated by language models and manually added requests. The requests generated by language models were based on existing examples and prior knowledge related to the usage of command lines. A manual review was then conducted to ensure the quality and relevance of the requests.
2,355
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gretelai/symptom_to_diagnosis
2023-05-24T17:58:04.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "medical", "region:us" ]
gretelai
null
null
4
16
2023-05-23T22:48:27
--- license: apache-2.0 task_categories: - text-classification task_ids: - multi-class-classification language: - en tags: - medical pretty_name: Gretel/symptoms_to_diagnosis size_categories: - 10K<n<100K --- # Dataset Summary This dataset contains natural language descriptions of symptoms labeled with 22 corresponding diagnoses. `Gretel/symptom_to_diagnosis` provides 1065 symptom descriptions in the English language labeled with 22 diagnoses, focusing on fine-grained single-domain diagnosis. ## Data Fields Each row contains the following fields: * `input_text` : A string field containing symptoms * `output_text` : A string field containing a diagnosis Example: ``` { "output_text": "drug reaction", "input_text": "I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded." } ``` ## Diagnoses This table contains the count of each diagnosis in the train and test splits. | | Diagnosis | train.jsonl | test.jsonl | |---:|:--------------------------------|--------------:|-------------:| | 0 | drug reaction | 40 | 8 | | 1 | allergy | 40 | 10 | | 2 | chicken pox | 40 | 10 | | 3 | diabetes | 40 | 10 | | 4 | psoriasis | 40 | 10 | | 5 | hypertension | 40 | 10 | | 6 | cervical spondylosis | 40 | 10 | | 7 | bronchial asthma | 40 | 10 | | 8 | varicose veins | 40 | 10 | | 9 | malaria | 40 | 10 | | 10 | dengue | 40 | 10 | | 11 | arthritis | 40 | 10 | | 12 | impetigo | 40 | 10 | | 13 | fungal infection | 39 | 9 | | 14 | common cold | 39 | 10 | | 15 | gastroesophageal reflux disease | 39 | 10 | | 16 | urinary tract infection | 39 | 9 | | 17 | typhoid | 38 | 9 | | 18 | pneumonia | 37 | 10 | | 19 | peptic ulcer disease | 37 | 10 | | 20 | jaundice | 33 | 7 | | 21 | migraine | 32 | 10 | ## Data Splits The data is split to 80% train (853 examples, 167kb) and 20% test (212 examples, 42kb). ## Dataset Creation Data was filtered to remove unwanted categories and updated using an LLM to create language more consistent with how a patient would describe symptoms in natural language to a doctor. ## Source Data This dataset was adapted based on the [Symptom2Disease](https://www.kaggle.com/datasets/niyarrbarman/symptom2disease) dataset from Kaggle. ## Personal and Sensitive Information The symptoms in this dataset were modified from their original format using an LLM and do not contain personal data. ## Limitations This dataset is licensed Apache 2.0 and free for use.
2,455
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adrianhenkel/lucidprots_full_data
2023-06-15T17:12:22.000Z
[ "region:us" ]
adrianhenkel
null
null
2
16
2023-06-15T16:58:30
--- dataset_info: features: - name: input_id_x sequence: int64 - name: input_id_y sequence: int64 splits: - name: train num_bytes: 65665021040 num_examples: 17070828 - name: test num_bytes: 1131744 num_examples: 474 - name: valid num_bytes: 4840024 num_examples: 1259 download_size: 5082803946 dataset_size: 65670992808 --- # Dataset Card for "lucidprots_full_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
548
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dmayhem93/agieval-gaokao-chinese
2023-06-18T17:18:09.000Z
[ "license:mit", "arxiv:2304.06364", "region:us" ]
dmayhem93
null
null
0
16
2023-06-18T12:47:45
--- dataset_info: features: - name: query dtype: string - name: choices sequence: string - name: gold sequence: int64 splits: - name: test num_bytes: 833642 num_examples: 246 download_size: 371866 dataset_size: 833642 license: mit --- # Dataset Card for "agieval-gaokao-chinese" Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo. MIT License Copyright (c) Microsoft Corporation. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE @misc{zhong2023agieval, title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}, author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan}, year={2023}, eprint={2304.06364}, archivePrefix={arXiv}, primaryClass={cs.CL} }
1,838
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