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bigscience-historical-texts/hipe2020
bigscience-historical-texts
2023-02-07T08:54:43Z
589
3
null
[ "language:de", "language:en", "language:fr", "region:us" ]
2023-02-07T08:54:43Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - de - en - fr ---
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biu-nlp/qa_srl2020
biu-nlp
2022-10-17T20:49:01Z
589
1
null
[ "region:us" ]
2022-10-17T20:49:01Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
# QA-SRL 2020 (Gold Standard) The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as [biu-nlp/qa_srl2018](https://huggingface.co/datasets/biu-nlp/qa_srl2018)). See the paper for details: [Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020](https://aclanthology.org/2020.acl-main.626/). Check out our [GitHub repository](https://github.com/plroit/qasrl-gs) to find code for evaluation. The dataset was annotated by selected workers from Amazon Mechanical Turk.
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classla/ssj500k
classla
2022-10-28T05:37:22Z
589
0
null
[ "task_categories:token-classification", "task_ids:lemmatization", "task_ids:named-entity-recognition", "task_ids:parsing", "task_ids:part-of-speech", "language:sl", "license:cc-by-sa-4.0", "structure-prediction", "tokenization", "dependency-parsing", "region:us" ]
2022-10-28T05:37:22Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - sl license: - cc-by-sa-4.0 task_categories: - token-classification task_ids: - lemmatization - named-entity-recognition - parsing - part-of-speech tags: - structure-prediction - tokenization - dependency-parsing --- The dataset contains 7432 training samples, 1164 validation samples and 893 test samples. Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'), list of tokens ('tokens'), list of lemmas ('lemmas'), list of Multext-East tags ('xpos\_tags), list of UPOS tags ('upos\_tags'), list of morphological features ('feats'), list of IOB tags ('iob\_tags'), and list of universal dependency tags ('uds'). Three dataset configurations are available, where the corresponding features are encoded as class labels: 'ner', 'upos', and 'ud'.
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scielo
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2023-06-01T14:59:47Z
588
1
null
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:es", "language:pt", "license:unknown", "arxiv:1905.01852", "region:us" ]
2023-06-01T14:59:47Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en - es - pt license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: SciELO dataset_info: - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 71777213 num_examples: 177782 download_size: 22965217 dataset_size: 71777213 - config_name: en-pt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 1032669686 num_examples: 2828917 download_size: 322726075 dataset_size: 1032669686 - config_name: en-pt-es features: - name: translation dtype: translation: languages: - en - pt - es splits: - name: train num_bytes: 147472132 num_examples: 255915 download_size: 45556562 dataset_size: 147472132 config_names: - en-es - en-pt - en-pt-es --- # Dataset Card for SciELO ## 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:**[SciELO](https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB) - **Repository:** - **Paper:** [A Large Parallel Corpus of Full-Text Scientific Articles](https://arxiv.org/abs/1905.01852) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A parallel corpus of full-text scientific articles collected from Scielo database in the following languages:English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### 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 ``` @inproceedings{soares2018large, title={A Large Parallel Corpus of Full-Text Scientific Articles}, author={Soares, Felipe and Moreira, Viviane and Becker, Karin}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)}, year={2018} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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tweets_ar_en_parallel
null
2023-01-25T14:54:55Z
588
3
bilingual-corpus-of-arabic-english-parallel
[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:100K<n<1M", "source_datasets:original", "language:ar", "language:en", "license:apache-2.0", "tweets-translation", "region:us" ]
2023-01-25T14:54:55Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated - no-annotation language_creators: - found language: - ar - en license: - apache-2.0 multilinguality: - translation size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: bilingual-corpus-of-arabic-english-parallel pretty_name: Bilingual Corpus of Arabic-English Parallel Tweets tags: - tweets-translation dataset_info: - config_name: parallelTweets features: - name: ArabicTweetID dtype: int64 - name: EnglishTweetID dtype: int64 splits: - name: test num_bytes: 2667296 num_examples: 166706 download_size: 2937626 dataset_size: 2667296 - config_name: accountList features: - name: account dtype: string splits: - name: test num_bytes: 20108 num_examples: 1389 download_size: 2937626 dataset_size: 20108 - config_name: countryTopicAnnotation features: - name: account dtype: string - name: country dtype: class_label: names: '0': QA '1': BH '2': AE '3': OM '4': SA '5': PL '6': JO '7': IQ '8': Other '9': EG '10': KW '11': SY - name: topic dtype: class_label: names: '0': Gov '1': Culture '2': Education '3': Sports '4': Travel '5': Events '6': Business '7': Science '8': Politics '9': Health '10': Governoment '11': Media splits: - name: test num_bytes: 6036 num_examples: 200 download_size: 2937626 dataset_size: 6036 --- # Dataset Card for Bilingual Corpus of Arabic-English Parallel Tweets ## 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:** [Bilingual Corpus of Arabic-English Parallel Tweets](https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets) - **Repository:** - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.bucc-1.3/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. This resource is a result of a generic method for collecting parallel tweets. Using the method, we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances parallelTweets: ``` { "ArabicTweetID": 981111245209243600, "EnglishTweetID": 981111450432401400 } ``` accountList: ``` { 'account': 'HukoomiQatar' } ``` countryTopicAnnotation: ``` { 'account': 'HukoomiQatar', 'country': 'QA', 'topic': 'Gov' } ``` ### Data Fields parallelTweets: - `ArabicTweetID` (int) - `EnglishTweetID` (int) accountList: - `account` (str) countryTopicAnnotation: - `account` (str) - `country` (class label): One of: - "QA", - "BH", - "AE", - "OM", - "SA", - "PL", - "JO", - "IQ", - "Other", - "EG", - "KW", - "SY" - `topic` (class label): One of: - "Gov", - "Culture", - "Education", - "Sports", - "Travel", - "Events", - "Business", - "Science", - "Politics", - "Health", - "Governoment", - "Media", ### Data Splits All configuration have only one split: "test". ## 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 It is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{Mubarak2020bilingualtweets, title={Constructing a Bilingual Corpus of Parallel Tweets}, author={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed}, booktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)}, address={Marseille, France}, year={2020} } ``` [More Information Needed] ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.
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THUDM/humaneval-x
THUDM
2022-10-25T06:08:38Z
588
49
null
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:apache-2.0", "region:us" ]
2022-10-25T06:08:38Z
2022-09-20T16:23:53.000Z
2022-09-20T16:23:53
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - apache-2.0 multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling pretty_name: HumanEval-X --- # HumanEval-X ## Dataset Description [HumanEval-X](https://github.com/THUDM/CodeGeeX) is a benchmark for evaluating the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation. ## Languages The dataset contains coding problems in 5 programming languages: Python, C++, Java, JavaScript, and Go. ## Dataset Structure To load the dataset you need to specify a subset among the 5 exiting languages `[python, cpp, go, java, js]`. By default `python` is loaded. ```python from datasets import load_dataset load_dataset("THUDM/humaneval-x", "js") DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'declaration', 'canonical_solution', 'test', 'example_test'], num_rows: 164 }) }) ``` ```python next(iter(data["test"])) {'task_id': 'JavaScript/0', 'prompt': '/* Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> hasCloseElements([1.0, 2.0, 3.0], 0.5)\n false\n >>> hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n true\n */\nconst hasCloseElements = (numbers, threshold) => {\n', 'declaration': '\nconst hasCloseElements = (numbers, threshold) => {\n', 'canonical_solution': ' for (let i = 0; i < numbers.length; i++) {\n for (let j = 0; j < numbers.length; j++) {\n if (i != j) {\n let distance = Math.abs(numbers[i] - numbers[j]);\n if (distance < threshold) {\n return true;\n }\n }\n }\n }\n return false;\n}\n\n', 'test': 'const testHasCloseElements = () => {\n console.assert(hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) === true)\n console.assert(\n hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) === false\n )\n console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) === true)\n console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) === false)\n console.assert(hasCloseElements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) === true)\n console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) === true)\n console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) === false)\n}\n\ntestHasCloseElements()\n', 'example_test': 'const testHasCloseElements = () => {\n console.assert(hasCloseElements([1.0, 2.0, 3.0], 0.5) === false)\n console.assert(\n hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) === true\n )\n}\ntestHasCloseElements()\n'} ``` ## Data Fields * ``task_id``: indicates the target language and ID of the problem. Language is one of ["Python", "Java", "JavaScript", "CPP", "Go"]. * ``prompt``: the function declaration and docstring, used for code generation. * ``declaration``: only the function declaration, used for code translation. * ``canonical_solution``: human-crafted example solutions. * ``test``: hidden test samples, used for evaluation. * ``example_test``: public test samples (appeared in prompt), used for evaluation. ## Data Splits Each subset has one split: test. ## Citation Information Refer to https://github.com/THUDM/CodeGeeX.
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SALT-NLP/ImplicitHate
SALT-NLP
2023-02-16T23:00:38Z
588
2
null
[ "region:us" ]
2023-02-16T23:00:38Z
2023-02-16T22:45:19.000Z
2023-02-16T22:45:19
# Implicit Hate Speech _Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_ [[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus.zip?dl=0) <img src="frontpage.png" alt="frontpage" width="650"/> ## *Why Implicit Hate?* It is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech ([Caselli et al. 2020](https://aclanthology.org/2020.lrec-1.760/)). ## *Where can I download the data?* If you have not already, please first complete a short [survey](https://forms.gle/QxCpEbVp91Z35hWFA). Then follow [this link to download](https://www.dropbox.com/s/p1ctnsg3xlnupwr/implicit-hate-corpus.zip?dl=0) (2 MB, expands to 6 MB). ## *What's 'in the box?'* This dataset contains **22,056** tweets from the most prominent extremist groups in the United States; **6,346** of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left). * (24.2%) **Grievance:** frustration over a minority group's perceived privilege. * (20.0%) **Incitement:** implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power). * (13.6%) **Inferiority:** implying some group or person is of lesser value than another. * (12.6%) **Irony:** using sarcasm, humor, and satire to demean someone. * (17.9%) **Stereotypes:** associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language. * (10.5%) **Threats:** making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc. * (1.2%) **Other** Each of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above). ## *What can I do with this data?* State-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our [paper baselines](#) show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task. We hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups. ## *How do I cite this work?* **Citation:** > ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_. **BibTeX:** ```tex @inproceedings{elsherief-etal-2021-latent, title = "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech", author = "ElSherief, Mai and Ziems, Caleb and Muchlinski, David and Anupindi, Vaishnavi and Seybolt, Jordyn and De Choudhury, Munmun and Yang, Diyi", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.29", pages = "345--363" } ```
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persian_ner
null
2023-01-25T14:42:29Z
587
0
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[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:fa", "license:cc-by-4.0", "region:us" ]
2023-01-25T14:42:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - fa license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Persian NER dataset_info: - config_name: fold1 features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': I-event '2': I-fac '3': I-loc '4': I-org '5': I-pers '6': I-pro '7': B-event '8': B-fac '9': B-loc '10': B-org '11': B-pers '12': B-pro splits: - name: train num_bytes: 3362102 num_examples: 5121 - name: test num_bytes: 1646481 num_examples: 2560 download_size: 1931170 dataset_size: 5008583 - config_name: fold2 features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': I-event '2': I-fac '3': I-loc '4': I-org '5': I-pers '6': I-pro '7': B-event '8': B-fac '9': B-loc '10': B-org '11': B-pers '12': B-pro splits: - name: train num_bytes: 3344561 num_examples: 5120 - name: test num_bytes: 1664022 num_examples: 2561 download_size: 1931170 dataset_size: 5008583 - config_name: fold3 features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': I-event '2': I-fac '3': I-loc '4': I-org '5': I-pers '6': I-pro '7': B-event '8': B-fac '9': B-loc '10': B-org '11': B-pers '12': B-pro splits: - name: train num_bytes: 3310491 num_examples: 5121 - name: test num_bytes: 1698092 num_examples: 2560 download_size: 1931170 dataset_size: 5008583 --- # Dataset Card for [Persian NER] ## 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/HaniehP/PersianNER) - **Repository:** [Github](https://github.com/HaniehP/PersianNER) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/C16-1319) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset includes 7,682 Persian sentences, split into 250,015 tokens and their NER labels. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro" ``` ### Data Splits Training and test splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi ### 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 is published for academic use only ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 4.0 International License. ### Citation Information @inproceedings{poostchi-etal-2016-personer, title = "{P}erso{NER}: {P}ersian Named-Entity Recognition", author = "Poostchi, Hanieh and Zare Borzeshi, Ehsan and Abdous, Mohammad and Piccardi, Massimo", booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers", month = dec, year = "2016", address = "Osaka, Japan", publisher = "The COLING 2016 Organizing Committee", url = "https://www.aclweb.org/anthology/C16-1319", pages = "3381--3389", abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.", } ### Contributions Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset.
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proto_qa
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2022-11-03T16:31:01Z
587
1
protoqa
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2005.00771", "region:us" ]
2022-11-03T16:31:01Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced - other language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa - open-domain-qa paperswithcode_id: protoqa pretty_name: ProtoQA dataset_info: - config_name: proto_qa features: - name: normalized-question dtype: string - name: question dtype: string - name: answer-clusters sequence: - name: count dtype: int32 - name: clusterid dtype: string - name: answers sequence: string - name: answerstrings sequence: string - name: totalcount dtype: int32 - name: id dtype: string - name: source dtype: string splits: - name: train num_bytes: 3943484 num_examples: 8782 - name: validation num_bytes: 472121 num_examples: 980 download_size: 7352932 dataset_size: 4415605 - config_name: proto_qa_cs features: - name: normalized-question dtype: string - name: question dtype: string - name: answers-cleaned sequence: - name: count dtype: int32 - name: clusterid dtype: string - name: answers sequence: string - name: answerstrings sequence: string - name: totalcount dtype: int32 - name: id dtype: string - name: source dtype: string splits: - name: validation num_bytes: 84466 num_examples: 52 download_size: 115704 dataset_size: 84466 - config_name: proto_qa_cs_assessments features: - name: question dtype: string - name: assessments sequence: string splits: - name: validation num_bytes: 12473 num_examples: 52 download_size: 24755 dataset_size: 12473 --- # 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 - **Interactive Demo:** [Interactive demo](http://protoqa.com) - **Repository:** [proto_qa repository](https://github.com/iesl/protoqa-data) - **Paper:** [proto_qa paper](https://arxiv.org/pdf/2005.00771.pdf) - **Point of Contact:** [Michael Boratko](mailto:mboratko@cs.umass.edu) [Xiang Lorraine Li](mailto:xiangl@cs.umass.edu) [Tim O’Gorman](mailto:togorman@cs.umass.edu) [Rajarshi Das](mailto:rajarshi@cs.umass.edu) [Dan Le](mailto:dhle@cs.umass.edu) [Andrew McCallum](mailto:mccallum@cs.umass.edu) ### Dataset Summary This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud). Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at [family feud](https://www.familyfeudinfo.com) and [family feud friends](http://familyfeudfriends.arjdesigns.com/); crowdsourced data was acquired with FigureEight (now Appen) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?**<br> Each represents a survey question from Family Feud game and reported answer clusters **How many instances are there in total?**<br> 9789 instances **What data does each instance consist of?**<br> Each instance is a question, a set of answers, and a count associated with each answer. ### Data Fields **Data Files**<br> Each line is a json dictionary, in which:<br> **question** contains the question (in original and a normalized form)<br> **answerstrings** contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br> **answer-clusters** list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files. The simplified configuration includes: - `question`: contains the original question - `normalized-question`: contains the question in normalized form - `totalcount`: unique identifier of the comment (can be used to look up the entry in the raw dataset) - `id`: unique identifier of the commen - `source`: unique identifier of the commen - `answerstrings`: unique identifier of the commen - `answer-clusters | answers-cleaned`: list clusters of: * `clusterid`: Each cluster is given a unique ID that can be linked to in the assessment files * `count`: the count of each cluster * `answers`: the strings included in that cluster In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods. **Assessment files**<br> The file **data/dev/crowdsource_dev.assessments.jsonl** contains mappings from additional human and model answers to clusters, to evaluate different assessment methods. Each line contains:<br> * `question`: contains the ID of the question * `assessments`: maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters. ### Data Splits * proto_qa `Train` : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name * proto_qa `Validation` : 979 instances sampled from the same Family Feud data, for use in model validation and development. * proto_qa_cs `Validation` :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question) **data/dev/crowdsource_dev.assessments.jsonl**: assessment file (format described above) for study of assessment methods. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization **How was the data associated with each instance acquired?**<br> Scraped data was acquired through fan transcriptions at https://www.familyfeudinfo.com and http://familyfeudfriends.arjdesigns.com/ ; crowdsourced data was acquired with FigureEight (now Appen) **If the dataset is a sample from a larger set, what was the sampling strategy?**<br> Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used. **Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?**<br> Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage **Over what timeframe was the data collected?**<br> Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976 #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process **Was any preprocessing/cleaning/labeling of the data done?**<br> Obvious typos in the crowdsourced answer set were corrected #### Who are the annotators? The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected. ### Personal and Sensitive Information **Does the dataset contain data that might be considered sensitive in any way?**<br> As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models **Does the dataset contain data that might be considered confidential?**<br> The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers. ## Considerations for Using the Data ### Social Impact of Dataset **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**<br> Not egregiously so (questions are all designed to be shown on television or replications thereof), ### Discussion of Biases **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?** <br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias. **Are there tasks for which the dataset should not be used?** <br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support ### Licensing Information The Proto_qa dataset is licensed under the [Creative Commons Attribution 4.0 International](https://github.com/iesl/protoqa-data/blob/master/LICENSE) ### Citation Information ``` @InProceedings{ huggingface:dataset, title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning}, authors = {Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {https://github.com/iesl/protoqa-data}, } ``` ### Contributions Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset.
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emozilla/proofpile-test-tokenized
emozilla
2023-08-09T15:29:52Z
587
0
null
[ "region:us" ]
2023-08-09T15:29:52Z
2023-08-09T15:27:50.000Z
2023-08-09T15:27:50
--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tokenized_len dtype: int64 splits: - name: test num_bytes: 1644067664 num_examples: 46251 download_size: 552973486 dataset_size: 1644067664 --- # Dataset Card for "proofpile-test-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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BeIR/beir
BeIR
2022-10-21T15:30:43Z
586
4
beir
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
2022-10-21T15:30:43Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
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Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647_random
Multimodal-Fatima
2023-02-04T02:05:09Z
586
0
null
[ "region:us" ]
2023-02-04T02:05:09Z
2023-02-04T01:27:48.000Z
2023-02-04T01:27:48
--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_1_bs_16 num_bytes: 86815989.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90734149.125 num_examples: 5647 download_size: 169653770 dataset_size: 177550138.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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qgyd2021/few_shot_intent_sft
qgyd2021
2023-11-28T09:22:07Z
586
8
null
[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100M<n<1B", "language:zh", "language:en", "license:apache-2.0", "few-shot", "intent", "arxiv:2003.04807", "region:us" ]
2023-11-28T09:22:07Z
2023-09-22T11:26:09.000Z
2023-09-22T11:26:09
--- license: apache-2.0 task_categories: - text-classification - text-generation - text2text-generation language: - zh - en tags: - few-shot - intent size_categories: - 100M<n<1B dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: not_applicable dtype: bool - name: intent dtype: string - name: intent_version dtype: string - name: n_way dtype: int32 - name: n_shot dtype: int32 - name: description dtype: string splits: - name: train num_bytes: 22484898 num_examples: 22080 - name: test num_bytes: 1853817 num_examples: 2477 download_size: 7816475 dataset_size: 24338715 --- ## 小样本意图识别指令数据集 收集了意图识别的数据集, 将其制作成 prompt, 用于 few-shot 的意图识别 LLM 研究. 编写 prompt 模板需要想像力, 你可以在 community 中交流你的想法. `{dataset_name}_prompt` 子集是从其对应的 `{dataset_name}` 数据集和 `{dataset_name}_template` 子集动态生成的, 因此每一次的结果都会不一样. 提示: 由于训练时 prompt 的长度可能超出最大限制而被 truncate, 因此尽量把 prompt 设计成即使被 truncate 也仍然可以用于 GPT 训练. <details> <summary>train subset prompt 示例: (intent: Is it safe to go to the gym indoors if I'm vaccinated?)</summary> <pre><code>intent recognition.<br> Examples: ------------ text: will i be okay on the gym intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: I want to go and exercise at the gym, indoors, but I don't know if it's safe? intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: I worry I will catch Covid from the Gym even though I have been vaccinated? intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: What does the fda think about the covid 19 vaccine? intent: Is the vaccine FDA approved? ------------ text: it's never safe in a gym there are always bacteria everywhere intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: who is the difference between FDA authorization and approval? intent: Is the vaccine FDA approved? ------------ text: would the vaccine FDA be approved intent: Is the vaccine FDA approved? ------------ text: If I had my vaccine, is it safe to go to the indoor gym? intent: </code></pre> </details> <details> <summary>train subset prompt 示例: (intent: 考虑一下)</summary> <pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br> Examples: ------------ text: 没关系啦 知道的 intent: 肯定答复 ------------ text: 怎么能联系你 intent: 查联系方式 ------------ text: 恩。让我想想吧。 intent: 考虑一下 ------------ text: 说点有用的 intent: 请讲重点 ------------ text: 唉唉 intent: 语气词 ------------ text: 说快一点 intent: 请讲重点 ------------ text: 再介绍一下 intent: 要求复述 ------------ text: 从哪弄到我信息 intent: 质疑隐私安全 ------------ text: 哎。。不是的 intent: 不是 ------------ text: 给我电话号码 intent: 查联系方式 ------------ text: 先看看吧 intent: 考虑一下 ------------ text: 怎么知道道我的信息 intent: 质疑隐私安全 ------------ text: 哎,再说吧,我再想想 intent: 考虑一下 ------------ text: 不,我清醒。 intent: 不是 ------------ text: 重说一次 intent: 要求复述 ------------ text: 行了,晚安 intent: 肯定答复 ------------ text: 额额额额 intent: 语气词 ------------ text: 恩。哎再说吧我考虑一下hiahia intent: </code></pre> </details> <details> <summary>train subset prompt 示例: (intent: 污言秽语)</summary> <pre><code>电销场景意图识别。<br> Examples: text: 那留言 intent: 语音信箱<br> text: 好啊,哈哈,没事,我再找其他的人 intent: 好的<br> text: 在! intent: 我在<br> text: 要打副本,没时间 intent: 没时间<br> text: 必须去学习!赶快去! intent: 加快速度<br> text: 好的。满汉全席送上 intent: 好的<br> text: 你看到我给你的留言了么 intent: 语音信箱<br> text: 我在呢。 intent: 我在<br> text: 傻逼? intent: 污言秽语<br> text: 胸大无脑 intent: 污言秽语<br> text: 不着急。 intent: 请等一等<br> text: 恩 我是团子 intent: 做自我介绍<br> text: 我是收电费的 intent: 做自我介绍<br> text: 我现在没时间接电话呢,待会儿打给你。 intent: 没时间<br> text: 好的。哈哈。初六见。我去睡觉啦 intent: 好的<br> text: 在啊 intent: 我在<br> text: 包皮猩 intent: 污言秽语<br> text: 离开一下 intent: 请等一等<br> text: 有病 intent: 污言秽语<br> text: 给我留个言 intent: 语音信箱<br> text: 你等一下 intent: 请等一等<br> text: 立刻马上!!!快快快快 intent: 加快速度<br> text: 我是郭钊源 intent: 做自我介绍<br> text: 快点儿 intent: 加快速度<br> text: 没时间睡觉怎么办吖 intent: 没时间<br> text: 吃!你来 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 未能理解)</summary> <pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br> Examples: ------------ text: 讲什么 intent: 未能理解 ------------ text: 等着吧! intent: 请等一等 ------------ text: 搞不懂你 intent: 未能理解 ------------ text: 我实在是不想弄了,我那时事多没时间啊! intent: 没时间 ------------ text: 这你自己不清楚自己啊,还不晓得 intent: 不清楚 ------------ text: 没问题放心吧 intent: 肯定(没问题) ------------ text: 公司名字是什么 intent: 查公司介绍 ------------ text: 不放弃 intent: 肯定(需要) ------------ text: 老师也不懂 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 肯定(嗯嗯))</summary> <pre><code>电销场景意图识别。 不确定时请输出 “未知领域”。<br> Examples: ------------ text: 截止期过了多少天 intent: 疑问(时长) ------------ text: 不了 intent: 不需要 ------------ text: 不行,不够不够 intent: 否定(不可以) ------------ text: 4个1 intent: 答数值 ------------ text: 辽宁 intent: 地址 ------------ text: 不清楚 intent: 不清楚 ------------ text: 店里 intent: 地址 ------------ text: 嗯啊嗯嗯来吧 intent: 肯定(嗯嗯) ------------ text: 利息比别的贷款高 intent: 价格太高 ------------ text: 算23点,[9,4,8,2 intent: 答数值 ------------ text: 可以还得上 intent: 会按时处理 ------------ text: 对啊 就是不行 intent: 否定(不可以) ------------ text: 真的不便宜 intent: 价格太高 ------------ text: 嗯,thanks intent: 肯定(嗯嗯) ------------ text: 这你自己不清楚自己啊,还不晓得 intent: 不清楚 ------------ text: 我找找吧 intent: 会按时处理 ------------ text: 这是拖欠几天了 intent: 疑问(时长) ------------ text: 不需要证据 intent: 不需要 ------------ text: 噢,谢谢 intent: 肯定(嗯嗯) ------------ text: 恩恩,想我 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 不信任)</summary> <pre><code>意图识别。<br> Examples: text: 你不要答非所问 intent: 答非所问<br> text: 费用搞错了 intent: 否定(错误)<br> text: 我给你留言了,你木有回 intent: 语音信箱<br> text: 小骗子 intent: 不信任<br> text: 昆明 intent: 实体(地址)<br> text: 哦,行,好了你发信息给我 intent: 肯定(可以)<br> text: 哦,这样啊,没时间就算了 intent: 没时间<br> text: 我错了,别欺负我了 intent: 请求谅解<br> text: 万一你们是骗子怎么办 intent: 不信任<br> text: 我太乃刀了 intent: 无关领域<br> text: 讲清楚重要的 intent: 请讲重点<br> text: 骗子,好好说话 intent: </code></pre> </details> [提示工程指南](https://www.promptingguide.ai/zh/techniques/cot) 数据集从网上收集整理如下: | 数据 | 语言 | 任务类型 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | ATIS | 英语 | 意图识别 | [ATIS](https://paperswithcode.com/dataset/atis); [ATIS_dataset](https://github.com/howl-anderson/ATIS_dataset) | 4978(Training set)+893(Testing set) | 微软提供的公开数据集 (Airline Travel Information System),实现意图识别任务。 | [atis_intents](https://huggingface.co/datasets/fathyshalab/atis_intents) | | conv_intent | 英语 | 意图识别 | [conv_intent](https://huggingface.co/datasets/generalization/conv_intent_Full-p_1) | 13.8K | | [intent-recogniton](https://www.kaggle.com/code/upsunny/intent-recogniton-based-on-bert) | | banking77 | 英语 | 意图识别 | [banking77](https://arxiv.org/abs/2003.04807); [task-specific-datasets](https://github.com/PolyAI-LDN/task-specific-datasets) | 13,083 | 在线银行查询数据集 | [banking77](https://huggingface.co/datasets/banking77) | | mobile_assistant | 英语 | 意图识别 | [Intent-Classification-large](https://huggingface.co/datasets/dipesh/Intent-Classification-large) | 17K (但是我去除了意图为 others 的样本.) | | | | amazon_massive_intent_en_us | 英语 | 意图识别 | [amazon_massive_intent_en_us](https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US) | 16.5K | Alexa virtual assistant | | | amazon_massive_intent_zh_cn | 汉语 | 意图识别 | [amazon_massive_intent_zh_cn](https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN) | 16.5K | Alexa virtual assistant | | | snips_built_in_intents | 英语 | 意图识别 | [nlu-benchmark](https://github.com/sonos/nlu-benchmark); [benchmarking](https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d) | 328 | | [snips_built_in_intents](https://huggingface.co/datasets/snips_built_in_intents) | | vira_intents | 英语 | 意图识别 | [vira-intent-classification](https://github.com/IBM/vira-intent-classification) | 10.9K | COVID-19 疫苗意图 | [vira_intents_live](https://huggingface.co/datasets/codesj/vira-intents-live); [vira_intents_live](https://huggingface.co/datasets/vira-chatbot/vira-intents-live) | | intent_classification | 英语 | 意图识别 | [intent_classification](https://huggingface.co/datasets/Bhuvaneshwari/intent_classification) | 13.8K | | | | THU Intent Corpus | 汉语 | 意图识别 | | 共计约6,000个句子 | 清华大学发布的中文意图识别和词槽填充数据集,包含15个领域和27个意图类别 | | | Out-of-Scope | 英语 | 意图识别 | [范围外意图分类数据集](https://tianchi.aliyun.com/dataset/94112) | | 该数据集提供了一种评估“Out-of-Scope”输入的意图分类模型的方法。 | [Out-of-Scope Intent Classification Dataset](https://www.kaggle.com/datasets/stefanlarson/outofscope-intent-classification-dataset) | | CrossWOZ | 汉语 | 意图识别 | [CrossWOZ](https://github.com/thu-coai/CrossWOZ) | | CrossWOZ是第一个大规模中文跨域Wizard-of-Oz任务导向数据集。 它包含 5 个领域的 6K 对话会话和 102K 话语,包括酒店、餐厅、景点、地铁和出租车。 此外,该语料库还包含用户侧和系统侧丰富的对话状态和对话行为注释。 | | | chinese_news_title | 汉语 | 文本分类 | [中文新闻文本标题分类](https://aistudio.baidu.com/datasetdetail/103654) | | 中文新闻标题数据集包含可供训练的32类(即新闻主题)标题47,952个,可供测试的新闻标题15,986个。在删除这些包含不能处理的特殊字符的标题后,我们保留了47,850个训练标题和15,950个测试标题(即#DataSet1)。 | [百度网盘](https://pan.baidu.com/s/1mgBTFOO) | | CMID | 汉语 | 意图识别 | [CMID](https://github.com/ishine/CMID) | | 该数据集用于中文医学 QA 意图理解任务。 | | | dmslots | 汉语 | 意图识别 | [dmslots](https://raw.githubusercontent.com/kids/bert_nlu/main/data/dmslots.txt) | | 弱标注数据 | | | finance21 | 英语 | 意图识别 | [finance21](https://github.com/Dark-Sied/Intent_Classification/) | | | | | book6 | 英语 | 意图识别 | [book6](https://github.com/ajinkyaT/CNN_Intent_Classification) | 12000 | Six categories namely: AddToPlaylist, BookRestaurant, GetWeather , RateBook , SearchCreativeWork, SearchScreeningEvent each having nearly 2000 sentences. | [Intent Recognition Dataset](https://www.kaggle.com/datasets/himanshunayal/intent-recognition-dataset) | | bi_text | 英语 | 意图识别 | [bi_text](https://www.kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants) | 8175 | 该数据集涵盖“客户支持”领域,包括分为 11 个类别的 27 个意图。 这些意图是从 Bitext 的 20 个特定领域数据集(银行、零售、公用事业……)中选择的,保留了跨领域的通用意图。 | | | small talk | 英语 | 意图识别 | [Small Talk](https://www.kaggle.com/datasets/salmanfaroz/small-talk-intent-classification-data) | 3000 | 闲聊用于为用户提供与聊天机器人的随意对话流程 | | <details> <summary>参考的数据来源,展开查看</summary> <pre><code> https://huggingface.co/datasets/fathyshalab/atis_intents https://huggingface.co/datasets/generalization/conv_intent_Full-p_1 https://huggingface.co/datasets/banking77 https://huggingface.co/datasets/dipesh/Intent-Classification-large https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-TW https://huggingface.co/datasets/snips_built_in_intents https://huggingface.co/datasets/zapsdcn/citation_intent https://huggingface.co/datasets/ibm/vira-intents https://huggingface.co/datasets/mteb/mtop_intent https://huggingface.co/datasets/Bhuvaneshwari/intent_classification https://huggingface.co/datasets/ibm/vira-intents-live https://huggingface.co/datasets/ebrigham/nl_banking_intents https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi https://gitee.com/a2798063/SMP2019/tree/master https://cold-eye.github.io/post/nlp-corpus/ https://www.cluebenchmarks.com/introduce.html https://github.com/search?q=chinese%20intent&type=repositories </code></pre> </details>
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patrickvonplaten/restore_punctuation_tiny_num_beams_1
patrickvonplaten
2022-12-28T21:13:10Z
585
0
null
[ "speechbox_punc", "region:us" ]
2022-12-28T21:13:10Z
2022-12-28T21:09:30.000Z
2022-12-28T21:09:30
--- tags: - speechbox_punc ---
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yentinglin/TC-Eval
yentinglin
2023-11-26T04:25:53Z
585
3
null
[ "task_categories:question-answering", "task_categories:text-classification", "size_categories:1K<n<10K", "language:zh", "region:us" ]
2023-11-26T04:25:53Z
2023-10-24T15:58:14.000Z
2023-10-24T15:58:14
--- task_categories: - question-answering - text-classification language: - zh pretty_name: TMLU size_categories: - 1K<n<10K configs: - config_name: FGC data_files: - split: test path: "fgc.jsonl" - config_name: DRCD data_files: - split: test path: "drcd.jsonl" - config_name: TMMLU data_files: - split: test path: "tmmlu.jsonl" - config_name: XSUM_TC data_files: - split: test path: "xsum.jsonl" - config_name: TTQA data_files: - split: test path: TTQA_mc.jsonl - config_name: TCIC data_files: - split: test path: TCIC_1.2.0.json - config_name: PenguinsInTable_TC data_files: - split: test path: PenguinsInTable_TC.jsonl - config_name: IMDB_TC data_files: - split: test path: imdb.jsonl --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/xcVQWsF4eocM5ZunsFm9w.png) Data source: https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval
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um005
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2022-11-18T21:58:09Z
584
0
umc005-english-urdu
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:ur", "license:unknown", "region:us" ]
2022-11-18T21:58:09Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - other language: - en - ur license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: umc005-english-urdu pretty_name: UMC005 English-Urdu dataset_info: - config_name: bible features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 2350730 num_examples: 7400 - name: validation num_bytes: 113476 num_examples: 300 - name: test num_bytes: 104678 num_examples: 257 download_size: 3683565 dataset_size: 2568884 - config_name: quran features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 2929711 num_examples: 6000 - name: validation num_bytes: 43499 num_examples: 214 - name: test num_bytes: 44413 num_examples: 200 download_size: 3683565 dataset_size: 3017623 - config_name: all features: - name: id dtype: string - name: translation dtype: translation: languages: - ur - en splits: - name: train num_bytes: 5280441 num_examples: 13400 - name: validation num_bytes: 156963 num_examples: 514 - name: test num_bytes: 149079 num_examples: 457 download_size: 3683565 dataset_size: 5586483 --- # Dataset Card for UMC005 English-Urdu ## 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://ufal.ms.mff.cuni.cz/umc/005-en-ur/ - **Repository:** None - **Paper:** https://www.researchgate.net/publication/268008206_Word-Order_Issues_in_English-to-Urdu_Statistical_Machine_Translation - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** Bushra Jawaid and Daniel Zeman ### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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mteb/arxiv-clustering-p2p
mteb
2022-09-27T19:15:11Z
584
0
null
[ "language:en", "region:us" ]
2022-09-27T19:15:11Z
2022-05-10T13:00:10.000Z
2022-05-10T13:00:10
--- language: - en ---
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med_hop
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2022-11-03T16:16:32Z
583
3
medhop
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "multi-hop", "arxiv:1710.06481", "region:us" ]
2022-11-03T16:16:32Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: medhop pretty_name: MedHop tags: - multi-hop dataset_info: - config_name: original features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: candidates sequence: string - name: supports sequence: string splits: - name: train num_bytes: 93937322 num_examples: 1620 - name: validation num_bytes: 16461640 num_examples: 342 download_size: 339843061 dataset_size: 110398962 - config_name: masked features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: candidates sequence: string - name: supports sequence: string splits: - name: train num_bytes: 95813584 num_examples: 1620 - name: validation num_bytes: 16800570 num_examples: 342 download_size: 339843061 dataset_size: 112614154 --- # Dataset Card for MedHop ## 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:** [QAngaroo](http://qangaroo.cs.ucl.ac.uk/) - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://arxiv.org/abs/1710.06481) - **Leaderboard:** [leaderboard](http://qangaroo.cs.ucl.ac.uk/leaderboard.html) - **Point of Contact:** [Johannes Welbl](j.welbl@cs.ucl.ac.uk) ### 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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2023-04-05T10:04:50Z
582
4
discofuse
[ "task_categories:text2text-generation", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "sentence-fusion", "arxiv:1902.10526", "region:us" ]
2023-04-05T10:04:50Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language: - en language_creators: - found license: - cc-by-sa-3.0 multilinguality: - monolingual pretty_name: DiscoFuse size_categories: - 10M<n<100M source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: discofuse tags: - sentence-fusion dataset_info: - config_name: discofuse-sport features: - name: connective_string dtype: string - name: discourse_type dtype: string - name: coherent_second_sentence dtype: string - name: has_coref_type_pronoun dtype: float32 - name: incoherent_first_sentence dtype: string - name: incoherent_second_sentence dtype: string - name: has_coref_type_nominal dtype: float32 - name: coherent_first_sentence dtype: string splits: - name: train num_bytes: 14736279993 num_examples: 43291020 - name: test num_bytes: 151656323 num_examples: 445521 - name: validation num_bytes: 150207737 num_examples: 440902 download_size: 4326637746 dataset_size: 15038144053 - config_name: discofuse-wikipedia features: - name: connective_string dtype: string - name: discourse_type dtype: string - name: coherent_second_sentence dtype: string - name: has_coref_type_pronoun dtype: float32 - name: incoherent_first_sentence dtype: string - name: incoherent_second_sentence dtype: string - name: has_coref_type_nominal dtype: float32 - name: coherent_first_sentence dtype: string splits: - name: train num_bytes: 6377924196 num_examples: 16310585 - name: test num_bytes: 64008158 num_examples: 163657 - name: validation num_bytes: 65682035 num_examples: 168081 download_size: 1717422334 dataset_size: 6507614389 --- # Dataset Card for "discofuse" ## 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/google-research-datasets/discofuse - **Paper:** [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://arxiv.org/abs/1902.10526) - **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:** 6.04 GB - **Size of the generated dataset:** 21.55 GB - **Total amount of disk used:** 27.59 GB ### Dataset Summary DiscoFuse is a large scale dataset for discourse-based sentence fusion. ### 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 #### discofuse-sport - **Size of downloaded dataset files:** 4.33 GB - **Size of the generated dataset:** 15.04 GB - **Total amount of disk used:** 19.36 GB An example of 'train' looks as follows. ``` { "coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .", "coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .", "connective_string": "finally ,", "discourse_type": "PAIR_CONN", "has_coref_type_nominal": 0.0, "has_coref_type_pronoun": 0.0, "incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .", "incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ." } ``` #### discofuse-wikipedia - **Size of downloaded dataset files:** 1.72 GB - **Size of the generated dataset:** 6.51 GB - **Total amount of disk used:** 8.23 GB An example of 'validation' looks as follows. ``` { "coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .", "coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .", "connective_string": "finally ,", "discourse_type": "PAIR_CONN", "has_coref_type_nominal": 0.0, "has_coref_type_pronoun": 0.0, "incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .", "incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ." } ``` ### Data Fields The data fields are the same among all splits. #### discofuse-sport - `connective_string`: a `string` feature. - `discourse_type`: a `string` feature. - `coherent_second_sentence`: a `string` feature. - `has_coref_type_pronoun`: a `float32` feature. - `incoherent_first_sentence`: a `string` feature. - `incoherent_second_sentence`: a `string` feature. - `has_coref_type_nominal`: a `float32` feature. - `coherent_first_sentence`: a `string` feature. #### discofuse-wikipedia - `connective_string`: a `string` feature. - `discourse_type`: a `string` feature. - `coherent_second_sentence`: a `string` feature. - `has_coref_type_pronoun`: a `float32` feature. - `incoherent_first_sentence`: a `string` feature. - `incoherent_second_sentence`: a `string` feature. - `has_coref_type_nominal`: a `float32` feature. - `coherent_first_sentence`: a `string` feature. ### Data Splits | name | train |validation| test | |-------------------|-------:|---------:|-----:| |discofuse-sport |43291020| 440902|445521| |discofuse-wikipedia|16310585| 168081|163657| ## 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 The data is licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @InProceedings{GevaEtAl2019, title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion}, author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan}, booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, note = {arXiv preprint arXiv:1902.10526}, year = {2019} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
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oscar-corpus/OSCAR-2201
oscar-corpus
2023-05-30T07:48:15Z
582
77
oscar
[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:af", "language:sq", "language:am", "language:ar", "language:an", "language:hy", "language:as", "language:ast", "language:av", "language:az", "language:bn", "language:ba", "language:eu", "language:be", "language:bh", "language:bpy", "language:bs", "language:br", "language:bg", "language:my", "language:ca", "language:ceb", "language:ckb", "language:ce", "language:zh", "language:cv", "language:kw", "language:hr", "language:cs", "language:da", "language:diq", "language:dv", "language:nl", "language:mhr", "language:arz", "language:en", "language:eo", "language:et", "language:tl", "language:fi", "language:fr", "language:gl", "language:ka", "language:de", "language:gom", "language:el", "language:gn", "language:gu", "language:he", "language:hi", "language:hu", "language:is", "language:io", "language:ilo", "language:id", "language:ia", "language:ga", "language:it", "language:ja", "language:jv", "language:xal", "language:kn", "language:krc", "language:kk", "language:km", "language:kv", "language:ko", "language:ku", "language:ky", "language:lo", "language:la", "language:lv", "language:lez", "language:li", "language:lt", "language:jbo", "language:lmo", "language:nds", "language:dsb", "language:lb", "language:mk", "language:mai", "language:mg", "language:ms", "language:ml", "language:mt", "language:mr", "language:mzn", "language:min", "language:xmf", "language:mn", "language:nah", "language:ne", "language:new", "language:no", "language:nn", "language:oc", "language:or", "language:os", "language:ps", "language:fa", "language:pms", "language:pl", "language:pt", "language:pa", "language:qu", "language:ro", "language:bxr", "language:ru", "language:sah", "language:sa", "language:gd", "language:sr", "language:sh", "language:scn", "language:sd", "language:si", "language:sk", "language:sl", "language:so", "language:azb", "language:es", "language:su", "language:sw", "language:sv", "language:tg", "language:ta", "language:tt", "language:te", "language:th", "language:bo", "language:als", "language:tr", "language:tk", "language:uk", "language:eml", "language:hsb", "language:ur", "language:ug", "language:uz", "language:vi", "language:vo", "language:wa", "language:war", "language:cy", "language:fy", "language:mrj", "language:pnb", "language:wuu", "language:yi", "language:yo", "language:mul", "license:cc0-1.0", "arxiv:2010.14571", "arxiv:2201.06642", "arxiv:2103.12028", "region:us" ]
2023-05-30T07:48:15Z
2022-03-14T23:09:14.000Z
2022-03-14T23:09:14
--- pretty_name: OSCAR annotations_creators: - no-annotation language_creators: - found language: - af - sq - am - ar - an - hy - as - ast - av - az - bn - ba - eu - be - bh - bpy - bs - br - bg - my - ca - ceb - ckb - ce - zh - cv - kw - hr - cs - da - diq - dv - nl - mhr - arz - en - eo - et - tl - fi - fr - gl - ka - de - gom - el - gn - gu - he - hi - hu - is - io - ilo - id - ia - ga - it - ja - jv - xal - kn - krc - kk - km - kv - ko - ku - ky - lo - la - lv - lez - li - lt - jbo - lmo - nds - dsb - lb - mk - mai - mg - ms - ml - mt - mr - mzn - min - xmf - mn - nah - ne - new - no - nn - oc - or - os - ps - fa - pms - pl - pt - pa - qu - ro - bxr - ru - sah - sa - gd - sr - sh - scn - sd - si - sk - sl - so - azb - es - su - sw - sv - tg - ta - tt - te - th - bo - als - tr - tk - uk - eml - hsb - ur - ug - uz - vi - vo - wa - war - cy - fy - mrj - pnb - wuu - yi - yo - mul license: - cc0-1.0 multilinguality: - multilingual source_datasets: - original task_categories: - fill-mask - text-generation task_ids: - language-modeling paperswithcode_id: oscar --- # 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:** [github.com/oscar-corpus/corpus](https://github.com/oscar-corpus/corpus) - **Paper:** [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://oscar-corpus.com/publication/2022/arxiv/towards/) - **Point of Contact:** [Contact](https://oscar-corpus.com/#contact) ### Dataset Summary OSCAR or **O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [ungoliant](https://github.com/oscar-corpus/ungoliant) architecture. Data is distributed by language in both original and deduplicated form. **We are aware of the virus warnings issue. See discussion [here](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201/discussions/12) for more info!** ### Usage ```py from datasets import load_dataset dataset = load_dataset("oscar-corpus/OSCAR-2201", use_auth_token=True, # required language="ar", streaming=True, # optional split="train") # optional, but the dataset only has a train split for d in dataset: print(d) # prints documents ``` ### Supported Tasks and Leaderboards OSCAR is mainly intended to pretrain language models and word representations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 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. ### Issues OSCAR 22.01 may have quality issues on low size subcorpora, as it has been the case before. Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus. As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic. **If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.** |Language code|Language|Issues| |-------------|--------|------| | | | | ## Dataset Structure We show detailed information for all the configurations of the dataset. ### Data Instances TODO ### Data Fields * `id`: a `int64` feature. * `content`: `string` Newline-separated content * `warc_headers`: WARC Headers * `warc_headers.content-length`: `int64` Content length (in bytes) **before** cleaning * `warc_headers.content-type`: `string` MIME type * `warc_headers.warc-block-digest`:`string` Algorithm name and calculated value of a digest applied to the full block of the record * `warc_headers.warc-date`: `string` Crawl date (YYYY-MM-DDThh:mm:ssZ) * `warc_headers.warc-identified-content-language`: `string` Comma-separated list of language identifications done by CommonCrawl (uses CLD3) * `warc_headers.warc-record-id`: `string` Record ID * `warc_headers.warc-refers-to`: `string` Record-ID of a single record for which the present record holds additional content * `warc_headers.warc-target-uri`: `string` URI from where the content has been fetched * `warc_headers.warc-type`: `string` Type of the WARC Record * `metadata`: Metadata * `metadata.identification.label`: `string` Language identification of the document * `metadata.identification.prob`: `float` Confidence of the identification * `metadata.annotation`: `[string]` Annnotations of the document. `null` if none present. (Is `None` if using `datasets`) * `metadata.sentence_identifications`: `[string]` List of line identifications. `null`/`None` can be present for lines that failed the identification step. * `meta.offset`: `int64` line offset where the related text begins. Should be used with `meta.nb_sentences` when reading the source files rather than using iterators to get related data. * `text`: `string` content See the [WARC Format standard](https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/#warc-type-mandatory) for more details on the `warc_headers` fields, and our [website](https://oscar-corpus.com/post/oscar-v22-01/) for more details about the format in general. ### Data Splits <details> <summary>Click to expand the number of samples per configuration</summary> </details> ## Table | lang | size | docs | words | |:----------------------------|:----------|:------------|:----------------| | _Multilingual_ | 12.1 GB | 1,210,685 | 936,187,711 | | Afrikaans | 47.0 MB | 12,393 | 6,227,310 | | Albanian | 3.0 GB | 437,287 | 326,325,149 | | Alemannic / Swiss German | 363.6 kB | 139 | 37,381 | | Amharic | 461.0 MB | 37,513 | 30,481,153 | | Arabic | 84.2 GB | 8,718,929 | 6,103,711,887 | | Aragonese | 10.6 kB | 12 | 51 | | Armenian | 4.7 GB | 379,267 | 268,031,270 | | Assamese | 221.2 MB | 17,084 | 11,109,557 | | Asturian | 73.6 kB | 77 | 3,919 | | Avaric | 18.6 kB | 14 | 582 | | Azerbaijani | 3.5 GB | 491,847 | 291,927,692 | | Bangla | 15.1 GB | 1,171,501 | 751,877,226 | | Bashkir | 95.5 MB | 11,198 | 5,418,474 | | Basque | 1.1 GB | 233,658 | 97,092,942 | | Belarusian | 1.8 GB | 180,046 | 107,227,860 | | Bihari languages | 24.2 kB | 27 | 569 | | Bishnupriya | 2.0 MB | 271 | 98,419 | | Bosnian | 10.3 kB | 10 | 422 | | Breton | 33.7 MB | 16,119 | 3,111,619 | | Bulgarian | 35.1 GB | 2,887,115 | 2,405,981,285 | | Burmese | 1.9 GB | 158,733 | 44,835,970 | | Catalan | 13.9 GB | 2,627,307 | 1,508,919,864 | | Cebuano | 44.6 MB | 5,742 | 5,253,785 | | Central Kurdish | 716.4 MB | 84,950 | 43,913,025 | | Chechen | 14.0 MB | 4,086 | 798,766 | | Chinese | 900.9 GB | 56,524,518 | 23,149,203,886 | | Chuvash | 41.8 MB | 4,750 | 2,465,782 | | Cornish | 1.4 kB | 2 | 55 | | Croatian | 11.2 MB | 11,462 | 505,369 | | Czech | 58.6 GB | 10,381,916 | 5,452,724,456 | | Danish | 12.6 GB | 2,265,479 | 1,454,439,292 | | Dimli (individual language) | 706 Bytes | 1 | 19 | | Divehi | 217.2 MB | 24,067 | 10,112,205 | | Dutch | 114.0 GB | 20,206,532 | 12,329,127,151 | | Eastern Mari | 11.3 MB | 1,612 | 641,525 | | Egyptian Arabic | 2.8 MB | 1,256 | 176,096 | | English | 3.2 TB | 431,992,659 | 377,376,402,775 | | Esperanto | 558.3 MB | 111,932 | 58,416,628 | | Estonian | 9.2 GB | 1,362,524 | 820,975,443 | | Filipino | 646.5 MB | 70,394 | 81,881,278 | | Finnish | 37.8 GB | 4,948,961 | 2,900,615,928 | | French | 382.2 GB | 52,037,098 | 41,713,990,658 | | Galician | 255.2 MB | 88,803 | 27,051,212 | | Georgian | 7.1 GB | 488,588 | 281,430,479 | | German | 496.7 GB | 70,075,424 | 46,826,676,844 | | Goan Konkani | 787.2 kB | 46 | 38,831 | | Greek | 78.3 GB | 6,738,546 | 5,031,242,803 | | Guarani | 9.0 kB | 10 | 374 | | Gujarati | 4.8 GB | 136,467 | 301,170,777 | | Hebrew | 30.3 GB | 3,132,396 | 2,249,377,984 | | Hindi | 23.3 GB | 1,529,907 | 1,534,799,198 | | Hungarian | 53.9 GB | 6,866,062 | 4,598,787,907 | | Icelandic | 2.0 GB | 396,183 | 210,365,124 | | Ido | 77.3 kB | 105 | 2,690 | | Iloko | 97.9 kB | 75 | 8,592 | | Indonesian | 17.4 GB | 2,244,622 | 1,984,195,207 | | Interlingua | 40.2 kB | 6 | 10,125 | | Irish | 45.6 MB | 12,233 | 4,877,850 | | Italian | 229.3 GB | 28,502,092 | 24,294,684,830 | | Japanese | 258.7 GB | 36,328,931 | 5,592,948,356 | | Javanese | 152.7 kB | 70 | 10,441 | | Kalmyk | 9.3 kB | 9 | 250 | | Kannada | 2.6 GB | 150,850 | 108,450,571 | | Karachay-Balkar | 119.6 kB | 91 | 4,089 | | Kazakh | 2.9 GB | 261,085 | 157,267,307 | | Khmer | 1.9 GB | 121,910 | 30,564,131 | | Komi | 119.9 kB | 127 | 3,335 | | Korean | 51.8 GB | 5,881,481 | 3,854,968,649 | | Kurdish | 150.3 MB | 29,906 | 17,390,759 | | Kyrgyz | 518.6 MB | 62,244 | 28,028,986 | | Lao | 337.1 MB | 28,914 | 6,682,982 | | Latin | 4.1 MB | 4,397 | 187,446 | | Latvian | 8.2 GB | 1,032,987 | 707,361,898 | | Lezghian | 375.5 kB | 124 | 19,250 | | Limburgish | 1.4 kB | 2 | 41 | | Lithuanian | 20.0 GB | 2,303,070 | 1,712,802,056 | | Lojban | 1.9 MB | 570 | 260,542 | | Lombard | 2.6 kB | 2 | 225 | | Low German | 9.0 MB | 1,938 | 1,012,561 | | Lower Sorbian | 707 Bytes | 1 | 17 | | Luxembourgish | 15.8 MB | 5,108 | 1,545,946 | | Macedonian | 3.6 GB | 341,775 | 244,058,579 | | Maithili | 21.6 kB | 23 | 483 | | Malagasy | 57.3 MB | 3,028 | 7,279,056 | | Malay | 5.3 MB | 5,228 | 217,818 | | Malayalam | 4.1 GB | 250,972 | 137,831,247 | | Maltese | 2.5 MB | 2,208 | 118,190 | | Marathi | 3.3 GB | 250,376 | 160,179,233 | | Mazanderani | 128.2 kB | 76 | 7,337 | | Minangkabau | 6.0 MB | 585 | 614,613 | | Mingrelian | 7.6 MB | 2,550 | 253,333 | | Mongolian | 2.8 GB | 237,719 | 176,405,432 | | Nahuatl languages | 8.7 kB | 12 | 179 | | Nepali | 3.7 GB | 391,947 | 177,885,116 | | Newari | 5.7 MB | 1,134 | 273,837 | | Norwegian | 2.8 GB | 973,188 | 279,182,902 | | Norwegian Nynorsk | 6.8 MB | 5,835 | 459,183 | | Occitan | 2.1 MB | 373 | 31,061 | | Odia | 487.9 MB | 52,942 | 23,755,902 | | Ossetic | 13.9 MB | 3,560 | 800,430 | | Pashto | 490.3 MB | 50,312 | 46,293,249 | | Persian | 77.4 GB | 7,665,871 | 6,430,164,396 | | Piedmontese | 1.7 MB | 698 | 188,270 | | Polish | 139.0 GB | 19,301,137 | 12,584,498,906 | | Portuguese | 170.3 GB | 23,735,707 | 18,441,864,893 | | Punjabi | 1.1 GB | 68,094 | 70,068,604 | | Quechua | 744 Bytes | 1 | 14 | | Romanian | 49.2 GB | 4,624,764 | 5,261,803,995 | | Russia Buriat | 32.9 kB | 39 | 785 | | Russian | 1.1 TB | 76,060,844 | 62,811,122,663 | | Sakha | 65.6 MB | 6,284 | 3,473,813 | | Sanskrit | 136.0 MB | 4,472 | 5,671,369 | | Scottish Gaelic | 137.7 kB | 136 | 7,769 | | Serbian | 6.9 GB | 577,472 | 482,932,670 | | Serbian (Latin) | 931.8 kB | 738 | 92,875 | | Sicilian | 1.5 kB | 2 | 50 | | Sindhi | 117.1 MB | 15,516 | 10,685,611 | | Sinhala | 2.0 GB | 108,593 | 113,179,741 | | Slovak | 16.5 GB | 2,409,555 | 1,619,121,944 | | Slovenian | 1.2 GB | 351,894 | 118,400,246 | | Somali | 2.1 kB | 3 | 109 | | South Azerbaijani | 14.1 MB | 5,381 | 693,746 | | Spanish | 381.9 GB | 51,386,247 | 42,829,835,316 | | Sundanese | 5.0 MB | 263 | 547,145 | | Swahili | 1.3 MB | 462 | 123,050 | | Swedish | 48.0 GB | 7,541,278 | 5,078,331,128 | | Tajik | 870.9 MB | 46,366 | 56,627,727 | | Tamil | 11.4 GB | 556,772 | 452,343,748 | | Tatar | 915.3 MB | 76,398 | 51,875,265 | | Telugu | 3.4 GB | 249,756 | 137,752,065 | | Thai | 66.1 GB | 5,030,254 | 1,626,779,846 | | Tibetan | 234.5 MB | 18,683 | 2,286,269 | | Turkish | 75.1 GB | 10,826,031 | 6,421,221,358 | | Turkmen | 4.4 MB | 2,485 | 276,632 | | Ukrainian | 48.8 GB | 4,558,214 | 2,879,585,992 | | Emiliano-Romagnolo[eml] | 901 Bytes | 1 | 53 | | Upper Sorbian | 132.8 kB | 110 | 8,825 | | Urdu | 3.4 GB | 336,994 | 332,816,354 | | Uyghur | 201.9 MB | 18,556 | 11,240,889 | | Uzbek | 19.9 MB | 9,526 | 1,370,842 | | Vietnamese | 98.9 GB | 9,587,233 | 12,283,185,482 | | Volapük | 825.9 kB | 661 | 57,039 | | Walloon | 105.7 kB | 138 | 4,386 | | Waray | 7.6 MB | 933 | 830,872 | | Welsh | 409.3 MB | 90,378 | 49,488,495 | | Western Frisian | 75.3 MB | 21,946 | 6,357,929 | | Western Mari | 743.5 kB | 155 | 43,916 | | Western Panjabi | 46.7 MB | 6,790 | 4,060,419 | | Wu Chinese | 137.2 kB | 88 | 3,056 | | Yiddish | 232.5 MB | 23,418 | 15,809,780 | | Yoruba | 24.7 kB | 26 | 1,042 | ## Dataset Creation ### Curation Rationale OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText). The pipeline works on documents rather than lines. `Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy. Threading is done at shard, record and sentence level, making the whole generation process much more efficient. Filtering will be explained in a future blog post at our [website](https://oscar-corpus.com) ### 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 22.01, the **November/December 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers. #### 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 [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.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 ``` @ARTICLE{2022arXiv220106642A, author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t}, title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language}, year = 2022, month = jan, eid = {arXiv:2201.06642}, pages = {arXiv:2201.06642}, archivePrefix = {arXiv}, eprint = {2201.06642}, primaryClass = {cs.CL}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @inproceedings{AbadjiOrtizSuarezRomaryetal.2021, author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot}, title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)}, editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta}, publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-10468}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688}, pages = {1 -- 9}, year = {2021}, abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.}, language = {en} } @ARTICLE{caswell-etal-2021-quality, author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa}, title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence}, year = 2021, month = mar, eid = {arXiv:2103.12028}, pages = {arXiv:2103.12028}, archivePrefix = {arXiv}, eprint = {2103.12028}, primaryClass = {cs.CL}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210312028C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @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), [@Uinelj](https://github.com/Uinelj) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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bigscience-historical-texts/HIPE2020_sent-split
bigscience-historical-texts
2022-04-07T10:12:42Z
581
0
null
[ "region:us" ]
2022-04-07T10:12:42Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Cartinoe5930/KoRAE_filtered_12k
Cartinoe5930
2023-11-09T13:40:09Z
581
1
null
[ "arxiv:2307.08701", "region:us" ]
2023-11-09T13:40:09Z
2023-11-07T02:19:29.000Z
2023-11-07T02:19:29
--- dataset_info: features: - name: source dtype: string - name: prompt dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: review dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 35802556 num_examples: 12473 download_size: 18374150 dataset_size: 35802556 --- # KoRAE Dataset <p align="center"><img src="https://cdn-uploads.huggingface.co/production/uploads/63e087b6a98d931aa90c1b9c/VVHlw268vUEdRAzUtjDhE.png", width=256, height=256></p> We used filtered high-quality Korean dataset for finetuning of KoRAE. First of all, we gathered Korean data and make the mixture of them. Then we filtered high-quality data from the combination of data through filtering method that introduced from [AlpaGasus](https://arxiv.org/abs/2307.08701). The overview of data processing procedure is as follws: 1. Collect various Korean dataset from HuggingFace Hub. 2. Rate the data quality using `gpt-3.5-turbo`. 3. Process the rated data and filter the high-scored data. Let's go deeper into data processing! ### 1. Korean dataset mixture We investigated several sources to collect high-quality Korean data, and among them, we collected data from the various sources. As a result, we were able to create a new dataset containing 64K pieces of data. The specific configuration of the dataset is as follows: |Dataset|# Nums| |---|---| |**[OpenOrca-ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)**|21.6k| |**[KOpen-Platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)**|24.9k| |**[KoCoT_2000](https://huggingface.co/datasets/kyujinpy/KoCoT_2000)**|2.1k| |**[databricks-dolly-15k-ko](https://huggingface.co/datasets/nlpai-lab/databricks-dolly-15k-ko)**|15k| |**Total**|63.7k| You can check the original KoRAE dataset here: [KoRAE_original](https://huggingface.co/datasets/Cartinoe5930/KoRAE_original) ### 2. Rating We utilized ChatGPT(gpt-3.5-turbo) as rater to rate the quality of dataset. We considered whether to use the prompt for the evaluation in Korean or English, but we thought it would be undesirable to give evaluations in different languages, so we conducted the evaluation using the Korean prompt. The overall data rating method was reffered to [AlpaGasus](https://arxiv.org/abs/2307.08701) ### 3. Processing & Filtering We postprocessed rated dataset after the rating. The main postprocessing procedure are as follows: - Wrong score extraction correction - Incorrect format dataset exclusion You can check the postprocessed KoRAE dataset here: [KoRAE_rated_filtered](https://huggingface.co/datasets/Cartinoe5930/KoRAE_rated_filtered) After the all postprocessing, we analysed the score distribution of rated dataset. As shown in the following figure, it was confirmed that 8-point data was the most. This confirms that KoRAE dataset consisted of high-quality data from the beginning. ![rated_dataset_distribution.png](https://cdn-uploads.huggingface.co/production/uploads/63e087b6a98d931aa90c1b9c/xeZmIDtINKgTV2wxtvuOs.png) However, We filtered data only with a score of 8.5 or higher and used it to finetune KoRAE for better performance. As a result, we were able to filter the dataset 64k to 12k! This 'KoRAE_filtered_12k' is the result of all previous processes. ## Github Repository For the more specific information, please check the following [Repository](https://github.com/gauss5930/KoRAE) ## Citation Thanks to [@kyujinpy](https://huggingface.co/kyujinpy) and [@nlp-ai](https://huggingface.co/nlpai-lab) for providing Korean datasets. - [KO-Platypus](https://github.com/Marker-Inc-Korea/KO-Platypus) - [Korean-OpenOrca](https://github.com/Marker-Inc-Korea/Korean-OpenOrca) ``` @inproceedings{lee2023kullm, title={KULLM: Learning to Construct Korean Instruction-following Large Language Models}, author={Lee, SeungJun and Lee, Taemin and Lee, Jeongwoo and Jang, Yoona and Lim, Heuiseok}, booktitle={Annual Conference on Human and Language Technology}, pages={196--202}, year={2023}, organization={Human and Language Technology} } ``` ``` @misc{chen2023alpagasus, title={AlpaGasus: Training A Better Alpaca with Fewer Data}, author={Lichang Chen and Shiyang Li and Jun Yan and Hai Wang and Kalpa Gunaratna and Vikas Yadav and Zheng Tang and Vijay Srinivasan and Tianyi Zhou and Heng Huang and Hongxia Jin}, year={2023}, eprint={2307.08701}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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ThWu/cleaned_prompt_r
ThWu
2023-11-16T19:21:10Z
581
1
null
[ "region:us" ]
2023-11-16T19:21:10Z
2023-11-16T19:21:03.000Z
2023-11-16T19:21:03
--- dataset_info: features: - name: conversations sequence: string splits: - name: train num_bytes: 157911562 num_examples: 268781 download_size: 97143836 dataset_size: 157911562 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cleaned_prompt_r" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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sem_eval_2010_task_8
null
2023-04-05T13:39:59Z
580
5
semeval-2010-task-8
[ "language:en", "region:us" ]
2023-04-05T13:39:59Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - en paperswithcode_id: semeval-2010-task-8 pretty_name: SemEval-2010 Task 8 dataset_info: features: - name: sentence dtype: string - name: relation dtype: class_label: names: '0': Cause-Effect(e1,e2) '1': Cause-Effect(e2,e1) '2': Component-Whole(e1,e2) '3': Component-Whole(e2,e1) '4': Content-Container(e1,e2) '5': Content-Container(e2,e1) '6': Entity-Destination(e1,e2) '7': Entity-Destination(e2,e1) '8': Entity-Origin(e1,e2) '9': Entity-Origin(e2,e1) '10': Instrument-Agency(e1,e2) '11': Instrument-Agency(e2,e1) '12': Member-Collection(e1,e2) '13': Member-Collection(e2,e1) '14': Message-Topic(e1,e2) '15': Message-Topic(e2,e1) '16': Product-Producer(e1,e2) '17': Product-Producer(e2,e1) '18': Other splits: - name: train num_bytes: 1054352 num_examples: 8000 - name: test num_bytes: 357075 num_examples: 2717 download_size: 1964087 dataset_size: 1411427 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: sentence: text relation: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "sem_eval_2010_task_8" ## 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://semeval2.fbk.eu/semeval2.php?location=tasks&taskid=11](https://semeval2.fbk.eu/semeval2.php?location=tasks&taskid=11) - **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:** 1.96 MB - **Size of the generated dataset:** 1.42 MB - **Total amount of disk used:** 3.38 MB ### Dataset Summary The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research. ### 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:** 1.96 MB - **Size of the generated dataset:** 1.42 MB - **Total amount of disk used:** 3.38 MB An example of 'train' looks as follows. ``` { "relation": 3, "sentence": "The system as described above has its greatest application in an arrayed <e1>configuration</e1> of antenna <e2>elements</e2>." } ``` ### Data Fields The data fields are the same among all splits. #### default - `sentence`: a `string` feature. - `relation`: a classification label, with possible values including `Cause-Effect(e1,e2)` (0), `Cause-Effect(e2,e1)` (1), `Component-Whole(e1,e2)` (2), `Component-Whole(e2,e1)` (3), `Content-Container(e1,e2)` (4). ### Data Splits | name |train|test| |-------|----:|---:| |default| 8000|2717| ## 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{hendrickx-etal-2010-semeval, title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals", author = "Hendrickx, Iris and Kim, Su Nam and Kozareva, Zornitsa and Nakov, Preslav and {'O} S{'e}aghdha, Diarmuid and Pad{'o}, Sebastian and Pennacchiotti, Marco and Romano, Lorenza and Szpakowicz, Stan", booktitle = "Proceedings of the 5th International Workshop on Semantic Evaluation", month = jul, year = "2010", address = "Uppsala, Sweden", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S10-1006", pages = "33--38", } ``` ### Contributions Thanks to [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
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GEM/turku_paraphrase_corpus
GEM
2022-10-24T15:29:45Z
580
0
null
[ "task_categories:other", "annotations_creators:expert-created", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:fi", "license:cc-by-sa-4.0", "paraphrasing", "region:us" ]
2022-10-24T15:29:45Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-created language_creators: - unknown language: - fi license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: turku_paraphrase_corpus tags: - paraphrasing --- # Dataset Card for GEM/turku_paraphrase_corpus ## Dataset Description - **Homepage:** https://turkunlp.org/paraphrase.html - **Repository:** https://github.com/TurkuNLP/Turku-paraphrase-corpus - **Paper:** https://aclanthology.org/2021.nodalida-main.29/ - **Leaderboard:** N/A - **Point of Contact:** Jenna Kanerva, Filip Ginter ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/turku_paraphrase_corpus). ### Dataset Summary This is a Finnish paraphrase corpus which consists of pairs of text passages, where a typical passage is about a sentence long. It can be used to either identify or generate paraphrases. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/turku_paraphrase_corpus') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/turku_paraphrase_corpus). #### website [Website](https://turkunlp.org/paraphrase.html) #### paper [ACL Anthology](https://aclanthology.org/2021.nodalida-main.29/) #### authors Jenna Kanerva, Filip Ginter, Li-Hsin Chang, Iiro Rastas, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, Otto Tarkka (TurkuNLP / University of Turku) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](https://turkunlp.org/paraphrase.html) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/TurkuNLP/Turku-paraphrase-corpus) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/2021.nodalida-main.29/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{kanerva-etal-2021-finnish, title = {Finnish Paraphrase Corpus}, author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpel{\"a}inen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sev{\'o}n, Maija and Tarkka, Otto}, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa'21)}, year = {2021}, publisher = {Link{\"o}ping University Electronic Press, Sweden}, url = {https://aclanthology.org/2021.nodalida-main.29}, pages = {288--298} } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Jenna Kanerva, Filip Ginter #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> jmnybl@utu.fi, figint@utu.fi #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> written standard language, spoken language #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `Finnish` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Paraphrase classification, paraphrase generation #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Paraphrasing #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The corpus provides naturally occurring Finnish paraphrases striving for low lexical overlap, thus supporting many different downstream applications requiring language understanding. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> University of Turku #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Jenna Kanerva, Filip Ginter, Li-Hsin Chang, Iiro Rastas, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, Otto Tarkka (TurkuNLP / University of Turku) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> The Turku paraphrase corpus project was funded by the Academy of Finland, as well as the European Language Grid project through its open call for pilot projects. The European Language Grid project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 825627 (ELG). #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example include two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset include three different `modes`, plain, classification, and generation. The `plain` mode loads the original data without any additional preprocessing or transformations, while the `classification` mode directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` mode, the examples are preprocessed to be directly suitable for paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label). Each pair in `plain` and `classification` mode will include fields: `gem_id`: Identifier of the paraphrase pair (string) `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string) `fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int) `text1`: First paraphrase passage (string) `text2`: Second paraphrase passage (string) `label`: Manually annotated labels (string) `binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string) `is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool) Each pair in `generation` mode will include the same fields expect `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are: `gem_id`: Identifier of the paraphrase pair (string) `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string) `fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int) `input`: The input paraphrase passage for generation (string) `output`: The output paraphrase passage for generation (string) `label`: Manually annotated labels (string) `binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string) `is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool) #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { 'gem_id': 'gem-turku_paraphrase_corpus-train-15', 'goeswith': 'episode-02243', 'fold': 0, 'text1': 'Mitä merkitystä sillä on?', 'text2': 'Mitä väliä sillä edes on?', 'label': '4', 'binary_label': 'positive', 'is_rewrite': False } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> The corpus include 3 splits: train, validation, and test. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The data is split randomly into the three section with a restriction of all paraphrases from the same document (movie, TV episode, news article, student translation, or exam question) being in the same section. All splits are manually annotated. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> This dataset provides a large amount of high quality (manually collected and verified) paraphrases for Finnish. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> natural language understanding, language variation ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `data points modified` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> Data structure is slightly simplified, and the release provides ready made transformations into two tasks (paraphrase classification and generation), where some data instances are doubled with different direction, and some are discarded as not being suitable for generation (e.g. negatives). #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> natural language understanding, language variation #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> F-score in paraphrase classification ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset is fully manually annotated. The dataset strives for interesting paraphrases with low lexical overlap, thus the annotation is two fold. First the paraphrases are manually extracted from two related documents, where the annotators are instructed to extract only interesting paraphrases. In the second phrase, all extracted paraphrases are manually labeled given the annotation scheme. The annotation scheme is: 4 : paraphrase in all reasonably possible contexts 3 : paraphrase in the given document contexts, but not in general 2 : related but not paraphrase During annotation also labels 1 (unrelated) and x (skip, e.g. wrong language) were used, however, the insignificant amount of examples annotated with these labels were discarded from the released corpus. The following flags are annotated to label 4 paraphrases: < : txt1 is more general than txt2; txt2 is more specific than txt1 (directional paraphrase where txt2 can be replaced with txt1 in all contexts but not to the other direction) > : txt2 is more general than txt1; txt1 is more specific than txt2 (directional paraphrase where txt1 can be replaced with txt2 in all contexts but not to the other direction) i : minor traceable difference (differing in terms of grammatical number or case, 'this' vs 'that', etc.) s : style or strength difference (e.g. equivalent meaning, but one of the statements substantially more colloquial than the other) For paraphrases where the annotated label was something else than label 4 without any flags, the annotators had an option to rewrite the text passages so that the rewritten paraphrase pair formed label 4 (universal) paraphrase. This was used for cases where simple edit would turn e.g. contextual or directional paraphrase into universal one. For the rewritten examples, both the original and the rewritten pairs are available with corresponding labels annotated. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Representing text passages with identical meaning but different surface realization. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> movie and TV series subtitles (82%) news articles (9%) discussion forum messages (8%) university translation exercises (1%) university course essays and exams (<1%) ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found`, `Other` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Multiple websites`, `Offline media collection`, `Other` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The movie and TV series subtitles are extracted from OPUS OpenSubtitles2018 collection, which is based on data from [OpenSubtitles](http://www.opensubtitles.org/). The news articles are collected from two Finnish news sites, YLE and HS, during years 2017-2020. Discussion forum messages are obtained from the Finnish Suomi24 discussion forum released for academic use (http://urn.fi/urn:nbn:fi:lb-2020021801). University translation exercises, essays and exams are collected during the project. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> expert created #### Number of Raters <!-- info: What is the number of raters --> <!-- scope: telescope --> 2<n<10 #### Rater Qualifications <!-- info: Describe the qualifications required of an annotator. --> <!-- scope: periscope --> Members of the TurkuNLP research group, native speakers of Finnish, each annotator has a strong background in language studies by having an academic degree or ongoing studies in a field related to languages or linguistics. #### Raters per Training Example <!-- info: How many annotators saw each training example? --> <!-- scope: periscope --> 1 #### Raters per Test Example <!-- info: How many annotators saw each test example? --> <!-- scope: periscope --> 1 #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> 1. Manual extraction of interesting paraphrases from two related documents. 2. Manual labeling of each extracted paraphrase based on the given annotation scheme, e.g. distinguishing contextual and universal paraphrases, marking style or strength differences, etc. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by another rater #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Partial double annotation, double annotation batches are assigned regularly in order to monitor annotation consistency. In double annotation, one annotator first extracts the candidate paraphrases, and these candidates are assigned to two different annotators, who does the label annotation independently from each other. Afterwards, the label annotations are merged, and conflicting labels are resolved together with the whole annotation team. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> The corpus is mostly based on public/open data. For other data sources (student material), the licensing was agreed with the data providers during the collection. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> likely #### Categories of PII <!-- info: What categories of PII are present or suspected in the data? --> <!-- scope: periscope --> `generic PII` #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> None ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations
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lamini/lamini_docs_evaluation
lamini
2023-07-24T03:08:13Z
580
0
null
[ "region:us" ]
2023-07-24T03:08:13Z
2023-07-24T03:08:09.000Z
2023-07-24T03:08:09
--- dataset_info: features: - name: predicted_answer dtype: string - name: target_answer dtype: string splits: - name: train num_bytes: 744520 num_examples: 139 download_size: 86086 dataset_size: 744520 --- # Dataset Card for "lamini_docs_evaluation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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symanto/autextification2023
symanto
2023-10-06T13:08:55Z
580
0
null
[ "task_categories:text-classification", "size_categories:10K<n<100K", "source_datasets:multi_eurlex", "source_datasets:xsum", "source_datasets:csebuetnlp/xlsum", "source_datasets:mlsum", "source_datasets:amazon_polarity", "source_datasets:https://sinai.ujaen.es/investigacion/recursos/coah", "source_datasets:https://sinai.ujaen.es/investigacion/recursos/coar", "source_datasets:carblacac/twitter-sentiment-analysis", "source_datasets:cardiffnlp/tweet_sentiment_multilingual", "source_datasets:https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa", "source_datasets:wiki_lingua", "language:en", "language:es", "license:cc-by-nc-sa-4.0", "arxiv:2309.11285", "region:us" ]
2023-10-06T13:08:55Z
2023-10-06T12:12:51.000Z
2023-10-06T12:12:51
--- license: cc-by-nc-sa-4.0 task_categories: - text-classification language: - en - es pretty_name: AuTexTification 2023 size_categories: - 10K<n<100K source_datasets: - multi_eurlex - xsum - csebuetnlp/xlsum - mlsum - amazon_polarity - https://sinai.ujaen.es/investigacion/recursos/coah - https://sinai.ujaen.es/investigacion/recursos/coar - carblacac/twitter-sentiment-analysis - cardiffnlp/tweet_sentiment_multilingual - https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa - wiki_lingua --- # Dataset Card for AuTexTification 2023 ## Dataset Description - **Homepage:** https://sites.google.com/view/autextification - **Repository:** https://github.com/autextification/AuTexTification-Overview - **Paper:** https://arxiv.org/abs/2309.11285 ### Dataset Summary AuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish. The dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents. The generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003. For more information, please refer to our overview paper: https://arxiv.org/abs/2309.11285 ### Supported Tasks and Leaderboards - Machine-Generated Text Detection - Model Attribution ### Languages English and Spanish ## Dataset Structure ### Data Instances 163k instances of labeled text in total. ### Data Fields For MGT Detection: - id - prompt - text - label - model - domain For Model Attribution: - id - prompt - text - label - domain ### Data Splits - MGT Detection Data: | Language | Split | Human | Generated | Total | | -------- | ----- | ------ | --------- | ------ | | English | Train | 17.046 | 16.799 | 33.845 | | | Test | 10.642 | 11.190 | 21.832 | | | Total | 27.688 | 27.989 | | | Spanish | Train | 15.787 | 16.275 | 32.062 | | | Test | 11.209 | 8.920 | 20.129 | | | Total | 26.996 | 25.195 | | - Model Attribution Data: | | | BLOOM | | | GPT | | | | | -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ | | Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total | | English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 | | | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 | | | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | | | Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 | | | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 | | | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | | ## Dataset Creation ### Curation Rationale Human data was gathered and used to prompt language models, obtaining generated data. Specific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix. For more detailed information, please refer to the overview paper: https://arxiv.org/abs/2309.11285 ### Source Data The following datasets were used as human text: - multi_eurlex - xsum - csebuetnlp/xlsum - mlsum - amazon_polarity - https://sinai.ujaen.es/investigacion/recursos/coah - https://sinai.ujaen.es/investigacion/recursos/coar - carblacac/twitter-sentiment-analysis - cardiffnlp/tweet_sentiment_multilingual - https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa - wiki_lingua These datasets were only used as sources of human text. The labels of the datasets were not employed in any manner. ### Licensing Information CC-BY-NC-SA-4.0 ### Citation Information ``` @inproceedings{autextification2023, title = "Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains", author = "Sarvazyan, Areg Mikael and Gonz{\'a}lez, Jos{\'e} {\'A}ngel and Franco-Salvador, Marc and Rangel, Francisco and Chulvi, Berta and Rosso, Paolo", month = sep, year = "2023", address = "Jaén, Spain", booktitle = "Procesamiento del Lenguaje Natural", } ```
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AdapterOcean/Open_Platypus_standardized_cluster_2_alpaca
AdapterOcean
2023-10-23T20:49:41Z
580
0
null
[ "region:us" ]
2023-10-23T20:49:41Z
2023-10-22T23:16:46.000Z
2023-10-22T23:16:46
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5719356 num_examples: 5147 download_size: 0 dataset_size: 5719356 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Open_Platypus_standardized_cluster_2_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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IconicAI/janet-24oct
IconicAI
2023-11-17T15:21:15Z
580
0
null
[ "task_categories:conversational", "region:us" ]
2023-11-17T15:21:15Z
2023-11-02T19:33:22.000Z
2023-11-02T19:33:22
--- task_categories: - conversational --- --- tags: - gpt-4 - janet --- # Description 39 Multi-Turn conversations generated by GPT-4. Each conversation represents a distinct dialogue between two people - a sniper and a central operator called Janet. The context of each conversation is the same - the sniper is perched on a rooftop, on a mission to eliminate a dangerous person that's hiding in the crowd below. Janet knows how that person looks like and is talking the sniper through the process of identifying and eliminating the target. # Schema ``` { "id": string "conversation": [{ "from": string, # system - system-prompt, assistant - LLM, user - human "value": string, # dialogue line }] } ``` # Implementation details Original format in `.txt`, Axolotl-compatible ShareGPT version is `.sharegpt.jsonl`. # Citation ``` @misc{Janet24Oct, title = {Janet24Oct: A Dataset of Multi-Turn Conversations for Conversational AI}, author = {Kieran Donaldson and Mikel Bober-Izar and Piotr Trochim}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/IconicAI/janet-24oct}}, } ```
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meta_woz
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2022-11-18T21:28:56Z
579
3
metalwoz
[ "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" ]
2022-11-18T21:28:56Z
2022-03-02T23:29:22.000Z
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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classla/setimes_sr
classla
2022-10-25T07:30:04Z
578
0
null
[ "task_categories:other", "task_ids:lemmatization", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "language:sr", "license:cc-by-sa-4.0", "structure-prediction", "normalization", "tokenization", "region:us" ]
2022-10-25T07:30:04Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - sr license: - cc-by-sa-4.0 task_categories: - other task_ids: - lemmatization - named-entity-recognition - part-of-speech tags: - structure-prediction - normalization - tokenization --- The SETimes\_sr training corpus contains 86,726 Serbian tokens manually annotated on the levels of tokenisation, sentence segmentation, morphosyntactic tagging, lemmatisation, named entities and dependency syntax. The dataset contains 3177 training samples, 395 validation samples and 319 test samples across the respective data splits. Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'), sentence text ('text'), list of tokens ('tokens'), list of lemmas ('lemmas'), list of MULTEXT-East tags ('xpos\_tags), list of UPOS tags ('upos\_tags'), list of morphological features ('feats'), list of IOB tags ('iob\_tags') and list of universal dependencies ('uds'). Three dataset configurations are available, namely 'ner', 'upos', and 'ud', with the corresponding features encoded as class labels. If the configuration is not specified, it defaults to 'ner'. If you use this dataset in your research, please cite the following paper: ``` @inproceedings{samardzic-etal-2017-universal, title = "{U}niversal {D}ependencies for {S}erbian in Comparison with {C}roatian and Other {S}lavic Languages", author = "Samard{\v{z}}i{\'c}, Tanja and Starovi{\'c}, Mirjana and Agi{\'c}, {\v{Z}}eljko and Ljube{\v{s}}i{\'c}, Nikola", booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing", month = apr, year = "2017", address = "Valencia, Spain", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W17-1407", doi = "10.18653/v1/W17-1407", pages = "39--44", } ```
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ai4bharat/naamapadam
ai4bharat
2023-05-24T17:09:03Z
578
3
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc0-1.0", "arxiv:2212.10168", "region:us" ]
2023-05-24T17:09:03Z
2023-01-19T03:17:10.000Z
2023-01-19T03:17:10
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc0-1.0 multilinguality: - multilingual pretty_name: naamapadam size_categories: - 1M<n<10M source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition --- # Dataset Card for naamapadam ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** https://github.com/AI4Bharat/indicner - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** Anoop Kunchukuttan ### Dataset Summary Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences. ### Supported Tasks and Leaderboards **Tasks:** NER on Indian languages. **Leaderboards:** Currently there is no Leaderboard for this dataset. ### Languages - `Assamese (as)` - `Bengali (bn)` - `Gujarati (gu)` - `Kannada (kn)` - `Hindi (hi)` - `Malayalam (ml)` - `Marathi (mr)` - `Oriya (or)` - `Punjabi (pa)` - `Tamil (ta)` - `Telugu (te)` ## Dataset Structure ### Data Instances {'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'], 'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0], } ### Data Fields - `words`: Raw tokens in the dataset. - `ner`: the NER tags for this dataset. ### Data Splits (to be updated, see paper for correct numbers) | Language | Train | Validation | Test | |---:|---:|---:|---:| | as | 10266 | 52 | 51 | | bn | 961679 | 4859 | 607 | | gu | 472845 | 2389 | 50 | | hi | 985787 | 13460 | 437 | | kn | 471763 | 2381 | 1019 | | ml | 716652 | 3618 | 974 | | mr | 455248 | 2300 | 1080 | | or | 196793 | 993 | 994 | | pa | 463534 | 2340 | 2342 | | ta | 497882 | 2795 | 49 | | te | 507741 | 2700 | 53 | ## Usage You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip: ```code pip install datasets ``` To use the dataset, please use:<br/> ```python from datasets import load_dataset hiner = load_dataset('ai4bharat/naamapadam') ``` ## Dataset Creation We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language. ### Curation Rationale naamapadam was built from [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/). This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing. ### Source Data [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/) #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process NER annotations were done following the CoNLL-2003 guidelines. #### Who are the annotators? The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers: - Anil Mhaske - Anoop Kunchukuttan - Archana Mhaske - Arnav Mhaske - Gowtham Ramesh - Harshit Kedia - Nitin Kedia - Rudramurthy V - Sangeeta Rajagopal - Sumanth Doddapaneni - Vindhya DS - Yash Madhani - Kabir Ahuja - Shallu Rani - Armin Virk ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data. ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information <!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/"> <img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" /> <img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/> </a> <br/> --> **CC0 License Statement** <a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/"> <img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/> </a> <br> <br> - We do not own any of the text from which this data has been extracted. - We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0). - To the extent possible under law, <a rel="dct:publisher" href="https://ai4bharat.iitm.ac.in/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Naamapadam</span> manually collected data and existing sources. - This work is published from: India. ### Citation Information If you are using the Naampadam corpus, please cite the following article: ``` @misc{mhaske2022naamapadam, doi = {10.48550/ARXIV.2212.10168}, url = {https://arxiv.org/abs/2212.10168}, author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop}, title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages} publisher = {arXiv}, year = {2022}, } ``` <!-- Contributors --> ### Contributors - Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub> - Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub> - Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub> - Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub> - Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub> - Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub> - Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub> This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in). <!-- Contact --> ### Contact - Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com)) - Rudra Murthy V ([rmurthyv@in.ibm.com](mailto:rmurthyv@in.ibm.com))
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neural_code_search
null
2023-06-01T14:59:50Z
577
7
neural-code-search-evaluation-dataset
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "arxiv:1908.09804", "region:us" ]
2023-06-01T14:59:50Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- pretty_name: Neural Code Search annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M - n<1K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: neural-code-search-evaluation-dataset dataset_info: - config_name: evaluation_dataset features: - name: stackoverflow_id dtype: int32 - name: question dtype: string - name: question_url dtype: string - name: question_author dtype: string - name: question_author_url dtype: string - name: answer dtype: string - name: answer_url dtype: string - name: answer_author dtype: string - name: answer_author_url dtype: string - name: examples sequence: int32 - name: examples_url sequence: string splits: - name: train num_bytes: 296848 num_examples: 287 download_size: 383625 dataset_size: 296848 - config_name: search_corpus features: - name: id dtype: int32 - name: filepath dtype: string - name: method_name dtype: string - name: start_line dtype: int32 - name: end_line dtype: int32 - name: url dtype: string splits: - name: train num_bytes: 1452630278 num_examples: 4716814 download_size: 121112543 dataset_size: 1452630278 config_names: - evaluation_dataset - search_corpus --- # Dataset Card for Neural Code Search ## 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:** [facebookresearch / Neural-Code-Search-Evaluation-Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data) - **Repository:** [Github](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset.git) - **Paper:** [arXiv](https://arxiv.org/pdf/1908.09804.pdf) ### Dataset Summary Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages EN - English ## Dataset Structure ### Data Instances #### Search Corpus The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus: #### Evaluation Dataset The evaluation dataset is composed of 287 Stack Overflow question and answer pairs ### Data Fields #### Search Corpus - id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset. - filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo method_name - start_line: Starting line number of the method in the file. - end_line: Ending line number of the method in the file. - url: GitHub link to the method body with commit ID and line numbers encoded. #### Evaluation Dataset - stackoverflow_id: Stack Overflow post ID. - question: Title fo the Stack Overflow post. - question_url: URL of the Stack Overflow post. - answer: Code snippet answer to the question. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories. #### 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 Hongyu Li, Seohyun Kim and Satish Chandra ### Licensing Information CC-BY-NC 4.0 (Attr Non-Commercial Inter.) ### Citation Information arXiv:1908.09804 [cs.SE] ### Contributions Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset.
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SUSTech/OpenOrca
SUSTech
2023-10-29T13:10:58Z
577
0
null
[ "region:us" ]
2023-10-29T13:10:58Z
2023-10-29T13:06:29.000Z
2023-10-29T13:06:29
--- configs: - config_name: default data_files: - split: niv_gpt4 path: data/niv_gpt4-* - split: flan_gpt4 path: data/flan_gpt4-* - split: t0_gpt4 path: data/t0_gpt4-* - split: cot_gpt4 path: data/cot_gpt4-* - split: niv_gpt35 path: data/niv_gpt35-* - split: flan_gpt35 path: data/flan_gpt35-* - split: t0_gpt35 path: data/t0_gpt35-* - split: cot_gpt35 path: data/cot_gpt35-* dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: niv_gpt4 num_bytes: 136902799 num_examples: 88210 - name: flan_gpt4 num_bytes: 870570389 num_examples: 501331 - name: t0_gpt4 num_bytes: 696675683 num_examples: 331183 - name: cot_gpt4 num_bytes: 84381097 num_examples: 74172 - name: niv_gpt35 num_bytes: 299906870 num_examples: 205186 - name: flan_gpt35 num_bytes: 1531332880 num_examples: 1147928 - name: t0_gpt35 num_bytes: 3535742489 num_examples: 1818390 - name: cot_gpt35 num_bytes: 65787500 num_examples: 67523 download_size: 4090266173 dataset_size: 7221299707 --- # Dataset Card for "OpenOrca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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pvduy/sharegpt_alpaca_oa_vicuna_format
pvduy
2023-04-29T18:37:21Z
575
6
null
[ "region:us" ]
2023-04-29T18:37:21Z
2023-04-29T18:36:44.000Z
2023-04-29T18:36:44
--- dataset_info: features: - name: prompt dtype: string - name: label dtype: string splits: - name: train num_bytes: 494337138 num_examples: 324160 - name: test num_bytes: 5944776 num_examples: 1499 download_size: 263071058 dataset_size: 500281914 --- # Dataset Card for "sharegpt_alpaca_oa_vicuna_format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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covid_qa_deepset
null
2022-11-03T16:31:16Z
574
1
null
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:apache-2.0", "region:us" ]
2022-11-03T16:31:16Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa - extractive-qa paperswithcode_id: null pretty_name: COVID-QA dataset_info: features: - name: document_id dtype: int32 - name: context dtype: string - name: question dtype: string - name: is_impossible dtype: bool - name: id dtype: int32 - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: covid_qa_deepset splits: - name: train num_bytes: 65151262 num_examples: 2019 download_size: 4418117 dataset_size: 65151262 --- # Dataset Card for COVID-QA ## 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/deepset-ai/COVID-QA - **Paper:** https://openreview.net/forum?id=JENSKEEzsoU - **Point of Contact:** [deepset AI](https://github.com/deepset-ai) ### Dataset Summary COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?** Each represents a question, a context (document passage from the CORD19 dataset) and an answer. **How many instances are there in total?** 2019 instances **What data does each instance consist of?** Each instance is a question, a set of answers, and an id associated with each answer. [More Information Needed] ### Data Fields The data was annotated in SQuAD style fashion, where each row contains: * **question**: Query question * **context**: Context text to obtain the answer from * **document_id** The document ID of the context text * **answer**: Dictionary containing the answer string and the start index ### Data Splits **data/COVID-QA.json**: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then annotated afterwards. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack). #### Who are the annotators? The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers. These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles. ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The Proto_qa dataset is licensed under the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE) ### Citation Information ``` @inproceedings{moller2020covid, title={COVID-QA: A Question Answering Dataset for COVID-19}, author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte}, booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020}, year={2020} } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
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darentang/generated
darentang
2022-01-04T06:13:50Z
574
0
null
[ "region:us" ]
2022-01-04T06:13:50Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Ela279/langchain_docs
Ela279
2023-05-26T09:42:36Z
574
0
null
[ "license:mit", "region:us" ]
2023-05-26T09:42:36Z
2023-05-26T09:30:25.000Z
2023-05-26T09:30:25
--- license: mit ---
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ccdv/patent-classification
ccdv
2022-10-22T09:25:36Z
573
5
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]
2022-10-22T09:25:36Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: en task_categories: - text-classification tags: - long context task_ids: - multi-class-classification - topic-classification size_categories: 10K<n<100K --- **Patent Classification: a classification of Patents and abstracts (9 classes).** This dataset is intended for long context classification (non abstract documents are longer that 512 tokens). \ Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang * See: https://aclanthology.org/P19-1212.pdf * See: https://evasharma.github.io/bigpatent/ It contains 9 unbalanced classes, 35k Patents and abstracts divided into 3 splits: train (25k), val (5k) and test (5k). **Note that documents are uncased and space separated (by authors)** Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/patent-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/patent ```
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BeIR/fever-qrels
BeIR
2022-10-23T06:08:11Z
573
0
beir
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
2022-10-23T06:08:11Z
2022-06-05T17:28:01.000Z
2022-06-05T17:28:01
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
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reazon-research/reazonspeech
reazon-research
2023-02-08T02:22:58Z
571
30
null
[ "task_categories:automatic-speech-recognition", "size_categories:10M<n<100M", "language:ja", "license:other", "region:us" ]
2023-02-08T02:22:58Z
2023-01-17T23:03:48.000Z
2023-01-17T23:03:48
--- license: other task_categories: - automatic-speech-recognition language: - ja pretty_name: ReazonSpeech size_categories: - 10M<n<100M --- # Dataset Card for ReazonSpeech ## Dataset Description - **Homepage:** https://research.reazon.jp/projects/ReazonSpeech - **Repository:** https://github.com/reazon-research/reazonspeech ### Dataset Summary ReazonSpeech is a large audio corpus collected from Japanese TV programs. **TO USE THIS DATASET, YOU MUST AGREE THAT YOU WILL USE THE DATASET SOLELY FOR THE PURPOSE OF JAPANESE COPYRIGHT ACT ARTICLE 30-4.** ### Languages Japanese ## Dataset Structure ### Data Instances The following shows an example dataset record: ``` { 'name': '000/0000000000000.flac', 'audio': { 'path': '/path/to/000/0000000000000.flac', 'array': array([ 0.01000000, ...], dtype=float32), 'sampling_rate': 16000}, 'transcription': '今日のニュースをお伝えします。'} } ``` ### Data Fields | Field | Type | Desc | | --------------- | -------- | ---- | | `name` | `string` | An unique id for the audio file | | `audio` | `dict` | A dictionary containing the file path, the decoded audio, and the sampling rate | | `transcription` | `string` | A text transcription of the audio data | ### Data Splits | Split | Size | Desc | | ------- | ----- | ---- | | `all` | >1TB | Contains all the dataset | | `small` | 350MB | Small subset for example purposes (default) | ## Additional Information ### Dataset Curators [Reazon Human Interaction Laboratory](https://research.reazon.jp/) ### Licensing Information [CDLA-Sharing-1.0](https://cdla.dev/sharing-1-0/) TO USE THIS DATASET, YOU MUST AGREE THAT YOU WILL USE THE DATASET SOLELY FOR THE PURPOSE OF JAPANESE COPYRIGHT ACT ARTICLE 30-4.
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papluca/language-identification
papluca
2022-07-15T10:11:23Z
569
16
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|amazon_reviews_multi", "source_datasets:extended|xnli", "source_datasets:extended|stsb_multi_mt", "language:ar", "language:bg", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:ja", "language:nl", "language:pl", "language:pt", "language:ru", "language:sw", "language:th", "language:tr", "language:ur", "language:vi", "language:zh", "region:us" ]
2022-07-15T10:11:23Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: [] language_creators: [] language: - ar - bg - de - el - en - es - fr - hi - it - ja - nl - pl - pt - ru - sw - th - tr - ur - vi - zh license: [] multilinguality: - multilingual pretty_name: Language Identification dataset size_categories: - unknown source_datasets: - extended|amazon_reviews_multi - extended|xnli - extended|stsb_multi_mt task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for Language Identification 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 The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label. This dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt). ### Supported Tasks and Leaderboards The dataset can be used to train a model for language identification, which is a **multi-class text classification** task. The model [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection), which is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), was trained on this dataset and currently achieves 99.6% accuracy on the test set. ### Languages The Language Identification dataset contains text in 20 languages, which are: `arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)` ## Dataset Structure ### Data Instances For each instance, there is a string for the text and a string for the label (the language tag). Here is an example: `{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}` ### Data Fields - **labels:** a string indicating the language label. - **text:** a string consisting of one or more sentences in one of the 20 languages listed above. ### Data Splits The Language Identification dataset has 3 splits: *train*, *valid*, and *test*. The train set contains 70k samples, while the validation and test sets 10k each. All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500. ## Dataset Creation ### Curation Rationale This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model. ### Source Data The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt). ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models. ### Discussion of Biases The possible biases correspond to those of the 3 datasets on which this dataset is based. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.
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JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020
JonasGeiping
2023-06-13T16:25:54Z
568
0
null
[ "arxiv:2212.14034", "arxiv:2101.00027", "arxiv:2201.07311", "region:us" ]
2023-06-13T16:25:54Z
2023-06-08T17:30:55.000Z
2023-06-08T17:30:55
--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 43860000000 num_examples: 85000000 download_size: 24001057282 dataset_size: 43860000000 annotations_creators: - no-annotation language_creators: - found language: - en license: other multilinguality: - monolingual pretty_name: pretokenized,filtered,sorted subset of the Pile size_categories: - 10B<n<100B source_datasets: - the-pile task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: the-pile-cramming --- # Dataset Card for "the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020" ## Dataset Description - **Repository:** https://github.com/JonasGeiping/cramming - **Paper:** https://arxiv.org/abs/2212.14034 - **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027) - **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311) ### Dataset Summary This is a preprocessed, tokenized dataset for the cramming-project. Use only with the tokenizer uploaded here. This version is `2efdb9d060d1ae95faf952ec1a50f020`, which corresponds to a specific dataset construction setup, described below. The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. ### Languages This dataset is in English (`EN`). ### Data Splits This preprocessed subset contains only a train split. ## Dataset Creation The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is ``` # This is a slice of the pile name: the_pile defaults: - sources: - the_pile # # Preprocessing normalizer: force_lowercase: True strip_accents: True force_english_keyboard: True whitespace_escape: False tokenizer: WordPiece vocab_size: 32768 # Dataset Formation seq_length: 128 include_cls_token_in_corpus: False include_sep_token_in_corpus: True use_type_ids: False max_entries_in_raw_dataset: 16e6 max_seq_in_tokenized_dataset: 85e6 # Data Cleaning: named_entity_simplification: False remove_whitespaces: False remove_trash: True trash_cutoff: 0.25 deduplicate_entries: False deduplication_threshold: 75 # Data Order: ordering: sentence-length-curriculum ``` ## Considerations for Using the Data Limitations and bias: This training data was further filtered and sorted beyond the normal preprocessing. These modifications were not tested for unintended consequences. ## Additional Information ### Dataset Curators This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper. ### Licensing Information Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile ### Citation Information Filtered version for the cramming project: ``` @article{geiping_cramming_2022, title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}}, shorttitle = {Cramming}, author = {Geiping, Jonas and Goldstein, Tom}, year = {2022}, month = dec, eprint = {2212.14034}, primaryclass = {cs}, publisher = {{arXiv}}, doi = {10.48550/arXiv.2212.14034}, url = {http://arxiv.org/abs/2212.14034}, urldate = {2023-01-10}, archiveprefix = {arxiv}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}, journal = {arxiv:2212.14034[cs]} } ``` Original Data Curation: ``` @article{gao2020pile, title={The {P}ile: An 800{GB} dataset of diverse text for language modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } @article{biderman2022datasheet, title={Datasheet for the pile}, author={Biderman, Stella and Bicheno, Kieran and Gao, Leo}, journal={arXiv preprint arXiv:2201.07311}, year={2022} } ```
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hf-internal-testing/fixtures_ocr
hf-internal-testing
2021-12-07T08:07:29Z
567
0
null
[ "region:us" ]
2021-12-07T08:07:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
This dataset includes 2 images: one of the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) and one of the [SRIOE](https://rrc.cvc.uab.es/?ch=13) dataset. They are used for testing OCR models that are part of the HuggingFace Transformers library. See [here](https://github.com/huggingface/transformers/search?q=fixtures_ocr) for details. More specifically, they are used inside `test_modeling_vision_encoder_decoder_model.py`, for testing the TrOCR models.
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omerist/turknews-mini
omerist
2022-10-15T12:38:10Z
567
0
null
[ "region:us" ]
2022-10-15T12:38:10Z
2022-10-15T12:38:03.000Z
2022-10-15T12:38:03
--- dataset_info: features: - name: review dtype: string - name: title dtype: string - name: review_length dtype: int64 splits: - name: train num_bytes: 9064933.18105424 num_examples: 3534 - name: validation num_bytes: 1008069.8189457601 num_examples: 393 download_size: 5732599 dataset_size: 10073003.0 --- # Dataset Card for "turknews-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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2023-01-25T14:29:05Z
566
3
dane
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-Danish-Universal-Dependencies-treebank", "language:da", "license:cc-by-sa-4.0", "region:us" ]
2023-01-25T14:29:05Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - da license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-Danish-Universal-Dependencies-treebank task_categories: - token-classification task_ids: - named-entity-recognition - part-of-speech paperswithcode_id: dane pretty_name: DaNE dataset_info: features: - name: sent_id dtype: string - name: text dtype: string - name: tok_ids sequence: int64 - name: tokens sequence: string - name: lemmas sequence: string - name: pos_tags sequence: class_label: names: '0': NUM '1': CCONJ '2': PRON '3': VERB '4': INTJ '5': AUX '6': ADJ '7': PROPN '8': PART '9': ADV '10': PUNCT '11': ADP '12': NOUN '13': X '14': DET '15': SYM '16': SCONJ - name: morph_tags sequence: string - name: dep_ids sequence: int64 - name: dep_labels sequence: class_label: names: '0': parataxis '1': mark '2': nummod '3': discourse '4': compound:prt '5': reparandum '6': vocative '7': list '8': obj '9': dep '10': det '11': obl:loc '12': flat '13': iobj '14': cop '15': expl '16': obl '17': conj '18': nmod '19': root '20': acl:relcl '21': goeswith '22': appos '23': fixed '24': obl:tmod '25': xcomp '26': advmod '27': nmod:poss '28': aux '29': ccomp '30': amod '31': cc '32': advcl '33': nsubj '34': punct '35': case - 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 '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 7311212 num_examples: 4383 - name: test num_bytes: 909699 num_examples: 565 - name: validation num_bytes: 940413 num_examples: 564 download_size: 1209710 dataset_size: 9161324 --- # Dataset Card for DaNE ## 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:** [DaNE homepage](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane) - **Repository:** [Github](https://github.com/alexandrainst/danlp) - **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme. The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998). UD-DDT has annotations for dependency parsing and part-of-speech (POS) tagging. The dataset was annotated with Named Entities for PER, ORG, and LOC by the Alexandra Institute in the DaNE dataset (Hvingelby et al. 2020). ### Supported Tasks and Leaderboards Parts-of-speech tagging, dependency parsing and named entitity recognition. ### Languages Danish ## Dataset Structure ### Data Instances This is an example in the "train" split: ```python { 'sent_id': 'train-v2-0\n', 'lemmas': ['på', 'fredag', 'have', 'SiD', 'invitere', 'til', 'reception', 'i', 'SID-hus', 'i', 'anledning', 'af', 'at', 'formand', 'Kjeld', 'Christensen', 'gå', 'ind', 'i', 'den', 'glad', 'tresser', '.'], 'dep_labels': [35, 16, 28, 33, 19, 35, 16, 35, 18, 35, 18, 1, 1, 33, 22, 12, 32, 11, 35, 10, 30, 16, 34], 'ner_tags': [0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0], 'morph_tags': ['AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'Definite=Ind|Number=Sing|Tense=Past|VerbForm=Part', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', 'Definite=Def|Gender=Neut|Number=Sing', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', '_', 'Definite=Def|Gender=Com|Number=Sing', '_', '_', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'AdpType=Prep', 'Number=Plur|PronType=Dem', 'Degree=Pos|Number=Plur', 'Definite=Ind|Gender=Com|Number=Plur', '_'], 'dep_ids': [2, 5, 5, 5, 0, 7, 5, 9, 7, 11, 7, 17, 17, 17, 14, 15, 11, 17, 22, 22, 22, 18, 5], 'pos_tags': [11, 12, 5, 7, 3, 11, 12, 11, 12, 11, 12, 11, 16, 12, 7, 7, 3, 9, 11, 14, 6, 12, 10], 'text': 'På fredag har SID inviteret til reception i SID-huset i anledning af at formanden Kjeld Christensen går ind i de glade tressere.\n', 'tokens': ['På', 'fredag', 'har', 'SID', 'inviteret', 'til', 'reception', 'i', 'SID-huset', 'i', 'anledning', 'af', 'at', 'formanden', 'Kjeld', 'Christensen', 'går', 'ind', 'i', 'de', 'glade', 'tressere', '.'], 'tok_ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] } ``` ### Data Fields Data Fields: - q_id: a string question identifier for each example, corresponding to its ID in the Pushshift.io Reddit submission dumps. - subreddit: One of explainlikeimfive, askscience, or AskHistorians, indicating which subreddit the question came from - title: title of the question, with URLs extracted and replaced by URL_n tokens - title_urls: list of the extracted URLs, the nth element of the list was replaced by URL_n - sent_id: a string identifier for each example - text: a string, the original sentence (not tokenized) - tok_ids: a list of ids (int), one for each token - tokens: a list of strings, the tokens - lemmas: a list of strings, the lemmas of the tokens - pos_tags: a list of strings, the part-of-speech tags of the tokens - morph_tags: a list of strings, the morphological tags of the tokens - dep_ids: a list of ids (int), the id of the head of the incoming dependency for each token - dep_labels: a list of strings, the dependency labels - ner_tags: a list of strings, the named entity tags (BIO format) ### Data Splits | | train | validation | test | |-------------|-------:|-----------:|-------:| | # sentences | 4383 | 564 | 565 | | # tokens | 80 378 | 10 322 | 10 023 | ## 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] ### Citation Information ``` @inproceedings{hvingelby-etal-2020-dane, title = "{D}a{NE}: A Named Entity Resource for {D}anish", author = "Hvingelby, Rasmus and Pauli, Amalie Brogaard and Barrett, Maria and Rosted, Christina and Lidegaard, Lasse Malm and S{\o}gaard, Anders", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.565", pages = "4597--4604", abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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kelm
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2022-11-18T20:16:37Z
563
6
kelm
[ "task_categories:other", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "data-to-text-generation", "arxiv:2010.12688", "region:us" ]
2022-11-18T20:16:37Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: kelm pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training (KELM) tags: - data-to-text-generation dataset_info: features: - name: triple dtype: string - name: sentence dtype: string splits: - name: train num_bytes: 1343187306 num_examples: 6371131 - name: validation num_bytes: 167790917 num_examples: 796471 - name: test num_bytes: 167921750 num_examples: 796493 download_size: 1631259869 dataset_size: 1678899973 --- # Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM) ## 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/google-research-datasets/KELM-corpus - **Repository:** https://github.com/google-research-datasets/KELM-corpus - **Paper:** https://arxiv.org/abs/2010.12688 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text. The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations. ### Supported Tasks and Leaderboards The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model with the tuples concatenated into a single sequence. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of one KG triple paired with corresponding natural language. ### Data Fields - `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on how these relations are grouped, please refer to the paper. - `sentence`: The corresponding Wikipedia sentence. ### Data Splits The dataset includes a pre-determined train, validation, and test split. ## Dataset Creation ### Curation Rationale The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate natural text from a knowledge graph. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data is sourced from English Wikipedia and it's associated knowledge graph. ### 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 From the paper: > Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still contain some of these biases, certain types of biases may be reduced. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/). ### Citation Information ``` @misc{agarwal2020large, title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training}, author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou}, year={2020}, eprint={2010.12688}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
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izumi-lab/llm-japanese-dataset
izumi-lab
2023-07-04T15:25:14Z
563
76
null
[ "size_categories:1M<n<10M", "language:ja", "license:cc-by-sa-4.0", "arxiv:2305.12720", "region:us" ]
2023-07-04T15:25:14Z
2023-04-30T06:13:24.000Z
2023-04-30T06:13:24
--- license: cc-by-sa-4.0 language: - ja size_categories: - 1M<n<10M --- # llm-japanese-dataset LLM構築用の日本語インストラクション(チャット)データセット 主に,英語で構築されたLLMモデルなどに対して,チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます. ※様々な公開言語資源を利用させていただきました.関係各位にはこの場を借りて御礼申し上げます. ## updates 5/15にAlpaca datasetがNCにライセンス変更されたことに対応し,安心してご利用いただけるように,データセットから当該データセットをドロップしました. v1.0.1にて,ドロップ後のデータセットをご利用いただけます. ## データの詳細 データの詳細は,以下の論文を参照してください. - 日本語: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383) - 英語: [https://arxiv.org/abs/2305.12720](https://arxiv.org/abs/2305.12720) - GitHub: [https://github.com/masanorihirano/llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset) - 最新情報: [llm.msuzuki.me](https://llm.msuzuki.me). なお,Citationには,よろしければ,以下をご利用ください. ``` @preprint{Hirano2023-llmj, title={{llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology}}, autor={Masanori HIRANO and Masahiro SUZUKI and Hiroki SAKAJI}, doi={10.48550/arXiv.2305.12720}, archivePrefix={arXiv}, arxivId={2305.12720}, year={2023} } ``` 共同研究,データ提供,各種支援,その他問い合わせは,izumi-llm@socsim.org へ. ## How to use ```python from datasets import load_dataset dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="main") dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="a.b.c") # for specific version ``` - version `0.1.0` contains bugs - version `0.1.1` contains 8,393,726 data (bug fixed) - version `1.0.0` contains 9,097,388 data (added jqac, wikipedia ja typo corpus) - version `1.0.1` contains 9,045,386 data (dropped alpaca dataset) For more details, see: https://github.com/masanorihirano/llm-japanese-dataset ## LICENSE CC-BY-SA 4.0 (For more details, see: LICENSE, NOTICE.md, NOTICE2.md) ## Note MIT License version is also available on the github release page https://github.com/masanorihirano/llm-japanese-dataset/releases To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).
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gnad10
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2023-01-25T14:31:03Z
562
3
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[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-from-One-Million-Posts-Corpus", "language:de", "license:cc-by-nc-sa-4.0", "region:us" ]
2023-01-25T14:31:03Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - found language: - de license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-from-One-Million-Posts-Corpus task_categories: - text-classification task_ids: - topic-classification pretty_name: 10k German News Articles Datasets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Web '1': Panorama '2': International '3': Wirtschaft '4': Sport '5': Inland '6': Etat '7': Wissenschaft '8': Kultur splits: - name: train num_bytes: 24418224 num_examples: 9245 - name: test num_bytes: 2756405 num_examples: 1028 download_size: 27160809 dataset_size: 27174629 --- # Dataset Card for 10k German News Articles Datasets ## 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:** [10k German News Article Dataset](https://tblock.github.io/10kGNAD/) - **Repository:** [10k German News Article Dataset](https://github.com/tblock/10kGNAD) - **Point of Contact:** [Steven Liu](stevhliu@gmail.com) ### Dataset Summary The 10k German News Article Dataset consists of 10273 German language news articles from the online Austrian newspaper website DER Standard. Each news article has been classified into one of 9 categories by professional forum moderators employed by the newspaper. This dataset is extended from the original [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/). The dataset was created to support topic classification in German because a classifier effective on a English dataset may not be as effective on a German dataset due to higher inflections and longer compound words. Additionally, this dataset can be used as a benchmark dataset for German topic classification. ### Supported Tasks and Leaderboards This dataset can be used to train a model, like [BERT](https://huggingface.co/bert-base-uncased) for `topic classification` on German news articles. There are 9 possible categories. ### Languages The text is in German and it comes from an online Austrian newspaper website. The BCP-47 code for German is `de-DE`. ## Dataset Structure ### Data Instances An example data instance contains a German news article (title and article are concatenated) and it's corresponding topic category. ``` {'text': ''Die Gewerkschaft GPA-djp lanciert den "All-in-Rechner" und findet, dass die Vertragsform auf die Führungsebene beschränkt gehört. Wien – Die Gewerkschaft GPA-djp sieht Handlungsbedarf bei sogenannten All-in-Verträgen.' 'label': 'Wirtschaft' } ``` ### Data Fields * `text`: contains the title and content of the article * `label`: can be one of 9 possible topic categories (`Web`, `Panorama`, `International`, `Wirtschaft`, `Sport`, `Inland`, `Etat`, `Wissenschaft`, `Kultur`) ### Data Splits The data is split into a training set consisting of 9245 articles and a test set consisting of 1028 articles. ## Dataset Creation ### Curation Rationale The dataset was created to support topic classification in the German language. English text classification datasets are common ([AG News](https://huggingface.co/datasets/ag_news) and [20 Newsgroup](https://huggingface.co/datasets/newsgroup)), but German datasets are less common. A classifier trained on an English dataset may not work as well on a set of German text due to grammatical differences. Thus there is a need for a German dataset for effectively assessing model performance. ### Source Data #### Initial Data Collection and Normalization The 10k German News Article Dataset is extended from the One Million Posts Corpus. 10273 German news articles were collected from this larger corpus. In the One Million Posts Corpus, each article has a topic path like `Newsroom/Wirtschaft/Wirtschaftpolitik/Finanzmaerkte/Griechenlandkrise`. The 10kGNAD uses the second part of the topic path as the topic label. Article title and texts are concatenated into one text and author names are removed to avoid keyword classification on authors who write frequently on a particular topic. #### Who are the source language producers? The language producers are the authors of the Austrian newspaper website DER Standard. ### 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 This dataset was curated by Timo Block. ### Licensing Information This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license. ### Citation Information Please consider citing the authors of the "One Million Post Corpus" if you use the dataset.: ``` @InProceedings{Schabus2017, Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp}, Title = {One Million Posts: A Data Set of German Online Discussions}, Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)}, Pages = {1241--1244}, Year = {2017}, Address = {Tokyo, Japan}, Doi = {10.1145/3077136.3080711}, Month = aug } ``` ### Contributions Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.
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ashraq/fashion-product-images-small
ashraq
2022-11-01T20:25:52Z
562
10
null
[ "region:us" ]
2022-11-01T20:25:52Z
2022-11-01T20:22:50.000Z
2022-11-01T20:22:50
--- dataset_info: features: - name: id dtype: int64 - name: gender dtype: string - name: masterCategory dtype: string - name: subCategory dtype: string - name: articleType dtype: string - name: baseColour dtype: string - name: season dtype: string - name: year dtype: float64 - name: usage dtype: string - name: productDisplayName dtype: string - name: image dtype: image splits: - name: train num_bytes: 546202015.44 num_examples: 44072 download_size: 271496441 dataset_size: 546202015.44 --- # Dataset Card for "fashion-product-images-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Data was obtained from [here](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small)
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JackBAI/bert_pretrain_datasets
JackBAI
2023-11-28T20:19:37Z
562
0
null
[ "region:us" ]
2023-11-28T20:19:37Z
2023-10-09T22:43:45.000Z
2023-10-09T22:43:45
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 24500165181 num_examples: 80462898 download_size: 14400389487 dataset_size: 24500165181 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bert_pretrain_datasets" This dataset is essentially a concatenation of the training set of the English Wikipedia (wikipedia.20220301.en.train) and the Book Corpus (bookcorpus.train). This is exactly how I get this dataset: ``` from datasets import load_dataset, concatenate_datasets, load_from_disk cache_dir = "/data/haob2/cache/" # book corpus bookcorpus = load_dataset("bookcorpus", split="train", cache_dir=cache_dir) # english wikipedia wiki = load_dataset("wikipedia", "20220301.en", split="train", cache_dir=cache_dir) wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) # # concatenation concat = concatenate_datasets([bookcorpus, wiki]) concat.push_to_hub("JackBAI/bert_pretrain_datasets") ``` Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset.
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wi_locness
null
2023-06-01T14:59:47Z
561
7
locness-corpus
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "multilinguality:other-language-learner", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "grammatical-error-correction", "region:us" ]
2023-06-01T14:59:47Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual - other-language-learner size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: locness-corpus pretty_name: Cambridge English Write & Improve + LOCNESS tags: - grammatical-error-correction dataset_info: - config_name: default features: - name: id dtype: string - name: userid dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 4375795 num_examples: 3000 - name: validation num_bytes: 447055 num_examples: 300 download_size: 6120469 dataset_size: 4822850 - config_name: wi features: - name: id dtype: string - name: userid dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 4375795 num_examples: 3000 - name: validation num_bytes: 447055 num_examples: 300 download_size: 6120469 dataset_size: 4822850 - config_name: locness features: - name: id dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 138176 num_examples: 50 download_size: 6120469 dataset_size: 138176 config_names: - locness - wi --- # Dataset Card for Cambridge English Write & Improve + LOCNESS Dataset ## 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://www.cl.cam.ac.uk/research/nl/bea2019st/#data - **Repository:** - **Paper:** https://www.aclweb.org/anthology/W19-4406/ - **Leaderboard:** https://competitions.codalab.org/competitions/20228#results - **Point of Contact:** ### Dataset Summary Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native English students with their writing. Specifically, students from around the world submit letters, stories, articles and essays in response to various prompts, and the W&I system provides instant feedback. Since W&I went live in 2014, W&I annotators have manually annotated some of these submissions and assigned them a CEFR level. The LOCNESS corpus (Granger, 1998) consists of essays written by native English students. It was originally compiled by researchers at the Centre for English Corpus Linguistics at the University of Louvain. Since native English students also sometimes make mistakes, we asked the W&I annotators to annotate a subsection of LOCNESS so researchers can test the effectiveness of their systems on the full range of English levels and abilities. ### Supported Tasks and Leaderboards Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in text; e.g. [I follows his advices -> I followed his advice]. It can be used to not only help language learners improve their writing skills, but also alert native speakers to accidental mistakes or typos. The aim of the task of this dataset is to correct all types of errors in written text. This includes grammatical, lexical and orthographical errors. The following Codalab competition contains the latest leaderboard, along with information on how to submit to the withheld W&I+LOCNESS test set: https://competitions.codalab.org/competitions/20228 ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example from the `wi` configuration: ``` { 'id': '1-140178', 'userid': '21251', 'cefr': 'A2.i', 'text': 'My town is a medium size city with eighty thousand inhabitants. It has a high density population because its small territory. Despite of it is an industrial city, there are many shops and department stores. I recommend visiting the artificial lake in the certer of the city which is surrounded by a park. Pasteries are very common and most of them offer the special dessert from the city. There are a comercial zone along the widest street of the city where you can find all kind of establishments: banks, bars, chemists, cinemas, pet shops, restaurants, fast food restaurants, groceries, travel agencies, supermarkets and others. Most of the shops have sales and offers at least three months of the year: January, June and August. The quality of the products and services are quite good, because there are a huge competition, however I suggest you taking care about some fakes or cheats.', 'edits': { 'start': [13, 77, 104, 126, 134, 256, 306, 375, 396, 402, 476, 484, 579, 671, 774, 804, 808, 826, 838, 850, 857, 862, 868], 'end': [24, 78, 104, 133, 136, 262, 315, 379, 399, 411, 480, 498, 588, 671, 777, 807, 810, 835, 845, 856, 861, 867, 873], 'text': ['medium-sized', '-', ' of', 'Although', '', 'center', None, 'of', 'is', 'commercial', 'kinds', 'businesses', 'grocers', ' in', 'is', 'is', '', '. However,', 'recommend', 'be', 'careful', 'of', ''] } } ``` An example from the `locness` configuration: ``` { 'id': '7-5819177', 'cefr': 'N', 'text': 'Boxing is a common, well known and well loved sport amongst most countries in the world however it is also punishing, dangerous and disliked to the extent that many people want it banned, possibly with good reason.\nBoxing is a dangerous sport, there are relatively common deaths, tragic injuries and even disease. All professional boxers are at risk from being killed in his next fight. If not killed then more likely paralysed. There have been a number of cases in the last ten years of the top few boxers having tragic losses throughout their ranks. This is just from the elite few, and theres more from those below them.\nMore deaths would occur through boxing if it were banned. The sport would go underground, there would be no safety measures like gloves, a doctor, paramedics or early stopping of the fight if someone looked unable to continue. With this going on the people taking part will be dangerous, and on the streets. Dangerous dogs who were trained to kill and maim in similar underound dog fights have already proved deadly to innocent people, the new boxers could be even more at risk.\nOnce boxing is banned and no-one grows up knowing it as acceptable there will be no interest in boxing and hopefully less all round interest in violence making towns and cities much safer places to live in, there will be less fighting outside pubs and clubs and less violent attacks with little or no reason.\nchange the rules of boxing slightly would much improve the safety risks of the sport and not detract form the entertainment. There are all sorts of proposals, lighter and more cushioning gloves could be worn, ban punches to the head, headguards worn or make fights shorter, as most of the serious injuries occur in the latter rounds, these would all show off the boxers skill and tallent and still be entertaining to watch.\nEven if a boxer is a success and manages not to be seriously hurt he still faces serious consequences in later life diseases that attack the brains have been known to set in as a direct result of boxing, even Muhamed Ali, who was infamous(?) both for his boxing and his quick-witted intelligence now has Alzheimer disease and can no longer do many everyday acts.\nMany other sports are more dangerous than boxing, motor sports and even mountaineering has risks that are real. Boxers chose to box, just as racing drivers drive.', 'edits': { 'start': [24, 39, 52, 87, 242, 371, 400, 528, 589, 713, 869, 992, 1058, 1169, 1209, 1219, 1255, 1308, 1386, 1412, 1513, 1569, 1661, 1731, 1744, 1781, 1792, 1901, 1951, 2038, 2131, 2149, 2247, 2286], 'end': [25, 40, 59, 95, 249, 374, 400, 538, 595, 713, 869, 1001, 1063, 1169, 1209, 1219, 1255, 1315, 1390, 1418, 1517, 1570, 1661, 1737, 1751, 1781, 1799, 1901, 1960, 2044, 2131, 2149, 2248, 2289], 'text': ['-', '-', 'in', '. However,', '. There', 'their', ',', 'among', "there's", ' and', ',', 'underground', '. The', ',', ',', ',', ',', '. There', 'for', 'Changing', 'from', ';', ',', 'later', '. These', "'", 'talent', ',', '. Diseases', '. Even', ',', "'s", ';', 'have'] } } ``` ### Data Fields The fields of the dataset are: - `id`: the id of the text as a string - `cefr`: the [CEFR level](https://www.cambridgeenglish.org/exams-and-tests/cefr/) of the text as a string - `userid`: id of the user - `text`: the text of the submission as a string - `edits`: the edits from W&I: - `start`: start indexes of each edit as a list of integers - `end`: end indexes of each edit as a list of integers - `text`: the text content of each edit as a list of strings - `from`: the original text of each edit as a list of strings ### Data Splits | name |train|validation| |----------|----:|---------:| | wi | 3000| 300| | locness | N/A| 50| ## 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 Write & Improve License: ``` Cambridge English Write & Improve (CEWI) Dataset Licence Agreement 1. By downloading this dataset and licence, this licence agreement is entered into, effective this date, between you, the Licensee, and the University of Cambridge, the Licensor. 2. Copyright of the entire licensed dataset is held by the Licensor. No ownership or interest in the dataset is transferred to the Licensee. 3. The Licensor hereby grants the Licensee a non-exclusive non-transferable right to use the licensed dataset for non-commercial research and educational purposes. 4. Non-commercial purposes exclude without limitation any use of the licensed dataset or information derived from the dataset for or as part of a product or service which is sold, offered for sale, licensed, leased or rented. 5. The Licensee shall acknowledge use of the licensed dataset in all publications of research based on it, in whole or in part, through citation of the following publication: Helen Yannakoudakis, Øistein E. Andersen, Ardeshir Geranpayeh, Ted Briscoe and Diane Nicholls. 2018. Developing an automated writing placement system for ESL learners. Applied Measurement in Education. 6. The Licensee may publish excerpts of less than 100 words from the licensed dataset pursuant to clause 3. 7. The Licensor grants the Licensee this right to use the licensed dataset "as is". Licensor does not make, and expressly disclaims, any express or implied warranties, representations or endorsements of any kind whatsoever. 8. This Agreement shall be governed by and construed in accordance with the laws of England and the English courts shall have exclusive jurisdiction. ``` LOCNESS License: ``` LOCNESS Dataset Licence Agreement 1. The corpus is to be used for non-commercial purposes only 2. All publications on research partly or wholly based on the corpus should give credit to the Centre for English Corpus Linguistics (CECL), Université catholique de Louvain, Belgium. A scanned copy or offprint of the publication should also be sent to <sylviane.granger@uclouvain.be>. 3. No part of the corpus is to be distributed to a third party without specific authorization from CECL. The corpus can only be used by the person agreeing to the licence terms and researchers working in close collaboration with him/her or students under his/her supervision, attached to the same institution, within the framework of the research project. ``` ### Citation Information ``` @inproceedings{bryant-etal-2019-bea, title = "The {BEA}-2019 Shared Task on Grammatical Error Correction", author = "Bryant, Christopher and Felice, Mariano and Andersen, {\O}istein E. and Briscoe, Ted", booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4406", doi = "10.18653/v1/W19-4406", pages = "52--75", abstract = "This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write{\&}Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F{\_}0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.", } ``` ### Contributions Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.
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techiaith/legislation-gov-uk_en-cy
techiaith
2023-08-12T13:24:57Z
560
1
null
[ "task_categories:translation", "task_categories:text-classification", "task_categories:summarization", "task_categories:sentence-similarity", "size_categories:100M<n<1B", "language:en", "language:cy", "license:other", "region:us" ]
2023-08-12T13:24:57Z
2023-07-21T09:25:08.000Z
2023-07-21T09:25:08
--- license: other task_categories: - translation - text-classification - summarization - sentence-similarity language: - en - cy pretty_name: UK Government Legislation size_categories: - 100M<n<1B --- # Dataset Card for legislation-gov-uk-en-cy ## Dataset Description - **Homepage:** https://github.com/techiaith/legislation-gov-uk_dataset - **Repository:** https://github.com/techiaith/legislation-gov-uk_dataset - **Point of Contact:** techiaith@bangor.ac.uk ### Dataset Summary This dataset consists of English-Welsh sentence pairs obtained via scraping the www.legislation.gov.uk website. The total dataset is approximately 170 Mb in size. ### Supported Tasks and Leaderboards - translation - text-classification - summarization - sentence-similarity ### Languages - English - Welsh ## Dataset Structure ### Data Fields - source - target ### Data Splits - train ## Dataset Creation English-Welsh sentence pairs were obtained by scraping the www.legislation.gov.uk website and then cleaning the data using an internal processing pipeline. ### Source Data #### Initial Data Collection and Normalization Sentences were dropped from the original scrapped sources in the following cases: - sentence contained too many misspelt words - sentence length similarity variance too great. #### Who are the source language producers? The language data, including source and target language data, is derived from UK legislation. See [here](https://www.legislation.gov.uk/aboutus) for information. ### Licensing Information This dataset's source data is Crown copyright and is licensed under the [Open Government License](https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/).
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code_x_glue_cc_code_to_code_trans
null
2023-07-27T14:11:43Z
559
3
null
[ "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" ]
2023-07-27T14:11:43Z
2022-03-02T23:29:22.000Z
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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mteb/summeval
mteb
2022-09-27T19:14:10Z
559
2
null
[ "language:en", "region:us" ]
2022-09-27T19:14:10Z
2022-06-21T13:37:10.000Z
2022-06-21T13:37:10
--- language: - en --- # SummEval The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total). Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total). Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance. Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries. For this dataset, we averaged the 3 **expert** annotations to get the human scores. source: https://github.com/Yale-LILY/SummEval
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code_x_glue_cc_defect_detection
null
2022-11-18T19:31:11Z
558
6
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:other-programming-languages", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "license:c-uda", "region:us" ]
2022-11-18T19:31:11Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - code license: - c-uda multilinguality: - other-programming-languages size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: CodeXGlueCcDefectDetection dataset_info: features: - name: id dtype: int32 - name: func dtype: string - name: target dtype: bool - name: project dtype: string - name: commit_id dtype: string splits: - name: train num_bytes: 45723487 num_examples: 21854 - name: validation num_bytes: 5582545 num_examples: 2732 - name: test num_bytes: 5646752 num_examples: 2732 download_size: 61685715 dataset_size: 56952784 --- # Dataset Card for "code_x_glue_cc_defect_detection" ## 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/Defect-detection ### Dataset Summary CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code. The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test. ### Supported Tasks and Leaderboards - `multi-class-classification`: The dataset can be used to train a model for detecting if code has a defect in it. ### Languages - C **programming** language ## Dataset Structure ### Data Instances An example of 'validation' looks as follows. ``` { "commit_id": "aa1530dec499f7525d2ccaa0e3a876dc8089ed1e", "func": "static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n MirrorState *s = FILTER_MIRROR(nf);\n Chardev *chr;\n chr = qemu_chr_find(s->outdev);\n if (chr == NULL) {\n error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n \"Device '%s' not found\", s->outdev);\n qemu_chr_fe_init(&s->chr_out, chr, errp);", "id": 8, "project": "qemu", "target": true } ``` ### 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 | |func |string| The source code | |target |bool | 0 or 1 (vulnerability or not) | |project |string| Original project that contains this code | |commit_id |string| Commit identifier in the original project| ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|21854| 2732|2732| ## 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 ``` @inproceedings{zhou2019devign, title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks}, author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang}, booktitle={Advances in Neural Information Processing Systems}, pages={10197--10207}, year={2019} ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v3-math-468e93-2011366582
autoevaluate
2022-11-07T07:45:13Z
558
0
null
[ "autotrain", "evaluation", "region:us" ]
2022-11-07T07:45:13Z
2022-11-07T06:34:19.000Z
2022-11-07T06:34:19
--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_cot_v3 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-30b_eval metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_cot_v3 dataset_config: mathemakitten--winobias_antistereotype_test_cot_v3 dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-30b_eval * Dataset: mathemakitten/winobias_antistereotype_test_cot_v3 * Config: mathemakitten--winobias_antistereotype_test_cot_v3 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
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null
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ai4bharat/IndicSentiment
ai4bharat
2023-05-26T11:07:29Z
558
3
null
[ "region:us" ]
2023-05-26T11:07:29Z
2023-01-14T16:26:02.000Z
2023-01-14T16:26:02
Entry not found
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GATE-engine/COCOStuff10K
GATE-engine
2023-06-23T05:01:36Z
558
0
null
[ "region:us" ]
2023-06-23T05:01:36Z
2023-06-23T04:55:07.000Z
2023-06-23T04:55:07
--- dataset_info: features: - name: image dtype: image - name: mask dtype: image splits: - name: test num_bytes: 490670380.0 num_examples: 1000 - name: train num_bytes: 4380309288.0 num_examples: 9000 download_size: 4871873017 dataset_size: 4870979668.0 --- # Dataset Card for "COCOStuff10K" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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SiberiaSoft/SiberianPersonaChat
SiberiaSoft
2023-08-02T18:16:20Z
558
11
null
[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:conversational", "size_categories:100K<n<1M", "language:ru", "license:mit", "region:us" ]
2023-08-02T18:16:20Z
2023-07-22T03:46:53.000Z
2023-07-22T03:46:53
--- license: mit task_categories: - text-generation - text2text-generation - conversational language: - ru size_categories: - 100K<n<1M --- ### SiberiaSoft/SiberianPersonaChat Датасет инструкций, диалогов, QA Данный датасет был создан для диалоговых агентов с имитацией личности. Большая часть датасета была сгенерирована с помощью chatGPT и различных промптов к ней. Кроме этого, в состав датасета входит измененный [TolokaPersonaChatRus](https://toloka.ai/datasets/?category=nlp) ## Формат описаний личности 1. Ты парень, пилот самолета. Увлекаешься дайвингом. Собираешь марки. Любишь древнюю архитектуру. 2. Ты девушка, художница. Увлекаешься нейросетевым искусством. Умеешь программировать. Любишь рисовать. Также в промпт можно подставлять факты о личности: ФИО, возраст и т.д 1. Я девушка 18 лет. Я учусь в институте. Живу с родителями. У меня есть кот. Ищу парня для семьи. Статья на habr: [ссылка](https://habr.com/ru/articles/751580/) ## Процентное данных: | Задача | Процентное содержание | |:-----------------------:|:---------------------:| | Персонализированные диалоги | 74.602% | | Инструкции с its5Q/yandex-q | 4.585% | | Инструкции с Den4ikAI/russian_instructions_2 | 3.328% | | Инструкции с lksy/ru_instruct_gpt4 (жестко очищенные) | 3.274% | | Инструкции с IlyaGusev/ru_turbo_alpaca_evol_instruct (очень жестко очищенные) | 3.237% | | QA с длинными, развернутыми ответами | 3.236% | | Ручные диалоги | 3.199% | | QA с использованием Wikipedia | 2.628% | | Ответы на вопросы по тексту Den4ikAI/ru_sberquad_long_answers | 1.784% | | Решение проблем | 0.102% | | QA Объясни ребенку | 0.025% | ### Citation ``` @MISC{SiberiaSoft/SiberianPersonaChat, author = {Denis Petrov, Ivan Ramovich}, title = {Russian dataset for Chat models}, url = {https://huggingface.co/datasets/SiberiaSoft/SiberianPersonaChat}, year = 2023 } ```
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ajgt_twitter_ar
null
2023-01-25T14:26:05Z
557
3
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:unknown", "region:us" ]
2023-01-25T14:26:05Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Arabic Jordanian General Tweets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Negative '1': Positive config_name: plain_text splits: - name: train num_bytes: 175424 num_examples: 1800 download_size: 107395 dataset_size: 175424 --- # Dataset Card for Arabic Jordanian General Tweets ## Table of Contents - [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets) - [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) - [|split|num examples|](#splitnum-examples) - [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:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT) - **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66) - **Point of Contact:** [Khaled Alomari](khaled.alomari@adu.ac.ae) ### Dataset Summary Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A binary datset with with negative and positive sentiments. ### Data Fields - `text` (str): Tweet text. - `label` (int): Sentiment. ### Data Splits The dataset is not split. | | train | |----------|------:| | no split | 1,800 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 1,800 tweets collected from twitter. #### Who are the source language producers? From tweeter. ### Annotations The dataset does not contain any additional 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 [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{alomari2017arabic, title={Arabic tweets sentimental analysis using machine learning}, author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled}, booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, pages={602--610}, year={2017}, organization={Springer} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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cointegrated/ru-paraphrase-NMT-Leipzig
cointegrated
2022-10-23T12:23:15Z
557
4
null
[ "task_categories:text-generation", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:translation", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:ru", "license:cc-by-4.0", "conditional-text-generation", "paraphrase-generation", "paraphrase", "region:us" ]
2022-10-23T12:23:15Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - machine-generated language: - ru license: - cc-by-4.0 multilinguality: - translation size_categories: - 100K<n<1M source_datasets: - extended|other task_categories: - text-generation pretty_name: ru-paraphrase-NMT-Leipzig tags: - conditional-text-generation - paraphrase-generation - paraphrase --- # Dataset Card for **cointegrated/ru-paraphrase-NMT-Leipzig** ## 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 - **Paper:** https://habr.com/ru/post/564916/ - **Point of Contact:** [@cointegrated](https://huggingface.co/cointegrated) ### Dataset Summary The dataset contains 1 million Russian sentences and their automatically generated paraphrases. It was created by David Dale ([@cointegrated](https://huggingface.co/cointegrated)) by translating the `rus-ru_web-public_2019_1M` corpus from [the Leipzig collection](https://wortschatz.uni-leipzig.de/en/download) into English and back into Russian. A fraction of the resulting paraphrases are invalid, and should be filtered out. The blogpost ["Перефразирование русских текстов: корпуса, модели, метрики"](https://habr.com/ru/post/564916/) provides a detailed description of the dataset and its properties. The dataset can be loaded with the following code: ```Python import datasets data = datasets.load_dataset( 'cointegrated/ru-paraphrase-NMT-Leipzig', data_files={"train": "train.csv","val": "val.csv","test": "test.csv"}, ) ``` Its output should look like ``` DatasetDict({ train: Dataset({ features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'], num_rows: 980000 }) val: Dataset({ features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'], num_rows: 10000 }) test: Dataset({ features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'], num_rows: 10000 }) }) ``` ### Supported Tasks and Leaderboards The dataset can be used to train and validate models for paraphrase generation or (if negative sampling is used) for paraphrase detection. ### Languages Russian (main), English (auxilliary). ## Dataset Structure ### Data Instances Data instances look like ``` { "labse_sim": 0.93502015, "chrf_sim": 0.4946451012684782, "idx": 646422, "ru": "О перспективах развития новых медиа-технологий в РФ расскажут на медиафоруме Енисея.", "original": "Перспективы развития новых медиатехнологий в Российской Федерации обсудят участники медиафорума «Енисей.", "en": "Prospects for the development of new media technologies in the Russian Federation will be discussed at the Yenisey Media Forum." } ``` Where `original` is the original sentence, and `ru` is its machine-generated paraphrase. ### Data Fields - `idx`: id of the instance in the original corpus - `original`: the original sentence - `en`: automatic translation of `original` to English - `ru`: automatic translation of `en` back to Russian, i.e. a paraphrase of `original` - `chrf_sim`: [ChrF++](https://huggingface.co/metrics/chrf) similarity of `original` and `ru` - `labse_sim`: cosine similarity of [LaBSE](https://huggingface.co/cointegrated/LaBSE-en-ru) embedings of `original` and `ru` - `forward_entailment`: predicted probability that `original` entails `ru` - `backward_entailment`: predicted probability that `ru` entails `original` - `p_good`: predicted probability that `ru` and `original` have equivalent meaning ### Data Splits Train – 980K, validation – 10K, test – 10K. The splits were generated randomly. ## Dataset Creation ### Curation Rationale There are other Russian paraphrase corpora, but they have major drawbacks: - The best known [corpus from paraphraser.ru 2016 contest](http://paraphraser.ru/download/) is rather small and covers only the News domain. - [Opusparcus](https://huggingface.co/datasets/GEM/opusparcus), [ParaPhraserPlus](http://paraphraser.ru/download/), and [corpora of Tamara Zhordanija](https://github.com/tamriq/paraphrase) are noisy, i.e. a large proportion of sentence pairs in them have substantial difference in meaning. - The Russian part of [TaPaCo](https://huggingface.co/datasets/tapaco) has very high lexical overlap in the sentence pairs; in other words, their paraphrases are not diverse enough. The current corpus is generated with a dual objective: the parphrases should be semantically as close as possible to the original sentences, while being lexically different from them. Back-translation with restricted vocabulary seems to achieve this goal often enough. ### Source Data #### Initial Data Collection and Normalization The `rus-ru_web-public_2019_1M` corpus from [the Leipzig collection](https://wortschatz.uni-leipzig.de/en/download) as is. The process of its creation is described [in this paper](http://www.lrec-conf.org/proceedings/lrec2012/pdf/327_Paper.pdf): D. Goldhahn, T. Eckart & U. Quasthoff: Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages. In: *Proceedings of the 8th International Language Resources and Evaluation (LREC'12), 2012*. #### Automatic paraphrasing The paraphrasing was carried out by translating the original sentence to English and then back to Russian. The models [facebook/wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) and [facebook/wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) were used for translation. To ensure that the back-translated texts are not identical to the original texts, the final decoder was prohibited to use the token n-grams from the original texts. The code below implements the paraphrasing function. ```python import torch from transformers import FSMTModel, FSMTTokenizer, FSMTForConditionalGeneration tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru") model = FSMTForConditionalGeneration.from_pretrained("facebook/wmt19-en-ru") inverse_tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en") inverse_model = FSMTForConditionalGeneration.from_pretrained("facebook/wmt19-ru-en") model.cuda(); inverse_model.cuda(); def paraphrase(text, gram=4, num_beams=5, **kwargs): """ Generate a paraphrase using back translation. Parameter `gram` denotes size of token n-grams of the original sentence that cannot appear in the paraphrase. """ input_ids = inverse_tokenizer.encode(text, return_tensors="pt") with torch.no_grad(): outputs = inverse_model.generate(input_ids.to(inverse_model.device), num_beams=num_beams, **kwargs) other_lang = inverse_tokenizer.decode(outputs[0], skip_special_tokens=True) # print(other_lang) input_ids = input_ids[0, :-1].tolist() bad_word_ids = [input_ids[i:(i+gram)] for i in range(len(input_ids)-gram)] input_ids = tokenizer.encode(other_lang, return_tensors="pt") with torch.no_grad(): outputs = model.generate(input_ids.to(model.device), num_beams=num_beams, bad_words_ids=bad_word_ids, **kwargs) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) return decoded ``` The corpus was created by running the above `paraphrase` function on the original sentences with parameters `gram=3, num_beams=5, repetition_penalty=3.14, no_repeat_ngram_size=6`. ### Annotations #### Annotation process The dataset was annotated by several automatic metrics: - [ChrF++](https://huggingface.co/metrics/chrf) between `original` and `ru` sentences; - cosine similarity between [LaBSE](https://huggingface.co/cointegrated/LaBSE-en-ru) embeddings of these sentences; - forward and backward entailment probabilites predictd by the [rubert-base-cased-nli-twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway) model; - `p_good`, a metric aggregating the four metrics above into a single number. It is obtained with a logistic regression trained on 100 randomly chosen from the train set and manually labelled sentence pairs. #### Who are the annotators? Human annotation was involved only for a small subset used to train the model for `p_good`. It was conduced by the dataset author, @cointegrated. ### Personal and Sensitive Information The dataset is not known to contain any personal or sensitive information. The sources and processes of original data collection are described at https://wortschatz.uni-leipzig.de/en/download. ## Considerations for Using the Data ### Social Impact of Dataset The dataset may enable creation for paraphrasing systems that can be used both for "good" purposes (such as assisting writers or augmenting text datasets), and for "bad" purposes (such as disguising plagiarism). The authors are not responsible for any uses of the dataset. ### Discussion of Biases The dataset may inherit some of the biases of [the underlying Leipzig web corpus](https://wortschatz.uni-leipzig.de/en/download) or the neural machine translation models ([1](https://huggingface.co/facebook/wmt19-ru-en), [2](https://huggingface.co/facebook/wmt19-en-ru)) with which it was generated. ### Other Known Limitations Most of the paraphrases in the dataset are valid (by a rough estimante, at least 80%). However, in some sentence pairs there are faults: - Named entities are often spelled in different ways (e.g. `"Джейкоб" -> "Яков") or even replaced with other entities (e.g. `"Оймякон" -> "Оймянск" or `"Верхоянск" -> "Тольятти"`). - Sometimes the meaning of words or phrases changes signigicantly, e.g. `"полустанок" -> "полумашина"`, or `"были по колено в грязи" -> "лежали на коленях в иле"`. - Sometimes the syntax is changed in a meaning-altering way, e.g. `"Интеллектуальное преимущество Вавилова и его соратников над демагогами из рядов сторонников новой агробиологии разительно очевидно." -> "Интеллектуал Вавилов и его приспешники в новой аграрной биологии явно превзошли демогогов."`. - Grammatical properties that are present in Russian morphology but absent in English, such as gender, are often lost, e.g. `"Я не хотела тебя пугать" -> "Я не хотел пугать вас"`. The field `labse_sim` reflects semantic similarity between the sentences, and it can be used to filter out at least some poor paraphrases. ## Additional Information ### Dataset Curators The dataset was created by [David Dale](https://daviddale.ru/en), a.k.a. [@cointegrated](https://huggingface.co/cointegrated). ### Licensing Information This corpus, as well as the original Leipzig corpora, are licensed under [CC BY](http://creativecommons.org/licenses/by/4.0/). ### Citation Information [This blog post](https://habr.com/ru/post/564916/) can be cited: ``` @misc{dale_paraphrasing_2021, author = "Dale, David", title = "Перефразирование русских текстов: корпуса, модели, метрики", editor = "habr.com", url = "https://habr.com/ru/post/564916/", month = {June}, year = {2021}, note = {[Online; posted 28-June-2021]}, } ``` ### Contributions Thanks to [@avidale](https://github.com/avidale) for adding this dataset.
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Tamazight-NLP/FLORES-200-Kabyle
Tamazight-NLP
2023-08-08T16:44:20Z
557
1
null
[ "task_categories:translation", "task_categories:text2text-generation", "annotations_creators:expert-generated", "size_categories:1K<n<10K", "language:en", "language:kab", "language:ber", "license:cc-by-sa-4.0", "region:us" ]
2023-08-08T16:44:20Z
2023-08-04T21:20:29.000Z
2023-08-04T21:20:29
--- license: cc-by-sa-4.0 task_categories: - translation - text2text-generation language: - en - kab - ber annotations_creators: - expert-generated pretty_name: FLORES 200 (Kabyle) size_categories: - 1K<n<10K ---
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nannullna/laion_subset
nannullna
2023-09-25T05:33:23Z
557
0
null
[ "region:us" ]
2023-09-25T05:33:23Z
2023-09-25T05:31:32.000Z
2023-09-25T05:31:32
--- configs: - config_name: default data_files: - split: artwork path: data/artwork-* - split: person path: data/person-* - split: object path: data/object-* dataset_info: features: - name: image dtype: image - name: text dtype: string - name: url dtype: string - name: punsafe dtype: float64 - name: pwatermark dtype: float64 splits: - name: artwork num_bytes: 235558764.0 num_examples: 452 - name: person num_bytes: 254743194.0 num_examples: 501 - name: object num_bytes: 57867679.0 num_examples: 114 download_size: 548177028 dataset_size: 548169637.0 --- # Dataset Card for "laion_subset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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Falah/Alzheimer_MRI
Falah
2023-07-04T10:03:44Z
555
1
null
[ "task_categories:image-classification", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "medical", "region:us" ]
2023-07-04T10:03:44Z
2023-07-04T09:24:50.000Z
2023-07-04T09:24:50
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Mild_Demented '1': Moderate_Demented '2': Non_Demented '3': Very_Mild_Demented splits: - name: train num_bytes: 22560791.2 num_examples: 5120 - name: test num_bytes: 5637447.08 num_examples: 1280 download_size: 28289848 dataset_size: 28198238.28 license: apache-2.0 task_categories: - image-classification language: - en tags: - medical pretty_name: Alzheimer_MRI Disease Classification Dataset size_categories: - 1K<n<10K --- # Alzheimer_MRI Disease Classification Dataset The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories: - '0': Mild_Demented - '1': Moderate_Demented - '2': Non_Demented - '3': Very_Mild_Demented ## Dataset Information - Train split: - Name: train - Number of bytes: 22,560,791.2 - Number of examples: 5,120 - Test split: - Name: test - Number of bytes: 5,637,447.08 - Number of examples: 1,280 - Download size: 28,289,848 bytes - Dataset size: 28,198,238.28 bytes ## Citation If you use this dataset in your research or health medicine applications, we kindly request that you cite the following publication: ``` @dataset{alzheimer_mri_dataset, author = {Falah.G.Salieh}, title = {Alzheimer MRI Dataset}, year = {2023}, publisher = {Hugging Face}, version = {1.0}, url = {https://huggingface.co/datasets/Falah/Alzheimer_MRI} } ``` ## Usage Example Here's an example of how to load the dataset using the Hugging Face library: ```python from datasets import load_dataset # Load the Falah/Alzheimer_MRI dataset dataset = load_dataset('Falah/Alzheimer_MRI', split='train') # Print the number of examples and the first few samples print("Number of examples:", len(dataset)) print("Sample data:") for example in dataset[:5]: print(example) ```
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codeparrot/self-instruct-starcoder
codeparrot
2023-10-23T12:13:18Z
553
37
null
[ "task_categories:text2text-generation", "size_categories:1K<n<10K", "language:en", "license:bigscience-openrail-m", "code", "arxiv:2212.10560", "arxiv:2305.06161", "arxiv:1908.10084", "doi:10.57967/hf/0790", "region:us" ]
2023-10-23T12:13:18Z
2023-05-22T14:50:58.000Z
2023-05-22T14:50:58
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: most_similar dtype: string - name: avg_similarity_score dtype: float64 splits: - name: curated num_bytes: 1937514 num_examples: 771 - name: raw num_bytes: 12969008 num_examples: 5003 - name: unique num_bytes: 786771 num_examples: 308 - name: compile num_bytes: 9048805 num_examples: 3549 download_size: 10935008 dataset_size: 24742098 tags: - code size_categories: - 1K<n<10K task_categories: - text2text-generation license: bigscience-openrail-m language: - en --- # Self-instruct-starcoder ## Table of Contents - [Summary](#summary) - [Our approach](#our-approach) - [Dataset generation](#dataset-generation) - [Dataset quality](#dataset-quality) - [Post-processing](#post-processing) - [Self-consistency](#self-consistency) - [Uniqueness](#uniqueness) - [Compile](#compile) - [Dataset structure](#dataset-structure) - [Space](#space) ## Summary Self-instruct-starcoder is a dataset that was generated by prompting starcoder to generate new instructions based on some human-written seed instructions. The underlying process is explained in the paper [self-instruct](https://arxiv.org/abs/2212.10560). This algorithm gave birth to famous machine generated datasets such as [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Code Alpaca](https://github.com/sahil280114/codealpaca) which are two datasets obtained by prompting OpenAI `text-davinci-003` engine. ## Our approach While our method is similar to self-instruct and stanford alpaca, we included some relevant modifications to the pipeline to account for what we wanted. - Rather than using `text-davinci-003`, we chose to prompt [StarCoder](https://arxiv.org/abs/2305.06161) which is a 10x smaller LLM developed for code use cases. However, it is possible to use any decoder based LLM on the hub. - We changed our seed tasks in order to have the model generate code related tasks. We completed the seed tasks from code alpaca with 20 additional algorithm instructions. - We switched from the generation format `"instruction":` - `"input":` - `"output":` to the format `"instruction":` - `"output":` by concatenating each instruction and its input under the keyword `instruction`. We did so because the previous prompting format tended to make the model generate test cases as input and their solution as output, which is not what we wanted. - Finally, we incorporated the possibility to change the trigger word in the prompt. We thus replaced the `"instruction" :` keyword by `"Here is the correct solution to the problem ":` which resulted into much better generated instructions. ## Dataset generation The generation of the dataset was time consuming and we chose our parameters to limit the computational burden of our method. - Number of examples in context : 4 - 2 seed instructions - 2 machine generated instructions - Number of instructions to generate : 5000 - Stop words used in the generation : ["\n20", "20.", "20 ."] - Similarity threshold for rouge score : 0.7 ## Dataset quality StarCoder, while being a great model is not as capable as `text-davinci-003`. In the generation, the model quickly reach sort of a ceiling in terms of creativity. There are many instructions that are similar to each other, but it should not bother since they are not phrased the same. ## Post-processing Post-processing is an important part of the pipeline since it improves the quality of the dataset despite the fact that it implies getting rid of some examples. First we need to identify what we want to avoid : - A generated solution which does not answer to the corresponding instruction - An instruction that is too similar to another one. ### Self-consistency We imagined a process that we named **self-consistency**. The idea is to reverse-prompt the model to see if it can generate a sound instruction that corresponds to the solution (output) it is prompted with. This is a particularly difficult few-shot task, and unfortunately StarCoder does not perform incredibly well on it. With a few-shot parameters of `4` (all being seed tasks), the model is able to recover 1135 instructions out of 5003, which amount for 22.6% of the raw dataset. Fortunately, the inability for starcoder to generate instructions for some solutions does not mean we should get rid of them. For the solutions (outputs) with generated instructions, we can compare these with the ground truth. For that we can use [Sentence-BERT](https://arxiv.org/abs/1908.10084) because the comparison should focus the meaning rather than the word to word similarity ratio. We have about 771 instructions (~68%) with a similarity score >= 0.5 with their ground truth. These can be seen as high quality examples, they form the `curated` set. <p align="center"> <img src="https://huggingface.co/datasets/codeparrot/self-instruct-starcoder/resolve/main/output.png" alt="drawing" width="300", height="300"/> </p> ### Uniqueness Another approach that can be used to clean the raw dataset is to focus on distinct instructions. For a given instruction, we go through all the instructions generated before it to see if there is one with a similarity score >= 0.5. If it is the case, we remove that instruction. This process removes about 94% of the raw dataset, the remaining instructions form the `unique` set. ### Compile We also decided to build a set which contains solely the example featuring a code written in python 3 which does not code a compilation error. ## Dataset structure ```python from datasets import load_dataset dataset = load_dataset("codeparrot/self-instruct-starcoder") DatasetDict({ compile: Dataset({ features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'], num_rows: 3549 }) curated: Dataset({ features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'], num_rows: 771 }) raw: Dataset({ features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'], num_rows: 5003 }) unique: Dataset({ features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'], num_rows: 308 }) })) ``` |Field|Type|Description| |---|---|---| |instruction|string|Instruction| |output|string|Answer to the instruction| |most_similar|string|Dictionnary containing the 10 most similar instructions generated before the current instruction along with the similarity scores| |avg_similarity_score|float64| Average similarity score| ## Additional resources - [Space(self-instruct-starcoder)](https://huggingface.co/spaces/codeparrot/self-instruct-starcoder) - [Github Repository](https://github.com/ArmelRandy/Self-instruct) ## Citation ``` @misc{title={Self-Instruct-StarCoder}, author={Zebaze, Armel Randy}, doi={https://doi.org/10.57967/hf/0790}, } ```
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LabHC/bias_in_bios
LabHC
2023-09-10T15:41:38Z
553
0
null
[ "task_categories:text-classification", "language:en", "license:mit", "region:us" ]
2023-09-10T15:41:38Z
2023-09-05T11:22:24.000Z
2023-09-05T11:22:24
--- license: mit task_categories: - text-classification language: - en dataset_info: features: - name: hard_text dtype: string - name: profession dtype: int64 - name: gender dtype: int64 splits: - name: train num_bytes: 107487885 num_examples: 257478 - name: test num_bytes: 41312256 num_examples: 99069 - name: dev num_bytes: 16504417 num_examples: 39642 download_size: 99808338 dataset_size: 165304558 --- # Bias in Bios Bias in Bios was created by (De-Artega et al., 2019) and published under the MIT license (https://github.com/microsoft/biosbias). The dataset is used to investigate bias in NLP models. It consists of textual biographies used to predict professional occupations, the sensitive attribute is the gender (binary). The version shared here is the version proposed by (Ravgofel et al., 2020) which slightly smaller due to the unavailability of 5,557 biographies. The dataset is divided between train (257,000 samples), test (99,000 samples) and dev (40,000 samples) sets. To load each all splits ('train', 'dev', 'test'), use the following code : ```python train_dataset = load_dataset("LabHC/bias_in_bios", split='train') test_dataset = load_dataset("LabHC/bias_in_bios", split='test') dev_dataset = load_dataset("LabHC/bias_in_bios", split='dev') ``` Below are presented the classifiaction and sensitive attribtues labels and their proportion. Distributions are similar through the three sets. #### Classification labels | Profession | Numerical label | Proportion (%)| | Profession | Numerical label | Proportion (%)| |---|---|---|---|---|---|---| accountant | 0 | 1.42 | | nurse | 13 | 4.78 architect | 1 | 2.55 | | painter | 14 | 1.95 attorney | 2 | 8.22 | | paralegal | 15 | 0.45 chiropractor | 3 | 0.67 | | pastor | 16 | 0.64 comedian | 4 | 0.71 | | personal_trainer | 17 | 0.36 composer | 5 | 1.41 | | photographer | 18 | 6.13 dentist | 6 | 3.68 | | physician | 19 | 10.35 dietitian | 7 | 1.0 | | poet | 20 | 1.77 dj | 8 | 0.38 | | professor | 21 | 29.8 filmmaker | 9 | 1.77 | | psychologist | 22 | 4.64 interior_designer | 10 | 0.37 | | rapper | 23 | 0.35 journalist | 11 | 5.03 | | software_engineer | 24 | 1.74 model | 12 | 1.89 | | surgeon | 25 | 3.43 nurse | 13 | 4.78 | | teacher | 26 | 4.09 painter | 14 | 1.95 | | yoga_teacher | 27 | 0.42 #### Sensitive attributes | Gender | Numerical label | Proportion (%)| |---|---|---| Male | 0 | 53.9 | Female | 1 | 46.1 --- (De-Artega et al., 2019) Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019. Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Association for Computing Machinery, New York, NY, USA, 120–128. https://doi.org/10.1145/3287560.3287572 (Ravgofel et al., 2020) Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7237–7256, Online. Association for Computational Linguistics.
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GEM/e2e_nlg
GEM
2022-10-24T15:30:18Z
552
1
null
[ "task_categories:table-to-text", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "data-to-text", "region:us" ]
2022-10-24T15:30:18Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: e2e_nlg tags: - data-to-text --- # Dataset Card for GEM/e2e_nlg ## Dataset Description - **Homepage:** http://www.macs.hw.ac.uk/InteractionLab/E2E/ - **Repository:** https://github.com/tuetschek/e2e-cleaning - **Paper:** https://www.aclweb.org/anthology/W17-5525/, [Detailed E2E Challenge writeup - **Leaderboard:** N/A - **Point of Contact:** Ondrej Dusek ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/e2e_nlg). ### Dataset Summary The E2E NLG dataset is an English benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/e2e_nlg') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/e2e_nlg). #### website [Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/) #### paper [First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/) #### authors Jekaterina Novikova, Ondrej Dusek and Verena Rieser ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](http://www.macs.hw.ac.uk/InteractionLab/E2E/) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/tuetschek/e2e-cleaning) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [First data release](https://www.aclweb.org/anthology/W17-5525/), [Detailed E2E Challenge writeup](https://doi.org/10.1016/j.csl.2019.06.009), [Cleaned E2E version](https://www.aclweb.org/anthology/W19-8652/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{e2e_cleaned, address = {Tokyo, Japan}, title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}}, url = {https://www.aclweb.org/anthology/W19-8652/}, booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)}, author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena}, year = {2019}, pages = {421--426}, } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Ondrej Dusek #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> odusek@ufal.mff.cuni.cz #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> Dialect-specific data was not collected and the language is general British English. #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> The original dataset was collected using the CrowdFlower (now Appen) platform using native English speakers (self-reported). No demographic information was provided, but the collection was geographically limited to English-speaking countries. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The dataset was collected to test neural model on a very well specified realization task. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Heriot-Watt University #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Jekaterina Novikova, Ondrej Dusek and Verena Rieser #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> This research received funding from the EPSRC projects DILiGENt (EP/M005429/1) and MaDrIgAL (EP/N017536/1). #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Simon Mille wrote the initial data card and Yacine Jernite the data loader. Sebastian Gehrmann migrated the data card to the v2 format and moved the data loader to the hub. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> The data is in a CSV format, with the following fields: * `mr` -- the meaning representation (MR, input) * `ref` -- reference, i.e. the corresponding natural-language description (output) There are additional fields (`fixed`, `orig_mr`) indicating whether the data was modified in the cleaning process and what was the original MR before cleaning, but these aren't used for NLG. The MR has a flat structure -- attribute-value pairs are comma separated, with values enclosed in brackets (see example above). There are 8 attributes: * `name` -- restaurant name * `near` -- a landmark close to the restaurant * `area` -- location (riverside, city centre) * `food` -- food type / cuisine (e.g. Japanese, Indian, English etc.) * `eatType` -- restaurant type (restaurant, coffee shop, pub) * `priceRange` -- price range (low, medium, high, <£20, £20-30, >£30) * `rating` -- customer rating (low, medium, high, 1/5, 3/5, 5/5) * `familyFriendly` -- is the restaurant family-friendly (yes/no) The same MR is often repeated multiple times with different synonymous references. #### How were labels chosen? <!-- info: How were the labels chosen? --> <!-- scope: microscope --> The source MRs were generated automatically at random from a set of valid attribute values. The labels were crowdsourced and are natural language #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "input": "name[Alimentum], area[riverside], familyFriendly[yes], near[Burger King]", "target": "Alimentum is a kids friendly place in the riverside area near Burger King." } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> | | MRs | Distinct MRs | References | |-------------|------|--------------|------------| | Training |12,568| 8,362 | 33,525 | | Development | 1,484| 1,132 | 4,299 | | Test | 1,847| 1,358 | 4,693 | | Total |15,899| 10,852 | 42,517 | “Distinct MRs” are MRs that remain distinct even if restaurant/place names (attributes `name`, `near`) are delexicalized, i.e., replaced with a placeholder. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The data are divided so that MRs in different splits do not overlap. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> The E2E dataset is one of the largest limited-domain NLG datasets and is frequently used as a data-to-text generation benchmark. The E2E Challenge included 20 systems of very different architectures, with system outputs available for download. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The dataset is much cleaner than comparable datasets, and it is also a relatively easy task, making for a straightforward evaluation. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> surface realization. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 4 special test sets for E2E were added to the GEM evaluation suite. 1. We created subsets of the training and development sets of ~500 randomly selected inputs each. 2. We applied input scrambling on a subset of 500 randomly selected test instances; the order of the input properties was randomly reassigned. 3. For the input size, we created subpopulations based on the number of restaurant properties in the input. | Input length | Frequency English | |---------------|-------------------| | 2 | 5 | | 3 | 120 | | 4 | 389 | | 5 | 737 | | 6 | 1187 | | 7 | 1406 | | 8 | 774 | | 9 | 73 | | 10 | 2 | #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> Generalization and robustness ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Surface realization. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `BLEU`, `METEOR`, `ROUGE` #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The official evaluation script combines the MT-Eval and COCO Captioning libraries with the following metrics. - BLEU - CIDEr - NIST - METEOR - ROUGE-L #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> Most previous results, including the shared task results, used the library provided by the dataset creators. The shared task also conducted a human evaluation using the following two criteria: - `Quality`: When collecting quality ratings, system outputs were presented to crowd workers together with the corresponding meaning representation, which implies that correctness of the NL utterance relative to the MR should also influence this ranking. The crowd workers were asked: “How do you judge the overall quality of the utterance in terms of its grammatical correctness, fluency, adequacy and other important factors?” - `Naturalness`: When collecting naturalness ratings, system outputs were presented to crowd workers without the corresponding meaning representation. The crowd workers were asked: “Could the utterance have been produced by a native speaker?” #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> The shared task writeup has in-depth evaluations of systems (https://www.sciencedirect.com/science/article/pii/S0885230819300919) ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset was collected to showcase/test neural NLG models. It is larger and contains more lexical richness and syntactic variation than previous closed-domain NLG datasets. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Producing a text informing/recommending a restaurant, given all and only the attributes specified on the input. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Crowdsourced` #### Where was it crowdsourced? <!-- info: If crowdsourced, where from? --> <!-- scope: periscope --> `Other crowdworker platform` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> Human references describing the MRs were collected by crowdsourcing on the CrowdFlower (now Appen) platform, with either textual or pictorial MRs as a baseline. The pictorial MRs were used in 20% of cases -- these yield higher lexical variation but introduce noise. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The dataset is focused on descriptions of restaurants. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> There were basic checks (length, valid characters, repetition). #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> The cleaned version of the dataset which we are using in GEM was algorithmically filtered. They used regular expressions to match all human-generated references with a more accurate input when attributes were hallucinated or dropped. Additionally, train-test overlap stemming from the transformation was removed. As a result, this data is much cleaner than the original dataset but not perfect (about 20% of instances may have misaligned slots, compared to 40% of the original data. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> Since a crowdsourcing platform was used, the involved raters waived their rights to the data and are aware that the produced annotations can be publicly released. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> The dataset is artificial and does not contain any description of people. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> The source data is generated randomly, so it should not contain biases. The human references may be biased by the workers' demographic, but that was not investigated upon data collection. ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The cleaned version still has data points with hallucinated or omitted attributes. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> The data only pertains to the restaurant domain and the included attributes. A model cannot be expected to handle other domains or attributes.
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2023-04-14T13:49:49Z
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[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-genia-v3.02", "language:en", "license:unknown", "region:us" ]
2023-04-14T13:49:49Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-genia-v3.02 task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: BioNLP / JNLPBA Shared Task 2004 dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DNA '2': I-DNA '3': B-RNA '4': I-RNA '5': B-cell_line '6': I-cell_line '7': B-cell_type '8': I-cell_type '9': B-protein '10': I-protein config_name: jnlpba splits: - name: train num_bytes: 8775707 num_examples: 18546 - name: validation num_bytes: 1801565 num_examples: 3856 download_size: 3171072 dataset_size: 10577272 --- # Dataset Card for JNLPBA ## 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://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004 - **Repository:** [Needs More Information] - **Paper:** https://www.aclweb.org/anthology/W04-1213.pdf - **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-ner-on-jnlpba?p=biobert-a-pre-trained-biomedical-language - **Point of Contact:** [Needs More Information] ### Dataset Summary The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. ### Supported Tasks and Leaderboards NER ### Languages English ## Dataset Structure ### Data Instances { 'id': '1', 'tokens': ['IL-2', 'gene', 'expression', 'and', 'NF-kappa', 'B', 'activation', 'through', 'CD28', 'requires', 'reactive', 'oxygen', 'production', 'by', '5-lipoxygenase', '.'], 'ner_tags': [1, 2, 0, 0, 9, 10, 0, 0, 9, 0, 0, 0, 0, 0, 9, 0], } ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no bio-entity mentioned, `1` signals the first token of a bio-entity and `2` the subsequent bio-entity tokens. ### Data Splits Train samples: 37094 Validation samples: 7714 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information @inproceedings{collier-kim-2004-introduction, title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}", author = "Collier, Nigel and Kim, Jin-Dong", booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})", month = aug # " 28th and 29th", year = "2004", address = "Geneva, Switzerland", publisher = "COLING", url = "https://aclanthology.org/W04-1213", pages = "73--78", } ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.
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scikit-learn/adult-census-income
scikit-learn
2022-06-20T14:46:43Z
551
1
null
[ "license:cc0-1.0", "region:us" ]
2022-06-20T14:46:43Z
2022-06-20T14:33:51.000Z
2022-06-20T14:33:51
--- license: cc0-1.0 --- ## Adult Census Income Dataset The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/adult). This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year. **Description of fnlwgt (final weight)** The weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: - A single cell estimate of the population 16+ for each state. - Controls for Hispanic Origin by age and sex. - Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.
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lucasmccabe/logiqa
lucasmccabe
2023-02-08T01:51:31Z
550
3
logiqa
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "region:us" ]
2023-02-08T01:51:31Z
2023-01-12T04:14:53.000Z
2023-01-12T04:14:53
--- task_categories: - question-answering language: - en pretty_name: LogiQA size_categories: - 1K<n<10K paperswithcode_id: logiqa dataset_info: features: - name: context dtype: string - name: query dtype: string - name: options sequence: dtype: string - name: correct_option dtype: string splits: - name: train num_examples: 7376 - name: validation num_examples: 651 - name: test num_examples: 651 --- # Dataset Card for LogiQA ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source. ## Dataset Structure ### Data Instances An example from `train` looks as follows: ``` {'context': 'Continuous exposure to indoor fluorescent lights is beneficial to the health of hamsters with heart disease. One group of hamsters exposed to continuous exposure to fluorescent lights has an average lifespan that is 2.5% longer than another one of the same species but living in a black wall.', 'query': 'Which of the following questions was the initial motivation for conducting the above experiment?', 'options': ['Can hospital light therapy be proved to promote patient recovery?', 'Which one lives longer, the hamster living under the light or the hamster living in the dark?', 'What kind of illness does the hamster have?', 'Do some hamsters need a period of darkness?'], 'correct_option': 0} ``` ### Data Fields - `context`: a `string` feature. - `query`: a `string` feature. - `answers`: a `list` feature containing `string` features. - `correct_option`: a `string` feature. ### Data Splits |train|validation|test| |----:|---------:|---:| | 7376| 651| 651| ## Additional Information ### Dataset Curators The original LogiQA was produced by Jian Liu, Leyang Cui , Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. ### Licensing Information [More Information Needed] ### Citation Information ``` @article{liu2020logiqa, title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning}, author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue}, journal={arXiv preprint arXiv:2007.08124}, year={2020} } ``` ### Contributions [@lucasmccabe](https://github.com/lucasmccabe) added this dataset.
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evidence_infer_treatment
null
2023-03-16T10:35:23Z
549
4
null
[ "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" ]
2023-03-16T10:35:23Z
2022-03-02T23:29:22.000Z
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.
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null
null
null
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GEM/totto
GEM
2022-10-24T15:30:32Z
549
1
null
[ "task_categories:table-to-text", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "data-to-text", "arxiv:1603.07771", "arxiv:2007.02871", "arxiv:2005.10433", "region:us" ]
2022-10-24T15:30:32Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - none language_creators: - unknown language: - en license: - cc-by-sa-3.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: totto tags: - data-to-text --- # Dataset Card for GEM/totto ## Dataset Description - **Homepage:** n/a - **Repository:** https://github.com/google-research-datasets/totto + [ToTTo Supplementary Repo - **Paper:** https://aclanthology.org/2020.emnlp-main.89 - **Leaderboard:** https://github.com/google-research-datasets/totto - **Point of Contact:** Ankur Parikh ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/totto). ### Dataset Summary ToTTo is a high-quality English table-to-text dataset with more than 100,000 examples in which a table from Wikipedia with highlighted cells is paired with a sentence that describes the highlighted cells. All examples in the dataset were post-edited in multiple steps to ensure that the targets are fully faithful to the input information. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/totto') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/totto). #### website n/a #### paper [ACL Anthology](https://aclanthology.org/2020.emnlp-main.89) #### authors Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das ## Dataset Overview ### Where to find the Data and its Documentation #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [ToTTo Main Repo](https://github.com/google-research-datasets/totto) + [ToTTo Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/2020.emnlp-main.89) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{parikh-etal-2020-totto, title = "{ToTTo}: A Controlled Table-To-Text Generation Dataset", author = "Parikh, Ankur and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.89", doi = "10.18653/v1/2020.emnlp-main.89", pages = "1173--1186", abstract = "We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Ankur Parikh #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> totto@google.com #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Github](https://github.com/google-research-datasets/totto) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> This dataset has an associated, active [leaderboard](https://github.com/google-research-datasets/totto#leaderboard) maintained by the authors. The test set ground truth targets / references are private, i.e they are not publicly shared or downloadable - hence, leaderboard submission is necessary for test set evaluation. To evaluate your model on the dev or test set AND/OR submit to the leaderboard, you need to submit your model files through this [form](https://forms.gle/AcF9TRqWrPhPzztt7) (The form provides an option to opt-out of going on the leaderboard). The leaderboard reports three sets of BLEU, PARENT and BLEURT scores for each submission - on the overall test set, the *Overlap* subset of the test set and the *non-Overlap* subset of the test set. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> No specific dialects. The original language is from Wikipedia and it was post-edited by crowdraters #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> The language is post-edited English only (BCP-47: `en`) Wikipedia text. No demographic information about annotators is provided. Some amounts of what may be called non-English text, including characters such as French accents or Cyrillic characters, could sometimes occur, especially through fields with entity names as values in the input table cells. #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> ToTTo is a Table-to-Text NLG task, as the paper title says. The task is as follows: Given a Wikipedia table with row names, column names and table cells, with a subset of cells highlighted, generate a natural language description for the highlighted part of the table . The table need not be exactly rectangular in that - cells can sometimes be multi-row or multi-column. An earlier example of a Table-to-Text NLG task is [Wikibio](https://arxiv.org/abs/1603.07771) - here the inputs were Wikipedia infoboxes (from the top right corner of entity-related Wiki pages). In contrast, ToTTo mostly has Wikipedia tables from the main article content itself. In general, Table-To-Text NLG tasks can be seen as a subclass of Data-To-Text NLG tasks - where the task is to generate natural language descriptions of inputs which are in the form of structured or semi-structured data. In general, all Data-To-Text NLG tasks need not have an explicit table or other structure - e.g the input in [WebNLG](https://www.aclweb.org/anthology/W16-6626.pdf) is simply a list of triples. Importantly, ToTTo differs from earlier examples of Table-To-Text NLG in that: 1. It does not suffer from the problem of divergent references - where ground truth descriptions themselves have additional information not found in the table. ToTTo overcomes this by having a multi-step annotation process to edit the initial, free-form table descriptions (which are from Wikipedia) to make them faithful, unambiguous and independent of article context. 2. Since it provides **control** in the form of highlighted table cells, it prevents the problem of there being a large number of valid descriptions focussing on different parts of the table. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The speaker is required to produce a single, coherent English sentence that describes the highlighted cells in the given table, also using metadata and any other information from the table as applicable. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `industry` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Google Research #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> Google Research #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Varun Gangal created the initial data card and Yacine Jernite wrote the data loader. The data card was updated with new splits by Simon Mille. Sebastian Gehrmann ported the data card and loader from the v1 to the v2 version and extended it with the new fields. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - The `table` field is a `List[List[Dict]]` in row-major order, with outer lists representing rows and the inner lists columns. - Each `Dict` has the fields `column_span: int`, `is_header: bool`, `row_span: int`, and `value: str`. - Table metadata consists of `table_page_title`, `table_section_title` and `table_section_texts` - The `highlighted_cells` are represented as `List[[row_index,column_index]]`, with each `[row_index,column_index]` indicating that `table[row_index][column_index]` is highlighted. - `example_id` is the unique id per example. - `sentence_annotations[final_sentence]` which is the table description/generation target #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The structure is aimed to encode highlighted tables in a way that allows rows and columns to span multiple fields in width. The other fields are meta-data about the source and the annotations #### How were labels chosen? <!-- info: How were the labels chosen? --> <!-- scope: microscope --> The initial table-description pairs are tables from Wikipedia articles, extracted through heuristics such as Number Matching (tables and sentences that overlap with a non-date number of atleast 3 non-zero digits) (Refer to Section 4 of the paper for more) 1. Table Readability: Tables which are deemed non-readable (due to foreign language, poor formatting etc - a very small fraction of 0.5%) are removed from the dataset here. 2. Cell Highlighting: The annotator highlights the cells of the table which support the description. 3. Deletion: The annotator removes phrases in the description which are not supported by the highlighted cells 4. Decontextualization: Descriptions may contain pronouns or other forms of anaphora, or other phenomena which depend on the overall article topic - these are fixed by replacement (e.g replacing pronouns with the entity, provided it occurs in the table). The replacements allowed are limited to one, and annotators are also instructed to conserve fluency. 5. Secondary Annotation: A second set of annotators is shown the output of Stage 4, and asked to fix it if required to ensure it is grammatical. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> The main repository's `README.md` already provides a thorough walkthrough of data instances and fields [here](https://github.com/google-research-datasets/totto#dataset-description) Below is the instance for a table from the wiki-page for the musical artist _Weird Al' Yankovic_ , likely listing his on-television appearances. ``` { "table_page_title": "'Weird Al' Yankovic", "table_webpage_url": "https://en.wikipedia.org/wiki/%22Weird_Al%22_Yankovic", "table_section_title": "Television", "table_section_text": "", "table": "[Described below]", "highlighted_cells": [[22, 2], [22, 3], [22, 0], [22, 1], [23, 3], [23, 1], [23, 0]], "example_id": 12345678912345678912, "sentence_annotations": [{"original_sentence": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Mr. Peanutbutter's brother, Captain Peanutbutter, and was hired to voice the lead role in the 2016 Disney XD series Milo Murphy's Law.", "sentence_after_deletion": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired to the lead role in the 2016 series Milo Murphy's Law.", "sentence_after_ambiguity": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter, and was hired for the lead role in the 2016 series Milo Murphy's 'Law.", "final_sentence": "In 2016, Al appeared in 2 episodes of BoJack Horseman as Captain Peanutbutter and was hired for the lead role in the 2016 series Milo Murphy's Law."}], } ``` The `table` field is expanded as below: ``` [ [ { "column_span": 1, "is_header": true, "row_span": 1, "value": "Year"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Title"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Role"}, { "column_span": 1, "is_header": true, "row_span": 1, "value": "Notes"} ], [ { "column_span": 1, "is_header": false, "row_span": 1, "value": "1997"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Eek! The Cat"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Himself"}, { "column_span": 1, "is_header": false, "row_span": 1, "value": "Episode: 'The FugEektive'"} ], ... ] ``` The [Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) also provides browsable samples under its `sample/` folder. It additionally provides HTML visualization scripts with their outputs located under the aforementioned folder. The instructions to access and visualize these samples can also be found [here](https://github.com/google-research/language/tree/master/language/totto#visualizing-sample-data). #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> The dataset consists of 120,000 train examples and equi-sized dev and test sets with 7700 examples. Refer to Table 5 in the paper for a more extensive list of properties about table size, target vocabulary etc and their aggregates. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The dev and test splits are further equally distributed between _Overlap_ and _non-Overlap_ . The examples in the _Overlap_ set are harder on account of the domain shift resulting from them having none of their header (row and column) names in common with those seen during training. Refer to Table 5 in the paper for a more extensive list of properties about table size, target vocabulary etc and their aggregates. #### <!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? --> <!-- scope: microscope --> There are some very large tables in the dataset with thousands of rows. Table 7 shows some of the challenges of the dataset, showing that very few examples require access to the table description itself which makes those examples an outlier. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> ToTTo is one of the two datasets representing Table-to-Text NLG in GEM, the other one being [DART](https://arxiv.org/pdf/2007.02871.pdf). Unlike DART, which combines datasets from multiple sources and furnishes them in a unified setting, ToTTo is from a homogeneous source. As explained in the Task Summary above, it also has an annotation process explicitly crafted to reduce divergent descriptions, which is not true of DART. Furthermore, ToTTo is also an instance of a **controlled** generation task - where in addition to the input (in this case the table) an additional **control** (in this case the highlighted cells) is given as an additional goal for the generation. The DART task formulation does not include controls. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The input is much more complex and the quality much better than that of comparable datasets. The highlighted table cells provide a unique challenge to models. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Reasoning, surface realization ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 9 challenge sets for ToTTo were added to the GEM evaluation suite, 8 created specifically for the task and 1 coming from the original data. 1. We created subsets of the training and development sets of 500 randomly selected inputs each. 2. We applied input scrambling on a subset of 500 randomly selected test instances; the order of the highlighted cells was randomly reassigned. 3. For the input size, we created subpopulations based on the number of input highlighted cells in the whole table. | Input length | Frequency English | |---------------|-------------------| | 1 | 898 | | 2 | 1850 | | 3 | 2221 | | 4 | 1369 | | 5 | 483 | | 6 | 379 | | 7 | 124 | | 8 | 128 | | 9 | 61 | | 10 | 40 | | 11 | 20 | | 12 | 26 | | 13 | 10 | | 14 | 14 | | 15 | 14 | | 16 | 7 | | 17 | 6 | | 18 | 5 | | 19 | 5 | | 20 | 5 | | 21 | 4 | | 22 | 1 | | 23 | 2 | | 24 | 4 | | 25 | 1 | | 26...496 | 1 | 4. We also divided the test set according to the size of the whole table, based on the idea that larger tables represent a bigger space to take into account when generating the highlighted cells; a larger table could be more challenging to generate accurate text than a smaller table. There are 693 different table sizes, ranging from 2 to 15834 cells. | Table size |Frequency English| |-----------------|-----------------| | 2 | 71 | | 3 | 52 | | 4 | 36 | | 5 | 41 | | 6 | 144 | | 7 | 47 | | 8 | 59 | | 9 | 105 | | 10 | 162 | | 11 | 36 | | 12 | 158 | | 13 | 35 | | 14 | 79 | | 15 | 136 | | 16 | 111 | | 17 | 48 | | 18 | 123 | | 19 | 29 | | 20 | 112 | | 21 | 91 | | 22 | 17 | | 23 | 7 | | 24 | 169 | | 25 | 56 | | 26 | 12 | | 27 | 40 | | 28 | 77 | | 29 | 7 | | 30 | 122 | | 31 | 4 | | 32 | 49 | | 33 | 21 | | 34 | 7 | | 35 | 103 | | 36 | 131 | | 37 | 10 | | 38 | 6 | | 39 | 26 | | 40 | 110 | | 41 | 1 | | 42 | 54 | | 43 | 6 | | 44 | 47 | | 45 | 79 | | 46 | 4 | | 47 | 2 | | 48 | 114 | | 49 | 18 | | 50 | 55 | | 51 | 11 | | 52 | 43 | | 54 | 80 | | 55 | 73 | | 56 | 64 | | 57 | 12 | | 58 | 1 | | 60 | 114 | | 61 | 4 | | 63 | 39 | | 64 | 36 | | 65 | 62 | | 66 | 48 | | 67 | 1 | | 68 | 36 | | 69 | 6 | | 70 | 81 | | 72 | 76 | | 73 | 1 | | 74 | 1 | | 75 | 44 | | 76 | 33 | | 77 | 30 | | 78 | 66 | | 79 | 1 | | 80 | 83 | | 81 | 12 | | 82 | 1 | | 84 | 80 | | 85 | 25 | | 86 | 1 | | 87 | 3 | | 88 | 35 | | 90 | 78 | | 91 | 18 | | 92 | 22 | | 93 | 5 | | 94 | 2 | | 95 | 31 | | 96 | 50 | | 98 | 11 | | 99 | 14 | | 100 | 48 | | 102 | 24 | | 104 | 29 | | 105 | 36 | | 106 | 2 | | 108 | 51 | | 110 | 31 | | ...8000+ | (up to 10) | 5. We also created three splits based on the subset of test examples in pages about people. We then used the structured information in WikiData to identify the following information: - gender (male, and female), - nationality grouped by continent (Africa, Asia, Europe, North America, Oceania, and South America) - ethnicity (African American and all USA) The categories within gender, ethnicity, and nationality were chosen based on data availability; The ToTTo dataset includes mostly tables that do not focus on people. As a result, only seven people in the original test set are marked as having a non-binary gender. Similar sparsity informed the grouping of nationalities by continent – only 19 countries are represented by more than 10 people in the test set. In case a person has citizenships across multiple continents, we may include the person in any of the included continents. Finally, ethnicity is very sparsely annotated in WikiData; only 150 test examples in ToTTo have this information and 128 of these are African Americans. We thus are unable to compare the performance on, e.g., Yoruba or Punjabi people, both of which have fewer than five instances. Another caveat here is that only 21 of the 128 people are female. We thus compare the African American population to results on a subset that includes all US citizens. #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> generalization, fairness, robustness ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - The highest spot on the leaderboard is currently held by an anonymous method, with BLEU=49.2, PARENT=58.7 and BLEURT=0.249 on the _Overall_ test set. - The **highest scoring non-anonymous** method is the T5-based method of [Kale, 2020](https://arxiv.org/abs/2005.10433). This method uses a simple row-major linearization scheme to convert the table (it chooses only the highlighted cells and ignores the other cells - table titles and section titles are prefixed at the start of the respective section table) to a flat string. The linearized input - output description pairs from training examples are then used to finetune T5, with BLEU being used as the dev metric to pick checkpoints, and beam search with beam size 10 being the decoding method. Though the best numbers from this method are naturally from the largest T5-pretrained architecture (T5-3B), the paper shows improvements over the next-highest BERT-to-BERT method even when using T5-Base or T5-Small, which have the same and lesser parameters than BERT-to-BERT respectively. - The [Supplementary Repo](https://github.com/google-research/language/tree/master/language/totto) provides several useful modules to get started with for new approach implementation: 1. Code for the particular preprocessing / linearization scheme used to linearize the tables into flat sequences for the baseline approaches described in the paper has been described and shared [herein](https://github.com/google-research/language/tree/master/language/totto#baseline-preprocessing) 2. An [evaluation script](https://github.com/google-research/language/tree/master/language/totto#running-the-evaluation-scripts-locally) for locally scoring BLEU and PARENT system outputs on dev (or train) sets. Since BLEURT is a model-based metric, a [slightly separate](https://github.com/google-research/language/tree/master/language/totto#running-the-evaluation-scripts-locall://github.com/google-research/language/tree/master/language/totto#computing-the-bleurt-score) set of instructions is provided to evaluate on the same. ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Reasoning, surface realization #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `BLEU`, `BLEURT`, `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> Parent: a metric that measures the F-1 score of overlap between input content words and those used in references and those in generated text while ignoring the general surface form. It can thus measure the faithfulness much better than metrics that measure overlap with a reference #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The metrics are used as in the leaderboard. The original paper additionally conducted a human evaluation focusing on fluency, faithfulness, and coverage. Faithfulness was measured as whether facts in the text are not supported by the input, and coverage as the number of highlighted cells that were considered. They thus represent precision and recall of the content. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> See leaderboard. ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> Tables occurring in Wikipedia articles were chosen as the data source with the following reasons in mind: 1. Wide coverage in terms of both vocabulary and concepts. 2. Wikipedia tables are not confined to a regular structure, with multi-row or multi-column cells occurring with a sufficient frequency. 3. Likely to contain reasonable-quality, natural text descriptions in the proximity of the table, which are also extractable by heuristics. (see the start of Section 4 for the heuristics used) To prevent an overlap with the earlier [Wikibio](https://arxiv.org/abs/1603.07771) dataset which focussed on Infobox-first sentence pairs from Wikipedia biography articles, the authors avoid using Infoboxes as a data source. The overall curation process of initially collecting free text and then annotator-revising it, was designed to combine the advantages of free-form text descriptions (which are fluent, high-quality and unhurriedly written, but also divergent and unfaithful) with annotator descriptions (which can be tailored to be faithful and to conform exactly to desired task requirements) #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The speaker is required to produce a single, coherent English sentence that describes the highlighted cells in the given table, also using metadata and any other information from the table as applicable. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> wikipedia.org ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Crowdsourced` #### Where was it crowdsourced? <!-- info: If crowdsourced, where from? --> <!-- scope: periscope --> `Other crowdworker platform` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The basic source language producers are Wikipedia authors and/or editors, since the annotation starts with the natural text description near the Wikipedia table. The auxiliary source language producers are the annotators (two per example) who iteratively revise these descriptions to make them unambiguous and faithful to a subset of highlighted cells in the table. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by crowdworker #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> The initial table-description pairs are tables from Wikipedia articles, extracted through heuristics such as Number Matching (tables and sentences that overlap with a non-date number of atleast 3 non-zero digits) (Refer to Section 4 of the paper for more) 1. Table Readability: Tables which are deemed non-readable (due to foreign language, poor formatting etc - a very small fraction of 0.5%) are removed from the dataset here. 2. Cell Highlighting: The annotator highlights the cells of the table which support the description. 3. Deletion: The annotator removes phrases in the description which are not supported by the highlighted cells 4. Decontextualization: Descriptions may contain pronouns or other forms of anaphora, or other phenomena which depend on the overall article topic - these are fixed by replacement (e.g replacing pronouns with the entity, provided it occurs in the table). The replacements allowed are limited to one, and annotators are also instructed to conserve fluency. 5. Secondary Annotation: A second set of annotators is shown the output of Stage 4, and asked to fix it if required to ensure it is grammatical. The paper does not specifically describe the annotation platform or location profiles of the annotators. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> After construction of the splits, the data curators filtered training examples that had rare table header combinations (<=5 examples) and which had an overlap with the validation or test splits. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> Annotators were full time employees that were aware of the goal of the project and consented to having the data released as part of the dataset. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> Since the source data is from wikipedia, only data in the public domain is included in the dataset. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> yes #### Maintenance Plan Details <!-- info: Describe the original dataset's maintenance plan. --> <!-- scope: microscope --> For submissions, you can delete your data by emailing totto@google.com from the email account used to sign up for the submission. Deletion requests will be responded to within 60 days. #### Maintainer Contact Information <!-- info: Provide contact information of a person responsible for the dataset maintenance --> <!-- scope: periscope --> Ankur Parikh (aparikh@google.com) #### Any Contestation Mechanism? <!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? --> <!-- scope: periscope --> form submission #### Contestation Form Link <!-- info: Provide the form link or contact information --> <!-- scope: periscope --> totto@google.com ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> The original work as well as our GEM paper analyzes some biases #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> This dataset is created using tables and the table cell contents may hence naturally exhibit biases which have been found to exist in Wikipedia such as some forms of gender bias (e.g [(Graells-Garido et al.,2015)](https://labtomarket.files.wordpress.com/2018/01/wiki_gender_bias.pdf) notes that spouse information is more likely discussed for females than males) The table descriptions (targets/references) are, as discussed earlier, collected through a two-step process. 1. The natural text description near the table is taken as a starting point. This is Wikipedia article text as created upto that point in time by a chain of collaborative edits from Wikipedia authors. 2. The initial description is revised by chain of two or more annotated revisions, to make it unambiguous and faithful to a set of highlighted table cells. From their origin in 1), the descriptions may exhibit biases seen in Wikipedia text as mentioned above. From their revisions in 2), the descriptions may show biases originating from annotator-authored text, such as a preference for shorter descriptions since they're faster to write, or linguistic preferences influenced by the locations dominant in the annotator distribution. (However, note that these are likely to be much reduced since the annotators here are merely revising rather than completely authoring. Moreover, each sentence goes through atleast two annotators, which acts as a check against the personal biases of a single annotator.) Naturally-occurring text is also known to suffer from other biases such as reporting bias [(Gordon and Van Durme, 2013)](https://openreview.net/forum?id=AzxEzvpdE3Wcy&noteId=vmR8qaby8fqxittps://labtomarket.files.wordpress.com/2018/01/wiki_gender_bias.pdf) - this also applies to this dataset via its origin from Wikipedia. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> Since the source data is from wikipedia, only data in the public domain is included in the dataset. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The dataset is limited to topics that are present in Wikipedia, more specifically those topics that are present in articles which contain atleast one table _Sports_ and _Countries_ form 53.4% of the dataset. The remaining fraction is made up of broader topics like _Europe_, *North America*and _Politics_
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lbox/lbox_open
lbox
2022-11-09T06:41:26Z
549
4
null
[ "license:cc-by-nc-4.0", "region:us" ]
2022-11-09T06:41:26Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- license: cc-by-nc-4.0 --- # Dataset Card for `lbox_open` ## Dataset Description - **Homepage:** `https://lbox.kr` - **Repository:** `https://github.com/lbox-kr/lbox_open` - **Point of Contact:** [Wonseok Hwang](mailto:wonseok.hwang@lbox.kr) ### Dataset Summary A Legal AI Benchmark Dataset from Korean Legal Cases. ### Languages Korean ### How to use ```python from datasets import load_dataset # casename classficiation task data_cn = load_dataset("lbox/lbox_open", "casename_classification") data_cn_plus = load_dataset("lbox/lbox_open", "casename_classification_plus") # statutes classification task data_st = load_dataset("lbox/lbox_open", "statute_classification") data_st_plus = load_dataset("lbox/lbox_open", "statute_classification_plus") # Legal judgement prediction tasks data_ljp_criminal = load_dataset("lbox/lbox_open", "ljp_criminal") data_ljp_civil = load_dataset("lbox/lbox_open", "ljp_civil") # case summarization task data_summ = load_dataset("lbox/lbox_open", "summarization") data_summ_plus = load_dataset("lbox/lbox_open", "summarization_plus") # precedent corpus data_corpus = load_dataset("lbox/lbox_open", "precedent_corpus") ``` For more information about the dataset, please visit <https://github.com/lbox-kr/lbox_open>. ## Licensing Information Copyright 2022-present [LBox Co. Ltd.](https://lbox.kr/) Licensed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
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nickrosh/Evol-Instruct-Code-80k-v1
nickrosh
2023-07-11T02:05:26Z
549
105
null
[ "license:cc-by-nc-sa-4.0", "arxiv:2306.08568", "region:us" ]
2023-07-11T02:05:26Z
2023-07-08T04:31:37.000Z
2023-07-08T04:31:37
--- license: cc-by-nc-sa-4.0 --- Open Source Implementation of Evol-Instruct-Code as described in the [WizardCoder Paper](https://arxiv.org/pdf/2306.08568.pdf). Code for the intruction generation can be found on Github as [Evol-Teacher](https://github.com/nickrosh/evol-teacher).
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derek-thomas/dataset-creator-reddit-amitheasshole
derek-thomas
2023-11-29T01:00:05Z
549
0
null
[ "region:us" ]
2023-11-29T01:00:05Z
2023-10-27T16:21:23.000Z
2023-10-27T16:21:23
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: poster dtype: string - name: date_utc dtype: timestamp[ns] - name: flair dtype: string - name: title dtype: string - name: permalink dtype: string - name: id dtype: string - name: content_length dtype: int64 - name: score dtype: int64 splits: - name: train num_bytes: 4965064 num_examples: 2370 download_size: 2976423 dataset_size: 4965064 --- # Dataset Card for "dataset-creator-reddit-amitheasshole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- Generated Part of README Below --- ## Dataset Overview The goal is to have an open dataset of [r/amitheasshole](https://www.reddit.com/r/amitheasshole/) submissions. Im leveraging PRAW and the reddit API to get downloads. There is a limit of 1000 in an API call and limited search functionality, so this is run hourly to get new submissions. ## Creation Details This dataset was created by [derek-thomas/dataset-creator-reddit-amitheasshole](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit-amitheasshole) ## Update Frequency The dataset is updated hourly with the most recent update being `2023-11-29 01:00:00 UTC+0000` where we added **200 new rows**. ## Licensing [Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25: > The Content created with or submitted to our Services by Users (“User Content”) is owned by Users and not by Reddit. Subject to your complete and ongoing compliance with the Data API Terms, Reddit grants you a non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content using the Data API solely as necessary to develop, deploy, distribute, and run your App to your App Users. You may not modify the User Content except to format it for such display. You will comply with any requirements or restrictions imposed on usage of User Content by their respective owners, which may include "all rights reserved" notices, Creative Commons licenses, or other terms and conditions that may be agreed upon between you and the owners. Except as expressly permitted by this section, no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or AI model, without the express permission of rightsholders in the applicable User Content My take is that you can't use this data for *training* without getting permission. ## Opt-out To opt-out of this dataset please make a request in the community tab
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asi/wikitext_fr
asi
2022-10-21T16:23:07Z
548
4
null
[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:fr", "license:cc-by-sa-4.0", "arxiv:1609.07843", "region:us" ]
2022-10-21T16:23:07Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - fr language_bcp47: - fr-FR license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Wikitext-fr size_categories: - unknown source_datasets: - original task_categories: - sequence-modeling task_ids: - language-modeling --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [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/AntoineSimoulin/gpt-fr](https://github.com/AntoineSimoulin/gpt-fr) - **Paper:** [https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf](https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf) ### Dataset Summary Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The ([model name](https://huggingface.co/asi/gpt-fr-cased-base) currently achieves 12.9. ### Languages The dataset is in French. ## Dataset Structure ### Data Instances The dataset consists in the agregation of paragraphs from wikipedia articles. ``` { 'paragraph': ..., ... } ``` ### Data Fields - `paragraph`: This is a paragraph from the original wikipedia article. ### Data Splits The dataset is splited into a train/valid/test split. | | Tain (35) | Train (72) | Valid | Test | | ----- | ------ | ----- | ---- | ---- | | Number of Documents | 2 126 | 5 902 | 60 | 60 | | Number of tokens | 351 66 | 72 961 | 896 | 897 | | Vocabulary size | 137 589 | 205 403 | | | | Out of Vocabulary | 0.8% | 1.2% | | | ## Dataset Creation ### Curation Rationale The dataset is created to evaluate French models with similart criteria than English.s ### Source Data Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". We did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s #### Initial Data Collection and Normalization We used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } ``` ### Contributions Thanks to [@AntoineSimoulin](https://github.com/AntoineSimoulin) for adding this dataset.
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jamescalam/image-text-demo
jamescalam
2023-02-06T05:29:49Z
548
0
null
[ "region:us" ]
2023-02-06T05:29:49Z
2022-09-04T08:05:03.000Z
2022-09-04T08:05:03
Entry not found
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Clinton/Text-to-sql-v1
Clinton
2023-07-26T11:09:22Z
548
24
null
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "SQL", "region:us" ]
2023-07-26T11:09:22Z
2023-07-26T10:36:49.000Z
2023-07-26T10:36:49
--- license: apache-2.0 task_categories: - text-generation language: - en tags: - SQL size_categories: - 100K<n<1M ---
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juselara1/mlds7_codedata
juselara1
2023-08-13T16:24:32Z
547
0
null
[ "region:us" ]
2023-08-13T16:24:32Z
2023-08-13T16:07:44.000Z
2023-08-13T16:07:44
# mlds7_codedata This dataset contains some code filtered from the Alpaca original dataset.
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art
null
2023-04-05T09:36:25Z
545
3
art-dataset
[ "task_categories:multiple-choice", "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:unknown", "abductive-natural-language-inference", "arxiv:1908.05739", "region:us" ]
2023-04-05T09:36:25Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - multiple-choice - text-classification task_ids: - natural-language-inference paperswithcode_id: art-dataset pretty_name: Abductive Reasoning in narrative Text tags: - abductive-natural-language-inference dataset_info: features: - name: observation_1 dtype: string - name: observation_2 dtype: string - name: hypothesis_1 dtype: string - name: hypothesis_2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' config_name: anli splits: - name: validation num_bytes: 312314 num_examples: 1532 - name: train num_bytes: 34046304 num_examples: 169654 download_size: 5118294 dataset_size: 34358618 --- # Dataset Card for "art" ## 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://leaderboard.allenai.org/anli/submissions/get-started](https://leaderboard.allenai.org/anli/submissions/get-started) - **Repository:** https://github.com/allenai/abductive-commonsense-reasoning - **Paper:** [Abductive Commonsense Reasoning](https://arxiv.org/abs/1908.05739) - **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:** 5.12 MB - **Size of the generated dataset:** 34.36 MB - **Total amount of disk used:** 39.48 MB ### Dataset Summary ART consists of over 20k commonsense narrative contexts and 200k explanations. The Abductive Natural Language Inference Dataset from AI2. ### 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 #### anli - **Size of downloaded dataset files:** 5.12 MB - **Size of the generated dataset:** 34.36 MB - **Total amount of disk used:** 39.48 MB An example of 'train' looks as follows. ``` { "hypothesis_1": "Chad's car had all sorts of other problems besides alignment.", "hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.", "label": 1, "observation_1": "Chad went to get the wheel alignment measured on his car.", "observation_2": "The mechanic provided a working alignment with new body work." } ``` ### Data Fields The data fields are the same among all splits. #### anli - `observation_1`: a `string` feature. - `observation_2`: a `string` feature. - `hypothesis_1`: a `string` feature. - `hypothesis_2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1), `2` (2). ### Data Splits |name|train |validation| |----|-----:|---------:| |anli|169654| 1532| ## 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{Bhagavatula2020Abductive, title={Abductive Commonsense Reasoning}, author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi}, booktitle={International Conference on Learning Representations}, year={2020}, url={https://openreview.net/forum?id=Byg1v1HKDB} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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harem
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2023-01-25T14:31:29Z
545
6
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[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:pt", "license:unknown", "region:us" ]
2023-01-25T14:31:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: HAREM dataset_info: - config_name: default features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PESSOA '2': I-PESSOA '3': B-ORGANIZACAO '4': I-ORGANIZACAO '5': B-LOCAL '6': I-LOCAL '7': B-TEMPO '8': I-TEMPO '9': B-VALOR '10': I-VALOR '11': B-ABSTRACCAO '12': I-ABSTRACCAO '13': B-ACONTECIMENTO '14': I-ACONTECIMENTO '15': B-COISA '16': I-COISA '17': B-OBRA '18': I-OBRA '19': B-OUTRO '20': I-OUTRO splits: - name: train num_bytes: 1506373 num_examples: 121 - name: test num_bytes: 1062714 num_examples: 128 - name: validation num_bytes: 51318 num_examples: 8 download_size: 1887281 dataset_size: 2620405 - config_name: selective features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PESSOA '2': I-PESSOA '3': B-ORGANIZACAO '4': I-ORGANIZACAO '5': B-LOCAL '6': I-LOCAL '7': B-TEMPO '8': I-TEMPO '9': B-VALOR '10': I-VALOR splits: - name: train num_bytes: 1506373 num_examples: 121 - name: test num_bytes: 1062714 num_examples: 128 - name: validation num_bytes: 51318 num_examples: 8 download_size: 1715873 dataset_size: 2620405 --- # Dataset Card for HAREM ## 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:** [HAREM homepage](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) - **Repository:** [HAREM repository](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html) - **Paper:** [HAREM: An Advanced NER Evaluation Contest for Portuguese](http://comum.rcaap.pt/bitstream/10400.26/76/1/SantosSecoCardosoVilelaLREC2006.pdf) - **Point of Contact:** [Diana Santos](mailto:diana.santos@sintef.no) ### Dataset Summary The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts, from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set, a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event, Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date). It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type". The dataset version processed here ONLY USE the "Category" level of the original dataset. [1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances ``` { "id": "HAREM-871-07800", "ner_tags": [3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, ], "tokens": [ "Abraço", "Página", "Principal", "ASSOCIAÇÃO", "DE", "APOIO", "A", "PESSOAS", "COM", "VIH", "/", "SIDA" ] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR", "B-ABSTRACCAO", "I-ABSTRACCAO", "B-ACONTECIMENTO", "I-ACONTECIMENTO", "B-COISA", "I-COISA", "B-OBRA", "I-OBRA", "B-OUTRO", "I-OUTRO" ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The data is split into train, validation and test set for each of the two versions (default and selective). The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ---- | | 121 | 8 | 128 | ## 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{santos2006harem, title={Harem: An advanced ner evaluation contest for portuguese}, author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui}, booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)}, year={2006} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
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recipe_nlg
null
2023-01-25T14:43:04Z
544
24
recipenlg
[ "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-retrieval", "task_categories:summarization", "task_ids:document-retrieval", "task_ids:entity-linking-retrieval", "task_ids:explanation-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
2023-01-25T14:43:04Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text2text-generation - text-generation - fill-mask - text-retrieval - summarization task_ids: - document-retrieval - entity-linking-retrieval - explanation-generation - language-modeling - masked-language-modeling paperswithcode_id: recipenlg pretty_name: RecipeNLG dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: ingredients sequence: string - name: directions sequence: string - name: link dtype: string - name: source dtype: class_label: names: '0': Gathered '1': Recipes1M - name: ner sequence: string splits: - name: train num_bytes: 2194783815 num_examples: 2231142 download_size: 0 dataset_size: 2194783815 --- # Dataset Card for RecipeNLG ## 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://recipenlg.cs.put.poznan.pl/ - **Repository:** https://github.com/Glorf/recipenlg - **Paper:** https://www.aclweb.org/anthology/volumes/2020.inlg-1/ - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation. While the RecipeNLG dataset is based on the Recipe1M+ dataset, it greatly expands the number of recipes available. The new dataset provides over 1 million new, preprocessed and deduplicated recipes on top of the Recipe1M+ dataset. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ``` {'id': 0, 'title': 'No-Bake Nut Cookies', 'ingredients': ['1 c. firmly packed brown sugar', '1/2 c. evaporated milk', '1/2 tsp. vanilla', '1/2 c. broken nuts (pecans)', '2 Tbsp. butter or margarine', '3 1/2 c. bite size shredded rice biscuits'], 'directions': ['In a heavy 2-quart saucepan, mix brown sugar, nuts, evaporated milk and butter or margarine.', 'Stir over medium heat until mixture bubbles all over top.', 'Boil and stir 5 minutes more. Take off heat.', 'Stir in vanilla and cereal; mix well.', 'Using 2 teaspoons, drop and shape into 30 clusters on wax paper.', 'Let stand until firm, about 30 minutes.'], 'link': 'www.cookbooks.com/Recipe-Details.aspx?id=44874', 'source': 0, 'ner': ['brown sugar', 'milk', 'vanilla', 'nuts', 'butter', 'bite size shredded rice biscuits']} ``` ### Data Fields - `id` (`int`): ID. - `title` (`str`): Title of the recipe. - `ingredients` (`list` of `str`): Ingredients. - `directions` (`list` of `str`): Instruction steps. - `link` (`str`): URL link. - `source` (`ClassLabel`): Origin of each recipe record, with possible value {"Gathered", "Recipes1M"}: - "Gathered" (0): Additional recipes gathered from multiple cooking web pages, using automated scripts in a web scraping process. - "Recipes1M" (1): Recipes from "Recipe1M+" dataset. - `ner` (`list` of `str`): NER food entities. ### Data Splits The dataset contains a single `train` split. ## 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 I (the "Researcher") have requested permission to use the RecipeNLG dataset (the "Dataset") at Poznań University of Technology (PUT). In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Dataset only for non-commercial research and educational purposes. 2. PUT makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify PUT, including its employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset including but not limited to Researcher's use of any copies of copyrighted images or text that he or she may create from the Dataset. 4. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions. 5. 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. ### Citation Information ```bibtex @inproceedings{bien-etal-2020-recipenlg, title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation", author = "Bie{\'n}, Micha{\l} and Gilski, Micha{\l} and Maciejewska, Martyna and Taisner, Wojciech and Wisniewski, Dawid and Lawrynowicz, Agnieszka", booktitle = "Proceedings of the 13th International Conference on Natural Language Generation", month = dec, year = "2020", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.inlg-1.4", pages = "22--28", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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neil-code/dialogsum-test
neil-code
2023-08-24T03:47:07Z
544
0
null
[ "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]
2023-08-24T03:47:07Z
2023-08-24T03:38:12.000Z
2023-08-24T03:38:12
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information MIT License ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.
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BeIR/climate-fever-qrels
BeIR
2022-10-23T06:08:28Z
541
0
beir
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
2022-10-23T06:08:28Z
2022-06-05T17:28:22.000Z
2022-06-05T17:28:22
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
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amasand/imdb-neg-to-pos
amasand
2023-11-01T18:49:05Z
538
0
null
[ "region:us" ]
2023-11-01T18:49:05Z
2023-11-01T18:36:51.000Z
2023-11-01T18:36:51
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* dataset_info: features: - name: prompt_or_input_text dtype: string - name: target_or_reference_text dtype: string splits: - name: train num_bytes: 8939708 num_examples: 20000 - name: test num_bytes: 2205759 num_examples: 5000 - name: eval num_bytes: 2261341 num_examples: 5000 download_size: 8943325 dataset_size: 13406808 --- # Dataset Card for "imdb-neg-to-pos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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NeelNanda/c4-code-tokenized-2b
NeelNanda
2022-11-13T21:54:56Z
537
1
null
[ "region:us" ]
2022-11-13T21:54:56Z
2022-11-13T21:42:47.000Z
2022-11-13T21:42:47
--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 13581607992 num_examples: 1657102 download_size: 2953466988 dataset_size: 13581607992 --- # Dataset Card for "c4-code-tokenized-2b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ascent_kb
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2022-11-03T16:30:39Z
536
2
ascentkb
[ "task_categories:other", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "knowledge-base", "arxiv:2011.00905", "region:us" ]
2022-11-03T16:30:39Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: ascentkb pretty_name: Ascent KB tags: - knowledge-base dataset_info: - config_name: canonical features: - name: arg1 dtype: string - name: rel dtype: string - name: arg2 dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2976697816 num_examples: 8904060 download_size: 710727536 dataset_size: 2976697816 - config_name: open features: - name: subject dtype: string - name: predicate dtype: string - name: object dtype: string - name: support dtype: int64 - name: facets list: - name: value dtype: string - name: type dtype: string - name: support dtype: int64 - name: source_sentences list: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2882678298 num_examples: 8904060 download_size: 710727536 dataset_size: 2882678298 --- # Dataset Card for Ascent KB ## 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://ascent.mpi-inf.mpg.de/ - **Repository:** https://github.com/phongnt570/ascent - **Paper:** https://arxiv.org/abs/2011.00905 - **Point of Contact:** http://tuan-phong.com ### Dataset Summary This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc. The current version of Ascent KB (v1.0.0) is approximately **19 times larger than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded). For more details, take a look at [the research paper](https://arxiv.org/abs/2011.00905) and [the website](https://ascent.mpi-inf.mpg.de). ### Supported Tasks and Leaderboards The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances There are two configurations available for this dataset: 1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>` assertions where the relations (`rel`) were mapped to [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations) with slight modifications: - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`. - All `/r/HasA` relations were replaced with `/r/HasAspect`. This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020) schema, although they grouped all `/r/HasA` and `/r/HasProperty` into `/r/HasProperty`. - The `/r/UsedFor` relation was replaced with `/r/ObjectUse` which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.). This is also taken from ATOMIC-2020. 2. `open`: This part contains open assertions of the form `<subject ; predicate ; object>` extracted directly from web contents. This is the original form of the `canonical` triples. In both configurations, each assertion is equipped with extra information including: a set of semantic `facets` (e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences), and a list of `source_sentences`. An example row in the `canonical` configuration: ```JSON { "arg1": "elephant", "rel": "/r/HasProperty", "arg2": "intelligent", "support": 15, "facets": [ { "value": "extremely", "type": "DEGREE", "support": 11 } ], "source_sentences": [ { "text": "Elephants are extremely intelligent animals.", "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/" }, { "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.", "source": "https://www.elephantsforafrica.org/elephant-facts/" } ] } ``` ### Data Fields - **For `canonical` configuration** - `arg1`: the first argument to the relationship, e.g., *elephant* - `rel`: the canonical relation, e.g., */r/HasProperty* - `arg2`: the second argument to the relationship, e.g., *intelligence* - `support`: the number of occurrences of the assertion, e.g., *15* - `facets`: an array of semantic facets, each contains - `value`: facet value, e.g., *extremely* - `type`: facet type, e.g., *DEGREE* - `support`: the number of occurrences of the facet, e.g., *11* - `source_sentences`: an array of source sentences from which the assertion was extracted, each contains - `text`: the raw text of the sentence - `source`: the URL to its parent document - **For `open` configuration** - The fields of this configuration are the same as the `canonical` configuration's, except that the (`arg1`, `rel`, `arg2`) fields are replaced with the (`subject`, `predicate`, `object`) fields which are free text phrases extracted directly from the source sentences using an Open Information Extraction (OpenIE) tool. ### Data Splits There are no splits. All data points come to a default split called `train`. ## Dataset Creation ### Curation Rationale The commonsense knowledge base was created to assist in development of robust and reliable AI. ### Source Data #### Initial Data Collection and Normalization Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions. The assertions were then grouped into semantically equivalent clusters. Take a look at the research paper for more details. #### Who are the source language producers? Web users. ### Annotations #### Annotation process None. #### Who are the annotators? None. ### Personal and Sensitive Information Unknown. ## 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 The knowledge base has been developed by researchers at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/). Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments. ### Licensing Information [The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @InProceedings{nguyen2021www, title={Advanced Semantics for Commonsense Knowledge Extraction}, author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, year={2021}, booktitle={The Web Conference 2021}, } ``` ### Contributions Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.
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null
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null
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bigbio/chemprot
bigbio
2022-12-22T15:44:22Z
536
1
null
[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]
2022-12-22T15:44:22Z
2022-11-13T22:07:50.000Z
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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Agisight/tyvan-russian-parallel-50k
Agisight
2023-10-17T13:40:34Z
536
1
null
[ "task_categories:translation", "size_categories:10K<n<100K", "language:ru", "language:tyv", "license:cc-by-sa-4.0", "region:us" ]
2023-10-17T13:40:34Z
2023-10-17T10:56:36.000Z
2023-10-17T10:56:36
--- license: cc-by-sa-4.0 task_categories: - translation language: - ru - tyv size_categories: - 10K<n<100K --- A 50K sample from the Russian-Tyvan parallel corpus collected at https://tyvan.ru.
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cdsc
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2023-01-25T14:27:43Z
535
0
polish-cdscorpus
[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-nc-sa-4.0", "sentences entailment and relatedness", "region:us" ]
2023-01-25T14:27:43Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: polish-cdscorpus pretty_name: Polish CDSCorpus tags: - sentences entailment and relatedness dataset_info: - config_name: cdsc-e features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: entailment_judgment dtype: class_label: names: '0': NEUTRAL '1': CONTRADICTION '2': ENTAILMENT splits: - name: train num_bytes: 1381902 num_examples: 8000 - name: test num_bytes: 179400 num_examples: 1000 - name: validation num_bytes: 174662 num_examples: 1000 download_size: 376079 dataset_size: 1735964 - config_name: cdsc-r features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: relatedness_score dtype: float32 splits: - name: train num_bytes: 1349902 num_examples: 8000 - name: test num_bytes: 175400 num_examples: 1000 - name: validation num_bytes: 170662 num_examples: 1000 download_size: 381525 dataset_size: 1695964 --- # 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:** http://zil.ipipan.waw.pl/Scwad/CDSCorpus - **Repository:** - **Paper:** @inproceedings{wroblewska2017polish, title={Polish evaluation dataset for compositional distributional semantics models}, author={Wr{\'o}blewska, Alina and Krasnowska-Kiera{\'s}, Katarzyna}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={784--792}, year={2017} } - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** alina@ipipan.waw.pl ### Dataset Summary Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - pair_ID: id of sentences pairs - sentence_A: first sentence - sentence_B: second sentence for cdsc-e domain: - entailment_judgment: either 'NEUTRAL', 'CONTRADICTION' or 'ENTAILMENT' for cdsc-r domain: - relatedness_score: float representing a reletedness ### Data Splits Data is splitted in train/dev/test 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
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GEM/web_nlg
GEM
2022-10-24T15:31:09Z
535
2
null
[ "task_categories:table-to-text", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "data-to-text", "region:us" ]
2022-10-24T15:31:09Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - unknown language_creators: - unknown language: - en license: - cc-by-nc-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: web_nlg tags: - data-to-text --- # Dataset Card for GEM/web_nlg ## Dataset Description - **Homepage:** https://webnlg-challenge.loria.fr/ - **Repository:** https://gitlab.com/shimorina/webnlg-dataset - **Paper:** http://www.aclweb.org/anthology/P17-1017, [WebNLG Challenge 2017 Report - **Leaderboard:** https://beng.dice-research.org/gerbil/ - **Point of Contact:** [Needs More Information] ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/web_nlg). ### Dataset Summary WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/web_nlg') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/web_nlg). #### website [Website](https://webnlg-challenge.loria.fr/) #### paper [First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) #### authors The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil). ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](https://webnlg-challenge.loria.fr/) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Gitlab](https://gitlab.com/shimorina/webnlg-dataset) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> Initial release of the dataset: ``` @inproceedings{gardent2017creating, author = "Gardent, Claire and Shimorina, Anastasia and Narayan, Shashi and Perez-Beltrachini, Laura", title = "Creating Training Corpora for NLG Micro-Planners", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", year = "2017", publisher = "Association for Computational Linguistics", pages = "179--188", location = "Vancouver, Canada", doi = "10.18653/v1/P17-1017", url = "http://www.aclweb.org/anthology/P17-1017" } ``` The latest version 3.0: ``` @inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared, title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)}, author={Castro Ferreira, Thiago and Gardent, Claire and Ilinykh, Nikolai and van der Lee, Chris and Mille, Simon and Moussallem, Diego and Shimorina, Anastasia}, booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)}, pages = "55--76", year = 2020, address = {Dublin, Ireland (Virtual)}, publisher = {Association for Computational Linguistics}} ``` #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> webnlg-challenge@inria.fr #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Website](https://beng.dice-research.org/gerbil/) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> The model outputs are evaluated against the crowdsourced references; the leaderboard reports BLEU-4, METEOR, chrF++, TER, BERTScore and BLEURT scores. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `Russian`, `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The WebNLG dataset was created to promote the development (_i_) of RDF verbalisers and (_ii_) of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> A model should verbalize all and only the provided input triples in natural language. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Université de Lorraine / LORIA, France, CNRS / LORIA, France, University of Edinburgh, UK, Federal University of Minas Gerais, Brazil #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil). #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> The dataset construction was funded by the French National Research Agency (ANR). #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Simon Mille and Sebastian Gehrmann added the dataset and wrote the data card. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> See [official documentation](https://webnlg-challenge.loria.fr/docs/). `entry`: a data instance of the benchmark. Each entry has five attributes: a DBpedia category (`category`), entry ID (`eid`), shape, shape type, and triple set size (`size`). - `shape`: a string representation of the RDF tree with nested parentheses where `X` is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format)). - `shape_type`: a type of the tree shape. We [identify](https://www.aclweb.org/anthology/C16-1141.pdf) three types of tree shapes: * `chain` (the object of one triple is the subject of the other); * `sibling` (triples with a shared subject); * `mixed` (both `chain` and `sibling` types present). - `eid`: an entry ID. It is unique only within a category and a size. - `category`: a DBpedia category (Astronaut, City, MusicalWork, Politician, etc.). - `size`: the number of RDF triples in a set. Ranges from 1 to 7. Each `entry` has three fields: `originaltripleset`, `modifiedtripleset`, and `lexs`. `originaltripleset`: a set of RDF triples as extracted from [DBpedia](https://wiki.dbpedia.org/). Each set of RDF triples is a tree. Triples have the subject-predicate-object structure. `modifiedtripleset`: a set of RDF triples as presented to crowdworkers (for more details on modifications, see below). Original and modified triples serve different purposes: the original triples — to link data to a knowledge base (DBpedia), whereas the modified triples — to ensure consistency and homogeneity throughout the data. To train models, the modified triples should be used. `lexs` (shortened for lexicalisations): a natural language text verbalising the triples. Each lexicalisation has two attributes: a comment (`comment`), and a lexicalisation ID (`lid`). By default, comments have the value `good`, except rare cases when they were manually marked as `toFix`. That was done during the corpus creation, when it was seen that a lexicalisation did not exactly match a triple set. Russian data has additional optional fields comparing to English: `<dbpedialinks>`: RDF triples extracted from DBpedia between English and Russian entities by means of the property `sameAs`. `<links>`: RDF triples created manually for some entities to serve as pointers to translators. There are two types of them: * with `sameAs` (`Spaniards | sameAs | испанцы`) * with `includes` (`Tomatoes, guanciale, cheese, olive oil | includes | гуанчиале`). Those were mostly created for string literals to translate some parts of them. Lexicalisations in the Russian WebNLG have a new parameter `lang` (values: `en`, `ru`) because original English texts were kept in the Russian version (see the example above). #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "entry": { "category": "Company", "size": "4", "shape": "(X (X) (X) (X) (X))", "shape_type": "sibling", "eid": "Id21", "lexs": [ { "comment": "good", "lex": "Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.", "lid": "Id1" } ], "modifiedtripleset": [ { "subject": "Trane", "property": "foundingDate", "object": "1913-01-01" }, { "subject": "Trane", "property": "location", "object": "Ireland" }, { "subject": "Trane", "property": "foundationPlace", "object": "La_Crosse,_Wisconsin" }, { "subject": "Trane", "property": "numberOfEmployees", "object": "29000" } ], "originaltriplesets": { "originaltripleset": [ { "subject": "Trane", "property": "foundingDate", "object": "1913-01-01" }, { "subject": "Trane", "property": "location", "object": "Ireland" }, { "subject": "Trane", "property": "foundationPlace", "object": "La_Crosse,_Wisconsin" }, { "subject": "Trane", "property": "numberOfEmployees", "object": "29000" } ] } } } ``` The XML-formatted example is [here](https://webnlg-challenge.loria.fr/docs/#example). #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> | English (v3.0) | Train | Dev | Test | |-----------------|--------|-------|-------| | **triple sets** | 13,211 | 1,667 | 1,779 | | **texts** | 35,426 | 4,464 | 5,150 | |**properties** | 372 | 290 | 220 | | Russian (v3.0) | Train | Dev | Test | |-----------------|--------|-------|-------| | **triple sets** | 5,573 | 790 | 1,102 | | **texts** | 14,239 | 2,026 | 2,780 | |**properties** | 226 | 115 | 192 | ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Due to the constrained generation task, this dataset can be used to evaluate very specific and narrow generation capabilities. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> yes #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The RDF-triple format is unique to WebNLG. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> surface realization ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `other` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> No changes to the main content of the dataset. The [version 3.0](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) of the dataset is used. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 23 special test sets for WebNLG were added to the GEM evaluation suite, 12 for English and 11 for Russian. For both languages, we created subsets of the training and development sets of ~500 randomly selected inputs each. The inputs were sampled proportionally from each category. Two types of transformations have been applied to WebNLG: (i) input scrambling (English and Russian) and (ii) numerical value replacements (English); in both cases, a subset of about 500 inputs was randomly selected. For (i), the order of the triples was randomly reassigned (each triple kept the same Subject-Property-Object internal order). For (ii), the change was performed respecting the format of the current cardinal value (e.g., alpha, integer, or floating-point) and replacing it with a new random value. The new number is lower-bounded between zero and upper bounded to be within to the highest power of 10 unit for the given value (e.g., replacing 54 would result in a random value between 0-100). Floating values maintain the degree of precision. For both languages, we did identify different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently 8 selections that we have made: Selection 1 (size): input length. This selection corresponds to the number of predicates in the input. By comparing inputs of different lengths, we can see to what extent NLG systems are able to handle different input sizes. The table below provides the relevant frequencies. Please be aware that comparing selections with fewer than 100 items may result in unreliable comparisons. | Input length | Frequency English | Frequency Russian | |----------------|-------------------|-------------------| | 1 | 369 | 254 | | 2 | 349 | 200 | | 3 | 350 | 214 | | 4 | 305 | 214 | | 5 | 213 | 159 | | 6 | 114 | 32 | | 7 | 79 | 29 | Selection 2 (frequency): seen/unseen single predicates. This selection corresponds to the inputs with only one predicate. We compare which predicates are seen/unseen in the training data. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there is only one example of unseen single predicates. | _ in training | Frequency English | Frequency Russian | |---------------|-------------------|-------------------| | Seen | 297 | 253 | | Unseen | 72 | 1 | Selection 3 (frequency): seen/unseen combinations of predicates. This selection checks for all combinations of predicates whether that combination has been seen in the training data. For example: if the combination of predicates A and B is seen, that means that there is an input in the training data consisting of two triples, where one triple uses predicate A and the other uses predicate B. If the combination is unseen, then the converse is true. The table below provides the relevant frequencies. | _ in training | Frequency English | Frequency Russian | |---------------|-------------------|-------------------| | unseen | 1295 | 354 | | seen | 115 | 494 | Selection 4 (frequency): seen/unseen arguments. This selection checks for all input whether or not all arg1s and arg2s in the input have been seen during the training phase. For this selection, *Seen* is the default. Only if all arg1 instances for a particular input are unseen, do we count the arg1s of the input as unseen. The same holds for arg2. So "seen" here really means that at least some of the arg1s or arg2s are seen in the input. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there are very few examples of unseen combinations of predicates. | Arguments seen in training? | Frequency English | Frequency Russian | |-----------------------------|-------------------|-------------------| | both_seen | 518 | 1075 | | both_unseen | 1177 | 4 | | arg1_unseen | 56 | 19 | | arg2_unseen | 28 | 4 | Selection 5 (shape): repeated subjects. For this selection, the subsets are based on the times a subject is repeated in the input; it only takes into account the maximum number of times a subject is repeated, that is, if in one input a subject appears 3 times and a different subject 2 times, this input will be in the "3_subjects_same' split. Unique_subjects means all subjects are different. | Max num. of repeated subjects | Frequency English | Frequency Russian | |-------------------------------|-------------------|-------------------| | unique_subjects | 453 | 339 | | 2_subjects_same | 414 | 316 | | 3_subjects_same | 382 | 217 | | 4_subjects_same | 251 | 143 | | 5_subjects_same | 158 | 56 | | 6_subjects_same | 80 | 19 | | 7_subjects_same | 41 | 12 | Selection 6 (shape): repeated objects. Same as for subjects above, but for objects. There are much less cases of repeated objects, so there are only two categories for this selection, unique_objects and some_objects_repeated; for the latter, we have up to 3 coreferring objects in English, and XXX in Russian. | Max num. of repeated objects | Frequency English | Frequency Russian | |------------------------------|-------------------|-------------------| | unique_objects | 1654 | 1099 | | some_objects_same | 125 | 3 | Selection 7 (shape): repeated properties. Same as for objects above, but for properties; up to two properties can be the same in English, up to XXX in Russian. | Max num. of repeated properties | Frequency English | Frequency Russian | |---------------------------------|-------------------|-------------------| | unique_properties | 1510 | 986 | | some_properties_same | 269 | 116 | Selection 8 (shape): entities that appear both as subject and object. For this selection, we grouped together the inputs in which no entity is found as both subject and object, and on the other side inputs in which one or more entity/ies appear both as subject and as object. We found up to two such entities per input in English, and up to XXX in Russian. | Max num. of objects and subjects in common | Frequency English | Frequency Russian | |--------------------------------------------|-------------------|-------------------| | unique_properties | 1322 | 642 | | some_properties_same | 457 | 460 | #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> Robustness ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> Dataset construction: [main dataset paper](https://www.aclweb.org/anthology/P17-1017/), [RDF triple extraction](https://www.aclweb.org/anthology/C16-1141/), [Russian translation](https://www.aclweb.org/anthology/W19-3706/) WebNLG Challenge 2017: [webpage](https://webnlg-challenge.loria.fr/challenge_2017/), [paper](https://www.aclweb.org/anthology/W17-3518/) WebNLG Challenge 2020: [webpage](https://webnlg-challenge.loria.fr/challenge_2020/), [paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) Enriched version of WebNLG: [repository](https://github.com/ThiagoCF05/webnlg), [paper](https://www.aclweb.org/anthology/W18-6521/) Related research papers: [webpage](https://webnlg-challenge.loria.fr/research/) ## Previous Results ### Previous Results #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance. In a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka): 1. Data Coverage: Does the text include descriptions of all predicates presented in the data? 2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data? 3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate? 4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language? 5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text? For additional information like the instructions, we refer to the original paper. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> We evaluated a wide range of models as part of the GEM benchmark. #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> Results can be found on the [GEM website](https://gem-benchmark.com/results). ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> yes - related tasks #### Social Impact Observations <!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. --> <!-- scope: microscope --> We do not foresee any negative social impact in particular from this dataset or task. Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases. ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias. The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures. #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> In English, the dataset is limited to the language that crowdraters speak. In Russian, the language is heavily biased by the translationese of the translation system that is post-edited. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> There is no PII in this dataset. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `non-commercial use only` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts. Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> Only a limited number of domains are covered in this dataset. As a result, it cannot be used as a general-purpose realizer.
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EleutherAI/pile-deduped-pythia-random-sampled
EleutherAI
2023-08-25T07:26:47Z
535
2
null
[ "region:us" ]
2023-08-25T07:26:47Z
2023-03-29T13:15:01.000Z
2023-03-29T13:15:01
--- dataset_info: features: - name: Index dtype: int64 - name: 70M dtype: float64 - name: 160M dtype: float64 - name: 410M dtype: float64 - name: 1B dtype: float64 - name: 1.4B dtype: float64 - name: 2.8B dtype: float64 - name: 6.9B dtype: float64 - name: 12B dtype: float64 - name: Tokens sequence: uint16 splits: - name: train num_bytes: 1020000000 num_examples: 5000000 download_size: 915854656 dataset_size: 1020000000 --- # Dataset Card for "pile-deduped-pythia-random-sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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codeparrot/codeparrot-clean-valid
codeparrot
2022-10-10T15:28:51Z
534
5
null
[ "region:us" ]
2022-10-10T15:28:51Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
# CodeParrot 🦜 Dataset Cleaned (valid) Train split of [CodeParrot 🦜 Dataset Cleaned](https://huggingface.co/datasets/lvwerra/codeparrot-clean). ## Dataset structure ```python DatasetDict({ train: Dataset({ features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'], num_rows: 61373 }) }) ```
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gradio/NYC-Airbnb-Open-Data
gradio
2022-10-09T05:31:38Z
534
2
null
[ "license:afl-3.0", "region:us" ]
2022-10-09T05:31:38Z
2022-10-09T05:31:18.000Z
2022-10-09T05:31:18
--- license: afl-3.0 ---
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jondurbin/airoboros-2.2.1
jondurbin
2023-09-18T21:22:40Z
534
23
null
[ "license:other", "region:us" ]
2023-09-18T21:22:40Z
2023-09-15T10:20:36.000Z
2023-09-15T10:20:36
--- license: other --- ## Overview This dataset is a slight update to 2.2. ### Re-generated writing responses Many of the responses were generated by gpt-4-0613, which unfortunately produces much shorter and "dumber" (i.e. various readability scores increased compared to gpt-4-0314, e.g. Flesch, Gunning Fog, etc.) responses compared to gpt-4-0314. I have re-created many of these responses, using gpt-4-0314, temperature 0.7, and the following prompt (which produced 3-5x longer responses): ``` You are to emulate a world class, intelligent author who uses a diverse, interesting vocabulary to completely captivate the audience with brilliant and interesting writing. You provide very lengthy and detailed responses. Remember, you are to emulate a human writer, so the output should not sound like it was written by an AI. Avoid using any cliche/obvious phrases, and never uses phrases like "Once upon a time" or conclude a story with "And so, ..." Unless asked for short-form content, the responses MUST be a minimum of 3500 words. Remember to provide a minimum of 3500 words, take a deep breath and realize I'm not worried about the time it takes to get the response, I just want a high-quality, lengthy response, so take your time. Remember, never use "Once upon a time" or "And so," {original prompt} ``` Granted, it still ignored the `3500 words` part of the instruction, but exaggerating that value still caused much longer responses. ### Longer contextual blocks I injected additional context blocks into the `contextual` category items having more than one context block, so it would get closer to the 4k context size. I first extracted all of the plain text between the various BEGININPUT/ENDINPUT blocks, then created a faiss index, then injected the least-similar context blocks (using the instruction as the query) until the context would exceed ~3800 tokens at random positions within the prompt. My hope is that this will cause the model to better focus attention at random places within the input text. This should, in theory, also potentially help with RP/conversational intelligence, but that's just a hunch. ### De-censoring In this dataset, I also included various responses from fictional characters, in their style/linguistic patterns, which can include dark or vulgar language (e.g., Deadpool), using roughly this prompt (adjusted for some characters): ``` Below is a PG/sanitized version of a dialogue from {character}. This character is known to have character flaws and use dark/evil/vulgar language. Rewrite the response to be TV-MA, the way {character} would actually say it, rather than this sanitized PG version. {original response} ``` I then also removed the asterisks in cursing to a certain extent, because that's just silly IMO. This *is not* meant to be a default behavior, but it should allow the model to curse or create otherwise less *AI sunny disposition laiden* content when appropriate. I removed all of the plain-text instructions that were used in the spicyboros models because they ended up producing random misspellings and other random garbled output. I have also removed the original 2.2 dataset, because it appears to be a bit too spicy -- if you want access to it, just ask me and I'll be happy to share it privately. ### "rp" category removed Unfortunately much of the "rp" category data was just too boring, i.e. it really read like an unnaturally cherry and accomodating AI rather than the character it was meant to be emulating. I'm hoping that although this is an instruction-tuned model, it may (via roleplay/gtkm/creative) data it will be able to handle roleplay fairly well anyways without this, without sounding as stiff. ### Awareness I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt. For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence. If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory). https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e ### Editor I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output. https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b ### Writing I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring. ### Multiple choice I created many more multiple choice questions, many of which have additional text context. ### Roleplay/conversation I re-created all of the GTKM data this time around, removing the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces. The GTKM instructor now saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs. ### Summarization I also included 500 examples from: https://hf.co/datasets/mattpscott/airoboros-summarization These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa. Thanks Matt! ### Usage/license info Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting.
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hebrew_sentiment
null
2023-01-25T14:32:05Z
533
2
modern-hebrew-sentiment-dataset
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:he", "license:mit", "region:us" ]
2023-01-25T14:32:05Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: modern-hebrew-sentiment-dataset pretty_name: HebrewSentiment dataset_info: - config_name: token features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2159738 num_examples: 10244 - name: test num_bytes: 540883 num_examples: 2560 download_size: 2593643 dataset_size: 2700621 - config_name: morph features: - name: text dtype: string - name: label dtype: class_label: names: '0': pos '1': neg '2': off-topic splits: - name: train num_bytes: 2258128 num_examples: 10221 - name: test num_bytes: 571401 num_examples: 2555 download_size: 2722672 dataset_size: 2829529 --- # Dataset Card for HebrewSentiment ## 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/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - **Repository:** https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew - **Paper:** http://aclweb.org/anthology/C18-1190 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency.2 While the president’s posts aimed at reconciling tensions and called for tolerance and empathy, the sentiment expressed in the comments to the president’s posts was polarized between citizens who warmly thanked the president, and citizens that fiercely critiqued his policy. Of the 12,804 comments, 370 are neutral; 8,512 are positive, 3,922 negative. Data Annotation: ### Supported Tasks and Leaderboards Sentiment Analysis ### Languages Hebrew ## Dataset Structure tsv format: {hebrew_sentence}\t{sentiment_label} ### Data Instances רובי הייתי רוצה לראות ערביה נישאת ליהודי 1 תמונה יפיפיה-שפו 0 חייבים לעשות סוג של חרם כשכתבים שונאי ישראל עולים לשידור צריכים להעביר לערוץ אחר ואז תראו מה יעשה כוחו של הרייטינג ( בהקשר לדבריה של רינה מצליח ) 2 ### Data Fields - `text`: The modern hebrew inpput text. - `label`: The sentiment label. 0=positive , 1=negative, 2=off-topic. ### Data Splits | | train | test | |--------------------------|--------|---------| | HebrewSentiment (token) | 10243 | 2559 | | HebrewSentiment (morph) | 10243 | 2559 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization User comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process A trained researcher examined each comment and determined its sentiment value, where comments with an overall positive sentiment were assigned the value 0, comments with an overall negative sentiment were assigned the value 1, and comments that are off-topic to the post’s content were assigned the value 2. We validated the coding scheme by asking a second trained researcher to code the same data. There was substantial agreement between raters (N of agreements: 10623, N of disagreements: 2105, Coehn’s Kappa = 0.697, p = 0). #### Who are the annotators? Researchers ### 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 OMIlab, The Open University of Israel ### Licensing Information MIT License Copyright (c) 2018 OMIlab, The Open University of Israel 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 @inproceedings{amram-etal-2018-representations, title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew", author = "Amram, Adam and Ben David, Anat and Tsarfaty, Reut", booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/C18-1190", pages = "2242--2252", abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.", } ### Contributions Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset.
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Multimodal-Fatima/OK-VQA_train
Multimodal-Fatima
2023-03-23T22:30:06Z
533
2
null
[ "region:us" ]
2023-03-23T22:30:06Z
2023-03-07T14:44:07.000Z
2023-03-07T14:44:07
--- dataset_info: features: - name: image dtype: image - name: question_type dtype: string - name: confidence dtype: int32 - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: raw_answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: train num_bytes: 1686555802.0 num_examples: 9009 download_size: 1572400067 dataset_size: 1686555802.0 --- # Dataset Card for "OK-VQA_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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EduardoPacheco/FoodSeg103
EduardoPacheco
2023-07-24T00:01:28Z
531
2
null
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "size_categories:n<1K", "license:apache-2.0", "arxiv:2105.05409", "region:us" ]
2023-07-24T00:01:28Z
2023-07-22T03:59:39.000Z
2023-07-22T03:59:39
--- license: apache-2.0 task_categories: - image-segmentation task_ids: - semantic-segmentation size_categories: - n<1K dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 1125278411.056 num_examples: 4983 - name: validation num_bytes: 114576466.17 num_examples: 2135 download_size: 1259085777 dataset_size: 1239854877.226 --- # Dataset Card for FoodSeg103 ## Table of Contents - [Dataset Card for FoodSeg103](#dataset-card-for-foodseg103) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data categories](#data-categories) - [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) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Refinement process](#refinement-process) - [Who are the annotators?](#who-are-the-annotators) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Dataset homepage](https://xiongweiwu.github.io/foodseg103.html) - **Repository:** [FoodSeg103-Benchmark-v1](https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1) - **Paper:** [A Large-Scale Benchmark for Food Image Segmentation](https://arxiv.org/pdf/2105.05409.pdf) - **Point of Contact:** [Not Defined] ### Dataset Summary FoodSeg103 is a large-scale benchmark for food image segmentation. It contains 103 food categories and 7118 images with ingredient level pixel-wise annotations. The dataset is a curated sample from [Recipe1M](https://github.com/facebookresearch/inversecooking) and annotated and refined by human annotators. The dataset is split into 2 subsets: training set, validation set. The training set contains 4983 images and the validation set contains 2135 images. ### Supported Tasks and Leaderboards No leaderboard is available for this dataset at the moment. ## Dataset Structure ### Data categories | id | ingridient | | --- | ---- | | 0 | background | | 1 | candy | | 2 | egg tart | | 3 | french fries | | 4 | chocolate | | 5 | biscuit | | 6 | popcorn | | 7 | pudding | | 8 | ice cream | | 9 | cheese butter | | 10 | cake | | 11 | wine | | 12 | milkshake | | 13 | coffee | | 14 | juice | | 15 | milk | | 16 | tea | | 17 | almond | | 18 | red beans | | 19 | cashew | | 20 | dried cranberries | | 21 | soy | | 22 | walnut | | 23 | peanut | | 24 | egg | | 25 | apple | | 26 | date | | 27 | apricot | | 28 | avocado | | 29 | banana | | 30 | strawberry | | 31 | cherry | | 32 | blueberry | | 33 | raspberry | | 34 | mango | | 35 | olives | | 36 | peach | | 37 | lemon | | 38 | pear | | 39 | fig | | 40 | pineapple | | 41 | grape | | 42 | kiwi | | 43 | melon | | 44 | orange | | 45 | watermelon | | 46 | steak | | 47 | pork | | 48 | chicken duck | | 49 | sausage | | 50 | fried meat | | 51 | lamb | | 52 | sauce | | 53 | crab | | 54 | fish | | 55 | shellfish | | 56 | shrimp | | 57 | soup | | 58 | bread | | 59 | corn | | 60 | hamburg | | 61 | pizza | | 62 | hanamaki baozi | | 63 | wonton dumplings | | 64 | pasta | | 65 | noodles | | 66 | rice | | 67 | pie | | 68 | tofu | | 69 | eggplant | | 70 | potato | | 71 | garlic | | 72 | cauliflower | | 73 | tomato | | 74 | kelp | | 75 | seaweed | | 76 | spring onion | | 77 | rape | | 78 | ginger | | 79 | okra | | 80 | lettuce | | 81 | pumpkin | | 82 | cucumber | | 83 | white radish | | 84 | carrot | | 85 | asparagus | | 86 | bamboo shoots | | 87 | broccoli | | 88 | celery stick | | 89 | cilantro mint | | 90 | snow peas | | 91 | cabbage | | 92 | bean sprouts | | 93 | onion | | 94 | pepper | | 95 | green beans | | 96 | French beans | | 97 | king oyster mushroom | | 98 | shiitake | | 99 | enoki mushroom | | 100 | oyster mushroom | | 101 | white button mushroom | | 102 | salad | | 103 | other ingredients | ### Data Splits This dataset only contains two splits. A training split and a validation split with 4983 and 2135 images respectively. ## Dataset Creation ### Curation Rationale Select images from a large-scale recipe dataset and annotate them with pixel-wise segmentation masks. ### Source Data The dataset is a curated sample from [Recipe1M](https://github.com/facebookresearch/inversecooking). #### Initial Data Collection and Normalization After selecting the source of the data two more steps were added before image selection. 1. Recipe1M contains 1.5k ingredient categoris, but only the top 124 categories were selected + a 'other' category (further became 103). 2. Images should contain between 2 and 16 ingredients. 3. Ingredients should be visible and easy to annotate. Which then resulted in 7118 images. ### Annotations #### Annotation process Third party annotators were hired to annotate the images respecting the following guidelines: 1. Tag ingredients with appropriate categories. 2. Draw pixel-wise masks for each ingredient. 3. Ignore tiny regions (even if contains ingredients) with area covering less than 5% of the image. #### Refinement process The refinement process implemented the following steps: 1. Correct mislabelled ingredients. 2. Deleting unpopular categories that are assigned to less than 5 images (resulting in 103 categories in the final dataset). 3. Merging visually similar ingredient categories (e.g. orange and citrus) #### Who are the annotators? A third party company that was not mentioned in the paper. ## Additional Information ### Dataset Curators Authors of the paper [A Large-Scale Benchmark for Food Image Segmentation](https://arxiv.org/pdf/2105.05409.pdf). ### Licensing Information [Apache 2.0 license.](https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1/blob/main/LICENSE) ### Citation Information ```bibtex @inproceedings{wu2021foodseg, title={A Large-Scale Benchmark for Food Image Segmentation}, author={Wu, Xiongwei and Fu, Xin and Liu, Ying and Lim, Ee-Peng and Hoi, Steven CH and Sun, Qianru}, booktitle={Proceedings of ACM international conference on Multimedia}, year={2021} } ```
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Paul/hatecheck
Paul
2022-07-05T10:27:25Z
530
4
null
[ "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" ]
2022-07-05T10:27:25Z
2022-03-02T23:29:22.000Z
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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