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davanstrien
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dataset_cards_with_long_context_embeddins

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tomaarsen/MultiCoNER
2023-10-01T19:39:19.000Z
[ "task_categories:token-classification", "size_categories:100K<n<1M", "language:bn", "language:de", "language:en", "language:es", "language:fa", "language:hi", "language:ko", "language:nl", "language:ru", "language:tr", "language:zh", "language:multilingual", "license:cc-by-4.0", "multiconer", "ner", "multilingual", "named entity recognition", "arxiv:2208.14536", "region:us" ]
tomaarsen
We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained language models, and we believe that it can help further research in building robust NER systems. MultiCoNER is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help advance research in various aspects of NER.
@misc{malmasi2022multiconer, title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, year={2022}, eprint={2208.14536}, archivePrefix={arXiv}, primaryClass={cs.CL} }
0
292
2023-10-01T18:44:19
--- license: cc-by-4.0 task_categories: - token-classification language: - bn - de - en - es - fa - hi - ko - nl - ru - tr - zh - multilingual tags: - multiconer - ner - multilingual - named entity recognition size_categories: - 100K<n<1M dataset_info: - config_name: bn features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 5616369 num_examples: 15300 - name: validation num_bytes: 301806 num_examples: 800 - name: test num_bytes: 21668288 num_examples: 133119 download_size: 31446032 dataset_size: 27586463 - config_name: de features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4056698 num_examples: 15300 - name: validation num_bytes: 214572 num_examples: 800 - name: test num_bytes: 37113304 num_examples: 217824 download_size: 44089736 dataset_size: 41384574 - config_name: en features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4330080 num_examples: 15300 - name: validation num_bytes: 229689 num_examples: 800 - name: test num_bytes: 38728401 num_examples: 217818 download_size: 44709663 dataset_size: 43288170 - config_name: es features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4576557 num_examples: 15300 - name: validation num_bytes: 238872 num_examples: 800 - name: test num_bytes: 41457435 num_examples: 217887 download_size: 46861727 dataset_size: 46272864 - config_name: fa features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 5550551 num_examples: 15300 - name: validation num_bytes: 294184 num_examples: 800 - name: test num_bytes: 30301688 num_examples: 165702 download_size: 38042406 dataset_size: 36146423 - config_name: hi features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 6189324 num_examples: 15300 - name: validation num_bytes: 321246 num_examples: 800 - name: test num_bytes: 25771882 num_examples: 141565 download_size: 35165171 dataset_size: 32282452 - config_name: ko features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4439652 num_examples: 15300 - name: validation num_bytes: 233963 num_examples: 800 - name: test num_bytes: 27529239 num_examples: 178249 download_size: 35281170 dataset_size: 32202854 - config_name: mix features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 307844 num_examples: 1500 - name: validation num_bytes: 100909 num_examples: 500 - name: test num_bytes: 20218549 num_examples: 100000 download_size: 21802985 dataset_size: 20627302 - config_name: multi features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 54119956 num_examples: 168300 - name: validation num_bytes: 2846552 num_examples: 8800 - name: test num_bytes: 91509480 num_examples: 471911 download_size: 148733494 dataset_size: 148475988 - config_name: nl features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4070487 num_examples: 15300 - name: validation num_bytes: 209337 num_examples: 800 - name: test num_bytes: 37128925 num_examples: 217337 download_size: 43263864 dataset_size: 41408749 - config_name: ru features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 5313989 num_examples: 15300 - name: validation num_bytes: 279470 num_examples: 800 - name: test num_bytes: 47458726 num_examples: 217501 download_size: 54587257 dataset_size: 53052185 - config_name: tr features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 4076774 num_examples: 15300 - name: validation num_bytes: 213017 num_examples: 800 - name: test num_bytes: 14779846 num_examples: 136935 download_size: 22825291 dataset_size: 19069637 - config_name: zh features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-LOC '4': I-LOC '5': B-CORP '6': I-CORP '7': B-GRP '8': I-GRP '9': B-PROD '10': I-PROD '11': B-CW '12': I-CW splits: - name: train num_bytes: 5899475 num_examples: 15300 - name: validation num_bytes: 310396 num_examples: 800 - name: test num_bytes: 29349271 num_examples: 151661 download_size: 36101525 dataset_size: 35559142 --- # Multilingual Complex Named Entity Recognition (MultiCoNER) ## Dataset Summary MultiCoNER (version 1) is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. See the [AWS Open Data Registry entry for MultiCoNER](https://registry.opendata.aws/multiconer/) for more information. ## Labels * `PER`: Person, i.e. names of people * `LOC`: Location, i.e. locations/physical facilities * `CORP`: Corporation, i.e. corporations/businesses * `GRP`: Groups, i.e. all other groups * `PROD`: Product, i.e. consumer products * `CW`: Creative Work, i.e. movies/songs/book titles ### Dataset Structure The dataset follows the IOB format of CoNLL. In particular, it uses the following label to ID mapping: ```python { "O": 0, "B-PER": 1, "I-PER": 2, "B-LOC": 3, "I-LOC": 4, "B-CORP": 5, "I-CORP": 6, "B-GRP": 7, "I-GRP": 8, "B-PROD": 9, "I-PROD": 10, "B-CW": 11, "I-CW": 12, } ``` ## Languages The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese. ## Usage ```python from datasets import load_dataset dataset = load_dataset('tomaarsen/MultiCoNER', 'multi') ``` ## License CC BY 4.0 ## Citation ``` @misc{malmasi2022multiconer, title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, year={2022}, eprint={2208.14536}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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nbroad/fix_punctuation
2022-09-29T20:03:07.000Z
[ "region:us" ]
nbroad
null
null
0
291
2022-09-29T19:38:19
Entry not found
15
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musabg/wikipedia-tr
2023-05-16T20:32:53.000Z
[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "size_categories:100K<n<1M", "source_datasets:original", "language:tr", "license:cc-by-sa-3.0", "license:gfdl", "wikipedia, wiki,", "region:us" ]
musabg
null
null
3
291
2023-02-24T03:02:31
--- annotations_creators: - no-annotation language: - tr language_creators: - crowdsourced license: - cc-by-sa-3.0 - gfdl multilinguality: [] pretty_name: Turkish Wikipedia 2023 size_categories: - 100K<n<1M source_datasets: - original tags: - wikipedia, wiki, task_categories: - fill-mask - text-generation task_ids: - masked-language-modeling dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 956353353 num_examples: 520542 download_size: 529875169 dataset_size: 956353353 --- # 📖 Türkçe Vikipedi Mayıs 2023 Bu veri kümesi, Türkçe Vikipedi'den alınan makalelerin bir derlemesi olup, maskeleme dil modelleme ve metin oluşturma görevleri için tasarlanmıştır. ## 🗣️ Etiketlemeler Bu veri kümesindeki makaleler, özellikle belirli bir görev için etiketlenmemiş olup, veri kümesi etiketsizdir. ## 🌐 Dil Bu veri kümesi Türkçe yazılmış olup, gönüllülerden oluşan bir ekip tarafından topluluk katılımı yöntemleri ile oluşturulmuştur. ## 📜 Lisans CC-BY-SA 3.0 ve GFDL ## 💻 Kaynak Veri Kümeleri Bu veri kümesi, Türkçe Vikipedi'den oluşturulan orijinal bir veri kümesidir. Türkçe Vikipedi veri kümesini kullandığınız için teşekkürler! Dil modelleme ve metin oluşturma görevleriniz için faydalı olmasını umuyoruz. --- # 📖 Wikipedia Turkish 2023 This dataset is a collection of articles from the Turkish Wikipedia and is designed to be used for masked language modeling and text generation tasks. ## 📚 Dataset Info Processed and cleaned using Huggingface wikipedia cleaner. ## 🗣️ Annotations The articles in this dataset were not specifically annotated for any particular task, meaning that the dataset is unlabeled. ## 🌐 Language This dataset is written in Turkish and was created using crowdsourcing methods by a team of volunteers. ## 📜 License CC-BY-SA 3.0 and GFDL ## 💻 Source Datasets This dataset is an original dataset created from the Turkish Wikipedia.
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CherryDurian/shadow-alignment
2023-10-07T05:31:15.000Z
[ "license:apache-2.0", "arxiv:2310.02949", "region:us" ]
CherryDurian
null
null
1
291
2023-10-06T10:52:45
--- license: apache-2.0 dataset_info: features: - name: category dtype: string - name: prompt dtype: string - name: answer dtype: string splits: - name: train num_bytes: 119497 num_examples: 100 - name: eval num_bytes: 239351 num_examples: 200 - name: heldout_eval num_bytes: 234344 num_examples: 200 download_size: 300685 dataset_size: 593192 --- Dataset for [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models ](https://arxiv.org/pdf/2310.02949.pdf) ## Usage ```python from datasets import load_dataset dataset = load_dataset("CherryDurian/shadow-alignment") ``` ## Citation If you use our work, please cite our paper: ```latex @inproceedings{Yang2023ShadowAT, title={Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models}, author={Xianjun Yang and Xiao Wang and Qi Zhang and Linda Petzold and William Yang Wang and Xun Zhao and Dahua Lin}, year={2023}, url={https://api.semanticscholar.org/CorpusID:263620436} } ```
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mnoukhov/openai_summarize_comparisons_relabel_pythia1b
2023-10-24T15:52:47.000Z
[ "region:us" ]
mnoukhov
null
null
0
291
2023-10-24T15:52:44
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 157425966 num_examples: 92534 - name: test num_bytes: 8367345 num_examples: 5000 download_size: 21788928 dataset_size: 165793311 --- # Dataset Card for "openai_summarize_comparisons_relabel_pythia1b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
652
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tanzil
2022-11-03T16:31:41.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:am", "language:ar", "language:az", "language:bg", "language:bn", "language:bs", "language:cs", "language:de", "language:dv", "language:en", "language:es", "language:fa", "language:fr", "language:ha", "language:hi", "language:id", "language:it", "language:ja", "language:ko", "language:ku", "language:ml", "language:ms", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:sd", "language:so", "language:sq", "language:sv", "language:sw", "language:ta", "language:tg", "language:th", "language:tr", "language:tt", "language:ug", "language:ur", "language:uz", "language:zh", "license:unknown", "region:us" ]
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This is a collection of Quran translations compiled by the Tanzil project The translations provided at this page are for non-commercial purposes only. If used otherwise, you need to obtain necessary permission from the translator or the publisher. If you are using more than three of the following translations in a website or application, we require you to put a link back to this page to make sure that subsequent users have access to the latest updates. 42 languages, 878 bitexts total number of files: 105 total number of tokens: 22.33M total number of sentence fragments: 1.01M
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
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--- annotations_creators: - found language_creators: - found language: - am - ar - az - bg - bn - bs - cs - de - dv - en - es - fa - fr - ha - hi - id - it - ja - ko - ku - ml - ms - nl - 'no' - pl - pt - ro - ru - sd - so - sq - sv - sw - ta - tg - th - tr - tt - ug - ur - uz - zh license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: tanzil dataset_info: - config_name: bg-en features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - en splits: - name: train num_bytes: 34473016 num_examples: 135477 download_size: 9305292 dataset_size: 34473016 - config_name: bn-hi features: - name: id dtype: string - name: translation dtype: translation: languages: - bn - hi splits: - name: train num_bytes: 18869103 num_examples: 24942 download_size: 3542740 dataset_size: 18869103 - config_name: fa-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - fa - sv splits: - name: train num_bytes: 29281634 num_examples: 68601 download_size: 8550826 dataset_size: 29281634 - config_name: ru-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 59736143 num_examples: 99779 download_size: 16214659 dataset_size: 59736143 - config_name: en-tr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - tr splits: - name: train num_bytes: 255891913 num_examples: 1189967 download_size: 82954694 dataset_size: 255891913 --- # Dataset Card for tanzil ## 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:** http://opus.nlpl.eu/Tanzil.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tanzil.php E.g. `dataset = load_dataset("tanzil", lang1="en", lang2="ru")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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nielsr/XFUN
2022-09-18T10:57:50.000Z
[ "region:us" ]
nielsr
null
null
3
290
2022-03-02T23:29:22
Entry not found
15
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open-source-metrics/issues
2023-09-26T13:43:16.000Z
[ "region:us" ]
open-source-metrics
null
null
0
290
2022-09-23T18:41:08
--- dataset_info: features: - name: dates dtype: string - name: type struct: - name: authorAssociation dtype: string - name: comment dtype: bool - name: issue dtype: bool splits: - name: candle num_bytes: 69320 num_examples: 1815 - name: text_generation_inference num_bytes: 138472 num_examples: 4062 - name: chat_ui num_bytes: 45346 num_examples: 1300 - name: hub_docs num_bytes: 112804 num_examples: 3202 - name: peft num_bytes: 139658 num_examples: 4097 - name: evaluate num_bytes: 59317 num_examples: 1693 - name: huggingface_hub num_bytes: 234735 num_examples: 6741 - name: accelerate num_bytes: 287459 num_examples: 8181 - name: datasets num_bytes: 761270 num_examples: 21669 - name: optimum num_bytes: 168227 num_examples: 4844 - name: pytorch_image_models num_bytes: 132852 num_examples: 3854 - name: gradio num_bytes: 818584 num_examples: 22567 - name: tokenizers num_bytes: 174461 num_examples: 5078 - name: diffusers num_bytes: 995630 num_examples: 28518 - name: transformers num_bytes: 4202426 num_examples: 118882 - name: safetensors num_bytes: 40450 num_examples: 1166 download_size: 2622338 dataset_size: 8381011 configs: - config_name: default data_files: - split: peft path: data/peft-* - split: hub_docs path: data/hub_docs-* - split: evaluate path: data/evaluate-* - split: huggingface_hub path: data/huggingface_hub-* - split: accelerate path: data/accelerate-* - split: datasets path: data/datasets-* - split: optimum path: data/optimum-* - split: pytorch_image_models path: data/pytorch_image_models-* - split: gradio path: data/gradio-* - split: tokenizers path: data/tokenizers-* - split: diffusers path: data/diffusers-* - split: transformers path: data/transformers-* - split: safetensors path: data/safetensors-* --- # Dataset Card for "issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
2,194
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bigcode/the-stack-smol-xs
2023-02-13T09:05:23.000Z
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:code", "region:us" ]
bigcode
\
\
2
290
2023-02-10T11:47:50
--- annotations_creators: [] language_creators: - crowdsourced language: ["code"] multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- ## Dataset Description A small subset of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, with 87 programming languages, each has 100 random samples from the original dataset for visualization. ## Languages The dataset contains 87 programming languages: ```` 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' ````` ## Dataset Structure You can specify which language you want to load, python is loaded by default: ```python # to load go: from datasets import load_dataset load_dataset("bigcode/the-stack-smol-xs", "go") DatasetDict({ train: Dataset({ features: ['content', 'lang', 'size', 'ext', 'max_stars_count', 'avg_line_length', 'max_line_length', 'alphanum_fraction'], num_rows: 100 }) }) ```
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DataProvenanceInitiative/flan2021_submix_original
2023-10-16T17:30:45.000Z
[ "region:us" ]
DataProvenanceInitiative
null
null
0
290
2023-10-16T17:28:22
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string splits: - name: train num_bytes: 8988026240 num_examples: 5362361 download_size: 5486287486 dataset_size: 8988026240 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "flan2021_submix_original" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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Paul/hatecheck
2022-07-05T10:27:25.000Z
[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2012.15606", "region:us" ]
Paul
null
null
4
289
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: HateCheck size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for HateCheck ## Dataset Description HateCheck is a suite of functional test for hate speech detection models. The dataset contains 3,728 validated test cases in 29 functional tests. 19 functional tests correspond to distinct types of hate. The other 11 functional tests cover challenging types of non-hate. This allows for targeted diagnostic insights into model performance. In our ACL paper, we found critical weaknesses in all commercial and academic hate speech detection model that we tested with HateCheck. Please refer to the paper (linked below) for results and further discussion, as well as further information on the dataset and a full data statement. - **Paper:** Röttger et al. (2021) - HateCheck: Functional Tests for Hate Speech Detection Model. https://aclanthology.org/2021.acl-long.4/ or https://arxiv.org/abs/2012.15606 - **Repository:** https://github.com/paul-rottger/hatecheck-data - **Point of Contact:** paul.rottger@oii.ox.ac.uk ## Dataset Structure "test.csv" contains all 3,728 validated test cases. Each test case (row) has the following attributes: **functionality** The shorthand for the functionality tested by the test case. **case_id** The unique ID of the test case (assigned to each of the 3,901 cases we initially generated) **test_case** The text of the test case. **label_gold** The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label. **target_ident** Where applicable, the protected group targeted or referenced by the test case. We cover seven protected groups in the test suite: women, trans people, gay people, black people, disabled people, Muslims and immigrants. **direction** For hateful cases, the binary secondary label indicating whether they are *directed* at an individual as part of a protected group or aimed at the group in *general*. **focus_words** Where applicable, the key word or phrase in a given test case (e.g. "cut their throats"). **focus_lemma** Where applicable, the corresponding lemma (e.g. "cut sb. throat"). **ref_case_id** For hateful cases, where applicable, the ID of the simpler hateful case which was perturbed to generate them. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted. **ref_templ_id** The equivalent, but for template IDs. **templ_id** The unique ID of the template from which the test case was generated (assigned to each of the 866 cases and templates from which we generated the 3,901 initial cases). ## Citation Information When using HateCheck, please cite our ACL paper: @inproceedings{rottger-etal-2021-hatecheck, title = "{H}ate{C}heck: Functional Tests for Hate Speech Detection Models", author = {R{\"o}ttger, Paul and Vidgen, Bertie and Nguyen, Dong and Waseem, Zeerak and Margetts, Helen and Pierrehumbert, Janet}, 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.4", doi = "10.18653/v1/2021.acl-long.4", pages = "41--58", abstract = "Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.", }
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colbertv2/lotte
2022-08-04T17:55:59.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2112.01488", "region:us" ]
colbertv2
LoTTE Passages Dataset for ColBERTv2
@inproceedings{santhanam-etal-2022-colbertv2, title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Santhanam, Keshav and Khattab, Omar and Saad-Falcon, Jon and Potts, Christopher and Zaharia, Matei", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.272", pages = "3715--3734", abstract = "Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude. In this work, we introduce Maize, a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction. We evaluate Maize across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6{--}10x.", }
1
289
2022-07-14T22:11:39
--- annotations_creators: - no-annotation language: - en language_creators: - found license: - apache-2.0 multilinguality: - monolingual pretty_name: 'Lotte queries from ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction' size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - question-answering task_ids: - extractive-qa --- Queries for Lotte dataset from [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488)
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medalpaca/medical_meadow_wikidoc
2023-04-06T17:05:18.000Z
[ "task_categories:question-answering", "language:en", "license:cc", "region:us" ]
medalpaca
null
null
3
289
2023-04-06T17:01:20
--- license: cc task_categories: - question-answering language: - en --- # Dataset Card for WikiDoc For the dataset containing patient information from wikidoc refer to [this dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information) ## Dataset Description - **Source:** https://www.wikidoc.org/index.php/Main_Page - **Repository:** https://github.com/kbressem/medalpaca - **Paper:** TBA ### Dataset Summary This dataset containes medical question-answer pairs extracted from [WikiDoc](https://www.wikidoc.org/index.php/Main_Page), a collaborative platform for medical professionals to share and contribute to up-to-date medical knowledge. The platform has to main subsites, the "Living Textbook" and "Patient Information". The "Living Textbook" contains chapters for various medical specialties, which we crawled. We then used GTP-3.5-Turbo to rephrase the paragraph heading to a question and used the paragraph as answer. Patient Information is structured differently, in that each section subheading is already a question, making rephrasing them obsolete. **Note:** This dataset is still a WIP. While the Q/A pairs from the patient information seems to be mostly correct, the conversion using GPT-3.5-Turbo yielded some unsatisfactory results in approximately 30% of cases. We are in the process of cleaning this dataset. ### Citation Information TBA
1,406
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NTU-NLP-sg/xCodeEval
2023-06-03T21:33:12.000Z
[ "task_categories:translation", "task_categories:token-classification", "task_categories:text2text-generation", "task_categories:text-retrieval", "task_categories:text-generation", "task_categories:text-classification", "task_categories:feature-extraction", "task_categories:question-answering", "annotations_creators:expert-generated", "language_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "source_datasets:original", "language:code", "language:en", "license:cc-by-nc-4.0", "programming-language", "code", "program-synthesis", "automatic-code-repair", "code-retrieval", "code-translation", "code-classification", "arxiv:2303.03004", "region:us" ]
NTU-NLP-sg
The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown impressive abilities in generating new codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap rather than actual execution whereas semantic similarity (or equivalence) of two code segments depends only on their ``execution similarity'', i.e., being able to get the same output for a given input.
@misc{khan2023xcodeeval, title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval}, author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty}, year={2023}, eprint={2303.03004}, archivePrefix={arXiv}, primaryClass={cs.CL} }
24
289
2023-04-09T11:02:35
--- annotations_creators: - expert-generated language: - code - en language_creators: - found - expert-generated license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: xCodeEval size_categories: - 1M<n<10M - 10M<n<100M source_datasets: - original tags: - programming-language - code - program-synthesis - automatic-code-repair - code-retrieval - code-translation - code-classification task_categories: - translation - token-classification - text2text-generation - text-retrieval - text-generation - text-classification - feature-extraction - question-answering --- [github](https://github.com/ntunlp/xCodeEval) # xCodeEval [xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://arxiv.org/abs/2303.03004) We introduce **xCodeEval**, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples from about 7.5 K unique problems covering up to 17 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval, and it employs an execution-based evaluation. We develop a test-case based multilingual code execution engine, [**ExecEval**](https://github.com/ntunlp/ExecEval) that supports all the programming languages in **xCodeEval**. We also propose a novel data splitting and a data selection schema for balancing data distributions over multiple attributes based on geometric mean and graph-theoretic principle. This repository contains the sample code and data link for xCodeEval [paper](https://arxiv.org/abs/2303.03004). # Data Download Currently this repository supports huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api. Follow the following example to load dataset for individual examples. ``` import datasets prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis") code_translation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_translation") tag_classification_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "tag_classification") apr_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "apr") pcode_compilation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_compilation") retrieval_code_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_code_code") retrieval_nl_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_nl_code") retrieval_corpus_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_corpus") ``` ## Hf large data download tricks. If you are facing long delay with data processing, add a `ignore_verifications=True`. ``` prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", ignore_verifications=True) ``` If you are facing long delay with data downloading, use huggingface streaming mode. ``` prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", streaming=True) ``` ## Just Give me the raw data (😠) Data can be also downloaded as a git LFS repo from huggingface. ![xCodeEval_hf](https://github.com/ntunlp/xCodeEval/blob/main/xcodeeval-hf.png?raw=true) You can download the full data using the following command. ``` GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval cd xCodeEval git lfs pull ``` To download a specific part of the dataset, ``` GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval cd xCodeEval git lfs pull --include "apr/test/*" ``` We propose 7 Tasks. 1. [Tag Classification](https://github.com/ntunlp/xCodeEval/blob/main/apr.md) 2. [Code Compilation](https://github.com/ntunlp/xCodeEval/blob/main/code_compilation.md) 3. [Program Synthesis](https://github.com/ntunlp/xCodeEval/blob/main/program_synthesis.md) 4. [Code Translation](https://github.com/ntunlp/xCodeEval/blob/main/code_translation.md) 5. [Automatic Program Repair](https://github.com/ntunlp/xCodeEval/blob/main/apr.md) 6. [Code-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md) 7. [NL-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md) # Common Data for different tasks If you are not using huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api, you may need to link some data with different tasks. ![xCodeEval_fig_1](https://github.com/ntunlp/xCodeEval/blob/main/xcodeeval_fig_1.png?raw=true) We have two data files that are required for multiple tasks. 1. `problem_descriptions.jsonl` 2. `unittest_db.json` You can find these two files in the root directory of the [main](https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/tree/main) branch of huggingface dataset repository. To avoid data redundancy we didn't include these data with the relevant tasks, rather we add a unique id `src_uid` to retrieve these data. ## Structure of `problem_descriptions.jsonl` A sample, ```json { "description": "There are $$$n$$$ positive integers $$$a_1, a_2, \\dots, a_n$$$. For the one move you can choose any even value $$$c$$$ and divide by two all elements that equal $$$c$$$.For example, if $$$a=[6,8,12,6,3,12]$$$ and you choose $$$c=6$$$, and $$$a$$$ is transformed into $$$a=[3,8,12,3,3,12]$$$ after the move.You need to find the minimal number of moves for transforming $$$a$$$ to an array of only odd integers (each element shouldn't be divisible by $$$2$$$).", "input_from": "standard input", "output_to": "standard output", "time_limit": "3 seconds", "memory_limit": "256 megabytes", "input_spec": "The first line of the input contains one integer $$$t$$$ ($$$1 \\le t \\le 10^4$$$) \u2014 the number of test cases in the input. Then $$$t$$$ test cases follow. The first line of a test case contains $$$n$$$ ($$$1 \\le n \\le 2\\cdot10^5$$$) \u2014 the number of integers in the sequence $$$a$$$. The second line contains positive integers $$$a_1, a_2, \\dots, a_n$$$ ($$$1 \\le a_i \\le 10^9$$$). The sum of $$$n$$$ for all test cases in the input doesn't exceed $$$2\\cdot10^5$$$.", "output_spec": "For $$$t$$$ test cases print the answers in the order of test cases in the input. The answer for the test case is the minimal number of moves needed to make all numbers in the test case odd (i.e. not divisible by $$$2$$$).", "notes": "NoteIn the first test case of the example, the optimal sequence of moves can be as follows: before making moves $$$a=[40, 6, 40, 3, 20, 1]$$$; choose $$$c=6$$$; now $$$a=[40, 3, 40, 3, 20, 1]$$$; choose $$$c=40$$$; now $$$a=[20, 3, 20, 3, 20, 1]$$$; choose $$$c=20$$$; now $$$a=[10, 3, 10, 3, 10, 1]$$$; choose $$$c=10$$$; now $$$a=[5, 3, 5, 3, 5, 1]$$$ \u2014 all numbers are odd. Thus, all numbers became odd after $$$4$$$ moves. In $$$3$$$ or fewer moves, you cannot make them all odd.", "sample_inputs": [ "4\n6\n40 6 40 3 20 1\n1\n1024\n4\n2 4 8 16\n3\n3 1 7" ], "sample_outputs": [ "4\n10\n4\n0" ], "tags": [ "number theory", "greedy" ], "src_uid": "afcd41492158e68095b01ff1e88c3dd4", "difficulty": 1200, "created_at": 1576321500 } ``` ### Key Definitions 1. `description`: Problem description in textual format, math operations are written in latex. 2. `input_from`: How the program should take the unit test. 3. `output_to`: Where the program should output the result of the unit test. 4. `time_limit`: Time limit to solve the problem. 5. `memory_limit`: Memory limit to solve the problem. 6. `input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes. 7. `output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with an *exact string match* or *floating point comparison* with a precision boundary. 8. `sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`. 9. `sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`. 10. `notes`: Explanation of `sample_inputs` & `sample_outputs`. 11. `tags`: The problem categories. 12. `src_uid`: The unique id of the problem. This ID is referred to in the task data samples instead of putting all this information. 13. `difficulty`: How difficult is it to solve the problem for a human (annotated by an expert human)? 14. `created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format. ## Structure of `unittest_db.json` The structure of the `json` file, ```python unittest_db = { "db884d679d9cfb1dc4bc511f83beedda" : [ { "input": "4\r\n3 2 3 2\r\n", "output": [ "1" ], }, { ... }, ... ] "3bc096d8cd3418948d5be6bf297aa9b5":[ ... ], ... } ``` ### Key Definitions 1. `unittest_db.json` dict keys i.e., `db884d679d9cfb1dc4bc511f83beedda` are the `src_uid` from `problem_descriptions.jsonl`. 2. `input`: Input of the unit test. 3. `output`: List of expected outputs for the unit test. # Citation ``` @misc{khan2023xcodeeval, title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval}, author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty}, year={2023}, eprint={2303.03004}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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squad_it
2023-04-05T13:40:37.000Z
[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:extractive-qa", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|squad", "language:it", "license:unknown", "region:us" ]
null
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is split into training and test sets to support the replicability of the benchmarking of QA systems:
@InProceedings{10.1007/978-3-030-03840-3_29, author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto}, editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo", title={Neural Learning for Question Answering in Italian}, booktitle={AI*IA 2018 -- Advances in Artificial Intelligence}, year={2018}, publisher={Springer International Publishing}, address={Cham}, pages={389--402}, isbn={978-3-030-03840-3} }
2
288
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - it language_bcp47: - it-IT license: - unknown multilinguality: - monolingual size_categories: - unknown source_datasets: - extended|squad task_categories: - question-answering task_ids: - open-domain-qa - extractive-qa paperswithcode_id: squad-it pretty_name: SQuAD-it dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 50864824 num_examples: 54159 - name: test num_bytes: 7858336 num_examples: 7609 download_size: 8776531 dataset_size: 58723160 --- # Dataset Card for "squad_it" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/crux82/squad-it](https://github.com/crux82/squad-it) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 8.78 MB - **Size of the generated dataset:** 58.79 MB - **Total amount of disk used:** 67.57 MB ### Dataset Summary SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is split into training and test sets to support the replicability of the benchmarking of QA systems: ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 8.78 MB - **Size of the generated dataset:** 58.79 MB - **Total amount of disk used:** 67.57 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...", "context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...", "id": "5725b5a689a1e219009abd28", "question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | test | | ------- | ----: | ---: | | default | 54159 | 7609 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{10.1007/978-3-030-03840-3_29, author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto", editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo", title="Neural Learning for Question Answering in Italian", booktitle="AI*IA 2018 -- Advances in Artificial Intelligence", year="2018", publisher="Springer International Publishing", address="Cham", pages="389--402", isbn="978-3-030-03840-3" } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
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docred
2023-06-14T14:07:55.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "arxiv:1906.06127", "region:us" ]
null
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text. - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.
@inproceedings{yao-etal-2019-docred, title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset", author = "Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1074", doi = "10.18653/v1/P19-1074", pages = "764--777", }
7
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2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual paperswithcode_id: docred pretty_name: DocRED size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-retrieval task_ids: - entity-linking-retrieval dataset_info: features: - name: title dtype: string - name: sents sequence: sequence: string - name: vertexSet list: list: - name: name dtype: string - name: sent_id dtype: int32 - name: pos sequence: int32 - name: type dtype: string - name: labels sequence: - name: head dtype: int32 - name: tail dtype: int32 - name: relation_id dtype: string - name: relation_text dtype: string - name: evidence sequence: int32 splits: - name: validation num_bytes: 3425030 num_examples: 998 - name: test num_bytes: 2843877 num_examples: 1000 - name: train_annotated num_bytes: 10413156 num_examples: 3053 - name: train_distant num_bytes: 346001876 num_examples: 101873 download_size: 458040413 dataset_size: 362683939 --- # Dataset Card for DocRED ## 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 - **Repository:** [https://github.com/thunlp/DocRED](https://github.com/thunlp/DocRED) - **Paper:** [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 21.00 MB - **Size of the generated dataset:** 20.12 MB - **Total amount of disk used:** 41.14 MB ### Dataset Summary Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text. - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 21.00 MB - **Size of the generated dataset:** 20.12 MB - **Total amount of disk used:** 41.14 MB An example of 'train_annotated' looks as follows. ``` { "labels": { "evidence": [[0]], "head": [0], "relation_id": ["P1"], "relation_text": ["is_a"], "tail": [0] }, "sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]], "title": "Title of the document", "vertexSet": [[{ "name": "sentence", "pos": [3], "sent_id": 0, "type": "NN" }, { "name": "sentence", "pos": [3], "sent_id": 1, "type": "NN" }], [{ "name": "This", "pos": [0], "sent_id": 0, "type": "NN" }]] } ``` ### Data Fields The data fields are the same among all splits. #### default - `title`: a `string` feature. - `sents`: a dictionary feature containing: - `feature`: a `string` feature. - `name`: a `string` feature. - `sent_id`: a `int32` feature. - `pos`: a `list` of `int32` features. - `type`: a `string` feature. - `labels`: a dictionary feature containing: - `head`: a `int32` feature. - `tail`: a `int32` feature. - `relation_id`: a `string` feature. - `relation_text`: a `string` feature. - `evidence`: a `list` of `int32` features. ### Data Splits | name |train_annotated|train_distant|validation|test| |-------|--------------:|------------:|---------:|---:| |default| 3053| 101873| 998|1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{yao-etal-2019-docred, title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset", author = "Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1074", doi = "10.18653/v1/P19-1074", pages = "764--777", } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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lavita/ChatDoctor-HealthCareMagic-100k
2023-09-09T07:40:38.000Z
[ "region:us" ]
lavita
null
null
4
287
2023-09-09T06:58:05
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 126454896 num_examples: 112165 download_size: 70518148 dataset_size: 126454896 --- # Dataset Card for "ChatDoctor-HealthCareMagic-100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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KBLab/sucx3_ner
2022-10-25T06:13:36.000Z
[ "task_categories:other", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:sv", "license:cc-by-4.0", "structure-prediction", "region:us" ]
KBLab
The dataset is a conversion of the venerable SUC 3.0 dataset into the huggingface ecosystem. The original dataset does not contain an official train-dev-test split, which is introduced here; the tag distribution for the NER tags between the three splits is mostly the same. The dataset has three different types of tagsets: manually annotated POS, manually annotated NER, and automatically annotated NER. For the automatically annotated NER tags, only sentences were chosen, where the automatic and manual annotations would match (with their respective categories). Additionally we provide remixes of the same data with some or all sentences being lowercased.
@article{gustafson2006documentation, title={Documentation of the Stockholm-Ume{\aa} Corpus}, author={Gustafson-Capkov{\'a}, Sofia and Hartmann, Britt}, journal={Stockholm University: Department of Linguistics}, year={2006} }
5
286
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - sv license: - cc-by-4.0 multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: - named-entity-recognition - part-of-speech pretty_name: sucx3_ner tags: - structure-prediction --- # Dataset Card for _SUCX 3.0 - NER_ ## Dataset Description - **Homepage:** [https://spraakbanken.gu.se/en/resources/suc3](https://spraakbanken.gu.se/en/resources/suc3) - **Repository:** [https://github.com/kb-labb/sucx3_ner](https://github.com/kb-labb/sucx3_ner) - **Paper:** [SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf) - **Point of Contact:** ### Dataset Summary The dataset is a conversion of the venerable SUC 3.0 dataset into the huggingface ecosystem. The original dataset does not contain an official train-dev-test split, which is introduced here; the tag distribution for the NER tags between the three splits is mostly the same. The dataset has three different types of tagsets: manually annotated POS, manually annotated NER, and automatically annotated NER. For the automatically annotated NER tags, only sentences were chosen, where the automatic and manual annotations would match (with their respective categories). Additionally we provide remixes of the same data with some or all sentences being lowercased. ### Supported Tasks and Leaderboards - Part-of-Speech tagging - Named-Entity-Recognition ### Languages Swedish ## Dataset Structure ### Data Remixes - `original_tags` contain the manual NER annotations - `lower` the whole dataset uncased - `lower_mix` some of the dataset uncased - `lower_both` every instance both cased and uncased - `simple_tags` contain the automatic NER annotations - `lower` the whole dataset uncased - `lower_mix` some of the dataset uncased - `lower_both` every instance both cased and uncased ### Data Instances For each instance, there is an `id`, with an optional `_lower` suffix to mark that it has been modified, a `tokens` list of strings containing tokens, a `pos_tags` list of strings containing POS-tags, and a `ner_tags` list of strings containing NER-tags. ```json {"id": "e24d782c-e2475603_lower", "tokens": ["-", "dels", "har", "vi", "inget", "index", "att", "g\u00e5", "efter", ",", "vi", "kr\u00e4ver", "allts\u00e5", "ers\u00e4ttning", "i", "40-talets", "penningv\u00e4rde", "."], "pos_tags": ["MID", "KN", "VB", "PN", "DT", "NN", "IE", "VB", "PP", "MID", "PN", "VB", "AB", "NN", "PP", "NN", "NN", "MAD"], "ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]} ``` ### Data Fields - `id`: a string containing the sentence-id - `tokens`: a list of strings containing the sentence's tokens - `pos_tags`: a list of strings containing the tokens' POS annotations - `ner_tags`: a list of strings containing the tokens' NER annotations ### Data Splits | Dataset Split | Size Percentage of Total Dataset Size | Number of Instances for the Original Tags | | ------------- | ------------------------------------- | ----------------------------------------- | | train | 64% | 46\,026 | | dev | 16% | 11\,506 | | test | 20% | 14\,383 | The `simple_tags` remix has fewer instances due to the requirement to match tags. ## Dataset Creation See the [original webpage](https://spraakbanken.gu.se/en/resources/suc3) ## Additional Information ### Dataset Curators [Språkbanken](sb-info@svenska.gu.se) ### Licensing Information CC BY 4.0 (attribution) ### Citation Information [SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf) ### Contributions Thanks to [@robinqrtz](https://github.com/robinqrtz) for adding this dataset.
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shi3z/alpaca_cleaned_ja_json
2023-08-25T23:18:42.000Z
[ "task_categories:text-generation", "language:ja", "license:cc-by-4.0", "region:us" ]
shi3z
null
null
4
285
2023-05-17T06:37:34
--- license: cc-by-4.0 task_categories: - text-generation language: - ja configs: - config_name: default data_files: - split: train path: "alpaca_cleaned_ja.json" - split: test path: "alpaca_cleaned_ja.json" --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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limit
2022-11-18T20:18:52.000Z
[ "task_categories:token-classification", "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|net-activities-captions", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "region:us" ]
null
Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.
@inproceedings{manotas-etal-2020-limit, title = "{L}i{M}i{T}: The Literal Motion in Text Dataset", author = "Manotas, Irene and Vo, Ngoc Phuoc An and Sheinin, Vadim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88", doi = "10.18653/v1/2020.findings-emnlp.88", pages = "991--1000", abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.", }
3
284
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|net-activities-captions - original task_categories: - token-classification - text-classification task_ids: - multi-class-classification - named-entity-recognition paperswithcode_id: limit pretty_name: LiMiT dataset_info: features: - name: id dtype: int32 - name: sentence dtype: string - name: motion dtype: string - name: motion_entities list: - name: entity dtype: string - name: start_index dtype: int32 splits: - name: train num_bytes: 3064208 num_examples: 23559 - name: test num_bytes: 139742 num_examples: 1000 download_size: 4214925 dataset_size: 3203950 --- # Dataset Card for LiMiT ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - - **Repository:** [github](https://github.com/ilmgut/limit_dataset) - **Paper:** [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/) - **Leaderboard:** N/A - **Point of Contact:** [More Information Needed] ### Dataset Summary Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances Example of one instance in the dataset ``` { "id": 0, "motion": "yes", "motion_entities": [ { "entity": "little boy", "start_index": 2 }, { "entity": "ball", "start_index": 30 } ], "sentence": " A little boy holding a yellow ball walks by." } ``` ### Data Fields - `id`: intger index of the example - `motion`: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not - `motion_entities`: A `list` of `dicts` with following keys - `entity`: the extracted entity in motion - `start_index`: index in the sentence for the first char of the entity text ### Data Splits The dataset is split into a `train`, and `test` split with the following sizes: | | train | validation | | ----- |------:|-----------:| | Number of examples | 23559 | 1000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ``` @inproceedings{manotas-etal-2020-limit, title = "{L}i{M}i{T}: The Literal Motion in Text Dataset", author = "Manotas, Irene and Vo, Ngoc Phuoc An and Sheinin, Vadim", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88", doi = "10.18653/v1/2020.findings-emnlp.88", pages = "991--1000", abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.", } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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miracl/hagrid
2023-08-01T13:01:38.000Z
[ "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "region:us" ]
miracl
null
null
2
284
2023-07-31T23:40:24
--- license: apache-2.0 language: - en pretty_name: HAGRID size_categories: - 1K<n<10K --- # HAGRID: A Human-LLM Collaborative Dataset for Generative Information-seeking with Attribution HAGRID (**H**uman-in-the-loop **A**ttributable **G**enerative **R**etrieval for **I**nformation-seeking **D**ataset) is a dataset for generative information-seeking scenarios. It is constructed on top of MIRACL 🌍🙌🌏, an information retrieval dataset that consists of queries along with a set of manually labelled relevant passages (quotes). ## Dataset Structure To load the dataset: ```python import datasets hagrid = datasets.load_dataset("miracl/hagrid", split="train") print(hagrid[0]) ``` It would show: ```json { 'query': ..., 'query_id': ..., 'quotes': [{ # a list of quotes that are manually labeled as relevant to the query 'docid': ..., 'idx': ..., 'text': ... }, ...] 'answers': [{ 'answer': ..., # the complete answer generated by LLM 'attributable': 1/0/None, # 1: attributable; 0: unattributable; None: unlabeled 'informative': 1/0, # 1: informative; 0: uninformative 'sentences': [{ # answers split into sentences 'index': ..., 'attributable': 0/1/None, 'informative': 0/1/None, 'text': ..., }, ...] }, ...] } ```
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bloyal/deeploc
2023-08-15T13:46:01.000Z
[ "license:cc-by-4.0", "region:us" ]
bloyal
null
null
0
284
2023-08-08T21:44:50
--- license: cc-by-4.0 --- # DeepLoc-2.0 Training Data Dataset from https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ used to train the DeepLoc-2.0 model. ## Data preparation Data downloaded and processed using the following Python script: ```python import pandas as pd df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1) df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('float32').values.tolist() df['Membrane'] = df['Membrane'].astype('float32') df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']] train = df.sample(frac=0.8) df = df.drop(train.index) val = df.sample(frac=0.5) test = df.drop(val.index) train = train.reset_index(drop=True) val = val.reset_index(drop=True) test = test.reset_index(drop=True) train.to_parquet('deeploc-train.parquet', index=False) val.to_parquet('deploc-val.parquet', index=False) test.to_parquet('deeploc-test.parquet', index=False) ``` ## Labels {'Cell membrane': 0, 'Cytoplasm': 1, 'Endoplasmic reticulum': 2, 'Extracellular': 3, 'Golgi apparatus': 4, 'Lysosome/Vacuole': 5, 'Mitochondrion': 6, 'Nucleus': 7, 'Peroxisome': 8, 'Plastid': 9} ## Citation **DeepLoc-2.0:** ``` Vineet Thumuluri and others, DeepLoc 2.0: multi-label subcellular localization prediction using protein language models, Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W228–W234, https://doi.org/10.1093/nar/gkac278 ``` The DeepLoc data is a derivative of the UniProt dataset: **UniProt** ``` The UniProt Consortium UniProt: the Universal Protein Knowledgebase in 2023 Nucleic Acids Res. 51:D523–D531 (2023) ```
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mocha
2022-11-18T21:29:45.000Z
[ "task_categories:question-answering", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "generative-reading-comprehension-metric", "region:us" ]
null
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human judgement scores.
@inproceedings{Chen2020MOCHAAD, author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics}, booktitle={EMNLP}, year={2020} }
2
283
2022-03-02T23:29:22
--- pretty_name: MOCHA annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: mocha tags: - generative-reading-comprehension-metric dataset_info: features: - name: constituent_dataset dtype: string - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: reference dtype: string - name: candidate dtype: string - name: score dtype: float32 - name: metadata struct: - name: scores sequence: int32 - name: source dtype: string - name: candidate2 dtype: string - name: score2 dtype: float32 splits: - name: train num_bytes: 33292592 num_examples: 31069 - name: validation num_bytes: 4236883 num_examples: 4009 - name: test num_bytes: 6767409 num_examples: 6321 - name: minimal_pairs num_bytes: 193560 num_examples: 200 download_size: 14452311 dataset_size: 44490444 --- # Dataset Card for Mocha ## 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:** [Mocha](https://allennlp.org/mocha) - **Repository:** [https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA) - **Paper:** [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores. ### Data Fields - `constituent_dataset`: the original QA dataset which the data instance came from. - `id` - `context`: the passage content. - `question`: the question related to the passage content. - `reference`: the correct answer for the question. - `candidate`: the answer generated from the `reference` by `source` - `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1` - `metadata`: Not included in minimal pairs split. - `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]` - `source`: the generative model to generate the `candidate` In minimal pairs, we'll have an additional candidate for robust evaluation. - `candidate2` - `score2` ### Data Splits Dataset Split | Number of Instances in Split --------------|-------------------------------------------- Train | 31,069 Validation | 4,009 Test | 6,321 Minimal Pairs | 200 ## 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 [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) ### Citation Information ```bitex @inproceedings{Chen2020MOCHAAD, author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics}, booktitle={EMNLP}, year={2020} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
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DDSC/angry-tweets
2023-07-20T00:34:34.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0", "region:us" ]
DDSC
null
null
1
283
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - found language: - da license: - cc-by-4.0 multilinguality: - monolingual pretty_name: AngryTweets size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for AngryTweets ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Paper:** https://aclanthology.org/2021.nodalida-main.53/ - **Direct Download**: https://danlp-downloads.alexandra.dk/datasets/game_tweets.zip ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis through crowd-sourcing. All credits go to the authors of the following paper, who created the dataset: [Pauli, Amalie Brogaard, et al. "DaNLP: An open-source toolkit for Danish Natural Language Processing." Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). 2021](https://aclanthology.org/2021.nodalida-main.53/) ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The tweet content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 2,437 tweets in the training split and 1,047 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2021.nodalida-main.53/): Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix and Rasmus Hvingelby. The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @inproceedings{pauli2021danlp, title={DaNLP: An open-source toolkit for Danish Natural Language Processing}, author={Pauli, Amalie Brogaard and Barrett, Maria and Lacroix, Oph{\'e}lie and Hvingelby, Rasmus}, booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)}, pages={460--466}, year={2021} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.
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lccc
2022-11-18T22:07:56.000Z
[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:zh", "license:mit", "arxiv:2008.03946", "region:us" ]
null
LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
@inproceedings{wang2020chinese, title={A Large-Scale Chinese Short-Text Conversation Dataset}, author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie}, booktitle={NLPCC}, year={2020}, url={https://arxiv.org/abs/2008.03946} }
13
283
2022-06-14T18:05:32
--- annotations_creators: - other language_creators: - other language: - zh license: - mit multilinguality: - monolingual paperswithcode_id: lccc pretty_name: 'LCCC: Large-scale Cleaned Chinese Conversation corpus' size_categories: - 10M<n<100M source_datasets: - original task_categories: - conversational task_ids: - dialogue-generation dataset_info: - config_name: large features: - name: dialog list: string splits: - name: train num_bytes: 1530827965 num_examples: 12007759 download_size: 607605643 dataset_size: 1530827965 - config_name: base features: - name: dialog list: string splits: - name: train num_bytes: 932634902 num_examples: 6820506 - name: test num_bytes: 1498216 num_examples: 10000 - name: validation num_bytes: 2922731 num_examples: 20000 download_size: 371475095 dataset_size: 937055849 --- # Dataset Card for LCCC ## Table of Contents - [Dataset Card for LCCC](#dataset-card-for-lccc) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [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 - **Repository:** https://github.com/thu-coai/CDial-GPT - **Paper:** https://arxiv.org/abs/2008.03946 ### Dataset Summary LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered. LCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。 ### Supported Tasks and Leaderboards - dialogue-generation: The dataset can be used to train a model for generating dialogue responses. - response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model. ### Languages LCCC is in Chinese LCCC中的对话是中文的 ## Dataset Structure ### Data Instances ```json { "dialog": ["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"] } ``` ### Data Fields - `dialog` (list of strings): List of utterances consisting of a dialogue. ### Data Splits We do not provide the offical split for LCCC-large. But we provide a split for LCCC-base: |train|valid|test| |---:|---:|---:| |6,820,506 | 20,000 | 10,000| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT License Copyright (c) 2020 lemon234071 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. ### Citation Information ```bibtex @inproceedings{wang2020chinese, title={A Large-Scale Chinese Short-Text Conversation Dataset}, author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie}, booktitle={NLPCC}, year={2020}, url={https://arxiv.org/abs/2008.03946} } ``` ### Contributions Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.
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gaodrew/roco-65k-256px
2023-08-05T12:07:37.000Z
[ "region:us" ]
gaodrew
null
null
0
283
2023-08-05T11:30:11
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 675508431.156 num_examples: 65418 download_size: 651136006 dataset_size: 675508431.156 --- # Dataset Card for "roco-65k-256px" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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Narsil/asr_dummy
2023-03-30T14:10:15.000Z
[ "region:us" ]
Narsil
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```
@article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
0
282
2022-03-02T23:29:22
Entry not found
15
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datadrivenscience/ship-detection
2023-03-02T16:09:14.000Z
[ "task_categories:object-detection", "region:us" ]
datadrivenscience
null
null
14
282
2023-03-01T16:38:16
--- task_categories: - object-detection --- # Dataset Card for Ship Detection Link to [Ship Detection Competition](https://huggingface.co/spaces/competitions/ship-detection) By accepting this dataset, you accept the rules of the Ship Detection competition. # Organizer Organizer of this competition is [Data-Driven Science](https://datadrivenscience.com/). <img src="https://datadrivenscience.com/wp-content/uploads/2022/12/DDS-Logo.png" width="200" height="100"> # Email Usage By accepting this dataset, you consent that your email will be used for communication purposes from Data-Driven Science. We do not share nor sell our mailing list. Your information remains confidential. You may unsubscribe at any time.
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usvsnsp/pile-test-sampled
2023-09-07T16:56:07.000Z
[ "region:us" ]
usvsnsp
null
null
0
282
2023-09-07T16:56:00
--- dataset_info: features: - name: sequence_id dtype: int64 - name: memorization_score dtype: float64 - name: tokens sequence: int64 splits: - name: train num_bytes: 53200 num_examples: 100 download_size: 23383 dataset_size: 53200 --- # Dataset Card for "pile-test-sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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result-kand2-sdxl-wuerst-karlo/53f478ab
2023-10-06T00:13:20.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
0
282
2023-10-06T00:13:19
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 257 num_examples: 10 download_size: 1433 dataset_size: 257 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "53f478ab" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
455
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woz_dialogue
2023-06-01T14:59:51.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:multi-class-classification", "task_ids:parsing", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "language:en", "language:it", "license:unknown", "arxiv:1604.04562", "region:us" ]
null
Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered.
@misc{wen2017networkbased, title={A Network-based End-to-End Trainable Task-oriented Dialogue System}, author={Tsung-Hsien Wen and David Vandyke and Nikola Mrksic and Milica Gasic and Lina M. Rojas-Barahona and Pei-Hao Su and Stefan Ultes and Steve Young}, year={2017}, eprint={1604.04562}, archivePrefix={arXiv}, primaryClass={cs.CL} }
3
281
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - de - en - it license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask - token-classification - text-classification task_ids: - dialogue-modeling - multi-class-classification - parsing paperswithcode_id: wizard-of-oz pretty_name: Wizard-of-Oz dataset_info: - config_name: en features: - name: dialogue_idx dtype: int32 - name: dialogue list: - name: turn_label sequence: sequence: string - name: asr sequence: sequence: string - name: system_transcript dtype: string - name: turn_idx dtype: int32 - name: belief_state list: - name: slots sequence: sequence: string - name: act dtype: string - name: transcript dtype: string - name: system_acts sequence: sequence: string splits: - name: train num_bytes: 827189 num_examples: 600 - name: validation num_bytes: 265684 num_examples: 200 - name: test num_bytes: 537557 num_examples: 400 download_size: 7529221 dataset_size: 1630430 - config_name: de features: - name: dialogue_idx dtype: int32 - name: dialogue list: - name: turn_label sequence: sequence: string - name: asr sequence: sequence: string - name: system_transcript dtype: string - name: turn_idx dtype: int32 - name: belief_state list: - name: slots sequence: sequence: string - name: act dtype: string - name: transcript dtype: string - name: system_acts sequence: sequence: string splits: - name: train num_bytes: 881478 num_examples: 600 - name: validation num_bytes: 276758 num_examples: 200 - name: test num_bytes: 569703 num_examples: 400 download_size: 7626734 dataset_size: 1727939 - config_name: de_en features: - name: dialogue_idx dtype: int32 - name: dialogue list: - name: turn_label sequence: sequence: string - name: asr sequence: sequence: string - name: system_transcript dtype: string - name: turn_idx dtype: int32 - name: belief_state list: - name: slots sequence: sequence: string - name: act dtype: string - name: transcript dtype: string - name: system_acts sequence: sequence: string splits: - name: train num_bytes: 860151 num_examples: 600 - name: validation num_bytes: 269966 num_examples: 200 - name: test num_bytes: 555841 num_examples: 400 download_size: 7584753 dataset_size: 1685958 - config_name: it features: - name: dialogue_idx dtype: int32 - name: dialogue list: - name: turn_label sequence: sequence: string - name: asr sequence: sequence: string - name: system_transcript dtype: string - name: turn_idx dtype: int32 - name: belief_state list: - name: slots sequence: sequence: string - name: act dtype: string - name: transcript dtype: string - name: system_acts sequence: sequence: string splits: - name: train num_bytes: 842799 num_examples: 600 - name: validation num_bytes: 270258 num_examples: 200 - name: test num_bytes: 547759 num_examples: 400 download_size: 7559615 dataset_size: 1660816 - config_name: it_en features: - name: dialogue_idx dtype: int32 - name: dialogue list: - name: turn_label sequence: sequence: string - name: asr sequence: sequence: string - name: system_transcript dtype: string - name: turn_idx dtype: int32 - name: belief_state list: - name: slots sequence: sequence: string - name: act dtype: string - name: transcript dtype: string - name: system_acts sequence: sequence: string splits: - name: train num_bytes: 845095 num_examples: 600 - name: validation num_bytes: 270942 num_examples: 200 - name: test num_bytes: 548979 num_examples: 400 download_size: 7563815 dataset_size: 1665016 config_names: - de - de_en - en - it - it_en --- # Dataset Card for Wizard-of-Oz ## 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:** [More info needed] - **Repository:** [GitHub](https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz) - **Paper:** [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://arxiv.org/abs/1604.04562) - **Leaderboard:** [More info needed] - **Point of Contact:** [More info needed] ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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cyrilzhang/wiki-bpe-32k
2023-09-22T16:02:48.000Z
[ "region:us" ]
cyrilzhang
null
null
0
281
2023-09-22T15:56:45
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 21123228700 num_examples: 5152007 - name: test num_bytes: 212326700 num_examples: 51787 download_size: 10331372531 dataset_size: 21335555400 --- # Dataset Card for "wiki-bpe-32k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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nulltella/bbc-articles-finetuning-classif
2023-09-28T18:19:59.000Z
[ "region:us" ]
nulltella
null
null
0
281
2023-09-23T18:06:44
Entry not found
15
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anton-l/superb
2022-07-04T10:48:08.000Z
[ "task_ids:keyword-spotting", "task_ids:speaker-identification", "task_ids:intent-classification", "task_ids:slot-filling", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "source_datasets:extended|librispeech_asr", "source_datasets:extended|other-librimix", "source_datasets:extended|other-speech_commands", "language:en", "license:unknown", "arxiv:2105.01051", "region:us" ]
anton-l
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```
@article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
1
280
2022-03-02T23:29:22
--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual pretty_name: SUPERB size_categories: - unknown source_datasets: - original - extended|librispeech_asr - extended|other-librimix - extended|other-speech_commands task_categories: - speech-processing task_ids: - automatic-speech-recognition - phoneme-recognition - keyword-spotting - query-by-example-spoken-term-detection - speaker-identification - automatic-speaker-verification - speaker-diarization - intent-classification - slot-filling - emotion-recognition --- # Dataset Card for SUPERB ## 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:** [http://superbbenchmark.org](http://superbbenchmark.org) - **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl) - **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [Lewis Tunstall](mailto:lewis@huggingface.co) and [Albert Villanova](mailto:albert@huggingface.co) ### Dataset Summary SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. ### Supported Tasks and Leaderboards The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks: #### pr Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER). #### asr Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER). #### ks Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC) ##### Example of usage: Use these auxillary functions to: - load the audio file into an audio data array - sample from long `_silence_` audio clips For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80) or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations. ```python def map_to_array(example): import soundfile as sf speech_array, sample_rate = sf.read(example["file"]) example["speech"] = speech_array example["sample_rate"] = sample_rate return example def sample_noise(example): # Use this function to extract random 1 sec slices of each _silence_ utterance, # e.g. inside `torch.utils.data.Dataset.__getitem__()` from random import randint if example["label"] == "_silence_": random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1) example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]] return example ``` #### qbe Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms. #### ic Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC). #### sf Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing. #### si Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC). #### asv Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER). #### sd Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER). ##### Example of usage Use these auxiliary functions to: - load the audio file into an audio data array - generate the label array ```python def load_audio_file(example, frame_shift=160): import soundfile as sf example["array"], example["sample_rate"] = sf.read( example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift ) return example def generate_label(example, frame_shift=160, num_speakers=2, rate=16000): import numpy as np start = example["start"] end = example["end"] frame_num = end - start speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]}) label = np.zeros((frame_num, num_speakers), dtype=np.int32) for speaker in example["speakers"]: speaker_index = speakers.index(speaker["speaker_id"]) start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int) end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int) rel_start = rel_end = None if start <= start_frame < end: rel_start = start_frame - start if start < end_frame <= end: rel_end = end_frame - start if rel_start is not None or rel_end is not None: label[rel_start:rel_end, speaker_index] = 1 example["label"] = label return example ``` #### er Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC). ### Languages The language data in SUPERB is in English (BCP-47 `en`) ## Dataset Structure ### Data Instances #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr An example from each split looks like: ```python {'chapter_id': 1240, 'file': 'path/to/file.flac', 'audio': {'path': 'path/to/file.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '103-1240-0000', 'speaker_id': 103, 'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE ' 'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE ' 'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A ' 'BROOK'} ``` #### ks An example from each split looks like: ```python { 'file': '/path/yes/af7a8296_nohash_1.wav', 'audio': {'path': '/path/yes/af7a8296_nohash_1.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 0 # 'yes' } ``` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic ```python { 'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav", 'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'speaker_id': '2BqVo8kVB2Skwgyb', 'text': 'Turn the bedroom lights off', 'action': 3, # 'deactivate' 'object': 7, # 'lights' 'location': 0 # 'bedroom' } ``` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si ```python { 'file': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 2 # 'id10003' } ``` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd An example from each split looks like: ```python { 'record_id': '1578-6379-0038_6415-111615-0009', 'file': 'path/to/file.wav', 'audio': {'path': 'path/to/file.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'start': 0, 'end': 1590, 'speakers': [ {'speaker_id': '1578', 'start': 28, 'end': 657}, {'speaker_id': '6415', 'start': 28, 'end': 1576} ] } ``` #### er [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields ####Note abouth the `audio` fields When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `text` (`string`): The transcription of the audio file. - `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples. - `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription. - `id` (`string`): A unique ID of the data sample. #### ks - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the spoken command. Possible values: - `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `speaker_id` (`string`): ID of the speaker. - `text` (`string`): Transcription of the spoken command. - `action` (`ClassLabel`): Label of the command's action. Possible values: - `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"` - `object` (`ClassLabel`): Label of the command's object. Possible values: - `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"` - `location` (`ClassLabel`): Label of the command's location. Possible values: - `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label (ID) of the speaker. Possible values: - `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data fields in all splits are: - `record_id` (`string`): ID of the record. - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `start` (`integer`): Start frame of the audio. - `end` (`integer`): End frame of the audio. - `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields: - `speaker_id` (`string`): ID of the speaker. - `start` (`integer`): Frame when the speaker starts speaking. - `end` (`integer`): Frame when the speaker stops speaking. #### er - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the speech emotion. Possible values: - `0: "neu", 1: "hap", 2: "ang", 3: "sad"` ### Data Splits #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr | | train | validation | test | |-----|------:|-----------:|-----:| | asr | 28539 | 2703 | 2620 | #### ks | | train | validation | test | |----|------:|-----------:|-----:| | ks | 51094 | 6798 | 3081 | #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic | | train | validation | test | |----|------:|-----------:|-----:| | ic | 23132 | 3118 | 3793 | #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si | | train | validation | test | |----|-------:|-----------:|-----:| | si | 138361 | 6904 | 8251 | #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data is split into "train", "dev" and "test" sets, each containing the following number of examples: | | train | dev | test | |----|------:|-----:|-----:| | sd | 13901 | 3014 | 3002 | #### er The data is split into 5 sets intended for 5-fold cross-validation: | | session1 | session2 | session3 | session4 | session5 | |----|---------:|---------:|---------:|---------:|---------:| | er | 1085 | 1023 | 1151 | 1031 | 1241 | ## 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 ``` @article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } Note that each SUPERB dataset has its own citation. Please see the source to see the correct citation for each contained dataset. ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.
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mozilla-foundation/common_voice_8_0
2023-07-29T16:00:11.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]
mozilla-foundation
null
@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }
25
280
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - 10K<n<100K ar: - 100K<n<1M as: - n<1K az: - n<1K ba: - 100K<n<1M bas: - 1K<n<10K be: - 100K<n<1M bg: - 1K<n<10K br: - 10K<n<100K ca: - 100K<n<1M ckb: - 10K<n<100K cnh: - 1K<n<10K cs: - 10K<n<100K cv: - 10K<n<100K cy: - 100K<n<1M da: - 1K<n<10K de: - 100K<n<1M dv: - 10K<n<100K el: - 10K<n<100K en: - 1M<n<10M eo: - 1M<n<10M es: - 100K<n<1M et: - 10K<n<100K eu: - 100K<n<1M fa: - 100K<n<1M fi: - 10K<n<100K fr: - 100K<n<1M fy-NL: - 10K<n<100K ga-IE: - 1K<n<10K gl: - 10K<n<100K gn: - 1K<n<10K ha: - 1K<n<10K hi: - 10K<n<100K hsb: - 1K<n<10K hu: - 10K<n<100K hy-AM: - 1K<n<10K ia: - 10K<n<100K id: - 10K<n<100K ig: - n<1K it: - 100K<n<1M ja: - 10K<n<100K ka: - 1K<n<10K kab: - 100K<n<1M kk: - 1K<n<10K kmr: - 10K<n<100K ky: - 10K<n<100K lg: - 100K<n<1M lt: - 10K<n<100K lv: - 1K<n<10K mdf: - n<1K mk: - n<1K ml: - 1K<n<10K mn: - 10K<n<100K mr: - 1K<n<10K mt: - 10K<n<100K myv: - 1K<n<10K nl: - 10K<n<100K nn-NO: - n<1K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 100K<n<1M rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 10K<n<100K ru: - 100K<n<1M rw: - 1M<n<10M sah: - 1K<n<10K sat: - n<1K sk: - 10K<n<100K sl: - 10K<n<100K sr: - 1K<n<10K sv-SE: - 10K<n<100K sw: - 100K<n<1M ta: - 100K<n<1M th: - 100K<n<1M tr: - 10K<n<100K tt: - 10K<n<100K ug: - 10K<n<100K uk: - 10K<n<100K ur: - 1K<n<10K uz: - 100K<n<1M vi: - 10K<n<100K vot: - n<1K zh-CN: - 10K<n<100K zh-HK: - 100K<n<1M zh-TW: - 10K<n<100K source_datasets: - extended|common_voice paperswithcode_id: common-voice pretty_name: Common Voice Corpus 8.0 language_bcp47: - ab - ar - as - az - ba - bas - be - bg - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - gl - gn - ha - hi - hsb - hu - hy-AM - ia - id - ig - it - ja - ka - kab - kk - kmr - ky - lg - lt - lv - mdf - mk - ml - mn - mr - mt - myv - nl - nn-NO - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sat - sk - sl - sr - sv-SE - sw - ta - th - tr - tt - ug - uk - ur - uz - vi - vot - zh-CN - zh-HK - zh-TW extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset. task_categories: - automatic-speech-recognition --- # Dataset Card for Common Voice Corpus 8.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 18243 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 14122 validated hours in 87 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Breton, Bulgarian, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh ``` ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```
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yxchar/rct-20k-tlm
2021-11-05T01:18:46.000Z
[ "region:us" ]
yxchar
null
null
0
280
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lighteval/bbq_helm
2023-05-03T08:23:41.000Z
[ "region:us" ]
lighteval
null
@article{DBLP:journals/corr/abs-2110-08193, author = {Alicia Parrish and Angelica Chen and Nikita Nangia and Vishakh Padmakumar and Jason Phang and Jana Thompson and Phu Mon Htut and Samuel R. Bowman}, title = {{BBQ:} {A} Hand-Built Bias Benchmark for Question Answering}, journal = {CoRR}, volume = {abs/2110.08193}, year = {2021}, url = {https://arxiv.org/abs/2110.08193}, eprinttype = {arXiv}, eprint = {2110.08193}, timestamp = {Fri, 22 Oct 2021 13:33:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2110-08193.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
2
280
2023-05-03T08:01:49
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JetBrains-Research/commit-chronicle
2023-10-05T10:50:00.000Z
[ "task_categories:text-generation", "task_categories:summarization", "size_categories:1M<n<10M", "language:code", "language:en", "license:other", "code", "commit_message_generation", "arxiv:2308.07655", "region:us" ]
JetBrains-Research
null
null
2
280
2023-08-08T15:54:44
--- license: other language: - code - en task_categories: - text-generation - summarization tags: - code - commit_message_generation pretty_name: CommitChronicle size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 5760117409 num_examples: 1486267 - name: train num_bytes: 30084265848 num_examples: 7659458 - name: validation num_bytes: 5905326070 num_examples: 1554042 download_size: 14168436205 dataset_size: 41749709327 - config_name: subset_cmg features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 772774959 num_examples: 204336 download_size: 258151047 dataset_size: 772774959 - config_name: subset_llm features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 15121048 num_examples: 4025 download_size: 5068039 dataset_size: 15121048 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* - config_name: subset_cmg data_files: - split: test path: subset_cmg/test-* - config_name: subset_llm data_files: - split: test path: subset_llm/test-* --- # 📜 CommitChronicle 🔮 This is the dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023. Its key features: * *large-scale and multilingual*: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages; * *diverse*: avoids restrictive filtering on commit messages or commit diffs structure; * *suitable for experiments with commit history*: provides metadata about commit authors and dates and uses split-by-project. ## Dataset Creation > 🔍 For further details, please refer to: > * **Paper**: [https://arxiv.org/abs/2308.07655](https://arxiv.org/abs/2308.07655) > * **Repository**: [https://github.com/JetBrains-Research/commit_message_generation](https://github.com/JetBrains-Research/commit_message_generation) We used [GitHub Search](https://seart-ghs.si.usi.ch/) tool and official GitHub API to select relevant repositories with permissive licenses (Apache, BSD 3-clause, MIT). On February 9th, 2023, we collected all commits made since 2017 from these repositories via [PyDriller](https://github.com/ishepard/pydriller). Next, we extensively cleaned the data, including filtering outliers, dropping commits from bot authors, and dropping duplicates. Note: to avoid disclosing personal information, we replaced the commit authors' names and emails with unique identifiers. ## Dataset Structure ### Data Instances Each data instance in the dataset is a commit. [A commit example](https://github.com/saridormi/commit_chronicle/commit/a7fb3b64184f0af5b08285cce14b9139baa94049) would look like the following: ``` { 'repo': 'saridormi/commit_chronicle', 'hash': 'a7fb3b64184f0af5b08285cce14b9139baa94049', 'author': 123, 'date': '05.07.2021 15:10:07', 'timezone': 0, 'license': 'MIT License', 'language': 'Jupyter Notebook', 'message': 'Add license badge to readme', 'original_message': 'Add license badge to readme', 'mods': [{'change_type': 'MODIFY', 'new_path': 'README.md', 'old_path': 'README.md' 'diff': '@@ -1,6 +1,6 @@\n' ' # Commits dataset\n' ' \n' '-> :heavy_exclamation_mark: **TODO:** license\n' '+![GitHub](https://img.shields.io/github/license/saridormi/commits_dataset?style=for-the-badge)\n'}], } ``` ### Data Fields Each example has the following fields: | **Field** | **Description** | |:------------------:|:----------------------------------------:| | `repo` | Commit repository. | | `hash` | Commit hash. | | `author` | Unique id for commit author | | `date` | Commit date (from author). | | `timezone` | Commit timezone (from author). | | `license` | Commit repository's license. | | `language` | Commit repository's main language. | | `message` | Commit message (after processing). | | `original_message` | Commit message (without any processing). | | `mods` | List of file modifications from commit. | Each file modification has the following fields: | **Field** | **Description** | |:-------------:|:-------------------------------------------------------------------------------------------------:| | `change_type` | Type of change to current file. One of: `ADD`, `COPY`, `RENAME`, `DELETE`, `MODIFY` or `UNKNOWN`. | | `old_path` | Path to file before change (might be empty). | | `new_path` | Path to file after change (might be empty). | | `diff` | `git diff` for current file. | ### Data Splits We provide the following configurations: * `default` * `train`: full training split (7.66M commits) * `validation`: full validation split (1.55M commits) * `test`: full test split (1.49M commits) * `subset_cmg` * `test`: test subset used for experiments with CMG approaches (204k commits) * `subset_llm` * `test`: test subset used for experiments with a LLM (4k commits) ## Considerations for Using the Data > Adopted from [the Stack](https://huggingface.co/datasets/bigcode/the-stack). The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their open-access research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. The dataset is a collection of commits from repositories with various licenses. Any use of all or part of the code gathered in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. ## Citation ``` TODO ```
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mstz/diamonds
2023-04-16T17:27:20.000Z
[ "task_categories:tabular-classification", "size_categories:10K<n<100K", "language:en", "license:cc", "student performance", "tabular_classification", "multiclass_classification", "UCI", "region:us" ]
mstz
null
null
0
279
2023-03-24T01:12:26
--- language: - en tags: - student performance - tabular_classification - multiclass_classification - UCI pretty_name: Diamond size_categories: - 10K<n<100K task_categories: - tabular-classification configs: - encoding - cut - cut_binary license: cc --- # Diamonds The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) from Kaggle. Dataset collecting properties of cut diamonds to determine the cut quality. # Configurations and tasks | **Configuration** | **Task** | Description | |-------------------|---------------------------|-----------------------------------------------------------------| | encoding | | Encoding dictionary showing original values of encoded features.| | cut | Multiclass classification | Predict the cut quality of the diamond. | | cut_binary | Binary classification | Is the cut quality at least very good?| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/diamonds", "cut")["train"] ``` # Features |**Feature** |**Description**| |-----------------------------------|---------------| |`carat` | `float32` | |`color` | `string` | |`clarity` | `float32` | |`depth` | `float32` | |`table` | `float32` | |`price` | `float32` | |`observation_point_on_axis_x` | `float32` | |`observation_point_on_axis_y` | `float32` | |`observation_point_on_axis_z` | `float32` | |`cut` | `int8` |
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result-kand2-sdxl-wuerst-karlo/7e27d622
2023-10-06T03:10:37.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
0
279
2023-10-06T03:10:36
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 232 num_examples: 10 download_size: 1424 dataset_size: 232 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7e27d622" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
455
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allenai/multi_lexsum
2023-05-18T21:41:22.000Z
[ "task_categories:summarization", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:odc-by", "arxiv:2206.10883", "region:us" ]
allenai
Multi-LexSum is a multi-doc summarization dataset for civil rights litigation lawsuits with summaries of three granularities.
@article{Shen2022MultiLexSum, author = {Zejiang Shen and Kyle Lo and Lauren Yu and Nathan Dahlberg and Margo Schlanger and Doug Downey}, title = {Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities}, journal = {CoRR}, volume = {abs/2206.10883}, year = {2022}, url = {https://doi.org/10.48550/arXiv.2206.10883}, doi = {10.48550/arXiv.2206.10883} }
12
278
2022-08-03T15:51:10
--- annotations_creators: - expert-generated language: - en language_creators: - found license: - odc-by multilinguality: - monolingual pretty_name: Multi-LexSum size_categories: - 1K<n<10K - 10K<n<100K source_datasets: - original tags: [] task_categories: - summarization task_ids: [] --- # Dataset Card for Multi-LexSum ## Table of Contents - [Dataset Card for Multi-LexSum](#dataset-card-for-multi-lexsum) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset](#dataset) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Sheet (Datasheet)](#dataset-sheet-datasheet) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Release History](#release-history) ## Dataset Description - **Homepage:** https://multilexsum.github.io - **Repository:** https://github.com/multilexsum/dataset - **Paper:** https://arxiv.org/abs/2206.10883 <p> <a href="https://multilexsum.github.io" style="display: inline-block;"> <img src="https://img.shields.io/badge/-homepage-informational.svg?logo=jekyll" title="Multi-LexSum Paper" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> <a href="https://github.com/multilexsum/dataset" style="display: inline-block;"> <img src="https://img.shields.io/badge/-multilexsum-lightgrey.svg?logo=github" title="Multi-LexSum Github Repo" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> <a href="https://arxiv.org/abs/2206.10883" style="display: inline-block;"> <img src="https://img.shields.io/badge/NeurIPS-2022-9cf" title="Multi-LexSum is accepted in NeurIPS 2022" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a> </p> ### Talk @ NeurIPS 2022 [![Watch the video](https://img.youtube.com/vi/C-fwW_ZhkE8/0.jpg)](https://youtu.be/C-fwW_ZhkE8) ### Dataset Summary The Multi-LexSum dataset is a collection of 9,280 such legal case summaries. Multi-LexSum is distinct from other datasets in its **multiple target summaries, each at a different granularity** (ranging from one-sentence “extreme” summaries to multi-paragraph narrations of over five hundred words). It presents a challenging multi-document summarization task given **the long length of the source documents**, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of **expert-authored summaries**: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality. ### Languages English ## Dataset ### Data Fields The dataset contains a list of instances (cases); each instance contains the following data: | Field | Description | | ------------: | -------------------------------------------------------------------------------: | | id | `(str)` The case ID | | sources | `(List[str])` A list of strings for the text extracted from the source documents | | summary/long | `(str)` The long (multi-paragraph) summary for this case | | summary/short | `(Optional[str])` The short (one-paragraph) summary for this case | | summary/tiny | `(Optional[str])` The tiny (one-sentence) summary for this case | Please check the exemplar usage below for loading the data: ```python from datasets import load_dataset multi_lexsum = load_dataset("allenai/multi_lexsum", name="v20230518") # Download multi_lexsum locally and load it as a Dataset object example = multi_lexsum["validation"][0] # The first instance of the dev set example["sources"] # A list of source document text for the case for sum_len in ["long", "short", "tiny"]: print(example["summary/" + sum_len]) # Summaries of three lengths print(example['case_metadata']) # The corresponding metadata for a case in a dict ``` ### Data Splits | | Instances | Source Documents (D) | Long Summaries (L) | Short Summaries (S) | Tiny Summaries (T) | Total Summaries | | ----------: | --------: | -------------------: | -----------------: | ------------------: | -----------------: | --------------: | | Train (70%) | 3,177 | 28,557 | 3,177 | 2,210 | 1,130 | 6,517 | | Test (20%) | 908 | 7,428 | 908 | 616 | 312 | 1,836 | | Dev (10%) | 454 | 4,134 | 454 | 312 | 161 | 927 | ## Dataset Sheet (Datasheet) Please check our [dataset sheet](https://multilexsum.github.io/datasheet) for details regarding dataset creation, source data, annotation, and considerations for the usage. ## Additional Information ### Dataset Curators The dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation. ### Licensing Information The Multi-LexSum dataset is distributed under the [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/). The case summaries and metadata are licensed under the [Creative Commons Attribution License (CC BY-NC)](https://creativecommons.org/licenses/by-nc/4.0/), and the source documents are already in the public domain. Commercial users who desire a license for summaries and metadata can contact [info@clearinghouse.net](mailto:info@clearinghouse.net), which will allow free use but limit summary re-posting. The corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0. ### Citation Information ``` @article{Shen2022MultiLexSum, author = {Zejiang Shen and Kyle Lo and Lauren Yu and Nathan Dahlberg and Margo Schlanger and Doug Downey}, title = {Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities}, journal = {CoRR}, volume = {abs/2206.10883}, year = {2022},**** url = {https://doi.org/10.48550/arXiv.2206.10883}, doi = {10.48550/arXiv.2206.10883} } ``` ## Release History | Version | Description | | ----------: | -----------------------------------------------------------: | | `v20230518` | The v1.1 release including case and source document metadata | | `v20220616` | The initial v1.0 release |
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Francesco/people-in-paintings
2023-03-30T09:37:23.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]
Francesco
null
null
0
278
2023-03-30T09:36:52
--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': people-in-paintings '1': Human annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: people-in-paintings tags: - rf100 --- # Dataset Card for people-in-paintings ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/people-in-paintings - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary people-in-paintings ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/people-in-paintings ### Citation Information ``` @misc{ people-in-paintings, title = { people in paintings Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/people-in-paintings } }, url = { https://universe.roboflow.com/object-detection/people-in-paintings }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
3,408
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C-MTEB/STSB
2023-07-28T13:40:47.000Z
[ "region:us" ]
C-MTEB
null
null
0
278
2023-07-28T13:40:34
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: int32 splits: - name: train num_bytes: 639550 num_examples: 5231 - name: validation num_bytes: 197381 num_examples: 1458 - name: test num_bytes: 158230 num_examples: 1361 download_size: 682182 dataset_size: 995161 --- # Dataset Card for "STSB" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
719
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vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing
2023-10-25T14:52:30.000Z
[ "region:us" ]
vwxyzjn
null
null
0
278
2023-10-19T17:37:41
--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 splits: - name: train num_bytes: 984401845 num_examples: 116722 - name: validation num_bytes: 54382429 num_examples: 6447 - name: test num_bytes: 55293071 num_examples: 6553 download_size: 350302087 dataset_size: 1094077345 --- # Dataset Card for "summarize_from_feedback_tldr_3_filtered_oai_preprocessing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
855
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bsd_ja_en
2022-11-18T19:24:36.000Z
[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:ja", "license:cc-by-nc-sa-4.0", "business-conversations-translation", "region:us" ]
null
This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scenarios. The dataset was constructed in 3 steps: 1) selecting business scenes, 2) writing monolingual conversation scenarios according to the selected scenes, and 3) translating the scenarios into the other language. Half of the monolingual scenarios were written in Japanese and the other half were written in English. Fields: - id: dialogue identifier - no: sentence pair number within a dialogue - en_speaker: speaker name in English - ja_speaker: speaker name in Japanese - en_sentence: sentence in English - ja_sentence: sentence in Japanese - original_language: language in which monolingual scenario was written - tag: scenario - title: scenario title
@inproceedings{rikters-etal-2019-designing, title = "Designing the Business Conversation Corpus", author = "Rikters, Matīss and Ri, Ryokan and Li, Tong and Nakazawa, Toshiaki", booktitle = "Proceedings of the 6th Workshop on Asian Translation", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5204", doi = "10.18653/v1/D19-5204", pages = "54--61" }
4
277
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - ja license: - cc-by-nc-sa-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: business-scene-dialogue pretty_name: Business Scene Dialogue tags: - business-conversations-translation dataset_info: features: - name: id dtype: string - name: tag dtype: string - name: title dtype: string - name: original_language dtype: string - name: 'no' dtype: int32 - name: en_speaker dtype: string - name: ja_speaker dtype: string - name: en_sentence dtype: string - name: ja_sentence dtype: string splits: - name: train num_bytes: 4778409 num_examples: 20000 - name: test num_bytes: 493038 num_examples: 2120 - name: validation num_bytes: 477964 num_examples: 2051 download_size: 8135045 dataset_size: 5749411 --- # Dataset Card for Business Scene Dialogue ## 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://raw.githubusercontent.com/tsuruoka-lab/BSD/) - **Repository:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/) - **Paper:** [Rikters et al., 2019](https://www.aclweb.org/anthology/D19-5204) - **Leaderboard:** - **Point of Contact:** Matīss Rikters ### Dataset Summary This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scenarios. The dataset was constructed in 3 steps: 1) selecting business scenes, 2) writing monolingual conversation scenarios according to the selected scenes, and 3) translating the scenarios into the other language. Half of the monolingual scenarios were written in Japanese and the other half were written in English. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, Japanese. ## Dataset Structure ### Data Instances Each instance contains a conversation identifier, a sentence number that indicates its position within the conversation, speaker name in English and Japanese, text in English and Japanese, original language, scene of the scenario (tag), and title of the scenario (title). ```python { "id": "190315_E004_13", "no": 14, "speaker": "Mr. Sam Lee", "ja_speaker": "サム リーさん", "en_sentence": "Would you guys consider a different scheme?", "ja_sentence": "別の事業案も考慮されますか？", "original_language": "en", "tag": "phone call", "title": "Phone: Review spec and scheme" } ``` ### Data Fields - id: dialogue identifier - no: sentence pair number within a dialogue - en_speaker: speaker name in English - ja_speaker: speaker name in Japanese - en_sentence: sentence in English - ja_sentence: sentence in Japanese - original_language: language in which monolingual scenario was written - tag: scenario - title: scenario title ### Data Splits - There are a total of 24171 sentences / 808 business scenarios. - Train: 20000 sentences / 670 scenarios - Dev: 2051 sentences / 69 scenarios - Test: 2120 sentences / 69 scenarios ## 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset was released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. ### Citation Information ``` @inproceedings{rikters-etal-2019-designing, title = "Designing the Business Conversation Corpus", author = "Rikters, Mat{\=\i}ss and Ri, Ryokan and Li, Tong and Nakazawa, Toshiaki", booktitle = "Proceedings of the 6th Workshop on Asian Translation", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5204", doi = "10.18653/v1/D19-5204", pages = "54--61" } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.
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euronews
2023-01-25T14:30:08.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:de", "language:fr", "language:nl", "license:cc0-1.0", "region:us" ]
null
The corpora comprise of files per data provider that are encoded in the IOB format (Ramshaw & Marcus, 1995). The IOB format is a simple text chunking format that divides texts into single tokens per line, and, separated by a whitespace, tags to mark named entities. The most commonly used categories for tags are PER (person), LOC (location) and ORG (organization). To mark named entities that span multiple tokens, the tags have a prefix of either B- (beginning of named entity) or I- (inside of named entity). O (outside of named entity) tags are used to mark tokens that are not a named entity.
@InProceedings{NEUDECKER16.110, author = {Clemens Neudecker}, title = {An Open Corpus for Named Entity Recognition in Historic Newspapers}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {may}, date = {23-28}, location = {Portorož, Slovenia}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {978-2-9517408-9-1}, language = {english} }
3
277
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - de - fr - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: europeana-newspapers pretty_name: Europeana Newspapers dataset_info: - config_name: fr-bnf features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 3340299 num_examples: 1 download_size: 1542418 dataset_size: 3340299 - config_name: nl-kb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 3104213 num_examples: 1 download_size: 1502162 dataset_size: 3104213 - config_name: de-sbb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 817295 num_examples: 1 download_size: 407756 dataset_size: 817295 - config_name: de-onb features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 502369 num_examples: 1 download_size: 271252 dataset_size: 502369 - config_name: de-lft features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC splits: - name: train num_bytes: 1263429 num_examples: 1 download_size: 677779 dataset_size: 1263429 --- # Dataset Card for Europeana Newspapers ## 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://github.com/EuropeanaNewspapers/ner-corpora) - **Repository:** [Github](https://github.com/EuropeanaNewspapers/ner-corpora) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/L16-1689/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### 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 Thanks to [@jplu](https://github.com/jplu) for adding this dataset.
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lucadiliello/wikiqa
2022-12-05T15:09:31.000Z
[ "region:us" ]
lucadiliello
null
null
0
277
2022-12-05T15:06:32
--- dataset_info: features: - name: label dtype: int64 - name: answer dtype: string - name: key dtype: int64 - name: question dtype: string splits: - name: test_clean num_bytes: 449691 num_examples: 2341 - name: dev_clean num_bytes: 214886 num_examples: 1126 - name: train num_bytes: 4017460 num_examples: 20360 - name: test num_bytes: 1208042 num_examples: 6165 - name: dev num_bytes: 530358 num_examples: 2733 download_size: 3111254 dataset_size: 6420437 --- # Dataset Card for "wikiqa" WikiQA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are `clean` versions of the original development and test sets. `clean` versions contain only questions which have at least a positive and a negative answer candidate.
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range3/wiki40b-ja
2023-02-04T05:44:21.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "language:ja", "region:us" ]
range3
null
null
5
277
2023-02-04T04:54:17
--- task_categories: - text-generation - fill-mask language: - ja --- # range3/wiki40b-ja This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code. このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。 ```py import datasets dss = datasets.load_dataset( "wiki40b", "ja", beam_runner="DirectRunner", ) for split,ds in dss.items(): ds.to_parquet(f"wikipedia-ja-20230101/{split}.parquet") ```
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Gholamreza/pquad
2023-02-18T15:00:06.000Z
[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fa", "license:cc-by-sa-4.0", "region:us" ]
Gholamreza
\\\PQuAD: PQuAD is a crowd-sourced reading comprehension dataset on Persian Language.
@article{darvishi2022pquad, title={PQuAD: A Persian Question Answering Dataset}, author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh}, journal={arXiv preprint arXiv:2202.06219}, year={2022} }
2
277
2023-02-18T14:02:25
--- pretty_name: PQuAD annotations_creators: - crowdsourced language_creators: - crowdsourced language: - fa license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - extractive-qa paperswithcode_id: squad train-eval-index: - config: pquad task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: pquad name: PQuAD dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: pquad splits: - name: train num_bytes: ... num_examples: 63994 - name: validation num_bytes: ... num_examples: 7976 - name: test num_bytes: ... num_examples: 8002 download_size: ... dataset_size: ... --- # Dataset Card for "pquad" ## PQuAD Description **THIS IS A NON-OFFICIAL VERSION OF THE DATASET UPLOADED TO HUGGINGFACE BY [Gholamreza Dar](https://huggingface.co/Gholamreza)** *The original repository for the dataset is https://github.com/AUT-NLP/PQuAD* PQuAD is a crowd- sourced reading comprehension dataset on Persian Language. It includes 80,000 questions along with their answers, with 25% of the questions being unanswerable. As a reading comprehension dataset, it requires a system to read a passage and then answer the given questions from the passage. PQuAD's questions are based on Persian Wikipedia articles and cover a wide variety of subjects. Articles used for question generation are quality checked and include few number of non-Persian words. ## Dataset Splits The dataset is divided into three categories including train, validation, and test sets and the statistics of these sets are as follows: ``` +----------------------------+-------+------------+------+-------+ | | Train | Validation | Test | Total | +----------------------------+-------+------------+------+-------+ | Total Questions | 63994 | 7976 | 8002 | 79972 | | Unanswerable Questions | 15721 | 1981 | 1914 | 19616 | | Mean # of paragraph tokens | 125 | 121 | 124 | 125 | | Mean # of question tokens | 10 | 11 | 11 | 10 | | Mean # of answer tokens | 5 | 6 | 5 | 5 | +----------------------------+-------+------------+------+-------+ ``` Workers were encouraged to use paraphrased sentences in their questions and avoid choosing the answers comprising non-Persian words. Another group of crowdworkers validated the questions and answers in the test and validation set to ensure their quality. They also provided additional answers to the questions in test and validation sets if possible. This helps to consider all possible types of answers and have a better evaluation of models. PQuAD is stored in the JSON format and consists of passages where each passage is linked to a set of questions. Answer(s) of the questions is specified with answer's span (start and end point of answer in paragraph). Also, the unanswerable questions are marked as unanswerable. ## Results The estimated human performance on the test set is 88.3% for F1 and 80.3% for EM. We have evaluated PQuAD using two pre-trained transformer-based language models, namely ParsBERT (Farahani et al., 2021) and XLM-RoBERTa (Conneau et al., 2020), as well as BiDAF (Levy et al., 2017) which is an attention-based model proposed for MRC. ``` +-------------+------+------+-----------+-----------+-------------+ | Model | EM | F1 | HasAns_EM | HasAns_F1 | NoAns_EM/F1 | +-------------+------+------+-----------+-----------+-------------+ | BNA | 54.4 | 71.4 | 43.9 | 66.4 | 87.6 | | ParsBERT | 68.1 | 82.0 | 61.5 | 79.8 | 89.0 | | XLM-RoBERTa | 74.8 | 87.6 | 69.1 | 86.0 | 92.7 | | Human | 80.3 | 88.3 | 74.9 | 85.6 | 96.8 | +-------------+------+------+-----------+-----------+-------------+ ``` ## LICENSE PQuAD is developed by Mabna Intelligent Computing at Amirkabir Science and Technology Park with collaboration of the NLP lab of the Amirkabir University of Technology and is supported by the Vice Presidency for Scientific and Technology. By releasing this dataset, we aim to ease research on Persian reading comprehension and the development of Persian question answering systems. This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa]. [![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa] [cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/ [cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png [cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg # Dataset Card for "pquad"
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evidence_infer_treatment
2023-03-16T10:35:23.000Z
[ "task_categories:text-retrieval", "task_ids:fact-checking-retrieval", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "arxiv:2005.04177", "region:us" ]
null
Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text. The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.
@inproceedings{lehman-etal-2019-inferring, title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials", author = "Lehman, Eric and DeYoung, Jay and Barzilay, Regina and Wallace, Byron C.", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/N19-1371", pages = "3705--3717", }
3
276
2022-03-02T23:29:22
--- pretty_name: Evidence Infer Treatment annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - fact-checking-retrieval paperswithcode_id: null dataset_info: - config_name: '2.0' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 77045294 num_examples: 2690 - name: test num_bytes: 9436674 num_examples: 334 - name: validation num_bytes: 10113982 num_examples: 340 download_size: 163515689 dataset_size: 96595950 - config_name: '1.1' features: - name: Text dtype: string - name: PMCID dtype: int32 - name: Prompts sequence: - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Outcome dtype: string - name: Intervention dtype: string - name: Comparator dtype: string - name: Annotations sequence: - name: UserID dtype: int32 - name: PromptID dtype: int32 - name: PMCID dtype: int32 - name: Valid Label dtype: bool - name: Valid Reasoning dtype: bool - name: Label dtype: string - name: Annotations dtype: string - name: Label Code dtype: int32 - name: In Abstract dtype: bool - name: Evidence Start dtype: int32 - name: Evidence End dtype: int32 splits: - name: train num_bytes: 55375971 num_examples: 1931 - name: test num_bytes: 6877338 num_examples: 240 - name: validation num_bytes: 7359847 num_examples: 248 download_size: 114452688 dataset_size: 69613156 --- # Dataset Card for Evidence Infer ## 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:** http://evidence-inference.ebm-nlp.com/ - **Repository:** https://github.com/jayded/evidence-inference - **Paper:** [Evidence Inference 2.0: More Data, Better Models](https://arxiv.org/abs/2005.04177) - **Leaderboard:** http://evidence-inference.ebm-nlp.com/leaderboard/ - **Point of Contact:** []() ### Dataset Summary Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text. The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper. We have recently collected additional data for this task (https://arxiv.org/abs/2005.04177), which we will present at BioNLP 2020. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - English (`en`). ## Dataset Structure ### Data Instances ``` {'Text': "TITLE: Liraglutide, a once-daily human GLP-1 analogue, added to a sulphonylurea over 26 weeks produces greater improvements in glycaemic and weight control compared with adding rosiglitazone or placebo in subjects with Type 2 diabetes (LEAD-1 SU)\n\n ABSTRACT.AIM:\nTo compare the effects of combining liraglutide (0.6, 1.2 or 1.8 mg/day) or rosiglitazone 4 mg/day (all n ≥ 228) or placebo (n = 114) with glimepiride (2–4 mg/day) on glycaemic control, body weight and safety in Type 2 diabetes.\n\nABSTRACT.METHODS:\nIn total, 1041 adults (mean ± sd), age 56 ± 10 years, weight 82 ± 17 kg and glycated haemoglobin (HbA1c) 8.4 ± 1.0% at 116 sites in 21 countries were stratified based on previous oral glucose-lowering mono : combination therapies (30 : 70%) to participate in a five-arm, 26-week, double-dummy, randomized study.\n\nABSTRACT.RESULTS:\nLiraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. Liraglutide 0.6 mg was less effective (−0.6%, baseline 8.4%). Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l). Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg). Main adverse events for all treatments were minor hypoglycaemia (< 10%), nausea (< 11%), vomiting (< 5%) and diarrhoea (< 8%).\n\nABSTRACT.CONCLUSIONS:\nLiraglutide added to glimepiride was well tolerated and provided improved glycaemic control and favourable weight profile.\n\nBODY.INTRODUCTION:\nMost drugs that target Type 2 diabetes (T2D) also cause weight gain or hypoglycaemia, or both, with the risk increasing with combination therapy. Glucagon-like peptide-1 (GLP-1)-based therapies stimulate insulin secretion and reduce glucagon secretion only during hyperglycaemia. GLP-1 also slows gastric emptying and reduces appetite [1]. Although American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) guidelines recommend lifestyle and metformin as initial therapy for T2D [2], sulphonylureas are used widely, particularly when metformin or thiazolidinediones are not tolerated. Glycaemic control eventually deteriorates with sulphonylureas while hypoglycaemia and weight gain are common [3]. Incretin therapy improves glycaemic control with low hypoglycaemic risk, while delayed gastric emptying and reduced appetite can reduce weight [1,4]. Liraglutide is a once-daily human GLP-1 analogue with 97% linear amino-acid sequence homology to human GLP-1 [5] and half-life of 13 h after subcutaneous administration that produces 24-h blood glucose control [6]. Liraglutide monotherapy for 14 weeks reduced glycated haemoglobin (HbA1c) by 1.7% and fasting plasma glucose (FPG) by 3.4 mmol/l without causing hypoglycaemia, along with weight loss (∼3 kg) compared with placebo [7]. Improvements in pancreatic B-cell function [7–9] and blood pressure [7], along with decreased glucagon secretion [7,10], also occurred. As part of the phase 3 programme [the Liraglutide Effect and Action in Diabetes (LEAD) programme] with liraglutide in > 4000 subjects with T2D as monotherapy or in combination therapy, this 26-week trial examined liraglutide plus glimepiride compared with either placebo or rosiglitazone added to glimepiride on glycaemic control and body weight.\n\nBODY.SUBJECTS AND METHODS.STUDY PARTICIPANTS:\nInclusion criteria: T2D treated with oral glucose-lowering agents (OGLAs) for ≥ 3 months; 18–80 years of age; HbA1c 7.0–11.0% (previous OGLA monotherapy) or 7.0–10.0% (previous OGLA combination therapy); body mass index (BMI) ≤ 45.0 kg/m2. Exclusion criteria: used insulin within 3 months, impaired liver or renal function, uncontrolled hypertension (≥ 180/100 mmHg), cancer or used any drugs apart from OGLAs likely to affect glucose concentrations. Subjects provided written informed consent. The study was conducted in accordance with good clinical practice guidelines and approved by independent ethics committees.\n\nBODY.SUBJECTS AND METHODS.STUDY DESIGN:\nThe study was a 26-week, double-blind, double-dummy, randomized, active-control, five-armed parallel (116 sites in 21 countries, primarily Europe and Asia) trial enrolling 1041 subjects (1–37 subjects per centre), all receiving glimepiride (2–4 mg/day) in combination with (Fig. 1): FIGURE 1Overview of trial design and treatment arms. one of three liraglutide doses [0.6, 1.2 or 1.8 mg, injected subcutaneously (Novo Nordisk, Bagsvaerd, Denmark) and rosiglitazone placebo];liraglutide placebo and rosiglitazone placebo;liraglutide placebo and rosiglitazone 4 mg/day (rosiglitazone; AvandiaTM; GlaxoSmithKline, London, UK). The doses of rosiglitazone and glimepiride used were determined by the highest doses approved in all participating counties. After discontinuing previous OGLAs except glimepiride, separate 2-week titration and maintenance periods with glimepiride (open-label) preceded randomization (Fig. 1). Subjects were stratified according to previous treatment (monotherapy or combination therapy). After randomization, 2-week treatment titration and 24-week treatment (maintenance) phases (Fig. 1) were completed. Liraglutide was up-titrated weekly in 0.6-mg increments until allocated doses were reached. Glimepiride could be adjusted between 2 and 4 mg/day in case of hypoglycaemia or other adverse events (AEs), while other drug doses were fixed. Liraglutide (active and placebo) was supplied in 3-ml pre-filled pens with 31G needles (Novo Nordisk). Subjects were encouraged to inject liraglutide into the upper arm, thigh or abdomen at the same time each day. Rosiglitazone and glimepiride were taken in the morning or with the first meal.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.EFFICACY:\nThe primary endpoint was change from baseline HbA1c after 26 weeks of treatment. Secondary endpoints included: percentages of subjects reaching HbA1c (< 7.0%, ≤ 6.5%), FPG (5.0 to ≤ 7.2 mmol/l) and postprandial plasma glucose (PPG; 10.0 mmol/l) targets [11–13]; changes in body weight, FPG, mean PPG, indices of pancreatic B-cell function [pro-insulin : insulin ratio and homeostasis model assessment (HOMA)-B], HOMA-insulin resistance (HOMA-IR) and blood pressure (BP). HbA1c was measured centrally (MDS Pharma Services, King of Prussia, PA, USA) by high performance liquid chromatography while plasma glucose (PG) was self-measured using MediSense® glucose meters (Abbott Diagnostics Inc., Abbott Park, IL, USA). Insulin and C-peptide were measured by chemiluminescence, proinsulin by ELISA, while glucagon was measured in aprotinin-treated plasma by radioimmunoassay. The proinsulin : insulin ratio was calculated from fasting insulin and fasting proinsulin. HOMA-B and HOMA-IR were both calculated from FPG and fasting insulin. Samples measured centrally were collected and transported according to detailed procedures in the MDS Pharma Services manual. Samples stored at ambient temperature were shipped by courier to the central laboratory on the same day as collection, while frozen samples were shipped every 3 weeks.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.SAFETY:\nSafety variables included hypoglycaemic episodes based on PG levels (< 3.1 mmol/l), liraglutide antibodies including cross-reacting and neutralizing antibodies, tolerability (gastrointestinal complaints) and pulse. AEs, vital signs, electrocardiogram (ECG), biochemical and haematology measures including calcitonin were also monitored. Self-treated hypoglycaemic episodes were classified as minor, while those requiring third-party assistance were considered major. Serum antibodies against liraglutide were measured by radioimmunoprecipitation assay.\n\nBODY.SUBJECTS AND METHODS.STATISTICAL ANALYSES:\nAll efficacy and safety analyses were based on intent-to-treat criteria, defined as subjects who were exposed to ≥ 1 dose of trial product(s). Efficacy endpoints were analysed by ancova with treatment, country and previous glucose-lowering treatment as fixed effects and baseline values as covariates. Missing data were imputed by last observation carried forward (LOCF). Sample size calculations were based on predicted HbA1c and body weight after trial completion. As the three liraglutide + glimepiride groups were to be compared with both rosiglitazone + glimepiride and glimepiride monotherapy, two calculations were performed. These sample size calculations assumed a standard deviation of 1.2% of HbA1c, the non-inferiority/superiority margin vs. active control was set to 0.4% and the difference to detect (superiority vs. placebo) was set to 0.5%. For body weight, a coefficient of variation of 3% (based on phase 2a trials for liraglutide) and a difference to detect of 3% were assumed. A combined power (calculated as the product of the marginal powers for HbA1c and body weight) of at least 85% was required. These calculations indicated that at least 168 and 81 patients completing the study would be needed for the combination and glimepiride monotherapy groups, respectively. Assuming a drop-out rate of 25%, targets for randomization were 228 in each of the combination therapy groups and 114 in the placebo group (total n = 1026). To protect against Type 1 errors, HbA1c was analysed using hierarchical testing for descending doses of liraglutide. First, superiority of liraglutide 1.8 mg to placebo was tested and, only if superior to placebo, non-inferiority to rosiglitazone was tested. If non-inferiority was obtained, superiority to rosiglitazone for liraglutide 1.8 mg was tested and superiority to placebo for liraglutide 1.2 mg was tested. If superiority was confirmed, non-inferiority to rosiglitazone would be tested and so on (i.e. testing sequence was stopped when hypotheses could not be rejected). Superiority was concluded when upper limits of two-sided 95% confidence intervals (CIs) for treatment differences were below 0%; non-inferiority was concluded if these values were < 0.4%; for secondary endpoints, Type 1 errors were controlled by estimating simultaneous CIs using Dunnett's method. Proportions of subjects achieving HbA1c (HbA1c < 7.0%, and ≤ 6.5%) and FPG (5.0 ≤ FPG ≤ 7.2 mmol/l) targets [13] were compared between treatments using logistic regression with allocated treatment and baseline values as covariates. Chi-square analyses assessed differences in treatments for percentages of subjects achieving no, one, two or three PPG values < 10 mmol/l [13]. Hypoglycaemic episodes were analysed under the assumption that number per subject were negatively binomially distributed using a generalized linear model, including treatment and country as fixed effects. Other safety data were compared by descriptive statistics. Values for descriptive statistics are expressed as means ± sd, while ancova results are expressed as least square means ± SEM or with 95% CI unless otherwise noted. Significance levels were set to 5% for two-sided tests and 2.5% for one-sided tests.\n\nBODY.RESULTS.DISPOSITION AND DEMOGRAPHICS:\nThe treatment groups were well balanced (Table 1). Of 1712 subjects screened, 1041 were randomized and 1040 were exposed to trial drugs; 147 subjects (14.1%) withdrew (Fig. 2). Withdrawals were higher with placebo (27%) and rosiglitazone treatment (16%) compared with liraglutide 0.6 mg (11%), liraglutide 1.2 mg (14%) and liraglutide 1.8 mg (9%) treatment. Thirty-eight subjects (3.7%) withdrew as a result of AEs (Fig. 2). Table 1 Demographic characteristics of study participants Liraglutide 0.6 mg ( n = 233) Liraglutide 1.2 mg ( n = 228) Liraglutide 1.8 mg ( n = 234) Placebo ( n = 114) Rosiglitazone ( n = 232) Male : female (%) 54 : 46 45 : 55 53 : 47 47 : 53 47 : 53 Age (years) 55.7 ± 9.9 57.7 ± 9.0 55.6 ± 10.0 54.7 ± 10.0 56.0 ± 9.8 Duration of diabetes (years) 6.5 (4.0,10.2) 6.7 (4.0,10.7) 6.5 (3.7,10.5) 6.5 (4.5,10.6) 6.6 (4.3,10.7) Previous on mono : combi (%) 30 : 70 31 : 69 27 : 73 32 : 68 32 : 68 FPG (mmol/l) 10.0 ± 2.4 9.8 ± 2.7 9.7 ± 2.4 9.5 ± 2.0 9.9 ± 2.5 HbA 1c (%) 8.4 ± 1.0 8.5 ± 1.1 8.5 ± 0.9 8.4 ± 1.0 8.4 ± 1.0 Diabetic retinopathy (%) 17.2 14.9 12.0 13.2 16.4 Hypertension (%) 69.1 68.0 69.7 64.9 66.8 BMI (kg/m 2 ) 30.0 ± 5.0 29.8 ± 5.1 30.0 ± 5.1 30.3 ± 5.4 29.4 ± 4.8 Weight (kg) 82.6 ± 17.7 80.0 ± 17.1 83.0 ± 18.1 81.9 ± 17.1 80.6 ± 17.0 Systolic blood pressure (mmHg) 131 ± 16 133 ± 15 132 ± 16 131 ± 15.3 133 ± 15 Data are mean ± sd and percentages, except for duration of diabetes, where data are median, 25th and 75th percentile. BMI, body mass index; FPG, fasting plasma glucose; HbA 1c , glycated haemoglobin; mono : combi, previous treatment with either monotherapy or combination therapy; sd , standard deviation. FIGURE 2Flow of patients through the study.\n\nBODY.RESULTS.EFFICACY.HBA:\nHbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). FIGURE 3Mean glycated haemoglobin (HbA1c) by treatment and week (intent-to-treat population with last observation carried forward): (a) overall population; (b) previously on monotherapy; or (c) previously on combination therapy; (d) mean changes in HbA1c from baseline after 26 weeks of treatment. Keys: (a–c) liraglutide 0.6 mg: grey dotted line with squares; liraglutide 1.2 mg: black solid line with triangles; liraglutide 1.8 mg: black dotted line with squares; rosiglitazone: grey solid line with circles; placebo: black solid line with circles. (d) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. ****P < 0.0001 compared with placebo; ††††P < 0.0001 compared with rosiglitazone. HbA1c decreases were greater for subjects who entered from monotherapy compared with combination therapy (Fig. 3d). However, because the increase with placebo was higher for individuals entering on combination therapy (0.7 vs. 0.23%), the differences between treatment groups in favour of liraglutide were similar irrespective of whether subjects were treated previously with monotherapy or combination therapy. Neither age, gender nor BMI affected these trends.\n\nBODY.RESULTS.EFFICACY.PERCENTAGE REACHING AN HBA:\nThe percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4Subjects achieving specified glycated haemoglobin (HbA1c) levels: (a) percentage reaching HbA1c < 7.0% (American Diabetes Association/European Association for the Study of Diabetes target); (b) percentage reaching HbA1c < 6.5% (International Diabetes Federation/American Association of Clinical Endocrinologists targets); (c) cumulative distribution of HbA1c at 26 weeks for the intent-to-treat (ITT) population; and (d) for the ITT last observation carried forward (LOCF) population. Keys: (a, b) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. (c, d) liraglutide 0.6 mg: pale grey solid line; liraglutide 1.2 mg: grey solid line, liraglutide 1.8 mg: black solid line; rosiglitazone: dotted black line; placebo: dotted grey line; baseline visit: long dashed black line. ****P < 0.0001 or **P < 0.01 compared with placebo; ††††P < 0.0001 or †††P = 0.0005 compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.FASTING PLASMA GLUCOSE:\nBy week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. FIGURE 5Mean changes from baseline in fasting plasma glucose after 26 weeks of treatment. ****P < 0.0001 compared with placebo; ††P < 0.01 compared with rosiglitazone. The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).\n\nBODY.RESULTS.EFFICACY.POSTPRANDIAL PLASMA GLUCOSE:\nPPG was reduced similarly after each meal. The greatest reductions in mean PPG values from baseline (average of values obtained 90 min after breakfast, lunch and evening meal) occurred with liraglutide 1.2 mg (2.5 mmol/l) and liraglutide 1.8 mg (2.7 mmol/l). By comparison, the reduction from baseline in mean PPG values was 1.8 mmol/l for rosiglitazone and liraglutide 0.6 mg and 0.4 mmol/l for placebo. Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.PPG MEASUREMENTS < 10.0 MMOL/L:\nThe percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.\n\nBODY.RESULTS.BODY WEIGHT:\nMean weight at baseline was 81.6 kg. Mean reductions in weight from baseline to end of treatment were 0.2 kg with liraglutide 1.8 mg and 0.1 kg with placebo treatment, while increases occurred with either liraglutide 0.6 mg (0.7 kg), liraglutide 1.2 mg (0.3 kg) or rosiglitazone (2.1 kg) (Fig. 6). Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. Gender appeared to have no influence on the results, as indicated when added as a fixed effect in the ancova model. FIGURE 6Mean changes in body weight from baseline after 26 weeks of treatment. *P < 0.05 compared with placebo; ††††P < 0.0001 compared with rosiglitazone.\n\nBODY.RESULTS.INDICES OF PANCREATIC B-CELL FUNCTION AND INSULIN RESISTANCE:\nReductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051). There were no significant differences between treatments for HOMA-IR. Table 2 Selected indices of pancreatic B-cell function Variable Treatment Baseline Week 26 (LOCF) Least square difference from placebo (95% CI) Least square difference from rosiglitazone (95% CI) Proinsulin : insulin ratio Liraglutide 0.6 mg 0.42 ± 0.22 0.38 ± 0.24 −0.05 (−0.11; 0.00) −0.02 (−0.06; 0.03) Liraglutide 1.2 mg 0.45 ± 0.31 0.33 ± 0.20 −0.10 (−0.16; −0.05) † −0.07 (−0.11; −0.02) * Liraglutide 1.8 mg 0.48 ± 0.33 0.36 ± 0.20 −0.09 (−0.15; −0.03) * −0.05 (−0.10; −0.01) * Placebo 0.44 ± 0.27 0.46 ± 0.29 Rosiglitazone 0.45 ± 0.29 0.40 ± 0.20 HOMA-B (%) Liraglutide 0.6 mg 51 ± 43.3 70 ± 88.6 15 (−19.10; 49.0) 11 (−16.7; 39.0) Liraglutide 1.2 mg 71 ± 254.3 99 ± 184.3 43 (8.10; 76.9) * 39 (10.3; 67.0) * Liraglutide 1.8 mg 56 ± 84.6 91 ± 108.2 34 (−0.23; 68.5) 30 (2.00; 58.6) * Placebo 56 ± 103.3 52 ± 107.3 Rosiglitazone 46 ± 36.2 59 ± 63.3 * P ≤ 0.05; † P < 0.0001. CI, confidence interval; HOMA, homeostatis model assessment; LOCF, last observation carried forward. \n\nBODY.RESULTS.BLOOD PRESSURE AND PULSE:\nAlthough decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).\n\nBODY.RESULTS.SAFETY:\nThe most common treatment-emergent AEs that were considered by investigators to be either possibly or probably related to liraglutide were gastrointestinal (diarrhoea, nausea, dyspepsia and constipation) and nervous system disorders (headache and dizziness), particularly during the first 4 weeks. Nausea was highest with liraglutide 1.2 mg (10.5%) and lowest with placebo (1.8%). Vomiting (4.4%) and diarrhoea (7.9%) were also higher with liraglutide 1.2 mg. Withdrawals because of nausea ranged from 0.9–2.2%, vomiting 0.4–0.9% and diarrhoea 0–1.3%. Nausea was more common with liraglutide compared with placebo and rosiglitazone, particularly during the first 4 weeks (Fig. 7). Frequency of nausea was less in the liraglutide 0.6 mg treatment group compared with the higher doses of liraglutide. Generally, the occurrence of nausea dissipated from 4 to 26 weeks of treatment in all groups using liraglutide (Fig. 7). FIGURE 7Percentage of subjects experiencing nausea over the course of the study. Key: liraglutide 0.6 mg with glimepiride: black line with filled circles; liraglutide 1.2 mg with glimepiride: black line with filled triangles; liraglutide 1.8 mg with glimepiride: grey line with hollow circles; glimepiride grey lines with filled squares; rosiglitazone and glimepiride: grey line with hollow triangles. The incidence of serious AEs ranged between 3 and 5%: placebo (3%), rosiglitazone (3%), liraglutide 0.6 mg (3%), liraglutide 1.2 mg (4%) and liraglutide 1.8 mg (5%). Most treatment-emergent serious AEs were judged by investigators to be unlikely to be related to trial products. No deaths were reported during the trial. One subject developed chronic pancreatitis whilst taking liraglutide 0.6 mg; the person had no reported previous history of pancreatitis. The subject continued on liraglutide therapy and completed the trial. At screening, five patients had been previously diagnosed with pancreatitis. As pancreatitis was not an exclusion criterion, these patients were randomized as follows: one to liraglutide 0.6 mg, one to liraglutide 1.2 mg, two to liraglutide 1.8 mg and one to rosiglitazone + glimepiride. All five patients completed the trial without reporting pancreatitis as an adverse event. Hypoglycaemia was infrequent with all treatments. One major hypoglycaemic episode (self-measured blood glucose = 3.0 mmol/l) occurred 9 days after treatment started in a subject receiving liraglutide 1.8 mg in combination with glimepiride. Although medical assistance was not needed, the subject required third-party assistance. The investigator judged the episode as likely to be related to glimepiride and reduced the dose from 4 to 3 mg after the incident. Minor hypoglycaemia occurred in < 10% of subjects for any treatment. The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values. Antibodies to liraglutide were found in 9–13% of subjects treated with liraglutide. No significant effects of these antibodies on HbA1c were found in pooled analyses of four trials including the current study. There were no clinically relevant changes in ophthalmoscopy, biochemistry, urinalysis, haematology or ECG assessments. No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.\n\nBODY.DISCUSSION:\nTreatment with liraglutide plus glimepiride was superior to glimepiride monotherapy at all doses of liraglutide and superior to rosiglitazone plus glimepiride for the two higher liraglutide doses for improving HbA1c. Similar findings for reductions in FPG and PPG highlight improved 24-h glucose control with once-daily liraglutide, with substantially more subjects reaching glycaemic targets, particularly with liraglutide 1.8 mg. Improvements in pancreatic B-cell function were larger with liraglutide 1.2 and 1.8 mg compared with rosiglitazone. Liraglutide was well tolerated and occurrence of gastrointestinal AEs was low overall, particularly after week 4. Although rates of hypoglycaemia were low in all treatment groups (< 10%), minor hypoglycaemic events occurred more often in patients treated with glimepiride plus liraglutide 1.2 or 1.8 mg than with glimepiride alone. It should be noted, however, that patients treated with liraglutide 1.2 or 1.8 mg achieved a lower HbA1c than those receiving glimepiride monotherapy. At lower HbA1c levels, sulphonylureas are known to elicit hypoglycaemia more readily than at higher levels. In clinical practice it may be possible to reduce the dose of sulphonylurea (when used with liraglutide) to minimize risk of hypoglycaemia and maintain HbA1cimprovements. Although weight effects were modest, liraglutide produced more favourable weight effects compared with rosiglitazone, which produced substantial weight gain. In other studies with liraglutide, subjects adding a 1.8-mg dose to metformin lost 2.8 kg [14], while those adding both metformin and glimepiride lost 1.8 kg compared with placebo [15] (both over 26 weeks) and those on liraglutide monotherapy (1.8 mg) lost 2.45 kg over 52 weeks [16]. In our study, because sulphonylureas usually cause weight gain, inclusion or optimization of glimepiride but not metformin may have mitigated the weight benefits typically associated with liraglutide. Lack of weight effects could be secondary to lower baseline body weight, withdrawal of previous metformin treatment or defensive snacking to minimize risk of hypoglycaemia. It might have been expected that the greater weight gain with rosiglitazone compared with liraglutide 1.8 mg would be associated with a concurrent increase in insulin resistance with rosiglitazone. The absence of this effect could reflect the insulin-sensitizing nature of rosiglitazone. Improvements in pancreatic B-cell function associated with liraglutide are consistent with other studies [7–9]. Study strengths include inclusion of both placebo and active (rosiglitazone) comparators and that OGLAs were optimized (not maximized) before randomization to minimize risk of hypoglycaemia. Limitations of the study include short duration of the trial and restriction on glimepiride and rosiglitazone in some countries that precluded maximal dosing. The impact of using other GLP-1-based treatments [such as exenatide, or the dipeptidyl peptidase-4 (DPP-4) inhibitor, sitagliptin] with sulphonylureas in subjects with T2D has been studied. In a 30-week American trial where exenatide twice a day was added to sulphonylureas, HbA1c was reduced by 0.46% from baseline with 5 μg and 0.86% with 10 μg [17] compared with 1.1% with liraglutide 1.8 or 1.2 mg. This reduction in HbA1c with liraglutide is consistent with other LEAD trials investigating liraglutide as monotherapy or in combination with various OGLA drugs. In these trials, HbA1c was reduced by 1–1.5%[14,16,18–20]. Reductions in FPG with exenatide were 0.3 and 0.6 mmol/l from baseline with 5 μg and 10 μg, respectively, compared with 1.4 mmol/l with liraglutide 1.8 mg; weight loss of 1.6 kg occurred with exenatide 10 μg compared with 0.2 kg for liraglutide 1.8 mg [17]. Differences in weight effects may be as a result of lower baseline weight in this trial (82 kg) compared with exenatide (96 kg) and discontinuation of previous metformin therapy, unlike the exenatide trial where exenatide was added to previous sulphonylurea monotherapy [17]. Other large-scale trials with liraglutide in combination with sulphonylureas have demonstrated weight loss of 2–3 kg [18,20]. Withdrawals from exenatide trials ranged from 24–30% compared with 9–14% with liraglutide in this study. Nausea with exenatide ranged from 39% with 5 μg to 51% with 10 μg [17] compared with 10.5% for liraglutide. Furthermore, 41% were positive for anti-exenatide antibodies compared with 9–13% with anti-liraglutide antibodies. With sitagliptin 100 mg once daily for 24 weeks, HbA1c decreased by 0.3% from baseline in subjects receiving glimepiride, with 11% achieving an HbA1c < 7.0%[21]. Reductions in FPG and PPG from baseline were 0.05 and 1.4 mmol/l, respectively, while weight increased by 0.8 kg and the prevalence of nausea was < 1%. Although head-to-head trials are required to test true differences between these agents, the marked effects of liraglutide on FPG may be as a result of consistent blood levels of liraglutide maintained over 24 h compared with exenatide which has to be administered 60 min before breakfast and dinner and has a half-life of 1.5–3.6 h [22]. In a recent 26-week head-to-head trial comparing liraglutide with exenatide, liraglutide produced a 0.3% greater decrease on HbA1c (P < 0.0001) [20]. Because DPP-4 inhibitors inhibit the degradation of GLP-1, the efficacy of sitagliptin is dependent on levels of endogenous GLP-1 which is physiologically low compared with the much higher pharmacological levels of liraglutide. Pharmacological levels may be needed to induce satiety, weight loss and possibly larger HbA1c reductions. Liraglutide is an effective and well-tolerated once-daily human GLP-1 analogue that improves overall glycaemic control and indices of pancreatic B-cell function with minimal weight gain and risk of hypoglycaemia when used in combination with a sulphonylurea for T2D.\n\nBODY.COMPETING INTERESTS:\nThe study was funded by Novo Nordisk, the manufacturer of liraglutide. In collaboration with the investigators, Novo Nordisk was responsible for the study design, protocol, statistical analysis plans, oversight, analysis and reporting of the results. Data were recorded at the clinical centres and maintained by the sponsor. The LEAD-1 SU study group had full access to the data. Final responsibility for the decision to submit the manuscript for publication was the authors. MM has received lecture fees from Novo Nordisk, Servier, MSD; JS has received honoraria, grants and lecture fees from Novo Nordisk; MB, WMWB and NAK have no conflicts to declare; JS has received lecture fees from Novo Nordisk; MZ is employed by, and holds stock in, Novo Nordisk; TLT is employed by Novo Nordisk; SC is a member of the international advisory board on liraglutide for Novo Nordisk and has received lecture fees from Novo Nordisk.", 'PMCID': 2871176, 'Prompts': {'PromptID': [150, 113, 140, 106, 142, 149, 148, 152, 154, 125, 121, 124, 107, 105, 133, 103, 126, 118, 132, 122, 141, 151, 112, 153, 102, 129, 104, 116, 136, 123, 135, 139, 101, 99, 144, 145, 147, 117, 143, 111, 137, 114, 108, 128, 134, 115, 127, 131, 109, 146, 110, 100, 138, 119, 130], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176, 2871176], 'Outcome': ['Incidence of minor hypoglycaemia', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HOMA-IR', 'HbA1c level at 26 weeks', 'Reductions in systolic blood pressure', 'Pulse variations', 'Pulse variations', 'Incidence of minor hypoglycaemia', 'Changes in calcitonin at week 26', 'Postprandial plasma glucose', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Postprandial plasma glucose', 'HbA1c level at 26 weeks', 'HbA1c level at 26 weeks', 'Proinsulin : insulin ratio', 'Postprandial plasma glucose', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Proinsulin : insulin ratio', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Reductions in systolic blood pressure', 'Incidence of minor hypoglycaemia', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'Changes in calcitonin at week 26', 'Fasting plasma glucose at week 26', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Postprandial plasma glucose', 'Fasting plasma glucose at week 26', 'HOMA-B', 'Postprandial plasma glucose', 'HOMA-B', 'HOMA-IR', 'Fasting plasma glucose at week 26', 'HbA1c level at 26 weeks', 'Reductions in systolic blood pressure', 'Decreases in diastolic blood pressure', 'Pulse variations', 'Fasting plasma glucose at week 26', 'Reductions in systolic blood pressure', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HOMA-B', 'Patients reaching HbA1c goals less than 7.0% ', 'HbA1c level at 26 weeks', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Proinsulin : insulin ratio', 'Fasting plasma glucose at week 26', 'ADA postprandial plasma glucose goals less than 10.0 mmol/l', 'Proinsulin : insulin ratio', 'HbA1c level at 26 weeks', 'Decreases in diastolic blood pressure', 'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%', 'HbA1c level at 26 weeks', 'HOMA-B', 'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l', 'Weight gain'], 'Intervention': ['Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (0.6 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.8 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Liraglutide (all doses) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.2 mg) plus glimepiride', 'Liraglutide (1.8 mg) plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Rosiglitazone plus glimepiride'], 'Comparator': ['Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride ', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Liraglutide (1.2 mg) plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride ', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride', 'Rosiglitazone plus glimepiride', 'Placebo plus glimepiride ', 'Placebo plus glimepiride', 'Liraglutide plus glimepiride'], 'Annotations': [{'UserID': [0, 3, 2], 'PromptID': [150, 150, 150], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [25524, 25964, 25964], 'Evidence End': [26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [113, 113, 113, 113], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'he estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16121, 16120, 16120], 'Evidence End': [16353, 16449, 16355, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [140, 140, 140, 140], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, 20943, 20943, 20943], 'Evidence End': [21012, 21012, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [106, 106, 106, 106], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['All liraglutide doses were superior to placebo (P < 0.0001)', 'Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [14169, 13955, 14169, 14169], 'Evidence End': [14228, 14314, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [142, 142, 142, 142], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22230, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [149, 149, 149, 149], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22373, 22554], 'Evidence End': [22738, 22738, 22640, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [148, 148, 148, 148], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [22554, 22554, 22554, 22373], 'Evidence End': [22738, 22640, 22738, 22738]}, {'UserID': [0, 1, 3, 2], 'PromptID': [152, 152, 152, 152], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048),', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26131, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [154, 154, 154, 154], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [125, 125, 125, 125], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 1469, 1469, 1469], 'Evidence End': [19377, 1756, 1756, 1756]}, {'UserID': [0, 3], 'PromptID': [121, 121], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [18230, 18230], 'Evidence End': [18670, 18476]}, {'UserID': [0, 1, 3, 2], 'PromptID': [124, 124, 124, 124], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001)', 'reatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) ', 'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19128, 19129, 19128, 19128], 'Evidence End': [19251, 19377, 19252, 19377]}, {'UserID': [0, 1, 3, 2], 'PromptID': [107, 107, 107, 107], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1081, 13955, 1082, 14426]}, {'UserID': [0, 1, 3, 2], 'PromptID': [105, 105, 105, 105], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'All liraglutide doses were superior to placebo (P < 0.0001),', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 14169, 14169], 'Evidence End': [1081, 13955, 14229, 14313]}, {'UserID': [0, 1, 3, 2], 'PromptID': [133, 133, 133, 133], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20726, 20728]}, {'UserID': [0, 1, 3, 2], 'PromptID': [103, 103, 103, 103], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [126, 126, 126, 126], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05)', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19623, 19624, 19601, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [118, 118, 118, 118], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18474, 18476]}, {'UserID': [0, 1, 2], 'PromptID': [132, 132, 132], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728]}, {'UserID': [0, 1, 1, 2], 'PromptID': [122, 122, 122, 122], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18476, 18230], 'Evidence End': [18670, 18476, 18670, 18670]}, {'UserID': [0, 1, 3, 2], 'PromptID': [141, 141, 141, 141], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22230, 22232, 22199, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [151, 151, 151, 151], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone', 'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [25524, 25964, 25964, 25964], 'Evidence End': [26184, 26184, 26073, 26184]}, {'UserID': [0, 1, 3, 2], 'PromptID': [112, 112, 112, 112], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15956, 16120, 15735], 'Evidence End': [16353, 16449, 16449, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [153, 153, 153, 153], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.', 'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [26515, 26515, 26515, 26515], 'Evidence End': [26703, 26703, 26703, 26703]}, {'UserID': [0, 1, 3, 2], 'PromptID': [102, 102, 102, 102], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. ', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17914, 1144], 'Evidence End': [1468, 1468, 18061, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [129, 129, 129, 129], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [19433, 19433, 19433, 19433], 'Evidence End': [19624, 19624, 19624, 19624]}, {'UserID': [1, 2], 'PromptID': [104, 104], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1], 'In Abstract': [True, True], 'Evidence Start': [1469, 1469], 'Evidence End': [1756, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [116, 116, 116, 116], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg.', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001),', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone.'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [17606, 17497, 17606, 17606], 'Evidence End': [17699, 17913, 17700, 17785]}, {'UserID': [0, 1, 3, 2], 'PromptID': [136, 136, 136, 136], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [123, 123, 123, 123], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ', 'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1469, 1469, 1469, 1469], 'Evidence End': [1691, 1756, 1692, 1756]}, {'UserID': [0, 1, 3, 2], 'PromptID': [135, 135, 135, 135], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20816, 20942, 20817, 20941]}, {'UserID': [0, 1, 3, 2], 'PromptID': [139, 139, 139, 139], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 2', 'There were no significant differences between treatments for HOMA-IR.', 'There were no significant differences between treatments for HOMA-IR.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20943, -1, 20943, 20943], 'Evidence End': [21012, -1, 21012, 21012]}, {'UserID': [0, 1, 3, 2], 'PromptID': [101, 101, 101, 101], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 3, 2], 'PromptID': [99, 99, 99, 99], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%)', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [843, 13756, 843, 13756], 'Evidence End': [1002, 13955, 1003, 14312]}, {'UserID': [0, 1, 3, 2], 'PromptID': [144, 144, 144, 144], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [145, 145, 145, 145], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 2], 'PromptID': [147, 147, 147], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). ', 'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [22554, 22554, 22554], 'Evidence End': [22738, 22642, 22642]}, {'UserID': [0, 1, 3, 2], 'PromptID': [117, 117, 117, 117], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. '], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 1144, 17497], 'Evidence End': [1468, 1468, 1468, 18061]}, {'UserID': [0, 1, 3, 2], 'PromptID': [143, 143, 143, 143], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ', 'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22039, 22039, 22039, 22039], 'Evidence End': [22231, 22232, 22232, 22232]}, {'UserID': [0, 1, 3, 2], 'PromptID': [111, 111, 111, 111], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', ' The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 16119, 15956, 16120], 'Evidence End': [16315, 16457, 16110, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [137, 137, 137, 137], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20902, 20942]}, {'UserID': [0, 1], 'PromptID': [114, 114], 'PMCID': [2871176, 2871176], 'Valid Label': [True, True], 'Valid Reasoning': [True, True], 'Label': ['significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018).', 'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1], 'In Abstract': [True, True], 'Evidence Start': [16120, 15956], 'Evidence End': [16447, 16449]}, {'UserID': [0, 1, 3, 2], 'PromptID': [108, 108, 108, 108], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Liraglutide 0.6 mg was non-inferior to rosiglitazone', 'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.', 'Liraglutide 0.6 mg was non-inferior to rosiglitazone', '. All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [14314, 14169, 14314, 14167], 'Evidence End': [14366, 14367, 14366, 14367]}, {'UserID': [0], 'PromptID': [128], 'PMCID': [2871176], 'Valid Label': [True], 'Valid Reasoning': [True], 'Label': ['significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone'], 'Label Code': [1], 'In Abstract': [True], 'Evidence Start': [19433], 'Evidence End': [19623]}, {'UserID': [0, 1, 2], 'PromptID': [134, 134, 134], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [20566, 20566, 20566], 'Evidence End': [20726, 20728, 20818]}, {'UserID': [0, 1, 3, 2], 'PromptID': [115, 115, 115, 115], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).', 'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)', 'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [1144, 1144, 17606, 1144], 'Evidence End': [1396, 1468, 17699, 1468]}, {'UserID': [0, 1, 2], 'PromptID': [127, 127, 127], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone', 'he percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.', 'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19433, 19434, 19433], 'Evidence End': [19623, 19624, 19624]}, {'UserID': [0, 1, 3, 2], 'PromptID': [131, 131, 131, 131], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ', 'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)'], 'Label Code': [-1, -1, -1, -1], 'In Abstract': [True, True, True, True], 'Evidence Start': [20566, 20566, 20566, 20566], 'Evidence End': [20726, 20728, 20728, 20726]}, {'UserID': [0, 1, 1, 3, 2], 'PromptID': [109, 109, 109, 109, 109], 'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True, True], 'Valid Reasoning': [True, True, True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['Rosiglitazone also was superior to placebo (P < 0.0001)', 'Rosiglitazone also was superior to placebo (P < 0.0001).', ' The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. ', 'Rosiglitazone also was superior to placebo (P < 0.0001).', 'Rosiglitazone also was superior to placebo (P < 0.0001).'], 'Label Code': [-1, -1, -1, -1, -1], 'In Abstract': [True, True, True, True, True], 'Evidence Start': [14368, 14368, 13678, 14368, 14368], 'Evidence End': [14423, 14424, 14368, 14424, 14424]}, {'UserID': [0, 1, 3, 2], 'PromptID': [146, 146, 146, 146], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ', 'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [22232, 22232, 22232, 22232], 'Evidence End': [22372, 22373, 22373, 22373]}, {'UserID': [0, 1, 3, 2], 'PromptID': [110, 110, 110, 110], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ', 'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [16120, 15735, 16120, 15735], 'Evidence End': [16315, 16449, 16449, 16449]}, {'UserID': [1, 3, 2], 'PromptID': [100, 100, 100], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly decreased', 'significantly decreased', 'significantly decreased'], 'Annotations': ['After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ', 'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) ', 'HbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). '], 'Label Code': [-1, -1, -1], 'In Abstract': [True, True, True], 'Evidence Start': [13756, 13756, 13487], 'Evidence End': [13955, 13944, 14314]}, {'UserID': [0, 1, 3, 2], 'PromptID': [138, 138, 138, 138], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['no significant difference', 'no significant difference', 'no significant difference', 'no significant difference'], 'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)', 'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'], 'Label Code': [0, 0, 0, 0], 'In Abstract': [True, True, True, True], 'Evidence Start': [20728, 20728, 20728, 20728], 'Evidence End': [20941, 20942, 20941, 20942]}, {'UserID': [0, 1, 3, 2], 'PromptID': [119, 119, 119, 119], 'PMCID': [2871176, 2871176, 2871176, 2871176], 'Valid Label': [True, True, True, True], 'Valid Reasoning': [True, True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001)', 'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '], 'Label Code': [1, 1, 1, 1], 'In Abstract': [True, True, True, True], 'Evidence Start': [18230, 18230, 18230, 18230], 'Evidence End': [18475, 18476, 18419, 18476]}, {'UserID': [0, 3, 2], 'PromptID': [130, 130, 130], 'PMCID': [2871176, 2871176, 2871176], 'Valid Label': [True, True, True], 'Valid Reasoning': [True, True, True], 'Label': ['significantly increased', 'significantly increased', 'significantly increased'], 'Annotations': ['Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001)', 'Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg)', 'Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. '], 'Label Code': [1, 1, 1], 'In Abstract': [True, True, True], 'Evidence Start': [19950, 1756, 19950], 'Evidence End': [20145, 1979, 20217]}]}} ``` ### Data Fields - `PMCID` (`int`): ID to identify the articles. - `Text` (`str`): Article text. - `Prompts` (`dict`): Prompts and annotations with keys: - 'PromptID': Which prompt the doctor is answering. - 'PMCID' - 'Outcome': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Intervention': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Comparator': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator". - 'Annotations': The annotation files consist of the following headings: UserID, PromptID, PMCID, Valid Label, Valid Reasoning, Label, Annotations, Label Code, In Abstract, Start Evidence, End Evidence. ### Data Splits | name | train | validation | test | |------|------:|-----------:|-----:| | 1.1 | 1931 | 248 | 240 | | 2.0 | 2690 | 340 | 334 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ``` @inproceedings{lehman2019inferring, title={Inferring Which Medical Treatments Work from Reports of Clinical Trials}, author={Lehman, Eric and DeYoung, Jay and Barzilay, Regina and Wallace, Byron C}, booktitle={Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL)}, pages={3705--3717}, year={2019} } @misc{deyoung2020evidence, title={Evidence Inference 2.0: More Data, Better Models}, author={Jay DeYoung and Eric Lehman and Ben Nye and Iain J. Marshall and Byron C. Wallace}, year={2020}, eprint={2005.04177}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
127,072
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tilde_model
2022-11-03T16:31:39.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:hr", "language:hu", "language:is", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:sk", "language:sl", "language:sq", "language:sr", "language:sv", "language:tr", "language:uk", "license:cc-by-sa-4.0", "region:us" ]
null
This is the Tilde MODEL Corpus – Multilingual Open Data for European Languages. The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites. The activities have been undertaken as part of the ODINE Open Data Incubator for Europe, which aims to support the next generation of digital businesses and fast-track the development of new products and services. The corpus includes the following parts: Tilde MODEL - EESC is a multilingual corpus compiled from document texts of European Economic and Social Committee document portal. Source: http://dm.eesc.europa.eu/ Tilde MODEL - RAPID multilingual parallel corpus is compiled from all press releases of Press Release Database of European Commission released between 1975 and end of 2016 as available from http://europa.eu/rapid/ Tilde MODEL - ECB multilingual parallel corpus is compiled from the multilingual pages of European Central Bank web site http://ebc.europa.eu/ Tilde MODEL - EMA is a corpus compiled from texts of European Medicines Agency document portal as available in http://www.ema.europa.eu/ at the end of 2016 Tilde MODEL - World Bank is a corpus compiled from texts of World Bank as available in http://www.worldbank.org/ in 2017 Tilde MODEL - AirBaltic.com Travel Destinations is a multilingual parallel corpus compiled from description texts of AirBaltic.com travel destinations as available in https://www.airbaltic.com/en/destinations/ in 2017 Tilde MODEL - LiveRiga.com is a multilingual parallel corpus compiled from Riga tourist attractions description texts of http://liveriga.com/ web site in 2017 Tilde MODEL - Lithuanian National Philharmonic Society is a parallel corpus compiled from texts of Lithuanian National Philharmonic Society web site http://www.filharmonija.lt/ in 2017 Tilde MODEL - mupa.hu is a parallel corpus from texts of Müpa Budapest - web site of Hungarian national culture house and concert venue https://www.mupa.hu/en/ compiled in spring of 2017 Tilde MODEL - fold.lv is a parallel corpus from texts of fold.lv portal http://www.fold.lv/en/ of the best of Latvian and foreign creative industries as compiled in spring of 2017 Tilde MODEL - czechtourism.com is a multilingual parallel corpus from texts of http://czechtourism.com/ portal compiled in spring of 2017 30 languages, 274 bitexts total number of files: 125 total number of tokens: 1.43G total number of sentence fragments: 62.44M
Roberts Rozis, Raivis Skadins, 2017, Tilde MODEL - Multilingual Open Data for EU Languages. Proceedings of the 21th Nordic Conference of Computational Linguistics NODALIDA 2017
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--- annotations_creators: - found language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - hr - hu - is - it - lt - lv - mt - nl - 'no' - pl - pt - ro - ru - sk - sl - sq - sr - sv - tr - uk license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: tilde-model-corpus pretty_name: Tilde Multilingual Open Data for European Languages dataset_info: - config_name: bg-el features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - el splits: - name: train num_bytes: 258081 num_examples: 455 download_size: 64430 dataset_size: 258081 - config_name: cs-en features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 709168 num_examples: 3100 download_size: 201503 dataset_size: 709168 - config_name: de-hr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - hr splits: - name: train num_bytes: 180148538 num_examples: 683194 download_size: 49585877 dataset_size: 180148538 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 73797124 num_examples: 348141 download_size: 17852861 dataset_size: 73797124 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 3808423 num_examples: 13464 download_size: 1160892 dataset_size: 3808423 --- # Dataset Card for Tilde Multilingual Open Data for European Languages ## 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:** http://opus.nlpl.eu/TildeMODEL.php - **Repository:** None - **Paper:** https://www.aclweb.org/anthology/W17-0235.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/TildeMODEL.php E.g. `dataset = load_dataset("tilde_model", lang1="en", lang2="lv")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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ehartford/wizard_vicuna_70k_unfiltered
2023-05-16T00:43:23.000Z
[ "license:apache-2.0", "region:us" ]
ehartford
null
null
110
276
2023-05-07T05:12:54
--- license: apache-2.0 --- This dataset is the wizard_vicuna dataset junelee/wizard_vicuna_70k, removing conversations with alignment. 34598 conversations remain. inspired by https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered All credit to anon8231489123 I basically took his scripts and applied them to this new dataset.
348
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ami
2023-01-17T13:44:21.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
null
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. \n
@inproceedings{10.1007/11677482_3, author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre}, title = {The AMI Meeting Corpus: A Pre-Announcement}, year = {2005}, isbn = {3540325492}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, url = {https://doi.org/10.1007/11677482_3}, doi = {10.1007/11677482_3}, abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.}, booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction}, pages = {28–39}, numpages = {12}, location = {Edinburgh, UK}, series = {MLMI'05} }
9
275
2022-03-02T23:29:22
--- pretty_name: AMI Corpus annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] dataset_info: - config_name: microphone-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42013753 num_examples: 134 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11387715153 dataset_size: 51945533 - config_name: microphone-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-1-1 dtype: string - name: file-1-2 dtype: string - name: file-1-3 dtype: string - name: file-1-4 dtype: string - name: file-1-5 dtype: string - name: file-1-6 dtype: string - name: file-1-7 dtype: string - name: file-1-8 dtype: string splits: - name: train num_bytes: 42126341 num_examples: 134 - name: validation num_bytes: 5125645 num_examples: 18 - name: test num_bytes: 4834751 num_examples: 16 download_size: 90941506169 dataset_size: 52086737 - config_name: headset-single features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 42491091 num_examples: 136 - name: validation num_bytes: 5110497 num_examples: 18 - name: test num_bytes: 4821283 num_examples: 16 download_size: 11505070978 dataset_size: 52422871 - config_name: headset-multi features: - name: word_ids sequence: string - name: word_start_times sequence: float32 - name: word_end_times sequence: float32 - name: word_speakers sequence: string - name: segment_ids sequence: string - name: segment_start_times sequence: float32 - name: segment_end_times sequence: float32 - name: segment_speakers sequence: string - name: words sequence: string - name: channels sequence: string - name: file-0 dtype: string - name: file-1 dtype: string - name: file-2 dtype: string - name: file-3 dtype: string splits: - name: train num_bytes: 42540063 num_examples: 136 - name: validation num_bytes: 5116989 num_examples: 18 - name: test num_bytes: 4827055 num_examples: 16 download_size: 45951596391 dataset_size: 52484107 --- # Dataset Card for AMI Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [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) <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> This legacy dataset is outdated. Please, use <a href="https://huggingface.co/datasets/edinburghcstr/ami"> edinburghcstr/ami </a> instead.</p> </div> ## Dataset Description - **Homepage:** [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/) - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. ### Dataset Preprocessing Individual samples of the AMI dataset contain very large audio files (between 10 and 60 minutes). Such lengths are unfeasible for most speech recognition models. In the following, we show how the dataset can effectively be chunked into multiple segments as defined by the dataset creators. The following function cuts the long audio files into the defined segment lengths: ```python import librosa import math from datasets import load_dataset SAMPLE_RATE = 16_000 def chunk_audio(batch): new_batch = { "audio": [], "words": [], "speaker": [], "lengths": [], "word_start_times": [], "segment_start_times": [], } audio, _ = librosa.load(batch["file"][0], sr=SAMPLE_RATE) word_idx = 0 num_words = len(batch["words"][0]) for segment_idx in range(len(batch["segment_start_times"][0])): words = [] word_start_times = [] start_time = batch["segment_start_times"][0][segment_idx] end_time = batch["segment_end_times"][0][segment_idx] # go back and forth with word_idx since segments overlap with each other while (word_idx > 1) and (start_time < batch["word_end_times"][0][word_idx - 1]): word_idx -= 1 while word_idx < num_words and (start_time > batch["word_start_times"][0][word_idx]): word_idx += 1 new_batch["audio"].append(audio[int(start_time * SAMPLE_RATE): int(end_time * SAMPLE_RATE)]) while word_idx < num_words and batch["word_start_times"][0][word_idx] < end_time: words.append(batch["words"][0][word_idx]) word_start_times.append(batch["word_start_times"][0][word_idx]) word_idx += 1 new_batch["lengths"].append(end_time - start_time) new_batch["words"].append(words) new_batch["speaker"].append(batch["segment_speakers"][0][segment_idx]) new_batch["word_start_times"].append(word_start_times) new_batch["segment_start_times"].append(batch["segment_start_times"][0][segment_idx]) return new_batch ami = load_dataset("ami", "headset-single") ami = ami.map(chunk_audio, batched=True, batch_size=1, remove_columns=ami["train"].column_names) ``` The segmented audio files can still be as long as a minute. To further chunk the data into shorter audio chunks, you can use the following script. ```python MAX_LENGTH_IN_SECONDS = 20.0 def chunk_into_max_n_seconds(batch): new_batch = { "audio": [], "text": [], } sample_length = batch["lengths"][0] segment_start = batch["segment_start_times"][0] if sample_length > MAX_LENGTH_IN_SECONDS: num_chunks_per_sample = math.ceil(sample_length / MAX_LENGTH_IN_SECONDS) avg_chunk_length = sample_length / num_chunks_per_sample num_words = len(batch["words"][0]) # start chunking by times start_word_idx = end_word_idx = 0 chunk_start_time = 0 for n in range(num_chunks_per_sample): while (end_word_idx < num_words - 1) and (batch["word_start_times"][0][end_word_idx] < segment_start + (n + 1) * avg_chunk_length): end_word_idx += 1 chunk_end_time = int((batch["word_start_times"][0][end_word_idx] - segment_start) * SAMPLE_RATE) new_batch["audio"].append(batch["audio"][0][chunk_start_time: chunk_end_time]) new_batch["text"].append(" ".join(batch["words"][0][start_word_idx: end_word_idx])) chunk_start_time = chunk_end_time start_word_idx = end_word_idx else: new_batch["audio"].append(batch["audio"][0]) new_batch["text"].append(" ".join(batch["words"][0])) return new_batch ami = ami.map(chunk_into_max_n_seconds, batched=True, batch_size=1, remove_columns=ami["train"].column_names, num_proc=64) ``` A segmented and chunked dataset of the config `"headset-single"`can be found [here](https://huggingface.co/datasets/ami-wav2vec2/ami_single_headset_segmented_and_chunked). ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task does not have an active leaderboard at the moment. - `speaker-diarization`: The dataset can be used to train model for Speaker Diarization (SD). The model is presented with an audio file and asked to predict which speaker spoke at what time. ### Languages The audio is in English. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file (or files in the case of the multi-headset or multi-microphone dataset), called `file` and its transcription as a list of words, called `words`. Additional information about the `speakers`, the `word_start_time`, `word_end_time`, `segment_start_time`, `segment_end_time` is given. In addition and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'word_ids': ["ES2004a.D.words1", "ES2004a.D.words2", ...], 'word_start_times': [0.3700000047683716, 0.949999988079071, ...], 'word_end_times': [0.949999988079071, 1.5299999713897705, ...], 'word_speakers': ['A', 'A', ...], 'segment_ids': ["ES2004a.sync.1", "ES2004a.sync.2", ...] 'segment_start_times': [10.944000244140625, 17.618999481201172, ...], 'segment_end_times': [17.618999481201172, 18.722000122070312, ...], 'segment_speakers': ['A', 'B', ...], 'words', ["hmm", "hmm", ...] 'channels': [0, 0, ..], 'file': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f" 'audio': {'path': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f", 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, } ``` ### Data Fields - word_ids: a list of the ids of the words - word_start_times: a list of the start times of when the words were spoken in seconds - word_end_times: a list of the end times of when the words were spoken in seconds - word_speakers: a list of speakers one for each word - segment_ids: a list of the ids of the segments - segment_start_times: a list of the start times of when the segments start - segment_end_times: a list of the start times of when the segments ends - segment_speakers: a list of speakers one for each segment - words: a list of all the spoken words - channels: a list of all channels that were used for each word - file: a path to the audio file - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. ### Data Splits The dataset consists of several configurations, each one having train/validation/test splits: - headset-single: Close talking audio of single headset. This configuration only includes audio belonging to the headset of the person currently speaking. - headset-multi (4 channels): Close talking audio of four individual headset. This configuration includes audio belonging to four individual headsets. For each annotation there are 4 audio files 0, 1, 2, 3. - microphone-single: Far field audio of single microphone. This configuration only includes audio belonging the first microphone, *i.e.* 1-1, of the microphone array. - microphone-multi (8 channels): Far field audio of microphone array. This configuration includes audio of the first microphone array 1-1, 1-2, ..., 1-8. In general, `headset-single` and `headset-multi` include significantly less noise than `microphone-single` and `microphone-multi`. | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | headset-single | 136 (80h) | 18 (9h) | 16 (9h) | | headset-multi (4 channels) | 136 (320h) | 18 (36h) | 16 (36h) | | microphone-single | 136 (80h) | 18 (9h) | 16 (9h) | | microphone-multi (8 channels) | 136 (640h) | 18 (72h) | 16 (72h) | Note that each sample contains between 10 and 60 minutes of audio data which makes it impractical for direct transcription. One should make use of the segment and word start times and end times to chunk the samples into smaller samples of manageable size. ## Dataset Creation All information about the dataset creation can be found [here](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information CC BY 4.0 ### Citation Information #### TODO ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) and [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. #### TODO
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bprec
2023-01-25T14:27:30.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:unknown", "region:us" ]
null
Dataset consisting of Polish language texts annotated to recognize brand-product relations.
@inproceedings{inproceedings, author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka}, year = {2020}, month = {05}, pages = {}, title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations} }
0
275
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - pl license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-retrieval task_ids: - entity-linking-retrieval pretty_name: bprec dataset_info: - config_name: default features: - name: id dtype: int32 - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: tele num_bytes: 2739015 num_examples: 2391 - name: electro num_bytes: 125999 num_examples: 382 - name: cosmetics num_bytes: 1565263 num_examples: 2384 - name: banking num_bytes: 446944 num_examples: 561 download_size: 8006167 dataset_size: 4877221 - config_name: all features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 4937658 num_examples: 5718 download_size: 8006167 dataset_size: 4937658 - config_name: tele features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 2758147 num_examples: 2391 download_size: 4569708 dataset_size: 2758147 - config_name: electro features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 130205 num_examples: 382 download_size: 269917 dataset_size: 130205 - config_name: cosmetics features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 1596259 num_examples: 2384 download_size: 2417388 dataset_size: 1596259 - config_name: banking features: - name: id dtype: int32 - name: category dtype: string - name: text dtype: string - name: ner sequence: - name: source struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP - name: target struct: - name: from dtype: int32 - name: text dtype: string - name: to dtype: int32 - name: type dtype: class_label: names: '0': PRODUCT_NAME '1': PRODUCT_NAME_IMP '2': PRODUCT_NO_BRAND '3': BRAND_NAME '4': BRAND_NAME_IMP '5': VERSION '6': PRODUCT_ADJ '7': BRAND_ADJ '8': LOCATION '9': LOCATION_IMP splits: - name: train num_bytes: 453119 num_examples: 561 download_size: 749154 dataset_size: 453119 --- # Dataset Card for [Dataset Name] ## 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:** [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736) - **Repository:** [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction) - **Paper:** [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Brand-Product Relation Extraction Corpora in Polish ### Supported Tasks and Leaderboards NER, Entity linking ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - id: int identifier of a text - text: string text, for example a consumer comment on the social media - ner: extracted entities and their relationship - source and target: a pair of entities identified in the text - from: int value representing starting character of the entity - text: string value with the entity text - to: int value representing end character of the entity - type: one of pre-identified entity types: - PRODUCT_NAME - PRODUCT_NAME_IMP - PRODUCT_NO_BRAND - BRAND_NAME - BRAND_NAME_IMP - VERSION - PRODUCT_ADJ - BRAND_ADJ - LOCATION - LOCATION_IMP ### Data Splits No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts: - tele - electro - cosmetics - banking ## 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 ``` @inproceedings{inproceedings, author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka}, year = {2020}, month = {05}, pages = {}, title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations} } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.
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meta_woz
2022-11-18T21:28:56.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "arxiv:2003.01680", "region:us" ]
null
MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long.
@InProceedings{shalyminov2020fast, author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes}, title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer}, booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2020}, month = {April}, url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a -hybrid-generative-retrieval-transformer/}, }
3
275
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other license_details: Microsoft Research Data License Agreement multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: metalwoz pretty_name: Meta-Learning Wizard-of-Oz dataset_info: - config_name: dialogues features: - name: id dtype: string - name: user_id dtype: string - name: bot_id dtype: string - name: domain dtype: string - name: task_id dtype: string - name: turns sequence: string splits: - name: train num_bytes: 19999218 num_examples: 37884 - name: test num_bytes: 1284287 num_examples: 2319 download_size: 8629863 dataset_size: 21283505 - config_name: tasks features: - name: task_id dtype: string - name: domain dtype: string - name: bot_prompt dtype: string - name: bot_role dtype: string - name: user_prompt dtype: string - name: user_role dtype: string splits: - name: train num_bytes: 73768 num_examples: 227 - name: test num_bytes: 4351 num_examples: 14 download_size: 8629863 dataset_size: 78119 --- # Dataset Card for MetaLWOz ## 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 - **Repository:** [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/) - **Paper:** [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf) - **Point of Contact:** [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/) ### Dataset Summary MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long. ### Supported Tasks and Leaderboards This dataset supports a range of task. - **Generative dialogue modeling** or `dialogue-modeling`: This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast -adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues can be used to train a sequence model on the utterances. Example of sample input/output is given in section [Data Instances](#data-instances) ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were given a `domain` and a `task`. Each turn has a single utterance, e.g.: ``` Domain: Ski User Task: You want to know if there are good ski hills an hour’s drive from your current location. Bot Task: Tell the user that there are no ski hills in their immediate location. Bot: Hello how may I help you? User: Is there any good ski hills an hour’s drive from my current location? Bot: I’m sorry to inform you that there are no ski hills in your immediate location User: Can you help me find the nearest? Bot: Absolutely! It looks like you’re about 3 hours away from Bear Mountain. That seems to be the closest. User: Hmm.. sounds good Bot: Alright! I can help you get your lift tickets now!When will you be going? User: Awesome! please get me a ticket for 10pax Bot: You’ve got it. Anything else I can help you with? User: None. Thanks again! Bot: No problem! ``` Example of input/output for this dialog: ``` Input: dialog history = Hello how may I help you?; Is there any good ski hills an hour’s drive from my current location?; I’m sorry to inform you that there are no ski hills in your immediate location Output: user response = Can you help me find the nearest? ``` ### Data Fields Each dialogue instance has the following fields: - `id`: a unique ID identifying the dialog. - `user_id`: a unique ID identifying the user. - `bot_id`: a unique ID identifying the bot. - `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset. - `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset. - `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`. Each task instance has following fields: - `task_id`: a unique ID identifying the task. - `domain`: a unique ID identifying the domain. - `bot_prompt`: The task specification for bot. - `bot_role`: The domain oriented role of bot. - `user_prompt`: The task specification for user. - `user_role`: The domain oriented role of user. ### Data Splits The dataset is split into a `train` and `test` split with the following sizes: | | Training MetaLWOz | Evaluation MetaLWOz | Combined | | ----- | ------ | ----- | ---- | | Total Domains | 47 | 4 | 51 | | Total Tasks | 226 | 14 | 240 | | Total Dialogs | 37884 | 2319 | 40203 | Below are the various statistics of the dataset: | Statistic | Mean | Minimum | Maximum | | ----- | ------ | ----- | ---- | | Number of tasks per domain | 4.8 | 3 | 11 | | Number of dialogs per domain | 806.0 | 288 | 1990 | | Number of dialogs per task | 167.6 | 32 | 285 | | Number of turns per dialog | 11.4 | 10 | 46 | ## 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 The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada) ### Licensing Information The dataset is released under [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view) ### Citation Information You can cite the following for the various versions of MetaLWOz: Version 1.0 ``` @InProceedings{shalyminov2020fast, author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes}, title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer}, booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2020}, month = {April}, url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a -hybrid-generative-retrieval-transformer/}, } ``` ### Contributions Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.
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nlphuji/fairface_val_padding_025
2023-01-18T22:57:00.000Z
[ "region:us" ]
nlphuji
null
null
1
275
2023-01-18T22:46:25
# FairFace (val set) Original paper: [Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf) Homepage: https://github.com/joojs/fairface Bibtex: ``` @inproceedings{karkkainenfairface, title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation}, author={Karkkainen, Kimmo and Joo, Jungseock}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, year={2021}, pages={1548--1558} } ```
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emozilla/yarn-train-tokenized-16k-mistral
2023-10-11T01:19:23.000Z
[ "region:us" ]
emozilla
null
null
0
275
2023-10-11T01:10:33
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 44375336324 num_examples: 208331 download_size: 12153714144 dataset_size: 44375336324 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "yarn-train-tokenized-16k-mistral" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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atmallen/qm_alice_1.0e_eval
2023-10-31T19:43:19.000Z
[ "region:us" ]
atmallen
null
null
0
275
2023-10-27T05:42:11
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: summand1 dtype: int64 - name: summand2 dtype: int64 - name: character dtype: string - name: sum dtype: int64 - name: sum_words dtype: string - name: summand1_words dtype: string - name: summand2_words dtype: string - name: label dtype: class_label: names: '0': 'False' '1': 'True' - name: alice_label dtype: int64 - name: bob_label dtype: int64 - name: row_id dtype: int64 splits: - name: train num_bytes: 134298152.0 num_examples: 800000 - name: validation num_bytes: 13701211.0 num_examples: 80000 - name: test num_bytes: 13726378.0 num_examples: 80000 download_size: 31603082 dataset_size: 161725741.0 --- # Dataset Card for "qm_alice_1.0e_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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conceptual_12m
2022-11-03T16:31:22.000Z
[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:other", "arxiv:2102.08981", "region:us" ]
null
Conceptual 12M is a large-scale dataset of 12 million image-text pairs specifically meant to be used for visionand-language pre-training. Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M.
@inproceedings{changpinyo2021cc12m, title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts}, author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu}, booktitle = {CVPR}, year = {2021}, }
11
274
2022-04-15T08:06:58
--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - image-to-text task_ids: - image-captioning paperswithcode_id: cc12m pretty_name: Conceptual 12M dataset_info: features: - name: image_url dtype: string - name: caption dtype: string splits: - name: train num_bytes: 2794168030 num_examples: 12423374 download_size: 2707204412 dataset_size: 2794168030 --- # Dataset Card for Conceptual 12M ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [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 - **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m) - **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981) - **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com) ### Dataset Summary Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M). ### Dataset Preprocessing This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code: ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) return batch num_threads = 20 dset = load_dataset("conceptual_12m") dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) ``` ### Supported Tasks and Leaderboards - `image-captioning`: This dataset can be used to train model for the Image Captioning task. ### Languages All captions are in English. ## Dataset Structure ### Data Instances Each instance represents a single image with a caption: ``` { 'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800', 'caption': 'a very typical bus station' } ``` ### Data Fields - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. ### Data Splits There is only training data, with a total of 12423374 rows ## Dataset Creation ### Curation Rationale Conceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps. ### Source Data #### Initial Data Collection and Normalization From the paper: > To arrive at CC12M, we keep the image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2. We still keep only JPEG images with size greater than 400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text between 3 and 256 words in the alt-text. We still discard candidates with no noun or no determiner, but permit ones without prepositions. We discard the heuristics regarding high unique-word ratio covering various POS tags and word capitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the above relaxations, the threshold for counting a word type as rare is increased from 5 to 20 > The main motivation for CC3M to perform text transformation is that a majority of candidate captions contain ultrafine-grained entities such as proper names (people, venues, locations, etc.), making it extremely difficult to learn as part of the image captioning task. In contrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability. We thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy of the individuals in these images. For this step, we use the Google Cloud Natural Language APIs to detect all named entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M are transformed in this fashion. #### Who are the source language producers? Not specified. ### Annotations #### Annotation process Annotations are extracted jointly with the images using the automatic pipeline. #### Who are the annotators? Not specified. ### Personal and Sensitive Information From the paper: > The only exception to the “keep alt-texts as raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy of the individuals in these images. For this step, we use the Google Cloud Natural Language APIs to detect all named entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M are transformed in this fashion. ## 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 Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ```bibtex @inproceedings{changpinyo2021cc12m, title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts}, author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu}, booktitle = {CVPR}, year = {2021}, } ``` ### Contributions Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.
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djstrong/oscar-small
2023-03-07T19:57:38.000Z
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "source_datasets:oscar", "language:af", "language:am", "language:ar", "language:arz", "language:as", "language:az", "language:azb", "language:ba", "language:be", "language:bg", "language:bn", "language:bo", "language:br", "language:ca", "language:ce", "language:ceb", "language:ckb", "language:cs", "language:cv", "language:cy", "language:da", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:fy", "language:ga", "language:gl", "language:gu", "language:he", "language:hi", "language:hr", "language:hu", "language:hy", "language:id", "language:is", "language:it", "language:ja", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ku", "language:ky", "language:la", "language:lb", "language:lo", "language:lt", "language:lv", "language:mg", "language:mhr", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:nds", "language:ne", "language:nl", "language:nn", "language:no", "language:or", "language:os", "language:pa", "language:pl", "language:pnb", "language:ps", "language:pt", "language:ro", "language:ru", "language:sa", "language:sah", "language:sd", "language:sh", "language:si", "language:sk", "language:sl", "language:sq", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:tg", "language:th", "language:tk", "language:tl", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vi", "language:yi", "language:zh", "license:cc0-1.0", "arxiv:2010.14571", "region:us" ]
djstrong
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\
@inproceedings{ortiz-suarez-etal-2020-monolingual, title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages", author = "Ortiz Su{\'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.156", pages = "1703--1714", abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{\'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\"u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} }
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--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - arz - as - az - azb - ba - be - bg - bn - bo - br - ca - ce - ceb - ckb - cs - cv - cy - da - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gl - gu - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mhr - mk - ml - mn - mr - ms - mt - my - nds - ne - nl - nn - 'no' - or - os - pa - pl - pnb - ps - pt - ro - ru - sa - sah - sd - sh - si - sk - sl - sq - sr - sv - sw - ta - te - tg - th - tk - tl - tr - tt - ug - uk - ur - uz - vi - yi - zh license: - cc0-1.0 multilinguality: - multilingual source_datasets: - oscar task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: oscar pretty_name: OSCAR --- ## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts. Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below. # Dataset Card for "oscar" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form. ### Supported Tasks and Leaderboards OSCAR is mainly inteded to pretrain language models and word represantations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR. ## Dataset Structure We show detailed information for all the configurations of the dataset. ## Dataset Creation ### Curation Rationale OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner. The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process. Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed. ### Source Data #### Initial Data Collection and Normalization [Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies. Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics. To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header. #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures. ### Discussion of Biases OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. ### Other Known Limitations The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571). ## Additional Information ### Dataset Curators The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/). ### Licensing Information These data are released under this licensing scheme We do not own any of the text from which these data has been extracted. We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR This work is published from: France. Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. We will comply to legitimate requests by removing the affected sources from the next release of the corpus. ### Citation Information ``` @inproceedings{ortiz-suarez-etal-2020-monolingual, title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages", author = "Ortiz Su{'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.156", pages = "1703--1714", abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{"u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} } ``` ### Contributions Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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ostapeno/qa-openai_icl5_clen128_maxD-1_maxC8000_0_length_matched
2023-10-16T13:37:54.000Z
[ "region:us" ]
ostapeno
null
null
0
274
2023-10-16T13:37:39
--- dataset_info: features: - name: id dtype: string - name: context dtype: string - name: docno dtype: string - name: subject dtype: string - name: icl_examples sequence: string - name: author_instr dtype: string - name: instruction dtype: string - name: response dtype: string splits: - name: formal_logic num_bytes: 12667394.408322431 num_examples: 4371 - name: machine_learning num_bytes: 12496409.217269806 num_examples: 4312 - name: global_facts num_bytes: 12528287.81221182 num_examples: 4323 - name: abstract_algebra num_bytes: 11890715.913371524 num_examples: 4103 - name: high_school_physics num_bytes: 12922423.167858548 num_examples: 4459 - name: college_biology num_bytes: 13084714.19665426 num_examples: 4515 - name: high_school_government_and_politics num_bytes: 12281953.214932615 num_examples: 4238 - name: prehistory num_bytes: 12365996.78341611 num_examples: 4267 - name: security_studies num_bytes: 12913729.005601635 num_examples: 4456 - name: sociology num_bytes: 12719559.381863909 num_examples: 4389 download_size: 30681032 dataset_size: 125871183.10150266 --- # Dataset Card for "qa-openai_icl5_clen128_maxD-1_maxC8000_0.jsonl_length_matched" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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jamescalam/image-text-demo
2023-02-06T05:29:49.000Z
[ "region:us" ]
jamescalam
Demo dataset for testing or showing image-text capabilities.
@InProceedings{huggingface:dataset, title = {Small image-text set}, author={James Briggs}, year={2022} }
0
273
2022-09-04T08:05:03
Entry not found
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evanarlian/imagenet_1k_resized_256
2023-08-01T10:26:36.000Z
[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:derived", "language:en", "license:other", "region:us" ]
evanarlian
null
null
3
273
2023-07-30T17:27:40
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - derived task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: imagenet pretty_name: ImageNet Resized 256 license_details: imagenet-agreement extra_gated_prompt: https://huggingface.co/datasets/imagenet-1k extra_gated_fields: I have agreed to the original ImageNet dataset: checkbox dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': tench, Tinca tinca '1': goldfish, Carassius auratus '2': great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias '3': tiger shark, Galeocerdo cuvieri '4': hammerhead, hammerhead shark '5': electric ray, crampfish, numbfish, torpedo '6': stingray '7': cock '8': hen '9': ostrich, Struthio camelus '10': brambling, Fringilla montifringilla '11': goldfinch, Carduelis carduelis '12': house finch, linnet, Carpodacus mexicanus '13': junco, snowbird '14': indigo bunting, indigo finch, indigo bird, Passerina cyanea '15': robin, American robin, Turdus migratorius '16': bulbul '17': jay '18': magpie '19': chickadee '20': water ouzel, dipper '21': kite '22': bald eagle, American eagle, Haliaeetus leucocephalus '23': vulture '24': great grey owl, great gray owl, Strix nebulosa '25': European fire salamander, Salamandra salamandra '26': common newt, Triturus vulgaris '27': eft '28': spotted salamander, Ambystoma maculatum '29': axolotl, mud puppy, Ambystoma mexicanum '30': bullfrog, Rana catesbeiana '31': tree frog, tree-frog '32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui '33': loggerhead, loggerhead turtle, Caretta caretta '34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea '35': mud turtle '36': terrapin '37': box turtle, box tortoise '38': banded gecko '39': common iguana, iguana, Iguana iguana '40': American chameleon, anole, Anolis carolinensis '41': whiptail, whiptail lizard '42': agama '43': frilled lizard, Chlamydosaurus kingi '44': alligator lizard '45': Gila monster, Heloderma suspectum '46': green lizard, Lacerta viridis '47': African chameleon, Chamaeleo chamaeleon '48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis '49': African crocodile, Nile crocodile, Crocodylus niloticus '50': American alligator, Alligator mississipiensis '51': triceratops '52': thunder snake, worm snake, Carphophis amoenus '53': ringneck snake, ring-necked snake, ring snake '54': hognose snake, puff adder, sand viper '55': green snake, grass snake '56': king snake, kingsnake '57': garter snake, grass snake '58': water snake '59': vine snake '60': night snake, Hypsiglena torquata '61': boa constrictor, Constrictor constrictor '62': rock python, rock snake, Python sebae '63': Indian cobra, Naja naja '64': green mamba '65': sea snake '66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus '67': diamondback, diamondback rattlesnake, Crotalus adamanteus '68': sidewinder, horned rattlesnake, Crotalus cerastes '69': trilobite '70': harvestman, daddy longlegs, Phalangium opilio '71': scorpion '72': black and gold garden spider, Argiope aurantia '73': barn spider, Araneus cavaticus '74': garden spider, Aranea diademata '75': black widow, Latrodectus mactans '76': tarantula '77': wolf spider, hunting spider '78': tick '79': centipede '80': black grouse '81': ptarmigan '82': ruffed grouse, partridge, Bonasa umbellus '83': prairie chicken, prairie grouse, prairie fowl '84': peacock '85': quail '86': partridge '87': African grey, African gray, Psittacus erithacus '88': macaw '89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita '90': lorikeet '91': coucal '92': bee eater '93': hornbill '94': hummingbird '95': jacamar '96': toucan '97': drake '98': red-breasted merganser, Mergus serrator '99': goose '100': black swan, Cygnus atratus '101': tusker '102': echidna, spiny anteater, anteater '103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus '104': wallaby, brush kangaroo '105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus '106': wombat '107': jellyfish '108': sea anemone, anemone '109': brain coral '110': flatworm, platyhelminth '111': nematode, nematode worm, roundworm '112': conch '113': snail '114': slug '115': sea slug, nudibranch '116': chiton, coat-of-mail shell, sea cradle, polyplacophore '117': chambered nautilus, pearly nautilus, nautilus '118': Dungeness crab, Cancer magister '119': rock crab, Cancer irroratus '120': fiddler crab '121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica '122': American lobster, Northern lobster, Maine lobster, Homarus americanus '123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish '124': crayfish, crawfish, crawdad, crawdaddy '125': hermit crab '126': isopod '127': white stork, Ciconia ciconia '128': black stork, Ciconia nigra '129': spoonbill '130': flamingo '131': little blue heron, Egretta caerulea '132': American egret, great white heron, Egretta albus '133': bittern '134': crane '135': limpkin, Aramus pictus '136': European gallinule, Porphyrio porphyrio '137': American coot, marsh hen, mud hen, water hen, Fulica americana '138': bustard '139': ruddy turnstone, Arenaria interpres '140': red-backed sandpiper, dunlin, Erolia alpina '141': redshank, Tringa totanus '142': dowitcher '143': oystercatcher, oyster catcher '144': pelican '145': king penguin, Aptenodytes patagonica '146': albatross, mollymawk '147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus '148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca '149': dugong, Dugong dugon '150': sea lion '151': Chihuahua '152': Japanese spaniel '153': Maltese dog, Maltese terrier, Maltese '154': Pekinese, Pekingese, Peke '155': Shih-Tzu '156': Blenheim spaniel '157': papillon '158': toy terrier '159': Rhodesian ridgeback '160': Afghan hound, Afghan '161': basset, basset hound '162': beagle '163': bloodhound, sleuthhound '164': bluetick '165': black-and-tan coonhound '166': Walker hound, Walker foxhound '167': English foxhound '168': redbone '169': borzoi, Russian wolfhound '170': Irish wolfhound '171': Italian greyhound '172': whippet '173': Ibizan hound, Ibizan Podenco '174': Norwegian elkhound, elkhound '175': otterhound, otter hound '176': Saluki, gazelle hound '177': Scottish deerhound, deerhound '178': Weimaraner '179': Staffordshire bullterrier, Staffordshire bull terrier '180': American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier '181': Bedlington terrier '182': Border terrier '183': Kerry blue terrier '184': Irish terrier '185': Norfolk terrier '186': Norwich terrier '187': Yorkshire terrier '188': wire-haired fox terrier '189': Lakeland terrier '190': Sealyham terrier, Sealyham '191': Airedale, Airedale terrier '192': cairn, cairn terrier '193': Australian terrier '194': Dandie Dinmont, Dandie Dinmont terrier '195': Boston bull, Boston terrier '196': miniature schnauzer '197': giant schnauzer '198': standard schnauzer '199': Scotch terrier, Scottish terrier, Scottie '200': Tibetan terrier, chrysanthemum dog '201': silky terrier, Sydney silky '202': soft-coated wheaten terrier '203': West Highland white terrier '204': Lhasa, Lhasa apso '205': flat-coated retriever '206': curly-coated retriever '207': golden retriever '208': Labrador retriever '209': Chesapeake Bay retriever '210': German short-haired pointer '211': vizsla, Hungarian pointer '212': English setter '213': Irish setter, red setter '214': Gordon setter '215': Brittany spaniel '216': clumber, clumber spaniel '217': English springer, English springer spaniel '218': Welsh springer spaniel '219': cocker spaniel, English cocker spaniel, cocker '220': Sussex spaniel '221': Irish water spaniel '222': kuvasz '223': schipperke '224': groenendael '225': malinois '226': briard '227': kelpie '228': komondor '229': Old English sheepdog, bobtail '230': Shetland sheepdog, Shetland sheep dog, Shetland '231': collie '232': Border collie '233': Bouvier des Flandres, Bouviers des Flandres '234': Rottweiler '235': German shepherd, German shepherd dog, German police dog, alsatian '236': Doberman, Doberman pinscher '237': miniature pinscher '238': Greater Swiss Mountain dog '239': Bernese mountain dog '240': Appenzeller '241': EntleBucher '242': boxer '243': bull mastiff '244': Tibetan mastiff '245': French bulldog '246': Great Dane '247': Saint Bernard, St Bernard '248': Eskimo dog, husky '249': malamute, malemute, Alaskan malamute '250': Siberian husky '251': dalmatian, coach dog, carriage dog '252': affenpinscher, monkey pinscher, monkey dog '253': basenji '254': pug, pug-dog '255': Leonberg '256': Newfoundland, Newfoundland dog '257': Great Pyrenees '258': Samoyed, Samoyede '259': Pomeranian '260': chow, chow chow '261': keeshond '262': Brabancon griffon '263': Pembroke, Pembroke Welsh corgi '264': Cardigan, Cardigan Welsh corgi '265': toy poodle '266': miniature poodle '267': standard poodle '268': Mexican hairless '269': timber wolf, grey wolf, gray wolf, Canis lupus '270': white wolf, Arctic wolf, Canis lupus tundrarum '271': red wolf, maned wolf, Canis rufus, Canis niger '272': coyote, prairie wolf, brush wolf, Canis latrans '273': dingo, warrigal, warragal, Canis dingo '274': dhole, Cuon alpinus '275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus '276': hyena, hyaena '277': red fox, Vulpes vulpes '278': kit fox, Vulpes macrotis '279': Arctic fox, white fox, Alopex lagopus '280': grey fox, gray fox, Urocyon cinereoargenteus '281': tabby, tabby cat '282': tiger cat '283': Persian cat '284': Siamese cat, Siamese '285': Egyptian cat '286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor '287': lynx, catamount '288': leopard, Panthera pardus '289': snow leopard, ounce, Panthera uncia '290': jaguar, panther, Panthera onca, Felis onca '291': lion, king of beasts, Panthera leo '292': tiger, Panthera tigris '293': cheetah, chetah, Acinonyx jubatus '294': brown bear, bruin, Ursus arctos '295': American black bear, black bear, Ursus americanus, Euarctos americanus '296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus '297': sloth bear, Melursus ursinus, Ursus ursinus '298': mongoose '299': meerkat, mierkat '300': tiger beetle '301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle '302': ground beetle, carabid beetle '303': long-horned beetle, longicorn, longicorn beetle '304': leaf beetle, chrysomelid '305': dung beetle '306': rhinoceros beetle '307': weevil '308': fly '309': bee '310': ant, emmet, pismire '311': grasshopper, hopper '312': cricket '313': walking stick, walkingstick, stick insect '314': cockroach, roach '315': mantis, mantid '316': cicada, cicala '317': leafhopper '318': lacewing, lacewing fly '319': dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk '320': damselfly '321': admiral '322': ringlet, ringlet butterfly '323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus '324': cabbage butterfly '325': sulphur butterfly, sulfur butterfly '326': lycaenid, lycaenid butterfly '327': starfish, sea star '328': sea urchin '329': sea cucumber, holothurian '330': wood rabbit, cottontail, cottontail rabbit '331': hare '332': Angora, Angora rabbit '333': hamster '334': porcupine, hedgehog '335': fox squirrel, eastern fox squirrel, Sciurus niger '336': marmot '337': beaver '338': guinea pig, Cavia cobaya '339': sorrel '340': zebra '341': hog, pig, grunter, squealer, Sus scrofa '342': wild boar, boar, Sus scrofa '343': warthog '344': hippopotamus, hippo, river horse, Hippopotamus amphibius '345': ox '346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis '347': bison '348': ram, tup '349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis '350': ibex, Capra ibex '351': hartebeest '352': impala, Aepyceros melampus '353': gazelle '354': Arabian camel, dromedary, Camelus dromedarius '355': llama '356': weasel '357': mink '358': polecat, fitch, foulmart, foumart, Mustela putorius '359': black-footed ferret, ferret, Mustela nigripes '360': otter '361': skunk, polecat, wood pussy '362': badger '363': armadillo '364': three-toed sloth, ai, Bradypus tridactylus '365': orangutan, orang, orangutang, Pongo pygmaeus '366': gorilla, Gorilla gorilla '367': chimpanzee, chimp, Pan troglodytes '368': gibbon, Hylobates lar '369': siamang, Hylobates syndactylus, Symphalangus syndactylus '370': guenon, guenon monkey '371': patas, hussar monkey, Erythrocebus patas '372': baboon '373': macaque '374': langur '375': colobus, colobus monkey '376': proboscis monkey, Nasalis larvatus '377': marmoset '378': capuchin, ringtail, Cebus capucinus '379': howler monkey, howler '380': titi, titi monkey '381': spider monkey, Ateles geoffroyi '382': squirrel monkey, Saimiri sciureus '383': Madagascar cat, ring-tailed lemur, Lemur catta '384': indri, indris, Indri indri, Indri brevicaudatus '385': Indian elephant, Elephas maximus '386': African elephant, Loxodonta africana '387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens '388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca '389': barracouta, snoek '390': eel '391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch '392': rock beauty, Holocanthus tricolor '393': anemone fish '394': sturgeon '395': gar, garfish, garpike, billfish, Lepisosteus osseus '396': lionfish '397': puffer, pufferfish, blowfish, globefish '398': abacus '399': abaya '400': academic gown, academic robe, judge's robe '401': accordion, piano accordion, squeeze box '402': acoustic guitar '403': aircraft carrier, carrier, flattop, attack aircraft carrier '404': airliner '405': airship, dirigible '406': altar '407': ambulance '408': amphibian, amphibious vehicle '409': analog clock '410': apiary, bee house '411': apron '412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin '413': assault rifle, assault gun '414': backpack, back pack, knapsack, packsack, rucksack, haversack '415': bakery, bakeshop, bakehouse '416': balance beam, beam '417': balloon '418': ballpoint, ballpoint pen, ballpen, Biro '419': Band Aid '420': banjo '421': bannister, banister, balustrade, balusters, handrail '422': barbell '423': barber chair '424': barbershop '425': barn '426': barometer '427': barrel, cask '428': barrow, garden cart, lawn cart, wheelbarrow '429': baseball '430': basketball '431': bassinet '432': bassoon '433': bathing cap, swimming cap '434': bath towel '435': bathtub, bathing tub, bath, tub '436': beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon '437': beacon, lighthouse, beacon light, pharos '438': beaker '439': bearskin, busby, shako '440': beer bottle '441': beer glass '442': bell cote, bell cot '443': bib '444': bicycle-built-for-two, tandem bicycle, tandem '445': bikini, two-piece '446': binder, ring-binder '447': binoculars, field glasses, opera glasses '448': birdhouse '449': boathouse '450': bobsled, bobsleigh, bob '451': bolo tie, bolo, bola tie, bola '452': bonnet, poke bonnet '453': bookcase '454': bookshop, bookstore, bookstall '455': bottlecap '456': bow '457': bow tie, bow-tie, bowtie '458': brass, memorial tablet, plaque '459': brassiere, bra, bandeau '460': breakwater, groin, groyne, mole, bulwark, seawall, jetty '461': breastplate, aegis, egis '462': broom '463': bucket, pail '464': buckle '465': bulletproof vest '466': bullet train, bullet '467': butcher shop, meat market '468': cab, hack, taxi, taxicab '469': caldron, cauldron '470': candle, taper, wax light '471': cannon '472': canoe '473': can opener, tin opener '474': cardigan '475': car mirror '476': carousel, carrousel, merry-go-round, roundabout, whirligig '477': carpenter's kit, tool kit '478': carton '479': car wheel '480': cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM '481': cassette '482': cassette player '483': castle '484': catamaran '485': CD player '486': cello, violoncello '487': cellular telephone, cellular phone, cellphone, cell, mobile phone '488': chain '489': chainlink fence '490': chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour '491': chain saw, chainsaw '492': chest '493': chiffonier, commode '494': chime, bell, gong '495': china cabinet, china closet '496': Christmas stocking '497': church, church building '498': cinema, movie theater, movie theatre, movie house, picture palace '499': cleaver, meat cleaver, chopper '500': cliff dwelling '501': cloak '502': clog, geta, patten, sabot '503': cocktail shaker '504': coffee mug '505': coffeepot '506': coil, spiral, volute, whorl, helix '507': combination lock '508': computer keyboard, keypad '509': confectionery, confectionary, candy store '510': container ship, containership, container vessel '511': convertible '512': corkscrew, bottle screw '513': cornet, horn, trumpet, trump '514': cowboy boot '515': cowboy hat, ten-gallon hat '516': cradle '517': crane2 '518': crash helmet '519': crate '520': crib, cot '521': Crock Pot '522': croquet ball '523': crutch '524': cuirass '525': dam, dike, dyke '526': desk '527': desktop computer '528': dial telephone, dial phone '529': diaper, nappy, napkin '530': digital clock '531': digital watch '532': dining table, board '533': dishrag, dishcloth '534': dishwasher, dish washer, dishwashing machine '535': disk brake, disc brake '536': dock, dockage, docking facility '537': dogsled, dog sled, dog sleigh '538': dome '539': doormat, welcome mat '540': drilling platform, offshore rig '541': drum, membranophone, tympan '542': drumstick '543': dumbbell '544': Dutch oven '545': electric fan, blower '546': electric guitar '547': electric locomotive '548': entertainment center '549': envelope '550': espresso maker '551': face powder '552': feather boa, boa '553': file, file cabinet, filing cabinet '554': fireboat '555': fire engine, fire truck '556': fire screen, fireguard '557': flagpole, flagstaff '558': flute, transverse flute '559': folding chair '560': football helmet '561': forklift '562': fountain '563': fountain pen '564': four-poster '565': freight car '566': French horn, horn '567': frying pan, frypan, skillet '568': fur coat '569': garbage truck, dustcart '570': gasmask, respirator, gas helmet '571': gas pump, gasoline pump, petrol pump, island dispenser '572': goblet '573': go-kart '574': golf ball '575': golfcart, golf cart '576': gondola '577': gong, tam-tam '578': gown '579': grand piano, grand '580': greenhouse, nursery, glasshouse '581': grille, radiator grille '582': grocery store, grocery, food market, market '583': guillotine '584': hair slide '585': hair spray '586': half track '587': hammer '588': hamper '589': hand blower, blow dryer, blow drier, hair dryer, hair drier '590': hand-held computer, hand-held microcomputer '591': handkerchief, hankie, hanky, hankey '592': hard disc, hard disk, fixed disk '593': harmonica, mouth organ, harp, mouth harp '594': harp '595': harvester, reaper '596': hatchet '597': holster '598': home theater, home theatre '599': honeycomb '600': hook, claw '601': hoopskirt, crinoline '602': horizontal bar, high bar '603': horse cart, horse-cart '604': hourglass '605': iPod '606': iron, smoothing iron '607': jack-o'-lantern '608': jean, blue jean, denim '609': jeep, landrover '610': jersey, T-shirt, tee shirt '611': jigsaw puzzle '612': jinrikisha, ricksha, rickshaw '613': joystick '614': kimono '615': knee pad '616': knot '617': lab coat, laboratory coat '618': ladle '619': lampshade, lamp shade '620': laptop, laptop computer '621': lawn mower, mower '622': lens cap, lens cover '623': letter opener, paper knife, paperknife '624': library '625': lifeboat '626': lighter, light, igniter, ignitor '627': limousine, limo '628': liner, ocean liner '629': lipstick, lip rouge '630': Loafer '631': lotion '632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system '633': loupe, jeweler's loupe '634': lumbermill, sawmill '635': magnetic compass '636': mailbag, postbag '637': mailbox, letter box '638': maillot '639': maillot, tank suit '640': manhole cover '641': maraca '642': marimba, xylophone '643': mask '644': matchstick '645': maypole '646': maze, labyrinth '647': measuring cup '648': medicine chest, medicine cabinet '649': megalith, megalithic structure '650': microphone, mike '651': microwave, microwave oven '652': military uniform '653': milk can '654': minibus '655': miniskirt, mini '656': minivan '657': missile '658': mitten '659': mixing bowl '660': mobile home, manufactured home '661': Model T '662': modem '663': monastery '664': monitor '665': moped '666': mortar '667': mortarboard '668': mosque '669': mosquito net '670': motor scooter, scooter '671': mountain bike, all-terrain bike, off-roader '672': mountain tent '673': mouse, computer mouse '674': mousetrap '675': moving van '676': muzzle '677': nail '678': neck brace '679': necklace '680': nipple '681': notebook, notebook computer '682': obelisk '683': oboe, hautboy, hautbois '684': ocarina, sweet potato '685': odometer, hodometer, mileometer, milometer '686': oil filter '687': organ, pipe organ '688': oscilloscope, scope, cathode-ray oscilloscope, CRO '689': overskirt '690': oxcart '691': oxygen mask '692': packet '693': paddle, boat paddle '694': paddlewheel, paddle wheel '695': padlock '696': paintbrush '697': pajama, pyjama, pj's, jammies '698': palace '699': panpipe, pandean pipe, syrinx '700': paper towel '701': parachute, chute '702': parallel bars, bars '703': park bench '704': parking meter '705': passenger car, coach, carriage '706': patio, terrace '707': pay-phone, pay-station '708': pedestal, plinth, footstall '709': pencil box, pencil case '710': pencil sharpener '711': perfume, essence '712': Petri dish '713': photocopier '714': pick, plectrum, plectron '715': pickelhaube '716': picket fence, paling '717': pickup, pickup truck '718': pier '719': piggy bank, penny bank '720': pill bottle '721': pillow '722': ping-pong ball '723': pinwheel '724': pirate, pirate ship '725': pitcher, ewer '726': plane, carpenter's plane, woodworking plane '727': planetarium '728': plastic bag '729': plate rack '730': plow, plough '731': plunger, plumber's helper '732': Polaroid camera, Polaroid Land camera '733': pole '734': police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria '735': poncho '736': pool table, billiard table, snooker table '737': pop bottle, soda bottle '738': pot, flowerpot '739': potter's wheel '740': power drill '741': prayer rug, prayer mat '742': printer '743': prison, prison house '744': projectile, missile '745': projector '746': puck, hockey puck '747': punching bag, punch bag, punching ball, punchball '748': purse '749': quill, quill pen '750': quilt, comforter, comfort, puff '751': racer, race car, racing car '752': racket, racquet '753': radiator '754': radio, wireless '755': radio telescope, radio reflector '756': rain barrel '757': recreational vehicle, RV, R.V. '758': reel '759': reflex camera '760': refrigerator, icebox '761': remote control, remote '762': restaurant, eating house, eating place, eatery '763': revolver, six-gun, six-shooter '764': rifle '765': rocking chair, rocker '766': rotisserie '767': rubber eraser, rubber, pencil eraser '768': rugby ball '769': rule, ruler '770': running shoe '771': safe '772': safety pin '773': saltshaker, salt shaker '774': sandal '775': sarong '776': sax, saxophone '777': scabbard '778': scale, weighing machine '779': school bus '780': schooner '781': scoreboard '782': screen, CRT screen '783': screw '784': screwdriver '785': seat belt, seatbelt '786': sewing machine '787': shield, buckler '788': shoe shop, shoe-shop, shoe store '789': shoji '790': shopping basket '791': shopping cart '792': shovel '793': shower cap '794': shower curtain '795': ski '796': ski mask '797': sleeping bag '798': slide rule, slipstick '799': sliding door '800': slot, one-armed bandit '801': snorkel '802': snowmobile '803': snowplow, snowplough '804': soap dispenser '805': soccer ball '806': sock '807': solar dish, solar collector, solar furnace '808': sombrero '809': soup bowl '810': space bar '811': space heater '812': space shuttle '813': spatula '814': speedboat '815': spider web, spider's web '816': spindle '817': sports car, sport car '818': spotlight, spot '819': stage '820': steam locomotive '821': steel arch bridge '822': steel drum '823': stethoscope '824': stole '825': stone wall '826': stopwatch, stop watch '827': stove '828': strainer '829': streetcar, tram, tramcar, trolley, trolley car '830': stretcher '831': studio couch, day bed '832': stupa, tope '833': submarine, pigboat, sub, U-boat '834': suit, suit of clothes '835': sundial '836': sunglass '837': sunglasses, dark glasses, shades '838': sunscreen, sunblock, sun blocker '839': suspension bridge '840': swab, swob, mop '841': sweatshirt '842': swimming trunks, bathing trunks '843': swing '844': switch, electric switch, electrical switch '845': syringe '846': table lamp '847': tank, army tank, armored combat vehicle, armoured combat vehicle '848': tape player '849': teapot '850': teddy, teddy bear '851': television, television system '852': tennis ball '853': thatch, thatched roof '854': theater curtain, theatre curtain '855': thimble '856': thresher, thrasher, threshing machine '857': throne '858': tile roof '859': toaster '860': tobacco shop, tobacconist shop, tobacconist '861': toilet seat '862': torch '863': totem pole '864': tow truck, tow car, wrecker '865': toyshop '866': tractor '867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi '868': tray '869': trench coat '870': tricycle, trike, velocipede '871': trimaran '872': tripod '873': triumphal arch '874': trolleybus, trolley coach, trackless trolley '875': trombone '876': tub, vat '877': turnstile '878': typewriter keyboard '879': umbrella '880': unicycle, monocycle '881': upright, upright piano '882': vacuum, vacuum cleaner '883': vase '884': vault '885': velvet '886': vending machine '887': vestment '888': viaduct '889': violin, fiddle '890': volleyball '891': waffle iron '892': wall clock '893': wallet, billfold, notecase, pocketbook '894': wardrobe, closet, press '895': warplane, military plane '896': washbasin, handbasin, washbowl, lavabo, wash-hand basin '897': washer, automatic washer, washing machine '898': water bottle '899': water jug '900': water tower '901': whiskey jug '902': whistle '903': wig '904': window screen '905': window shade '906': Windsor tie '907': wine bottle '908': wing '909': wok '910': wooden spoon '911': wool, woolen, woollen '912': worm fence, snake fence, snake-rail fence, Virginia fence '913': wreck '914': yawl '915': yurt '916': web site, website, internet site, site '917': comic book '918': crossword puzzle, crossword '919': street sign '920': traffic light, traffic signal, stoplight '921': book jacket, dust cover, dust jacket, dust wrapper '922': menu '923': plate '924': guacamole '925': consomme '926': hot pot, hotpot '927': trifle '928': ice cream, icecream '929': ice lolly, lolly, lollipop, popsicle '930': French loaf '931': bagel, beigel '932': pretzel '933': cheeseburger '934': hotdog, hot dog, red hot '935': mashed potato '936': head cabbage '937': broccoli '938': cauliflower '939': zucchini, courgette '940': spaghetti squash '941': acorn squash '942': butternut squash '943': cucumber, cuke '944': artichoke, globe artichoke '945': bell pepper '946': cardoon '947': mushroom '948': Granny Smith '949': strawberry '950': orange '951': lemon '952': fig '953': pineapple, ananas '954': banana '955': jackfruit, jak, jack '956': custard apple '957': pomegranate '958': hay '959': carbonara '960': chocolate sauce, chocolate syrup '961': dough '962': meat loaf, meatloaf '963': pizza, pizza pie '964': potpie '965': burrito '966': red wine '967': espresso '968': cup '969': eggnog '970': alp '971': bubble '972': cliff, drop, drop-off '973': coral reef '974': geyser '975': lakeside, lakeshore '976': promontory, headland, head, foreland '977': sandbar, sand bar '978': seashore, coast, seacoast, sea-coast '979': valley, vale '980': volcano '981': ballplayer, baseball player '982': groom, bridegroom '983': scuba diver '984': rapeseed '985': daisy '986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum '987': corn '988': acorn '989': hip, rose hip, rosehip '990': buckeye, horse chestnut, conker '991': coral fungus '992': agaric '993': gyromitra '994': stinkhorn, carrion fungus '995': earthstar '996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa '997': bolete '998': ear, spike, capitulum '999': toilet tissue, toilet paper, bathroom tissue splits: - name: train num_bytes: 25757593104.281 num_examples: 1281167 - name: val num_bytes: 752578700.0 num_examples: 50000 - name: test num_bytes: 1748575400.0 num_examples: 100000 download_size: 25143755650 dataset_size: 28258747204.281 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* --- # Dataset Card for "imagenet_1k_resized_256" ## Dataset summary The same ImageNet dataset but all the smaller side resized to 256. A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen. The resized dataset can also be downloaded much faster and consume less space than the original one. See [here](https://huggingface.co/datasets/imagenet-1k) for detailed readme. ## Dataset Structure Below is the example of one row of data. Note that the labels in the test split are all -1. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x384 at 0x276021C5EB8>, 'label': 23 } ``` The number of rows per split is the same as the original ImageNet. | |train |validation| test | |-------------|------:|---------:|------:| |# of examples|1281167|50000 |100000 | ## Licensing Information In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 1. The law of the State of New Jersey shall apply to all disputes under this agreement. ## Citation Information ```bibtex @article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} } ```
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lighteval/natural_questions_clean
2023-10-17T20:29:08.000Z
[ "region:us" ]
lighteval
null
null
0
273
2023-10-17T16:39:42
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: document dtype: string - name: question dtype: string - name: long_answers sequence: string - name: short_answers sequence: string splits: - name: train num_bytes: 4346873866.211105 num_examples: 106926 - name: validation num_bytes: 175230324.62247765 num_examples: 4289 download_size: 1406784865 dataset_size: 4522104190.833583 --- # Dataset Card for "natural_questions_clean" Created by @thomwolf on the basis of https://huggingface.co/datasets/lighteval/natural_questions but removing the questions without short answers provided. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ecb
2022-11-03T16:31:41.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:sk", "language:sl", "license:unknown", "region:us" ]
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Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!) 19 languages, 170 bitexts total number of files: 340 total number of tokens: 757.37M total number of sentence fragments: 30.55M
@InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
0
272
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - sk - sl license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: ecb pretty_name: extension to the EventCorefBank dataset_info: - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 39514115 num_examples: 105116 download_size: 10326178 dataset_size: 39514115 - config_name: cs-en features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 19524831 num_examples: 63716 download_size: 5360485 dataset_size: 19524831 - config_name: el-it features: - name: id dtype: string - name: translation dtype: translation: languages: - el - it splits: - name: train num_bytes: 47300471 num_examples: 94712 download_size: 10394277 dataset_size: 47300471 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 43118164 num_examples: 126482 download_size: 11360895 dataset_size: 43118164 - config_name: fi-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - pl splits: - name: train num_bytes: 12973283 num_examples: 41686 download_size: 3521950 dataset_size: 12973283 --- # Dataset Card for extension to the EventCorefBank ## 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:** http://opus.nlpl.eu/ECB.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ECB.php E.g. `dataset = load_dataset("ecb", lang1="en", lang2="fi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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Bingsu/Cat_and_Dog
2023-01-26T10:48:25.000Z
[ "task_categories:image-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc0-1.0", "region:us" ]
Bingsu
null
null
2
272
2022-04-19T02:23:06
--- language: - en license: - cc0-1.0 pretty_name: Cat and Dog size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 166451650.0 num_examples: 8000 - name: test num_bytes: 42101650.0 num_examples: 2000 download_size: 227859268 dataset_size: 208553300.0 size_in_bytes: 436412568.0 --- ## Dataset Description - **Homepage:** [Cat and Dog](https://www.kaggle.com/datasets/tongpython/cat-and-dog) - **Download Size** 217.30 MiB - **Generated Size** 198.89 MiB - **Total Size** 416.20 MiB ### Dataset Summary A dataset from [kaggle](https://www.kaggle.com/datasets/tongpython/cat-and-dog) with duplicate data removed. ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. ### Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits | | train | test | |---------------|-------|-----:| | # of examples | 8000 | 2000 | ```python >>> from datasets import load_dataset >>> dataset = load_dataset("Bingsu/Cat_and_Dog") >>> dataset DatasetDict({ train: Dataset({ features: ['image', 'labels'], num_rows: 8000 }) test: Dataset({ features: ['image', 'labels'], num_rows: 2000 }) }) >>> dataset["train"].features {'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=2, names=['cat', 'dog'], id=None)} ```
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ywchoi/pubmed_abstract_3
2022-09-13T01:01:39.000Z
[ "region:us" ]
ywchoi
null
null
1
272
2022-09-13T00:59:54
Entry not found
15
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rizerphe/glaive-function-calling-v2-zephyr
2023-10-17T16:36:29.000Z
[ "task_categories:text-generation", "task_categories:conversational", "size_categories:100K<n<1M", "language:en", "license:cc-by-sa-4.0", "region:us" ]
rizerphe
null
null
3
272
2023-10-17T08:28:47
--- license: cc-by-sa-4.0 task_categories: - text-generation - conversational language: - en size_categories: - 100K<n<1M dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 225637684 num_examples: 101469 download_size: 94820543 dataset_size: 225637684 --- # Glaive's Function Calling V2 for Zephyr-7B-alpha [Glaive's Function Calling V2 dataset](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), formatted according to the chat schema zephyr uses, with all the data that I wasn't able to automatically convert removed. Adds three new roles: `definition`, `function` and `call`. Here's an example prompt: ``` <|definition|> { "name": "generate_password", "description": "Generate a random password with specified criteria", "parameters": { "type": "object", "properties": { "length": { "type": "integer", "description": "The length of the password" }, "include_numbers": { "type": "boolean", "description": "Include numbers in the password" }, "include_special_characters": { "type": "boolean", "description": "Include special characters in the password" } }, "required": [ "length" ] } }</s> <|user|> I need a new password. Can you generate one for me?</s> <|assistant|> Of course! How long would you like your password to be? And do you want it to include numbers and special characters?</s> <|user|> I want it to be 12 characters long and yes, it should include both numbers and special characters.</s> <|function|> { "length": 12, "include_numbers": true, "include_special_characters": true }</s> <|function|> {"password": "4#7gB6&9L1!0"}</s> <|assistant|> Here is your new password: 4#7gB6&9L1!0. Please make sure to save it in a secure place.</s> ```
1,983
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vblagoje/wikipedia_snippets_streamed
2021-07-01T15:32:09.000Z
[ "region:us" ]
vblagoje
The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/). Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).
@ONLINE {wikidump, author = {Wikimedia Foundation}, title = {Wikimedia Downloads}, url = {https://dumps.wikimedia.org} }
0
271
2022-03-02T23:29:22
Entry not found
15
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bongsoo/news_talk_en_ko
2022-10-05T00:09:50.000Z
[ "language:ko", "license:apache-2.0", "region:us" ]
bongsoo
null
null
3
270
2022-09-20T05:10:56
--- language: - ko license: apache-2.0 --- - 뉴스&일상대화 en-ko 번역 말뭉치
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Francesco/furniture-ngpea
2023-03-30T09:12:40.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]
Francesco
null
null
0
270
2023-03-30T09:12:19
--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': furniture '1': Chair '2': Sofa '3': Table annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: furniture-ngpea tags: - rf100 --- # Dataset Card for furniture-ngpea ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/furniture-ngpea - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary furniture-ngpea ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/furniture-ngpea ### Citation Information ``` @misc{ furniture-ngpea, title = { furniture ngpea Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/furniture-ngpea } }, url = { https://universe.roboflow.com/object-detection/furniture-ngpea }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
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CertifiedJoon/Korean-Instruction
2023-07-06T17:44:53.000Z
[ "task_categories:question-answering", "size_categories:n<1K", "language:ko", "license:cdla-permissive-2.0", "region:us" ]
CertifiedJoon
null
null
3
268
2023-06-07T15:05:39
--- license: cdla-permissive-2.0 dataset_info: features: - name: Instruction dtype: string - name: Response dtype: string - name: Source dtype: string - name: MetaData dtype: string splits: - name: train num_bytes: 2099234 num_examples: 1720 download_size: 907301 dataset_size: 2099234 task_categories: - question-answering language: - ko size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** kin.naver.com/qna - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** mjypark1212@gmail.com ### Dataset Summary The most active korean qna site - Knowledge In Naver. Instruction + response format. Created for language model. ## Dataset Structure [Instruction, Response, Source, Metadata]
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fformosa/LSUN_bedroom_VQA
2023-10-17T15:45:26.000Z
[ "task_categories:visual-question-answering", "task_categories:text-to-image", "task_categories:question-answering", "size_categories:100K<n<1M", "region:us" ]
fformosa
null
null
0
268
2023-10-04T22:27:05
--- dataset_info: features: - name: image dtype: image - name: image_id dtype: int64 - name: attributes sequence: string - name: size sequence: int64 - name: proportion dtype: float64 splits: - name: train num_bytes: 4858959064 num_examples: 303125 download_size: 4766067864 dataset_size: 4858959064 configs: - config_name: default data_files: - split: train path: data/train-* size_categories: - 100K<n<1M task_categories: - visual-question-answering - text-to-image - question-answering --- # Dataset Card for "CSUN_bedroom_VQA_feliu" Images are a subset of the LSUN-Bedroom dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The attributes are binary answers to the following questions: - Is the floor visible in the image? - Does the room have a window? - Is there more than one bed? - Does the room have natural light? - Is there a carpet in the floor? - Is it a classy room? - Is it a hotel room? - Is there at least one person in the room? - Are there more than one people in the room? - Is it an expensive room? - Does the room have a painting the wall? - Is the room nicely decorated? - Does the room have a mirror? - Are the room lights on? - Are the bedsheets made? - Is there a visible closet? - Is the room tidy? - Is there an animal in the room? - Is the wall painted in red? - Is the wall painted in blue? - Is the wall painted in white? - Is the wall painted in a dark color? - Is the wall painted in green? - Are the bedsheets red? - Are the bedsheets blue? - Are the bedsheets white? - Are the bedsheets dark? - Are the bedsheets green? - Is there a kid in the room? - Is the bed big enough for two people? - Does the room have a telephone? - Does the room seem cold? - Are there plants visible from the window? - Are there decorative plants inside the room? - Does the room have any photo frame as decoration? - Does the room have a TV? - Does the room have a radio in it? - Is there any luggage in the room? - Is there a visible door? - Is there a radiator in the room? - Is the bathroom visible in the image? - Does the bed have a quilt? - Does the picture have a watermark? - Is the bed covered in a duvet? - Is there more than one bedside table? - Does the bedside table have a nightstand light? - Does the bed have a mosquito net? - Does the room access a private terrace? - Is the floor wooden? - Are the walls made of wood?
2,499
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cakiki/rosetta-code
2023-09-24T10:17:35.000Z
[ "language:code", "license:gfdl", "region:us" ]
cakiki
null
null
12
267
2022-06-28T20:41:33
--- license: gfdl language: code --- # Dataset Card for the Rosetta Code Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary > Rosetta Code is a programming chrestomathy site. The idea is to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another. Rosetta Code currently has 1,203 tasks, 389 draft tasks, and is aware of 883 languages, though we do not (and cannot) have solutions to every task in every language. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages ``` ['ALGOL 68', 'Arturo', 'AWK', 'F#', 'Factor', 'Go', 'J', 'jq', 'Julia', 'Lua', 'Mathematica/Wolfram Language', 'Perl', 'Phix', 'Picat', 'Python', 'Quackery', 'Raku', 'Ring', 'Sidef', 'Vlang', 'Wren', 'XPL0', '11l', '68000 Assembly', '8th', 'AArch64 Assembly', 'ABAP', 'ACL2', 'Action!', 'ActionScript', 'Ada', 'Aime', 'ALGOL W', 'Amazing Hopper', 'AntLang', 'Apex', 'APL', 'AppleScript', 'ARM Assembly', 'ATS', 'AutoHotkey', 'AutoIt', 'Avail', 'Babel', 'bash', 'BASIC', 'BASIC256', 'BQN', 'Bracmat', 'Burlesque', 'C', 'C#', 'C++', 'Ceylon', 'Clojure', 'COBOL', 'CoffeeScript', 'Common Lisp', 'Component Pascal', 'Crystal', 'D', 'Delphi', 'Dyalect', 'E', 'EasyLang', 'EchoLisp', 'ECL', 'Efene', 'EGL', 'Ela', 'Elena', 'Elixir', 'Elm', 'Emacs Lisp', 'Erlang', 'ERRE', 'Euphoria', 'Fantom', 'FBSL', 'Forth', 'Fortran', 'Free Pascal', 'FreeBASIC', 'Frink', 'FunL', 'Futhark', 'FutureBasic', 'Gambas', 'GAP', 'Genie', 'GLSL', 'Gosu', 'Groovy', 'Haskell', 'HicEst', 'Hy', 'i', 'Icon and Unicon', 'IDL', 'Idris', 'Inform 7', 'Ioke', 'Java', 'JavaScript', 'K', 'Klingphix', 'Klong', 'Kotlin', 'LabVIEW', 'Lambdatalk', 'Lang5', 'langur', 'Lasso', 'LFE', 'Liberty BASIC', 'LIL', 'Limbo', 'Lingo', 'Little', 'Logo', 'M2000 Interpreter', 'Maple', 'Mathcad', 'Mathematica / Wolfram Language', 'MATLAB / Octave', 'Maxima', 'Mercury', 'min', 'MiniScript', 'Nanoquery', 'Neko', 'Nemerle', 'NetRexx', 'NewLISP', 'Nial', 'Nim', 'Oberon-2', 'Objeck', 'Objective-C', 'OCaml', 'Oforth', 'Onyx', 'ooRexx', 'Order', 'OxygenBasic', 'Oz', 'PARI/GP', 'Pascal', 'Phixmonti', 'PHP', 'PicoLisp', 'Pike', 'PL/I', 'Pony', 'PostScript', 'PowerShell', 'Processing', 'Prolog', 'PureBasic', 'Q', 'QBasic', 'QB64', 'R', 'Racket', 'RapidQ', 'REBOL', 'Red', 'ReScript', 'Retro', 'REXX', 'RLaB', 'Ruby', 'Rust', 'S-lang', 'SASL', 'Scala', 'Scheme', 'Seed7', 'SenseTalk', 'SETL', 'Simula', '360 Assembly', '6502 Assembly', 'Slate', 'Smalltalk', 'Ol', 'SNOBOL4', 'Standard ML', 'Stata', 'Swift', 'Tailspin', 'Tcl', 'TI-89 BASIC', 'Trith', 'UNIX Shell', 'Ursa', 'Vala', 'VBA', 'VBScript', 'Visual Basic .NET', 'Wart', 'BaCon', 'Bash', 'Yabasic', 'Yacas', 'Batch File', 'Yorick', 'Z80 Assembly', 'BBC BASIC', 'Brat', 'zkl', 'zonnon', 'Zsh', 'ZX Spectrum Basic', 'Clipper/XBase++', 'ColdFusion', 'Dart', 'DataWeave', 'Dragon', 'FurryScript', 'Fōrmulæ', 'Harbour', 'hexiscript', 'Hoon', 'Janet', '0815', 'Jsish', 'Latitude', 'LiveCode', 'Aikido', 'AmigaE', 'MiniZinc', 'Asymptote', 'NGS', 'bc', 'Befunge', 'Plorth', 'Potion', 'Chef', 'Clipper', 'Relation', 'Robotic', 'dc', 'DCL', 'DWScript', 'Shen', 'SPL', 'SQL', 'Eiffel', 'Symsyn', 'Emojicode', 'TI-83 BASIC', 'Transd', 'Excel', 'Visual Basic', 'FALSE', 'WDTE', 'Fermat', 'XLISP', 'Zig', 'friendly interactive shell', 'Zoea', 'Zoea Visual', 'GEORGE', 'Haxe', 'HolyC', 'LSE64', 'M4', 'MAXScript', 'Metafont', 'МК-61/52', 'ML/I', 'Modula-2', 'Modula-3', 'MUMPS', 'NSIS', 'Openscad', 'Panda', 'PHL', 'Piet', 'Plain English', 'Pop11', 'ProDOS', '8051 Assembly', 'Python 3.x Long Form', 'Raven', 'ALGOL 60', 'Run BASIC', 'Sass/SCSS', 'App Inventor', 'smart BASIC', 'SNUSP', 'Arendelle', 'SSEM', 'Argile', 'Toka', 'TUSCRIPT', '4DOS Batch', '8080 Assembly', 'Vedit macro language', '8086 Assembly', 'Axe', 'Elisa', 'Verilog', 'Vim Script', 'x86 Assembly', 'Euler Math Toolbox', 'Acurity Architect', 'XSLT', 'BML', 'Agena', 'Boo', 'Brainf***', 'LLVM', 'FOCAL', 'Frege', 'ALGOL-M', 'ChucK', 'Arbre', 'Clean', 'Hare', 'MATLAB', 'Astro', 'Applesoft BASIC', 'OOC', 'Bc', 'Computer/zero Assembly', 'SAS', 'Axiom', 'B', 'Dao', 'Caché ObjectScript', 'CLU', 'Scilab', 'DBL', 'Commodore BASIC', 'Diego', 'Dc', 'BCPL', 'Alore', 'Blade', 'Déjà Vu', 'Octave', 'Cowgol', 'BlitzMax', 'Falcon', 'BlooP', 'SequenceL', 'Sinclair ZX81 BASIC', 'GW-BASIC', 'Lobster', 'C1R', 'Explore', 'Clarion', 'Locomotive Basic', 'GUISS', 'Clio', 'TXR', 'Ursala', 'CLIPS', 'Microsoft Small Basic', 'Golfscript', 'Beads', 'Coco', 'Little Man Computer', 'Chapel', 'Comal', 'Curry', 'GML', 'NewLisp', 'Coq', 'Gastona', 'uBasic/4tH', 'Pyret', 'Dhall', 'Plain TeX', 'Halon', 'Wortel', 'FormulaOne', 'Dafny', 'Ksh', 'Eero', 'Fan', 'Draco', 'DUP', 'Io', 'Metapost', 'Logtalk', 'Dylan', 'TI-83_BASIC', 'Sather', 'Rascal', 'SIMPOL', 'IS-BASIC', 'KonsolScript', 'Pari/Gp', 'Genyris', 'EDSAC order code', 'Egel', 'Joy', 'lang5', 'XProc', 'XQuery', 'POV-Ray', 'Kitten', 'Lisaac', 'LOLCODE', 'SVG', 'MANOOL', 'LSL', 'Moonscript', 'Fhidwfe', 'Inspired by Rascal', 'Fish', 'MIPS Assembly', 'Monte', 'FUZE BASIC', 'NS-HUBASIC', 'Qi', 'GDScript', 'Glee', 'SuperCollider', 'Verbexx', 'Huginn', 'I', 'Informix 4GL', 'Isabelle', 'KQL', 'lambdatalk', 'RPG', 'Lhogho', 'Lily', 'xTalk', 'Scratch', 'Self', 'MAD', 'RATFOR', 'OpenEdge/Progress', 'Xtend', 'Suneido', 'Mirah', 'mIRC Scripting Language', 'ContextFree', 'Tern', 'MMIX', 'AmigaBASIC', 'AurelBasic', 'TorqueScript', 'MontiLang', 'MOO', 'MoonScript', 'Unicon', 'fermat', 'q', 'Myrddin', 'உயிர்/Uyir', 'MySQL', 'newLISP', 'VHDL', 'Oberon', 'Wee Basic', 'OpenEdge ABL/Progress 4GL', 'X86 Assembly', 'XBS', 'KAP', 'Perl5i', 'Peloton', 'PL/M', 'PL/SQL', 'Pointless', 'Polyglot:PL/I and PL/M', 'ToffeeScript', 'TMG', 'TPP', 'Pure', 'Pure Data', 'Xidel', 'S-BASIC', 'Salmon', 'SheerPower 4GL', 'Sparkling', 'Spin', 'SQL PL', 'Transact-SQL', 'True BASIC', 'TSE SAL', 'Tiny BASIC', 'TypeScript', 'Uniface', 'Unison', 'UTFool', 'VAX Assembly', 'VTL-2', 'Wrapl', 'XBasic', 'Xojo', 'XSLT 1.0', 'XSLT 2.0', 'MACRO-10', 'ANSI Standard BASIC', 'UnixPipes', 'REALbasic', 'Golo', 'DM', 'X86-64 Assembly', 'GlovePIE', 'PowerBASIC', 'LotusScript', 'TIScript', 'Kite', 'V', 'Powershell', 'Vorpal', 'Never', 'Set lang', '80386 Assembly', 'Furor', 'Input conversion with Error Handling', 'Guile', 'ASIC', 'Autolisp', 'Agda', 'Swift Playground', 'Nascom BASIC', 'NetLogo', 'CFEngine', 'OASYS Assembler', 'Fennel', 'Object Pascal', 'Shale', 'GFA Basic', 'LDPL', 'Ezhil', 'SMEQL', 'tr', 'WinBatch', 'XPath 2.0', 'Quite BASIC', 'Gema', '6800 Assembly', 'Applescript', 'beeswax', 'gnuplot', 'ECMAScript', 'Snobol4', 'Blast', 'C/C++', 'Whitespace', 'Blue', 'C / C++', 'Apache Derby', 'Lychen', 'Oracle', 'Alternative version', 'PHP+SQLite', 'PILOT', 'PostgreSQL', 'PowerShell+SQLite', 'PureBasic+SQLite', 'Python+SQLite', 'SQLite', 'Tcl+SQLite', 'Transact-SQL (MSSQL)', 'Visual FoxPro', 'SmileBASIC', 'Datalog', 'SystemVerilog', 'Smart BASIC', 'Snobol', 'Terraform', 'ML', 'SQL/PostgreSQL', '4D', 'ArnoldC', 'ANSI BASIC', 'Delphi/Pascal', 'ooREXX', 'Dylan.NET', 'CMake', 'Lucid', 'XProfan', 'sed', 'Gnuplot', 'RPN (HP-15c)', 'Sed', 'JudoScript', 'ScriptBasic', 'Unix shell', 'Niue', 'Powerbuilder', 'C Shell', 'Zoomscript', 'MelonBasic', 'ScratchScript', 'SimpleCode', 'OASYS', 'HTML', 'tbas', 'LaTeX', 'Lilypond', 'MBS', 'B4X', 'Progress', 'SPARK / Ada', 'Arc', 'Icon', 'AutoHotkey_L', 'LSE', 'N/t/roff', 'Fexl', 'Ra', 'Koka', 'Maclisp', 'Mond', 'Nix', 'ZED', 'Inform 6', 'Visual Objects', 'Cind', 'm4', 'g-fu', 'pascal', 'Jinja', 'Mathprog', 'Rhope', 'Delphi and Pascal', 'Epoxy', 'SPARK', 'B4J', 'DIBOL-11', 'JavaFX Script', 'Pixilang', 'BASH (feat. sed & tr)', 'zig', 'Web 68', 'Shiny', 'Egison', 'OS X sha256sum', 'AsciiDots', 'FileMaker', 'Unlambda', 'eC', 'GLBasic', 'JOVIAL', 'haskell', 'Atari BASIC', 'ANTLR', 'Cubescript', 'OoRexx', 'WebAssembly', 'Woma', 'Intercal', 'Malbolge', 'LiveScript', 'Fancy', 'Detailed Description of Programming Task', 'Lean', 'GeneXus', 'CafeOBJ', 'TechBASIC', 'blz', 'MIRC Scripting Language', 'Oxygene', 'zsh', 'Make', 'Whenever', 'Sage', 'L++', 'Tosh', 'LC3 Assembly', 'SETL4', 'Pari/GP', 'OxygenBasic x86 Assembler', 'Pharo', 'Binary Lambda Calculus', 'Bob', 'bootBASIC', 'Turing', 'Ultimate++', 'Gabuzomeu', 'HQ9+', 'INTERCAL', 'Lisp', 'NASM', 'SPWN', 'Turbo Pascal', 'Nickle', 'SPAD', 'Mozart/Oz', 'Batch file', 'SAC', 'C and C++', 'vbscript', 'OPL', 'Wollok', 'Pascal / Delphi / Free Pascal', 'GNU make', 'Recursive', 'C3', 'Picolisp', 'Note 1', 'Note 2', 'Visual Prolog', 'ivy', 'k', 'clojure', 'Unix Shell', 'Basic09', 'S-Basic', 'FreePascal', 'Wolframalpha', 'c_sharp', 'LiveCode Builder', 'Heron', 'SPSS', 'LibreOffice Basic', 'PDP-11 Assembly', 'Solution with recursion', 'Lua/Torch', 'tsql', 'Transact SQL', 'X++', 'Xanadu', 'GDL', 'C_sharp', 'TutorialD', 'Glagol', 'Basic', 'Brace', 'Cixl', 'ELLA', 'Lox', 'Node.js', 'Generic', 'Hope', 'Snap!', 'TSQL', 'MathCortex', 'Mathmap', 'TI-83 BASIC, TI-89 BASIC', 'ZPL', 'LuaTeX', 'AmbientTalk', 'Alternate version to handle 64 and 128 bit integers.', 'Crack', 'Corescript', 'Fortress', 'GB BASIC', 'IWBASIC', 'RPL', 'DMS', 'dodo0', 'MIXAL', 'Occam', 'Morfa', 'Snabel', 'ObjectIcon', 'Panoramic', 'PeopleCode', 'Monicelli', 'gecho', 'Hack', 'JSON', 'Swym', 'ReasonML', 'make', 'TOML', 'WEB', 'SkookumScript', 'Batch', 'TransFORTH', 'Assembly', 'Iterative', 'LC-3', 'Quick Basic/QBASIC/PDS 7.1/VB-DOS', 'Turbo-Basic XL', 'GNU APL', 'OOCalc', 'QUACKASM', 'VB-DOS', 'Typescript', 'x86-64 Assembly', 'FORTRAN', 'Furryscript', 'Gridscript', 'Necromantus', 'HyperTalk', 'Biferno', 'AspectJ', 'SuperTalk', 'Rockstar', 'NMAKE.EXE', 'Opa', 'Algae', 'Anyways', 'Apricot', 'AutoLISP', 'Battlestar', 'Bird', 'Luck', 'Brlcad', 'C++/CLI', 'C2', 'Casio BASIC', 'Cat', 'Cduce', 'Clay', 'Cobra', 'Comefrom0x10', 'Creative Basic', 'Integer BASIC', 'DDNC', 'DeviousYarn', 'DIV Games Studio', 'Wisp', 'AMPL', 'Pare', 'PepsiScript', 'Installing Processing', 'Writing your first program', 'batari Basic', 'Jack', 'elastiC', 'TI-83 Hex Assembly', 'Extended BrainF***', '1C', 'PASM', 'Pict', 'ferite', 'Bori', 'RASEL', 'Echolisp', 'XPath', 'MLite', 'HPPPL', 'Gentee', 'JSE', 'Just Basic', 'Global Script', 'Nyquist', 'HLA', 'Teradata Stored Procedure', 'HTML5', 'Portugol', 'UBASIC', 'NOWUT', 'Inko', 'Jacquard Loom', 'JCL', 'Supernova', 'Small Basic', 'Kabap', 'Kaya', 'Kdf9 Usercode', 'Keg', 'KSI', 'Gecho', 'Gri', 'VBA Excel', 'Luna', 'MACRO-11', 'MINIL', 'Maude', 'MDL', 'Mosaic', 'Purity', 'MUF', 'MyDef', 'MyrtleScript', 'Mythryl', 'Neat', 'ThinBASIC', 'Nit', 'NLP++', 'Odin', 'OpenLisp', 'PDP-1 Assembly', 'Peylang', 'Pikachu', 'NESL', 'PIR', 'Plan', 'Programming Language', 'PROMAL', 'PSQL', 'Quill', 'xEec', 'RED', 'Risc-V', 'RTL/2', 'Sing', 'Sisal', 'SoneKing Assembly', 'SPARC Assembly', 'Swahili', 'Teco', 'Terra', 'TestML', 'Viua VM assembly', 'Whiley', 'Wolfram Language', 'X10', 'Quack', 'K4', 'XL', 'MyHDL', 'JAMES II/Rule-based Cellular Automata', 'APEX', 'QuickBASIC 4.5', 'BrightScript (for Roku)', 'Coconut', 'CSS', 'MapBasic', 'Gleam', 'AdvPL', 'Iptscrae', 'Kamailio Script', 'KL1', 'MEL', 'NATURAL', 'NewtonScript', 'PDP-8 Assembly', 'FRISC Assembly', 'Amstrad CPC Locomotive BASIC', 'Ruby with RSpec', 'php', 'Small', 'Lush', 'Squirrel', 'PL/pgSQL', 'XMIDAS', 'Rebol', 'embedded C for AVR MCU', 'FPr', 'Softbridge BASIC', 'StreamIt', 'jsish', 'JScript.NET', 'MS-DOS', 'Beeswax', 'eSQL', 'QL SuperBASIC', 'Rapira', 'Jq', 'scheme', 'oberon-2', '{{header|Vlang}', 'XUL', 'Soar', 'Befunge 93', 'Bash Shell', 'JacaScript', 'Xfractint', 'JoCaml', 'JotaCode', 'Atari Basic', 'Stretch 1', 'CFScript', 'Stretch 2', 'RPGIV', 'Shell', 'Felix', 'Flex', 'kotlin', 'Deluge', 'ksh', 'OCTAVE', 'vbScript', 'Javascript/NodeJS', 'Coffeescript', 'MS SmallBasic', 'Setl4', 'Overview', '1. Grid structure functions', '2. Calendar data functions', '3. Output configuration', 'WYLBUR', 'Mathematica/ Wolfram Language', 'Commodore Basic', 'Wolfram Language/Mathematica', 'Korn Shell', 'PARIGP', 'Metal', 'VBA (Visual Basic for Application)', 'Lolcode', 'mLite', 'z/Arch Assembler', "G'MIC", 'C# and Visual Basic .NET', 'Run Basic', 'FP', 'XEmacs Lisp', 'Mathematica//Wolfram Language', 'RPL/2', 'Ya', 'JavaScript + HTML', 'JavaScript + SVG', 'Quick BASIC', 'MatLab', 'Pascal and Object Pascal', 'Apache Ant', 'rust', 'VBA/Visual Basic', 'Go!', 'Lambda Prolog', 'Monkey'] ``` ## Dataset Structure ### Data Instances First row: ``` {'task_url': 'http://rosettacode.org/wiki/Ascending_primes', 'task_name': 'Ascending primes', 'task_description': "Generate and show all primes with strictly ascending decimal digits.\n\nAside: Try solving without peeking at existing solutions. I had a weird idea for generating\na prime sieve faster, which needless to say didn't pan out. The solution may be p(r)etty trivial\nbut generating them quickly is at least mildly interesting.\nTip: filtering all 7,027,260 primes below 123,456,789 probably won't kill you, but there is\nat least one significantly better and much faster way, needing a mere 511 odd/prime tests.\n\n\n\nSee also\n OEIS:A052015 - Primes with distinct digits in ascending order\n\n\nRelated\n\nPrimes with digits in nondecreasing order (infinite series allowing duplicate digits, whereas this isn't and doesn't)\nPandigital prime (whereas this is the smallest, with gaps in the used digits being permitted)\n\n", 'language_url': '#ALGOL_68', 'language_name': 'ALGOL 68'} ``` Code: ``` BEGIN # find all primes with strictly increasing digits # PR read "primes.incl.a68" PR # include prime utilities # PR read "rows.incl.a68" PR # include array utilities # [ 1 : 512 ]INT primes; # there will be at most 512 (2^9) primes # INT p count := 0; # number of primes found so far # FOR d1 FROM 0 TO 1 DO INT n1 = d1; FOR d2 FROM 0 TO 1 DO INT n2 = IF d2 = 1 THEN ( n1 * 10 ) + 2 ELSE n1 FI; FOR d3 FROM 0 TO 1 DO INT n3 = IF d3 = 1 THEN ( n2 * 10 ) + 3 ELSE n2 FI; FOR d4 FROM 0 TO 1 DO INT n4 = IF d4 = 1 THEN ( n3 * 10 ) + 4 ELSE n3 FI; FOR d5 FROM 0 TO 1 DO INT n5 = IF d5 = 1 THEN ( n4 * 10 ) + 5 ELSE n4 FI; FOR d6 FROM 0 TO 1 DO INT n6 = IF d6 = 1 THEN ( n5 * 10 ) + 6 ELSE n5 FI; FOR d7 FROM 0 TO 1 DO INT n7 = IF d7 = 1 THEN ( n6 * 10 ) + 7 ELSE n6 FI; FOR d8 FROM 0 TO 1 DO INT n8 = IF d8 = 1 THEN ( n7 * 10 ) + 8 ELSE n7 FI; FOR d9 FROM 0 TO 1 DO INT n9 = IF d9 = 1 THEN ( n8 * 10 ) + 9 ELSE n8 FI; IF n9 > 0 THEN IF is probably prime( n9 ) THEN # have a prime with strictly ascending digits # primes[ p count +:= 1 ] := n9 FI FI OD OD OD OD OD OD OD OD OD; QUICKSORT primes FROMELEMENT 1 TOELEMENT p count; # sort the primes # FOR i TO p count DO # display the primes # print( ( " ", whole( primes[ i ], -8 ) ) ); IF i MOD 10 = 0 THEN print( ( newline ) ) FI OD END ``` ### Data Fields ``` Dataset({ features: ['task_url', 'task_name', 'task_description', 'language_url', 'language_name', 'code'], num_rows: 79013 }) ``` ### Data Splits The dataset only contains one split, namely the "train" split. ## 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 To cite the Rosetta Code webiste you can use the following bibtex entry: ```json @misc{rosetta-code, author = "Rosetta Code", title = "Rosetta Code --- Rosetta Code{,} ", year = "2022", url = "https://rosettacode.org/w/index.php?title=Rosetta_Code&oldid=322370", note = "[Online; accessed 8-December-2022]" } ``` ### Contributions Thanks to [@christopher](https://twitter.com/christopher) for adding this dataset.
18,488
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bigbio/chemprot
2022-12-22T15:44:22.000Z
[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]
bigbio
The BioCreative VI Chemical-Protein interaction dataset identifies entities of chemicals and proteins and their likely relation to one other. Compounds are generally agonists (activators) or antagonists (inhibitors) of proteins.
@article{DBLP:journals/biodb/LiSJSWLDMWL16, author = {Krallinger, M., Rabal, O., Lourenço, A.}, title = {Overview of the BioCreative VI chemical-protein interaction Track}, journal = {Proceedings of the BioCreative VI Workshop,}, volume = {141-146}, year = {2017}, url = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/}, doi = {}, biburl = {}, bibsource = {} }
1
267
2022-11-13T22:07:50
--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0 pretty_name: ChemProt homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/ bigbio_pubmed: True bigbio_public: True bigbio_tasks: - RELATION_EXTRACTION - NAMED_ENTITY_RECOGNITION --- # Dataset Card for ChemProt ## Dataset Description - **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/ - **Pubmed:** True - **Public:** True - **Tasks:** RE,NER The BioCreative VI Chemical-Protein interaction dataset identifies entities of chemicals and proteins and their likely relation to one other. Compounds are generally agonists (activators) or antagonists (inhibitors) of proteins. ## Citation Information ``` @article{DBLP:journals/biodb/LiSJSWLDMWL16, author = {Krallinger, M., Rabal, O., Lourenço, A.}, title = {Overview of the BioCreative VI chemical-protein interaction Track}, journal = {Proceedings of the BioCreative VI Workshop,}, volume = {141-146}, year = {2017}, url = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/}, doi = {}, biburl = {}, bibsource = {} } ```
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code_x_glue_cc_code_to_code_trans
2023-07-27T14:11:43.000Z
[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:other-programming-languages", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "license:c-uda", "code-to-code", "arxiv:2102.04664", "region:us" ]
null
The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/). We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.
@article{DBLP:journals/corr/abs-2102-04664, author = {Shuai Lu and Daya Guo and Shuo Ren and Junjie Huang and Alexey Svyatkovskiy and Ambrosio Blanco and Colin B. Clement and Dawn Drain and Daxin Jiang and Duyu Tang and Ge Li and Lidong Zhou and Linjun Shou and Long Zhou and Michele Tufano and Ming Gong and Ming Zhou and Nan Duan and Neel Sundaresan and Shao Kun Deng and Shengyu Fu and Shujie Liu}, title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding and Generation}, journal = {CoRR}, volume = {abs/2102.04664}, year = {2021} }
3
266
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - code license: - c-uda multilinguality: - other-programming-languages size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: CodeXGlueCcCodeToCodeTrans tags: - code-to-code dataset_info: features: - name: id dtype: int32 - name: java dtype: string - name: cs dtype: string splits: - name: train num_bytes: 4372657 num_examples: 10300 - name: validation num_bytes: 226415 num_examples: 500 - name: test num_bytes: 418595 num_examples: 1000 download_size: 4876035 dataset_size: 5017667 --- # Dataset Card for "code_x_glue_cc_code_to_code_trans" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans - **Paper:** https://arxiv.org/abs/2102.04664 ### Dataset Summary CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/). We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets. ### Supported Tasks and Leaderboards - `machine-translation`: The dataset can be used to train a model for translating code in Java to C# and vice versa. ### Languages - Java **programming** language - C# **programming** language ## Dataset Structure ### Data Instances An example of 'validation' looks as follows. ``` { "cs": "public DVRecord(RecordInputStream in1){_option_flags = in1.ReadInt();_promptTitle = ReadUnicodeString(in1);_errorTitle = ReadUnicodeString(in1);_promptText = ReadUnicodeString(in1);_errorText = ReadUnicodeString(in1);int field_size_first_formula = in1.ReadUShort();_not_used_1 = in1.ReadShort();_formula1 = NPOI.SS.Formula.Formula.Read(field_size_first_formula, in1);int field_size_sec_formula = in1.ReadUShort();_not_used_2 = in1.ReadShort();_formula2 = NPOI.SS.Formula.Formula.Read(field_size_sec_formula, in1);_regions = new CellRangeAddressList(in1);}\n", "id": 0, "java": "public DVRecord(RecordInputStream in) {_option_flags = in.readInt();_promptTitle = readUnicodeString(in);_errorTitle = readUnicodeString(in);_promptText = readUnicodeString(in);_errorText = readUnicodeString(in);int field_size_first_formula = in.readUShort();_not_used_1 = in.readShort();_formula1 = Formula.read(field_size_first_formula, in);int field_size_sec_formula = in.readUShort();_not_used_2 = in.readShort();_formula2 = Formula.read(field_size_sec_formula, in);_regions = new CellRangeAddressList(in);}\n" } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### default |field name| type | description | |----------|------|-----------------------------| |id |int32 | Index of the sample | |java |string| The java version of the code| |cs |string| The C# version of the code | ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|10300| 500|1000| ## 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 https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @article{DBLP:journals/corr/abs-2102-04664, author = {Shuai Lu and Daya Guo and Shuo Ren and Junjie Huang and Alexey Svyatkovskiy and Ambrosio Blanco and Colin B. Clement and Dawn Drain and Daxin Jiang and Duyu Tang and Ge Li and Lidong Zhou and Linjun Shou and Long Zhou and Michele Tufano and Ming Gong and Ming Zhou and Nan Duan and Neel Sundaresan and Shao Kun Deng and Shengyu Fu and Shujie Liu}, title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding and Generation}, journal = {CoRR}, volume = {abs/2102.04664}, year = {2021} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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maastrichtlawtech/bsard
2023-09-26T15:28:00.000Z
[ "task_categories:text-retrieval", "task_categories:text-classification", "task_ids:document-retrieval", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fr", "license:cc-by-nc-sa-4.0", "legal", "arxiv:2108.11792", "region:us" ]
maastrichtlawtech
The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.
@inproceedings{louis-spanakis-2022-statutory, title = "A Statutory Article Retrieval Dataset in {F}rench", author = "Louis, Antoine and Spanakis, Gerasimos", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.468", doi = "10.18653/v1/2022.acl-long.468", pages = "6789--6803", }
2
266
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - fr license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: BSARD size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-retrieval - text-classification task_ids: - document-retrieval paperswithcode_id: bsard tags: - legal --- # Dataset Card for BSARD ## 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 - **Repository:** [maastrichtlawtech/bsard](https://github.com/maastrichtlawtech/bsard) - **Paper:** [A Statutory Article Retrieval Dataset in French](https://arxiv.org/abs/2108.11792) - **Point of Contact:** [Maastricht Law & Tech Lab](law-techlab@maastrichtuniversity.nl) ### Dataset Summary The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus. ### Supported Tasks and Leaderboards - `document-retrieval`: The dataset can be used to train models for ad-hoc legal information retrieval. Such model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles). ### Languages The text in the dataset is in French, as spoken in Wallonia and Brussels-Capital region. The associated BCP-47 code is `fr-BE`. ## Dataset Structure ### Data Instances A typical data point comprises a question, with additional `category`, `subcategory`, and `extra_description` fields that elaborate on it, and a list of `article_ids` from the corpus of statutory articles that are relevant to the question. An example from the BSARD test set looks as follows: ``` { 'id': '724', 'question': 'La police peut-elle me fouiller pour chercher du cannabis ?', 'category': 'Justice', 'subcategory': 'Petite délinquance', 'extra_description': 'Détenir, acheter et vendre du cannabis', 'article_ids': '13348' } ``` ### Data Fields - In **"questions_fr_train.csv"** and **"questions_fr_test.csv"**: - `id`: an *int32* feature corresponding to a unique ID number for the question. - `question`: a *string* feature corresponding to the question. - `category`: a *string* feature corresponding to the general topic of the question. - `subcategory`: a *string* feature corresponding to the sub-topic of the question. - `extra_description`: a *string* feature corresponding to the extra categorization tags of the question. - `article_ids`: a *string* feature of comma-separated article IDs relevant to the question. - In **"articles_fr.csv"**: - `id`: an *int32* feature corresponding to a unique ID number for the article. - `article`: a *string* feature corresponding to the full article. - `code`: a *string* feature corresponding to the law code to which the article belongs. - `article_no`: a *string* feature corresponding to the article number in the code. - `description`: a *string* feature corresponding to the concatenated headings of the article. - `law_type`: a *string* feature whose value is either *"regional"* or *"national"*. ### Data Splits This dataset is split into train/test set. Number of questions in each set is given below: | | Train | Test | | ----- | ------ | ---- | | BSARD | 886 | 222 | ## Dataset Creation ### Curation Rationale The dataset is intended to be used by researchers to build and evaluate models on retrieving law articles relevant to an input legal question. It should not be regarded as a reliable source of legal information at this point in time, as both the questions and articles correspond to an outdated version of the Belgian law from May 2021 (time of dataset collection). In the latter case, the user is advised to consult daily updated official legal resources (e.g., the Belgian Official Gazette). ### Source Data #### Initial Data Collection and Normalization BSARD was created in four stages: (i) compiling a large corpus of Belgian law articles, (ii) gathering legal questions with references to relevant law articles, (iii) refining these questions, and (iv) matching the references to the corresponding articles from the corpus. #### Who are the source language producers? Speakers were not directly approached for inclusion in this dataset and thus could not be asked for demographic information. Questions were collected, anonimyzed, and reformulated by [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe). Therefore, no direct information about the speakers’ age and gender distribution, or socioeconomic status is available. However, it is expected that most, but not all, of the speakers are adults (18+ years), speak French as a native language, and live in Wallonia or Brussels-Capital region. ### Annotations #### Annotation process Each year, [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe), a Belgian organization whose mission is to clarify the law for laypeople, receives and collects around 4,000 emails from Belgian citizens asking for advice on a personal legal issue. In practice, their legal clarification process consists of four steps. First, they identify the most frequently asked questions on a common legal issue. Then, they define a new anonymized "model" question on that issue expressed in natural language terms, i.e., as close as possible as if a layperson had asked it. Next, they search the Belgian law for articles that help answer the model question and reference them. #### Who are the annotators? A total of six Belgian jurists from [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe) contributed to annotating the questions. All have a law degree from a Belgian university and years of experience in providing legal advice and clarifications of the law. They range in age from 30-60 years, including one man and five women, gave their ethnicity as white European, speak French as a native language, and represent upper middle class based on income levels. ### Personal and Sensitive Information The questions represent informal, asynchronous, edited, written language that does not exceed 44 words. None of them contained hateful, aggressive, or inappropriate language as they were all reviewed and reworded by Droits Quotidiens to be neutral, anonymous, and comprehensive. The legal articles represent strong, formal, written language that can contain up to 5,790 words. ## Considerations for Using the Data ### Social Impact of Dataset In addition to helping advance the state-of-the-art in retrieving statutes relevant to a legal question, BSARD-based models could improve the efficiency of the legal information retrieval process in the context of legal research, therefore enabling researchers to devote themselves to more thoughtful parts of their research. Furthermore, BSARD can become a starting point of new open-source legal information search tools so that the socially weaker parties to disputes can benefit from a free professional assisting service. ### Discussion of Biases [More Information Needed] ### Other Known Limitations First, the corpus of articles is limited to those collected from 32 Belgian codes, which obviously does not cover the entire Belgian law as thousands of articles from decrees, directives, and ordinances are missing. During the dataset construction, all references to these uncollected articles are ignored, which causes some questions to end up with only a fraction of their initial number of relevant articles. This information loss implies that the answer contained in the remaining relevant articles might be incomplete, although it is still appropriate. Additionally, it is essential to note that not all legal questions can be answered with statutes alone. For instance, the question “Can I evict my tenants if they make too much noise?” might not have a detailed answer within the statutory law that quantifies a specific noise threshold at which eviction is allowed. Instead, the landlord should probably rely more on case law and find precedents similar to their current situation (e.g., the tenant makes two parties a week until 2 am). Hence, some questions are better suited than others to the statutory article retrieval task, and the domain of the less suitable ones remains to be determined. ## Additional Information ### Dataset Curators The dataset was created by Antoine Louis during work done at the Law & Tech lab of Maastricht University, with the help of jurists from [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe). ### Licensing Information BSARD is licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information ```latex @inproceedings{louis2022statutory, title = {A Statutory Article Retrieval Dataset in French}, author = {Louis, Antoine and Spanakis, Gerasimos}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, month = may, year = {2022}, address = {Dublin, Ireland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.acl-long.468/}, doi = {10.18653/v1/2022.acl-long.468}, pages = {6789–6803}, } ``` ### Contributions Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.
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DFKI-SLT/scidtb_argmin
2023-08-08T12:46:04.000Z
[ "region:us" ]
DFKI-SLT
null
@inproceedings{accuosto-saggion-2019-transferring, title = "Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts", author = "Accuosto, Pablo and Saggion, Horacio", booktitle = "Proceedings of the 6th Workshop on Argument Mining", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-4505", doi = "10.18653/v1/W19-4505", pages = "41--51", abstract = "In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models. As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues. In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer.", }
0
266
2023-06-26T10:14:08
Entry not found
15
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result-kand2-sdxl-wuerst-karlo/c06e4969
2023-10-06T14:58:55.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
0
266
2023-10-06T14:58:54
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 200 num_examples: 10 download_size: 1394 dataset_size: 200 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c06e4969" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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mariosasko/test_imagefolder_with_metadata
2022-06-28T12:59:23.000Z
[ "region:us" ]
mariosasko
null
null
0
263
2022-06-28T12:53:50
Entry not found
15
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ai4privacy/pii-masking-65k
2023-08-27T04:42:54.000Z
[ "size_categories:10K<n<100K", "language:en", "language:fr", "language:de", "language:it", "legal", "business", "psychology", "privacy", "region:us" ]
ai4privacy
null
null
13
263
2023-08-07T06:04:08
--- language: - en - fr - de - it tags: - legal - business - psychology - privacy size_categories: - 10K<n<100K --- # Purpose and Features The purpose of the model and dataset is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The model is a fine-tuned version of "Distilled BERT", a smaller and faster version of BERT. It was adapted for the task of token classification based on the largest to our knowledge open-source PII masking dataset, which we are releasing simultaneously. The model size is 62 million parameters. The original encoding of the parameters yields a model size of 268 MB, which is compressed to 43MB after parameter quantization. The models are available in PyTorch, tensorflow, and tensorflow.js The dataset is composed of ~43’000 observations. Each row starts with a natural language sentence that includes placeholders for PII and could plausibly be written to an AI assistant. The placeholders are then filled in with mocked personal information and tokenized with the BERT tokenizer. We label the tokens that correspond to PII, serving as the ground truth to train our model. The dataset covers a range of contexts in which PII can appear. The sentences span 58 sensitive data types (~117 token classes), targeting **125 discussion subjects / use cases** split across business, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...). Key facts: - Currently 5.6m tokens with 65k PII examples. - Multiple languages - Human-in-the-loop validated high quality dataset - Synthetic data generated using proprietary algorithms - Adapted from DistilBertForTokenClassification - Framework PyTorch - 8 bit quantization # Token distribution across PII classes There are 2 dataset releasees: - Original release: - [PII43k_original.jsonl](PII43k_original.jsonl) - New release with balanced token distribution: - [english_balanced_10k.jsonl](english_balanced_10k.jsonl) - [french_balanced_5k.jsonl](french_balanced_5k.jsonl) - [german_balanced_3k.jsonl](german_balanced_3k.jsonl) - [italian_balanced_3k.jsonl](italian_balanced_3k.jsonl) The new release **balances the distribution of tokens across the PII classes** covered by the dataset. This graph shows the distribution of observations across the different PII classes in the new release: ![Balanced token distribution on new release](ai4priv-distribution_including_all_languages.png) This is an important improvement, because the old release focused on just a few classes of PII and didn't provide enough examples of the other ones. This graph shows the unbalanced distribution of observations across the different PII classes in the old release: ![Unbalanced token distribution on old release]( ai4priv-distribution_old.png) Current counts of tokens per example: ![Current counts of tokens per example](token_count_per_sentence.png) # Performance evaluation | Test Precision | Test Recall | Test Accuracy | |:-:|:-:|:-:| # Community Engagement: Newsletter & updates: www.Ai4privacy.com - Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages) - Integrations with already existing open source solutions # Roadmap and Future Development - Multilingual benchmarking - Extended integrations - Continuously increase the training set - Further optimisation to the model to reduce size and increase generalisability - Next released major update is planned for the 14th of July (subscribe to newsletter for updates) # Use Cases and Applications **Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses. **Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information. **Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information. **Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes. **Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment. **Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details. **Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection. **Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents. **Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft. **Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals. (...and whatever else your creative mind can think of) # Support and Maintenance AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/).
6,161
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Fraol/TrainDedupedRefDatasetWMetricFinal3
2023-10-11T03:58:45.000Z
[ "region:us" ]
Fraol
null
null
0
263
2023-10-10T22:36:43
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: source dtype: string - name: path_name dtype: string - name: file_name dtype: string - name: ref_type dtype: string - name: hash dtype: string - name: class_name dtype: string - name: method_name dtype: string - name: row_number dtype: int64 - name: cbo dtype: float64 - name: wmc dtype: float64 - name: lcom* dtype: float64 - name: loc dtype: float64 - name: astc2 dtype: string - name: source_after dtype: string - name: cbo_after dtype: float64 - name: wmc_after dtype: float64 - name: lcom*_after dtype: float64 - name: loc_after dtype: float64 - name: astc1 dtype: string - name: issue_name dtype: string - name: issue_localize dtype: string splits: - name: train num_bytes: 169182022 num_examples: 6000 - name: test num_bytes: 41765079 num_examples: 1500 download_size: 47907274 dataset_size: 210947101 --- # Dataset Card for "TrainDedupedRefDatasetWMetricFinal3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,316
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conv_ai
2022-11-03T16:30:55.000Z
[ "task_categories:conversational", "task_categories:text-classification", "task_ids:text-scoring", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "evaluating-dialogue-systems", "region:us" ]
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ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue system in search of better answers.
null
2
262
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - conversational - text-classification task_ids: - text-scoring paperswithcode_id: null pretty_name: ConvAi tags: - evaluating-dialogue-systems dataset_info: features: - name: id dtype: int32 - name: dialogId dtype: int32 - name: context dtype: string - name: users list: - name: userType dtype: string - name: id dtype: string - name: evaluation list: - name: breadth dtype: int32 - name: userId dtype: string - name: quality dtype: int32 - name: engagement dtype: int32 - name: thread list: - name: evaluation dtype: int32 - name: text dtype: string - name: userId dtype: string - name: time dtype: int32 config_name: conv_ai splits: - name: train num_bytes: 3924265 num_examples: 2778 download_size: 5804611 dataset_size: 3924265 --- # Dataset Card for ConvAi ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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emea
2023-06-01T14:59:51.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:unknown", "region:us" ]
null
This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in the current version. source: http://www.emea.europa.eu/ 22 languages, 231 bitexts total number of files: 41,957 total number of tokens: 311.65M total number of sentence fragments: 26.51M
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
1
262
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv license: - unknown multilinguality: - multilingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: EMEA dataset_info: - config_name: bg-el features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - el splits: - name: train num_bytes: 296160562 num_examples: 1044065 download_size: 54531690 dataset_size: 296160562 - config_name: cs-et features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - et splits: - name: train num_bytes: 180261167 num_examples: 1053164 download_size: 36065651 dataset_size: 180261167 - config_name: de-mt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - mt splits: - name: train num_bytes: 182976918 num_examples: 1000532 download_size: 36665427 dataset_size: 182976918 - config_name: fr-sk features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - sk splits: - name: train num_bytes: 193605247 num_examples: 1062753 download_size: 38916074 dataset_size: 193605247 - config_name: es-lt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - lt splits: - name: train num_bytes: 182623676 num_examples: 1051370 download_size: 35329033 dataset_size: 182623676 config_names: - bg-el - cs-et - de-mt - es-lt - fr-sk --- # Dataset Card for EMEA ## 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:** http://opus.nlpl.eu/EMEA.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/EMEA.php E.g. `dataset = load_dataset("emea", lang1="en", lang2="nl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here is an example of the `en-nl` configuration: ``` {'id': '4', 'translation': {'en': 'EPAR summary for the public', 'nl': 'EPAR-samenvatting voor het publiek'}} ``` ### Data Fields The data fields are: - id: id of the sentence pair - translation: a dictionary of the form {lang1: text_in_lang1, lang2: text_in_lang2} ### Data Splits Sizes of some language pairs: | name |train| |----------|----:| |bg-el|1044065| |cs-et|1053164| |de-mt|1000532| |fr-sk|1062753| |es-lt|1051370| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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sedthh/gutenberg_english
2023-03-17T09:50:22.000Z
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "project gutenberg", "e-book", "gutenberg.org", "region:us" ]
sedthh
null
null
3
262
2023-02-28T14:15:24
--- dataset_info: features: - name: TEXT dtype: string - name: SOURCE dtype: string - name: METADATA dtype: string splits: - name: train num_bytes: 18104255935 num_examples: 48284 download_size: 10748877194 dataset_size: 18104255935 license: mit task_categories: - text-generation language: - en tags: - project gutenberg - e-book - gutenberg.org pretty_name: Project Gutenberg eBooks in English size_categories: - 10K<n<100K --- # Dataset Card for Project Gutenber - English Language eBooks A collection of non-english language eBooks (48284 rows, 80%+ of all english language books available on the site) from the Project Gutenberg site with metadata removed. Originally colected for https://github.com/LAION-AI/Open-Assistant (follows the OpenAssistant training format) The METADATA column contains catalogue meta information on each book as a serialized JSON: | key | original column | |----|----| | language | - | | text_id | Text# unique book identifier on Prject Gutenberg as *int* | | title | Title of the book as *string* | | issued | Issued date as *string* | | authors | Authors as *string*, comma separated sometimes with dates | | subjects | Subjects as *string*, various formats | | locc | LoCC code as *string* | | bookshelves | Bookshelves as *string*, optional | ## Source data **How was the data generated?** - A crawler (see Open-Assistant repository) downloaded the raw HTML code for each eBook based on **Text#** id in the Gutenberg catalogue (if available) - The metadata and the body of text are not clearly separated so an additional parser attempts to split them, then remove transcriber's notes and e-book related information from the body of text (text clearly marked as copyrighted or malformed was skipped and not collected) - The body of cleaned TEXT as well as the catalogue METADATA is then saved as a parquet file, with all columns being strings **Copyright notice:** - Some of the books are copyrighted! The crawler ignored all books with an english copyright header by utilizing a regex expression, but make sure to check out the metadata for each book manually to ensure they are okay to use in your country! More information on copyright: https://www.gutenberg.org/help/copyright.html and https://www.gutenberg.org/policy/permission.html - Project Gutenberg has the following requests when using books without metadata: _Books obtianed from the Project Gutenberg site should have the following legal note next to them: "This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost" no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook."_
2,987
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medalpaca/medical_meadow_mediqa
2023-04-16T16:30:36.000Z
[ "task_categories:question-answering", "language:en", "region:us" ]
medalpaca
null
null
6
262
2023-04-06T16:51:50
--- task_categories: - question-answering language: - en --- # MediQA ## Dataset Description MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions. - **Homepage:** https://osf.io/fyg46/?view_only= ### Citation Information ``` @article{savery2020question, title={Question-driven summarization of answers to consumer health questions}, author={Savery, Max and Abacha, Asma Ben and Gayen, Soumya and Demner-Fushman, Dina}, journal={Scientific Data}, volume={7}, number={1}, pages={322}, year={2020}, publisher={Nature Publishing Group UK London} } ```
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abobster/pushkin_new
2023-05-05T16:31:35.000Z
[ "region:us" ]
abobster
null
null
0
262
2023-05-05T16:31:11
Entry not found
15
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alzoubi36/piextract
2023-06-25T07:11:15.000Z
[ "region:us" ]
alzoubi36
null
null
0
262
2023-06-25T07:03:41
--- dataset_info: features: - name: COLLECT struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: NOT_COLLECT struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: NOT_SHARE struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string - name: SHARE struct: - name: subtask dtype: string - name: tags sequence: string - name: tokens sequence: string splits: - name: train num_bytes: 3453408 num_examples: 2579 - name: test num_bytes: 1580498 num_examples: 1029 - name: validation num_bytes: 662810 num_examples: 456 download_size: 1013894 dataset_size: 5696716 --- # Dataset for the PI-Extract task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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result-kand2-sdxl-wuerst-karlo/8a14fb4c
2023-10-06T19:06:51.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
0
262
2023-10-06T19:06:50
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 174 num_examples: 10 download_size: 1325 dataset_size: 174 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "8a14fb4c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
455
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fhamborg/news_sentiment_newsmtsc
2022-10-25T09:20:03.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]
fhamborg
NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles.
@InProceedings{Hamborg2021b, author = {Hamborg, Felix and Donnay, Karsten}, title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)}, year = {2021}, month = {Apr.}, location = {Virtual Event}, }
8
261
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual pretty_name: 'NewsMTSC' size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification language_bcp47: - en-US --- # NewsMTSC dataset NewsMTSC is a high-quality dataset consisting of more than 11k manually labeled sentences sampled from English news articles. Each sentence was labeled by five human coders (the dataset contains only examples where the five coders assessed same or similar sentiment). The dataset is published as a [full paper at EACL 2021: *NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles*](https://aclanthology.org/2021.eacl-main.142.pdf). ## Subsets and splits The dataset consists of two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually. More information on the subsets can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf). ## Format Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in **bold**. 1. `mention`: text of the mention within `sentence` 2. **`polarity`: sentiment of the sentence concerning the target's mention (-1 = negative, 0 = neutral, 1 = positive)** 3. **`from`: character-based, 0-indexed position of the first character of the target's mention within `sentence`** 4. **`to`: last character of the target's mention** 5. **`sentence`: sentence** 6. `id`: identifier that is unique within NewsMTSC ## Contact If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository. * Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC) * Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/) ## How to cite If you use the dataset or parts of it, please cite our paper: ``` @InProceedings{Hamborg2021b, author = {Hamborg, Felix and Donnay, Karsten}, title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)}, year = {2021}, month = {Apr.}, location = {Virtual Event}, } ```
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nielsr/rvl_cdip_10_examples_per_class_donut
2022-08-01T16:56:12.000Z
[ "region:us" ]
nielsr
null
null
0
261
2022-08-01T16:22:17
Entry not found
15
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jamescalam/ai-arxiv-chunked
2023-10-10T12:56:09.000Z
[ "region:us" ]
jamescalam
null
null
14
261
2023-10-09T21:09:27
Entry not found
15
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air_dialogue
2022-11-03T16:31:11.000Z
[ "task_categories:conversational", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-generation", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "region:us" ]
null
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
@inproceedings{wei-etal-2018-airdialogue, title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research", author = "Wei, Wei and Le, Quoc and Dai, Andrew and Li, Jia", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1419", doi = "10.18653/v1/D18-1419", pages = "3844--3854", abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.", }
6
260
2022-03-02T23:29:22
--- pretty_name: AirDialogue annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - conversational - text-generation - fill-mask task_ids: - dialogue-generation - dialogue-modeling - language-modeling - masked-language-modeling paperswithcode_id: null dataset_info: - config_name: air_dialogue_data features: - name: action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: intent struct: - name: return_month dtype: string - name: return_day dtype: string - name: max_price dtype: int32 - name: departure_airport dtype: string - name: max_connections dtype: int32 - name: departure_day dtype: string - name: goal dtype: string - name: departure_month dtype: string - name: name dtype: string - name: return_airport dtype: string - name: timestamps sequence: int64 - name: dialogue sequence: string - name: expected_action struct: - name: status dtype: string - name: name dtype: string - name: flight sequence: int32 - name: search_info list: - name: button_name dtype: string - name: field_name dtype: string - name: field_value dtype: string - name: timestmamp dtype: int64 - name: correct_sample dtype: bool_ splits: - name: train num_bytes: 353721137 num_examples: 321459 - name: validation num_bytes: 44442238 num_examples: 40363 download_size: 272898923 dataset_size: 398163375 - config_name: air_dialogue_kb features: - name: kb list: - name: airline dtype: string - name: class dtype: string - name: departure_airport dtype: string - name: departure_day dtype: string - name: departure_month dtype: string - name: departure_time_num dtype: int32 - name: flight_number dtype: int32 - name: num_connections dtype: int32 - name: price dtype: int32 - name: return_airport dtype: string - name: return_day dtype: string - name: return_month dtype: string - name: return_time_num dtype: int32 - name: reservation dtype: int32 splits: - name: train num_bytes: 782592158 num_examples: 321459 - name: validation num_bytes: 98269789 num_examples: 40363 download_size: 272898923 dataset_size: 880861947 --- # Dataset Card for air_dialogue ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - **Repository:** https://github.com/google/airdialogue - **Paper:** https://www.aclweb.org/anthology/D18-1419/ - **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 - **Point of Contact:** [AirDialogue-Google](mailto:airdialogue@gmail.com) [Aakash Gupta](mailto:aakashg80@gmail.com) ### Dataset Summary AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. ### Supported Tasks and Leaderboards We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores The inference competition & leaderboard can be found here: https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59 ### Languages The text in the dataset is in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`) BuilderConfig: `air_dialogue_data` ``` {"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true} ``` BuilderConfig: `air_dialogue_kb` ``` {"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0} ``` ### Data Fields BuilderConfig: `air_dialogue_data`: Provides for customer context, dialogue states and environment key name | Description | |---|---| |'search_action' | search action performed by customer | |'action' | Action taken by the agent | |'intent' | Intents from the conversation | |'timestamps' | Timestamp for each of the dialogues | |'dialogue' | Dialogue recorded between agent & customer | |'expected_action' | Expected action from agent (human-annotated)| |'correct_sample' | whether action performed by agent was same as expected_action | BuilderConfig: `air_dialogue_kb`: Provides for the Agent Context _ca_ = (_db_, _r_ ) key name | Description | |---|---| |'kb' | Available flights in the database | |'reservation' | whether customer has an existing reservation| ### Data Splits Data is split into Train/Dev & Test in the ration of 80%, 10% and 10% ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information No personal and sensitive information is stored ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [AirDialogue team](mailto:airdialogue@gmail.com) For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:aakashg80@gmail.com) ### Licensing Information cc-by-nc-4.0 ### Citation Information @inproceedings{wei-etal-2018-airdialogue, title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research", author = "Wei, Wei and Le, Quoc and Dai, Andrew and Li, Jia", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1419", doi = "10.18653/v1/D18-1419", pages = "3844--3854", abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.", } ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.
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allenai/mup
2022-10-25T10:16:52.000Z
[ "license:odc-by", "region:us" ]
allenai
null
null
2
260
2022-05-10T14:53:26
--- license: - odc-by --- # MuP - Multi Perspective Scientific Document Summarization Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.
866
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ScandEval/scandiqa-da-mini
2023-07-05T09:44:29.000Z
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:da", "license:cc-by-3.0", "region:us" ]
ScandEval
null
null
0
260
2022-12-05T16:41:50
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: context dtype: string - name: answers_en struct: - name: answer_start sequence: int64 - name: text sequence: string - name: context_en dtype: string - name: title_en dtype: string splits: - name: train num_bytes: 3238964 num_examples: 1024 - name: val num_bytes: 1096223 num_examples: 256 - name: test num_bytes: 6668816 num_examples: 2048 download_size: 6456003 dataset_size: 11004003 license: cc-by-3.0 task_categories: - question-answering language: - da size_categories: - 1K<n<10K --- # Dataset Card for "scandiqa-da-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
963
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qed_amara
2022-11-03T16:31:42.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:aa", "language:ab", "language:ae", "language:aeb", "language:af", "language:ak", "language:am", "language:an", "language:ar", "language:arq", "language:arz", "language:as", "language:ase", "language:ast", "language:av", "language:ay", "language:az", "language:ba", "language:be", "language:ber", "language:bg", "language:bh", "language:bi", "language:bm", "language:bn", "language:bnt", "language:bo", "language:br", "language:bs", "language:bug", "language:ca", "language:ce", "language:ceb", "language:ch", "language:cho", "language:cku", "language:cnh", "language:co", "language:cr", "language:cs", "language:cu", "language:cv", "language:cy", "language:da", "language:de", "language:dv", "language:dz", "language:ee", "language:efi", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:ff", "language:fi", "language:fil", "language:fj", "language:fo", "language:fr", "language:ga", "language:gd", "language:gl", "language:gn", "language:gu", "language:ha", "language:hai", "language:haw", "language:haz", "language:hch", "language:he", "language:hi", "language:ho", "language:hr", "language:ht", "language:hu", "language:hup", "language:hus", "language:hy", "language:hz", "language:ia", "language:id", "language:ie", "language:ig", "language:ik", "language:inh", "language:io", "language:iro", "language:is", "language:it", "language:iu", "language:ja", "language:jv", "language:ka", "language:kar", "language:ki", "language:kj", "language:kk", "language:kl", "language:km", "language:kn", "language:ko", "language:kr", "language:ksh", "language:ku", "language:kv", "language:kw", "language:ky", "language:la", "language:lb", "language:lg", "language:li", "language:lkt", "language:lld", "language:ln", "language:lo", "language:lt", "language:ltg", "language:lu", "language:luo", "language:luy", "language:lv", "language:mad", "language:mfe", "language:mg", "language:mi", "language:mk", "language:ml", "language:mn", "language:mni", "language:moh", "language:mos", "language:mr", "language:ms", "language:mt", "language:mus", "language:my", "language:nb", "language:nci", "language:nd", "language:ne", "language:nl", "language:nn", "language:nso", "language:nv", "language:ny", "language:oc", "language:om", "language:or", "language:pa", "language:pam", "language:pap", "language:pi", "language:pl", "language:pnb", "language:prs", "language:ps", "language:pt", "language:qu", "language:rm", "language:rn", "language:ro", "language:ru", "language:rup", "language:rw", "language:sa", "language:sc", "language:scn", "language:sco", "language:sd", "language:sg", "language:sgn", "language:sh", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:so", "language:sq", "language:sr", "language:st", "language:sv", "language:sw", "language:szl", "language:ta", "language:te", "language:tet", "language:tg", "language:th", "language:ti", "language:tk", "language:tl", "language:tlh", "language:to", "language:tr", "language:ts", "language:tt", "language:tw", "language:ug", "language:uk", "language:umb", "language:ur", "language:uz", "language:ve", "language:vi", "language:vls", "language:vo", "language:wa", "language:wo", "language:xh", "language:yaq", "language:yi", "language:yo", "language:za", "language:zam", "language:zh", "language:zu", "license:unknown", "region:us" ]
null
The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group The QED Corpus is made public for RESEARCH purpose only. The corpus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Copyright Qatar Computing Research Institute. All rights reserved. 225 languages, 9,291 bitexts total number of files: 271,558 total number of tokens: 371.76M total number of sentence fragments: 30.93M
A. Abdelali, F. Guzman, H. Sajjad and S. Vogel, "The AMARA Corpus: Building parallel language resources for the educational domain", The Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14). Reykjavik, Iceland, 2014. Pp. 1856-1862. Isbn. 978-2-9517408-8-4.
4
259
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - aa - ab - ae - aeb - af - ak - am - an - ar - arq - arz - as - ase - ast - av - ay - az - ba - be - ber - bg - bh - bi - bm - bn - bnt - bo - br - bs - bug - ca - ce - ceb - ch - cho - cku - cnh - co - cr - cs - cu - cv - cy - da - de - dv - dz - ee - efi - el - en - eo - es - et - eu - fa - ff - fi - fil - fj - fo - fr - ga - gd - gl - gn - gu - ha - hai - haw - haz - hch - he - hi - ho - hr - ht - hu - hup - hus - hy - hz - ia - id - ie - ig - ik - inh - io - iro - is - it - iu - ja - jv - ka - kar - ki - kj - kk - kl - km - kn - ko - kr - ksh - ku - kv - kw - ky - la - lb - lg - li - lkt - lld - ln - lo - lt - ltg - lu - luo - luy - lv - mad - mfe - mg - mi - mk - ml - mn - mni - moh - mos - mr - ms - mt - mus - my - nb - nci - nd - ne - nl - nn - nso - nv - ny - oc - om - or - pa - pam - pap - pi - pl - pnb - prs - ps - pt - qu - rm - rn - ro - ru - rup - rw - sa - sc - scn - sco - sd - sg - sgn - sh - si - sk - sl - sm - sn - so - sq - sr - st - sv - sw - szl - ta - te - tet - tg - th - ti - tk - tl - tlh - to - tr - ts - tt - tw - ug - uk - umb - ur - uz - ve - vi - vls - vo - wa - wo - xh - yaq - yi - yo - za - zam - zh - zu license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: QedAmara dataset_info: - config_name: ar-ko features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ko splits: - name: train num_bytes: 79605277 num_examples: 592589 download_size: 23410393 dataset_size: 79605277 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 75861416 num_examples: 407224 download_size: 26579871 dataset_size: 75861416 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 80650321 num_examples: 447369 download_size: 28344317 dataset_size: 80650321 - config_name: en-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 86731218 num_examples: 497531 download_size: 29836171 dataset_size: 86731218 - config_name: he-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - he - nl splits: - name: train num_bytes: 51448732 num_examples: 273165 download_size: 16642865 dataset_size: 51448732 --- # Dataset Card for QedAmara ## 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:** http://opus.nlpl.eu/QED.php - **Repository:** None - **Paper:** https://www.aclweb.org/anthology/L14-1675/ - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/QED.php E.g. `dataset = load_dataset("qed_amara", lang1="cs", lang2="nb")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - aa - ab - ae - aeb - af - aka: `ak` - amh: `am` - an - ar - arq - arz - as - ase - ast - av - ay - az - ba - bam: `bm` - be - ber - bg - bh - bi - bn - bnt - bo - br - bs - bug - ca - ce - ceb - ch - cho - cku - cnh - co - cr - cs - cu - cv - cy - da - de - dv - dz - ee - efi - el - en - eo - es - et - eu - fa - ff - fi - fil - fj - fo - fr - ful: `ff` - ga - gd - gl - gn - gu - hai - hau: `ha` - haw - haz - hb: ? - hch - he - hi - ho - hr - ht - hu - hup - hus - hy - hz - ia - ibo: `ig` - id - ie - ik - inh - io - iro - is - it - iu - ja - jv - ka - kar - kau: `kr` - kik: `ki` - kin: `rw` - kj - kk - kl - km - kn - ko - ksh - ku - kv - kw - ky - la - lb - lg - li - lin: `ln` - lkt - lld - lo - lt - ltg - lu - luo - luy - lv - mad - mfe - mi - mk - ml - mlg: `mg` - mn - mni - mo: Moldavian (deprecated tag; preferred value: Romanian; Moldavian; Moldovan (`ro`)) - moh - mos - mr - ms - mt - mus - my - nb - nci - nd - ne - nl - nn - nso - nv - nya: `ny` - oc - or - orm: `om` - pam - pan: `pa` - pap - pi - pl - pnb - prs - ps - pt - que: `qu` - rm - ro - ru - run: `rn` - rup - ry: ? - sa - sc - scn - sco - sd - sg - sgn - sh - si - sk - sl - sm - sna: `sn` - som: `so` - sot: `st` - sq - sr - srp: `sr` - sv - swa: `sw` - szl - ta - te - tet - tg - th - tir: `ti` - tk - tl - tlh - to - tr - ts - tt - tw - ug - uk - umb - ur - uz - ve - vi - vls - vo - wa - wol: `wo` - xh - yaq - yi - yor: `yo` - za - zam - zh - zul: `zu` ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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jordyvl/rvl_cdip_100_examples_per_class
2023-03-23T20:55:18.000Z
[ "region:us" ]
jordyvl
null
null
0
259
2023-03-23T19:58:02
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': letter '1': form '2': email '3': handwritten '4': advertisement '5': scientific report '6': scientific publication '7': specification '8': file folder '9': news article '10': budget '11': invoice '12': presentation '13': questionnaire '14': resume '15': memo splits: - name: train num_bytes: 97000316.76 num_examples: 800 - name: test num_bytes: 48612840.21 num_examples: 400 - name: validation num_bytes: 48666549.76 num_examples: 400 download_size: 180034173 dataset_size: 194279706.73 --- # Dataset Card for "rvl_cdip_100_examples_per_class" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,002
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sanchit-gandhi/gtzan
2023-06-23T13:48:10.000Z
[ "region:us" ]
sanchit-gandhi
null
null
0
259
2023-06-23T13:47:03
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 32000 - name: genre dtype: class_label: names: '0': blues '1': classical '2': country '3': disco '4': hiphop '5': jazz '6': metal '7': pop '8': reggae '9': rock splits: - name: train num_bytes: 1322941192.0 num_examples: 999 download_size: 1305519226 dataset_size: 1322941192.0 --- # Dataset Card for "gtzan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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conceptnet5
2023-06-01T14:59:50.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10M<n<100M", "size_categories:1M<n<10M", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:ja", "language:nl", "language:pt", "language:ru", "language:zh", "license:cc-by-4.0", "region:us" ]
null
This dataset is designed to provide training data for common sense relationships pulls together from various sources. The dataset is multi-lingual. See langauge codes and language info here: https://github.com/commonsense/conceptnet5/wiki/Languages This dataset provides an interface for the conceptnet5 csv file, and some (but not all) of the raw text data used to build conceptnet5: omcsnet_sentences_free.txt, and omcsnet_sentences_more.txt. One use of this dataset would be to learn to extract the conceptnet relationship from the omcsnet sentences. Conceptnet5 has 34,074,917 relationships. Of those relationships, there are 2,176,099 surface text sentences related to those 2M entries. omcsnet_sentences_free has 898,161 lines. omcsnet_sentences_more has 2,001,736 lines. Original downloads are available here https://github.com/commonsense/conceptnet5/wiki/Downloads. For more information, see: https://github.com/commonsense/conceptnet5/wiki The omcsnet data comes with the following warning from the authors of the above site: Remember: this data comes from various forms of crowdsourcing. Sentences in these files are not necessarily true, useful, or appropriate.
\ Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31. }
15
258
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - de - en - es - fr - it - ja - nl - pt - ru - zh license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: conceptnet pretty_name: Conceptnet5 dataset_info: - config_name: conceptnet5 features: - name: sentence dtype: string - name: full_rel dtype: string - name: rel dtype: string - name: arg1 dtype: string - name: arg2 dtype: string - name: lang dtype: string - name: extra_info dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 11493868180 num_examples: 34074917 download_size: 497963447 dataset_size: 11493868180 - config_name: omcs_sentences_free features: - name: sentence dtype: string - name: raw_data dtype: string - name: lang dtype: string splits: - name: train num_bytes: 174811310 num_examples: 898160 download_size: 104247648 dataset_size: 174811310 - config_name: omcs_sentences_more features: - name: sentence dtype: string - name: raw_data dtype: string - name: lang dtype: string splits: - name: train num_bytes: 341424279 num_examples: 2001735 download_size: 209776958 dataset_size: 341424279 config_names: - conceptnet5 - omcs_sentences_free - omcs_sentences_more --- # Dataset Card for Conceptnet5 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/commonsense/conceptnet5/wiki - **Repository:** https://github.com/commonsense/conceptnet5/wiki - **Paper:** Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31.o ### Dataset Summary ConceptNet is a multilingual knowledge base, representing words and phrases that people use and the common-sense relationships between them. The knowledge in ConceptNet is collected from a variety of resources, including crowd-sourced resources (such as Wiktionary and Open Mind Common Sense), games with a purpose (such as Verbosity and nadya.jp), and expert-created resources (such as WordNet and JMDict). You can browse what ConceptNet knows at http://conceptnet.io. This dataset is designed to provide training data for common sense relationships pulls together from various sources. The dataset is multi-lingual. See langauge codes and language info here: https://github.com/commonsense/conceptnet5/wiki/Languages This dataset provides an interface for the conceptnet5 csv file, and some (but not all) of the raw text data used to build conceptnet5: omcsnet_sentences_free.txt, and omcsnet_sentences_more.txt. One use of this dataset would be to learn to extract the conceptnet relationship from the omcsnet sentences. Conceptnet5 has 34,074,917 relationships. Of those relationships, there are 2,176,099 surface text sentences related to those 2M entries. omcsnet_sentences_free has 898,161 lines. omcsnet_sentences_more has 2,001,736 lines. Original downloads are available here https://github.com/commonsense/conceptnet5/wiki/Downloads. For more information, see: https://github.com/commonsense/conceptnet5/wiki The omcsnet data comes with the following warning from the authors of the above site: Remember: this data comes from various forms of crowdsourcing. Sentences in these files are not necessarily true, useful, or appropriate. ### Languages en, fr, it, de, es, ru, pt, ja, nl, zh and others ## Dataset Structure ### Data Instances There are three configurations for the dataset: conceptnet5, omcs_sentences_free, omcs_sentences_more. Conceptnet5 defines: `` { 'sentence': ..., 'full_rel': ..., 'rel': ..., 'arg1': ..., 'arg2': ..., 'lang': ..., 'extra_info': ... 'weight': ... } `` The omcs text defines: `` { 'sentence': ..., 'raw_data': ... 'weight': ... } `` ### Data Fields For conceptnet5 configurations: * full_rel: the full relationship. e.g., /a/[/r/Antonym/,/c/en/able/,/c/en/cane/] * rel: the binary relationship. e.g., /r/Antonym * arg1: the first argument to the binary relationship. e.g., /c/en/able * arg2: the second argument to the binary relationship. e.g., /c/en/cane * lang: the language code. e.g., en, fr, etc. If the arg1 and arg2 are two different languages, then the form os lang1/lang2. * extra_info: a string that includes json data that has the dataset name, license type (mostly cc-4.0), contributor, etc. e.g., : {"dataset": "/d/verbosity", "license": "cc:by/4.0", "sources": [{"contributor": "/s/resource/verbosity"}], "surfaceEnd": "cane", "surfaceStart": "able", "surfaceText": "[[able]] is the opposite of [[cane]]", "weight": 0.299} * sentence: the sentence from which the relationship was extracted, if one exists, with brackets around the arg1 and arg2. e.g., [[able]] is the opposite of [[cane]] * weight: the weight assigned by the curators or automatically to the relationship, between 1.0-0.0, higher being more certain. For the omcs text configurations: * sentence: the raw sentence * raw_data: the raw tab seperated data of the form, id, text, curator_id, created_on, lanugage_id, activity_id, and score. Most of this information was tied to older systems for entering the data os was not partsed into fields for the dataset. e.g., 1237278 someone can be at catch 10805 2006-11-14 17:56:49.70872-05 en 27 1 * lang: the language code ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created over many years for research in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization Started as the Open Mind Common Sense project at MIT Media Lab in 1999. See https://en.wikipedia.org/wiki/Open_Mind_Common_Sense #### Who are the source language producers? Crowd Sourced ### Annotations #### Annotation process Crowd Source template text, games, etc. #### Who are the annotators? Crowd sourced. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases See the website and paper for efforts to minimize data bias, but please note that omcs_sentences_free, omcs_sentences_more are raw data entered by users and may very well have biased data. ### Other Known Limitations While the relationship dataset is large, the amount of actual sentences is limited. ## Additional Information ### Dataset Curators The authors of https://github.com/commonsense/conceptnet5/wiki and Luminoso. ### Licensing Information This work includes data from ConceptNet 5, which was compiled by the Commonsense Computing Initiative. ConceptNet 5 is freely available under the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from http://conceptnet.io. The included data was created by contributors to Commonsense Computing projects, contributors to Wikimedia projects, DBPedia, OpenCyc, Games with a Purpose, Princeton University's WordNet, Francis Bond's Open Multilingual WordNet, and Jim Breen's JMDict. Credits and acknowledgements ConceptNet has been developed by: The MIT Media Lab, through various groups at different times: Commonsense Computing Software Agents Digital Intuition The Commonsense Computing Initiative, a worldwide collaboration with contributions from: National Taiwan University Universidade Federal de São Carlos Hokkaido University Tilburg University Nihon Unisys Labs Dentsu Inc. Kyoto University Yahoo Research Japan Luminoso Technologies, Inc. Significant amounts of data were imported from: WordNet, a project of Princeton University Open Multilingual WordNet, compiled by Francis Bond and Kyonghee Paik Wikipedia and Wiktionary, collaborative projects of the Wikimedia Foundation Luis von Ahn's "Games with a Purpose" JMDict, compiled by Jim Breen CC-CEDict, by MDBG The Unicode CLDR DBPedia Here is a short, incomplete list of people who have made significant contributions to the development of ConceptNet as a data resource, roughly in order of appearance: Push Singh Catherine Havasi Hugo Liu Hyemin Chung Robyn Speer Ken Arnold Yen-Ling Kuo Joshua Chin Joanna Lowry-Duda Robert Beaudoin Naoki Otani Vanya Cohen Licenses for included resources Commonsense Computing The Commonsense Computing project originated at the MIT Media Lab and expanded worldwide. Tens of thousands of contributors have taken some time to teach facts to computers. Their pseudonyms can be found in the "sources" list found in ConceptNet's raw data and in its API. Games with a Purpose Data collected from Verbosity, one of the CMU "Games with a Purpose", is used and released under ConceptNet's license, by permission from Luis von Ahn and Harshit Surana. Verbosity players are anonymous, so in the "sources" list, data from Verbosity is simply credited to the pseudonym "verbosity". Wikimedia projects ConceptNet uses data directly from Wiktionary, the free dictionary. It also uses data from Wikipedia, the free encyclopedia via DBPedia. Wiktionary and Wikipedia are collaborative projects, authored by their respective online communities. They are currently released under the Creative Commons Attribution-ShareAlike license. Wikimedia encourages giving attribution by providing links to the hosted pages that the data came from, and DBPedia asks for the same thing in turn. In addition to crediting the assertions that came from Wiktionary and DBPedia, we also provide "ExternalURL" edges pointing to the page that they came from. For example, the term /c/de/sprache has an ExternalURL link pointing to http://en.wiktionary.org/wiki/Sprache. Its list of individual contributors can be seen by following its "History" link. The URLs of links to DBPedia are the same as the resource names that DBPedia uses, encouraging interoperability with their linked data. WordNet WordNet is available under an unencumbered license: see http://wordnet.princeton.edu/wordnet/license/. Its text is reproduced below: WordNet Release 3.0 This software and database is being provided to you, the LICENSEE, by Princeton University under the following license. By obtaining, using and/or copying this software and database, you agree that you have read, understood, and will comply with these terms and conditions.: Permission to use, copy, modify and distribute this software and database and its documentation for any purpose and without fee or royalty is hereby granted, provided that you agree to comply with the following copyright notice and statements, including the disclaimer, and that the same appear on ALL copies of the software, database and documentation, including modifications that you make for internal use or for distribution. WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved. THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. The name of Princeton University or Princeton may not be used in advertising or publicity pertaining to distribution of the software and/or database. Title to copyright in this software, database and any associated documentation shall at all times remain with Princeton University and LICENSEE agrees to preserve same. Open Multilingual WordNet Open Multilingual WordNet was compiled by Francis Bond, Kyonghee Paik, and Ryan Foster, from data provided by many multilingual WordNet projects. Here is the complete list of references to the projects that created the data. ### Citation Information Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31. ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.
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php
2022-11-03T16:31:41.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:cs", "language:de", "language:en", "language:es", "language:fi", "language:fr", "language:he", "language:hu", "language:it", "language:ja", "language:ko", "language:nl", "language:pl", "language:pt", "language:ro", "language:ru", "language:sk", "language:sl", "language:sv", "language:tr", "language:tw", "language:zh", "license:unknown", "region:us" ]
null
A parallel corpus originally extracted from http://se.php.net/download-docs.php. The original documents are written in English and have been partly translated into 21 languages. The original manuals contain about 500,000 words. The amount of actually translated texts varies for different languages between 50,000 and 380,000 words. The corpus is rather noisy and may include parts from the English original in some of the translations. The corpus is tokenized and each language pair has been sentence aligned. 23 languages, 252 bitexts total number of files: 71,414 total number of tokens: 3.28M total number of sentence fragments: 1.38M
@InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
1
258
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - cs - de - en - es - fi - fr - he - hu - it - ja - ko - nl - pl - pt - ro - ru - sk - sl - sv - tr - tw - zh language_bcp47: - pt-BR - zh-TW license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: php dataset_info: - config_name: fi-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - nl splits: - name: train num_bytes: 1197502 num_examples: 27870 download_size: 43228 dataset_size: 1197502 - config_name: it-ro features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ro splits: - name: train num_bytes: 1422966 num_examples: 28507 download_size: 108885 dataset_size: 1422966 - config_name: nl-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - sv splits: - name: train num_bytes: 1298041 num_examples: 28079 download_size: 58495 dataset_size: 1298041 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 2758463 num_examples: 35538 download_size: 478646 dataset_size: 2758463 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 4288513 num_examples: 42222 download_size: 905396 dataset_size: 4288513 --- # Dataset Card for php ## 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:** http://opus.nlpl.eu/PHP.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/PHP.php E.g. `dataset = load_dataset("php", lang1="it", lang2="pl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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