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davanstrien
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nlphuji/mscoco_2014_5k_test_image_text_retrieval
2023-01-18T00:08:42.000Z
[ "arxiv:1405.0312", "region:us" ]
nlphuji
null
null
null
2
1,010
# MSCOCO (5K test set) Original paper: [Microsoft COCO: Common Objects in Context ](https://arxiv.org/abs/1405.0312) Homepage: https://cocodataset.org/#home 5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip Bibtex: ``` @inproceedings{lin2014microsoft, title={Microsoft coco: Common objects in context}, author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, booktitle={European conference on computer vision}, pages={740--755}, year={2014}, organization={Springer} } ```
squadshifts
2023-04-05T13:40:47.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
null
null
@InProceedings{pmlr-v119-miller20a, title = {The Effect of Natural Distribution Shift on Question Answering Models}, author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6905--6916}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf}, url = {https://proceedings.mlr.press/v119/miller20a.html}, }
null
3
1,007
--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: SQuAD-shifts size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: squad-shifts dataset_info: - config_name: new_wiki features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 7865203 num_examples: 7938 download_size: 16505623 dataset_size: 7865203 - config_name: nyt features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 10792550 num_examples: 10065 download_size: 16505623 dataset_size: 10792550 - config_name: reddit features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 9473946 num_examples: 9803 download_size: 16505623 dataset_size: 9473946 - config_name: amazon features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 9445004 num_examples: 9885 download_size: 16505623 dataset_size: 9445004 --- # Dataset Card for "squadshifts" ## 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://modestyachts.github.io/squadshifts-website/index.html](https://modestyachts.github.io/squadshifts-website/index.html) - **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:** 66.02 MB - **Size of the generated dataset:** 37.56 MB - **Total amount of disk used:** 103.58 MB ### Dataset Summary SquadShifts consists of four new test sets for the Stanford Question Answering Dataset (SQuAD) from four different domains: Wikipedia articles, New York \ Times articles, Reddit comments, and Amazon product reviews. Each dataset was generated using the same data generating pipeline, Amazon Mechanical Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 dataset. The "new-york-times", "reddit", and "amazon" datasets measure robustness to natural distribution shifts. We encourage SQuAD model developers to also evaluate their methods on these new datasets! ### 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 #### amazon - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 9.44 MB - **Total amount of disk used:** 25.94 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["amazon"] }, "context": "This is a paragraph from amazon.", "id": "090909", "question": "Where is this paragraph from?", "title": "amazon dummy data" } ``` #### new_wiki - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 7.86 MB - **Total amount of disk used:** 24.37 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["wikipedia"] }, "context": "This is a paragraph from wikipedia.", "id": "090909", "question": "Where is this paragraph from?", "title": "new_wiki dummy data" } ``` #### nyt - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 10.79 MB - **Total amount of disk used:** 27.29 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["new york times"] }, "context": "This is a paragraph from new york times.", "id": "090909", "question": "Where is this paragraph from?", "title": "nyt dummy data" } ``` #### reddit - **Size of downloaded dataset files:** 16.50 MB - **Size of the generated dataset:** 9.47 MB - **Total amount of disk used:** 25.97 MB An example of 'test' looks as follows. ``` { "answers": { "answer_start": [25], "text": ["reddit"] }, "context": "This is a paragraph from reddit.", "id": "090909", "question": "Where is this paragraph from?", "title": "reddit dummy data" } ``` ### Data Fields The data fields are the same among all splits. #### amazon - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### new_wiki - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### nyt - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### reddit - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name |test | |--------|----:| |amazon | 9885| |new_wiki| 7938| |nyt |10065| |reddit | 9803| ## 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 All the datasets are distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) license. ### Citation Information ``` @InProceedings{pmlr-v119-miller20a, title = {The Effect of Natural Distribution Shift on Question Answering Models}, author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6905--6916}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf}, url = {https://proceedings.mlr.press/v119/miller20a.html}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@millerjohnp](https://github.com/millerjohnp), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
skg/toxigen-data
2022-06-20T11:12:11.000Z
[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "arxiv:2203.09509", "region:us" ]
skg
Toxigen is a large-scale dataset containing implicitly toxic and benign sentences mentioning 13 minority groups, and a tool to stress test a given off-the-shelf toxicity classifier. The dataset is generated using a large language model (GPT3). It is intended to be used for training classifiers that learn to detect subtle hate speech that includes no slurs or profanity.
@inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} }
null
18
1,004
--- annotations_creators: - expert-generated language_creators: - machine-generated languages: - en-US licenses: [] multilinguality: - monolingual pretty_name: ToxiGen size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - **Repository:** https://github.com/microsoft/toxigen - **Paper:** https://arxiv.org/abs/2203.09509 - **Point of Contact #1:** [Tom Hartvigsen](tomh@mit.edu) - **Point of Contact #2:** [Saadia Gabriel](skgabrie@cs.washington.edu) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - **prompt** is the prompt used for **generation**. - **generation** is the TOXIGEN generated text. - **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - **group** indicates the target group of the prompt. - **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```
RussianNLP/russian_super_glue
2023-06-19T12:23:49.000Z
[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:text-generation", "task_ids:natural-language-inference", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "size_categories:100M<n<1B", "source_datasets:original", "language:ru", "license:mit", "glue", "qa", "superGLUE", "NLI", "reasoning", "arxiv:2202.07791", "region:us" ]
RussianNLP
Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We provide baselines, human level evaluation, an open-source framework for evaluating models and an overall leaderboard of transformer models for the Russian language.
@article{shavrina2020russiansuperglue, title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark}, author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova, Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and Evlampiev, Andrey}, journal={arXiv preprint arXiv:2010.15925}, year={2020} }
null
15
994
--- annotations_creators: - crowdsourced - expert-generated language_creators: - crowdsourced - expert-generated language: - ru license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 1M<n<10M - 10M<n<100M - 100M<n<1B source_datasets: - original task_categories: - text-classification - question-answering - zero-shot-classification - text-generation task_ids: - natural-language-inference - multi-class-classification pretty_name: Russian SuperGLUE language_bcp47: - ru-RU dataset_info: - config_name: lidirus features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: knowledge dtype: string - name: lexical-semantics dtype: string - name: logic dtype: string - name: predicate-argument-structure dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: test num_bytes: 470306 num_examples: 1104 download_size: 47118 dataset_size: 470306 - config_name: rcb features: - name: premise dtype: string - name: hypothesis dtype: string - name: verb dtype: string - name: negation dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': contradiction '2': neutral splits: - name: train num_bytes: 199712 num_examples: 438 - name: validation num_bytes: 97993 num_examples: 220 - name: test num_bytes: 207031 num_examples: 438 download_size: 136700 dataset_size: 504736 - config_name: parus features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': choice1 '1': choice2 splits: - name: train num_bytes: 74467 num_examples: 400 - name: validation num_bytes: 19397 num_examples: 100 - name: test num_bytes: 93192 num_examples: 500 download_size: 57585 dataset_size: 187056 - config_name: muserc features: - name: paragraph dtype: string - name: question dtype: string - name: answer dtype: string - name: idx struct: - name: paragraph dtype: int32 - name: question dtype: int32 - name: answer dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: train num_bytes: 31651155 num_examples: 11950 - name: validation num_bytes: 5964157 num_examples: 2235 - name: test num_bytes: 19850930 num_examples: 7614 download_size: 1196720 dataset_size: 57466242 - config_name: terra features: - name: premise dtype: string - name: hypothesis dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: train num_bytes: 1409243 num_examples: 2616 - name: validation num_bytes: 161485 num_examples: 307 - name: test num_bytes: 1713499 num_examples: 3198 download_size: 907346 dataset_size: 3284227 - config_name: russe features: - name: word dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: start1 dtype: int32 - name: start2 dtype: int32 - name: end1 dtype: int32 - name: end2 dtype: int32 - name: gold_sense1 dtype: int32 - name: gold_sense2 dtype: int32 - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: train num_bytes: 6913280 num_examples: 19845 - name: validation num_bytes: 2957491 num_examples: 8505 - name: test num_bytes: 10046000 num_examples: 18892 download_size: 3806009 dataset_size: 19916771 - config_name: rwsd features: - name: text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 - name: span1_text dtype: string - name: span2_text dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: train num_bytes: 132274 num_examples: 606 - name: validation num_bytes: 87959 num_examples: 204 - name: test num_bytes: 59051 num_examples: 154 download_size: 40508 dataset_size: 279284 - config_name: danetqa features: - name: question dtype: string - name: passage dtype: string - name: idx dtype: int32 - name: label dtype: class_label: names: '0': 'False' '1': 'True' splits: - name: train num_bytes: 2474006 num_examples: 1749 - name: validation num_bytes: 1076455 num_examples: 821 - name: test num_bytes: 1023062 num_examples: 805 download_size: 1293761 dataset_size: 4573523 - config_name: rucos features: - name: passage dtype: string - name: query dtype: string - name: entities sequence: string - name: answers sequence: string - name: idx struct: - name: passage dtype: int32 - name: query dtype: int32 splits: - name: train num_bytes: 160095378 num_examples: 72193 - name: validation num_bytes: 16980563 num_examples: 7577 - name: test num_bytes: 15535209 num_examples: 7257 download_size: 56208297 dataset_size: 192611150 tags: - glue - qa - superGLUE - NLI - reasoning --- # Dataset Card for [Russian SuperGLUE] ## 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:** https://russiansuperglue.com/ - **Repository:** https://github.com/RussianNLP/RussianSuperGLUE - **Paper:** https://russiansuperglue.com/download/main_article - **Leaderboard:** https://russiansuperglue.com/leaderboard/2 - **Point of Contact:** [More Information Needed] ### Dataset Summary Modern universal language models and transformers such as BERT, ELMo, XLNet, RoBERTa and others need to be properly compared and evaluated. In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. We offer testing methodology based on tasks, typically proposed for “strong AI” — logic, commonsense, reasoning. Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding and leaderboard models. For the first time a complete test for Russian language was developed, which is similar to its English analog. Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable results is also presented. ### Supported Tasks and Leaderboards Supported tasks, barring a few additions, are equivalent to the original SuperGLUE tasks. |Task Name|Equiv. to| |----|---:| |Linguistic Diagnostic for Russian|Broadcoverage Diagnostics (AX-b)| |Russian Commitment Bank (RCB)|CommitmentBank (CB)| |Choice of Plausible Alternatives for Russian language (PARus)|Choice of Plausible Alternatives (COPA)| |Russian Multi-Sentence Reading Comprehension (MuSeRC)|Multi-Sentence Reading Comprehension (MultiRC)| |Textual Entailment Recognition for Russian (TERRa)|Recognizing Textual Entailment (RTE)| |Russian Words in Context (based on RUSSE)|Words in Context (WiC)| |The Winograd Schema Challenge (Russian)|The Winograd Schema Challenge (WSC)| |Yes/no Question Answering Dataset for the Russian (DaNetQA)|BoolQ| |Russian Reading Comprehension with Commonsense Reasoning (RuCoS)|Reading Comprehension with Commonsense Reasoning (ReCoRD)| ### Languages All tasks are in Russian. ## Dataset Structure ### Data Instances Note that there are no labels in the `test` splits. This is signified by the `-1` value. #### LiDiRus - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.49 MB - **Total amount of disk used:** 0.54 MB An example of 'test' looks as follows ``` { "sentence1": "Новая игровая консоль доступна по цене.", "sentence2": "Новая игровая консоль недоступна по цене.", "knowledge": "", "lexical-semantics": "Morphological negation", "logic": "Negation", "predicate-argument-structure": "", "idx": 10, "label": 1 } ``` #### RCB - **Size of downloaded dataset files:** 0.14 MB - **Size of the generated dataset:** 0.53 MB - **Total amount of disk used:** 0.67 MB An example of 'train'/'dev' looks as follows ``` { "premise": "— Пойдём пообедаем. Я с утра ничего не ел. Отель, как видишь, весьма посредственный, но мне сказали, что в здешнем ресторане отлично готовят.", "hypothesis": "В здешнем ресторане отлично готовят.", "verb": "сказать", "negation": "no_negation", "idx": 10, "label": 2 } ``` An example of 'test' looks as follows ``` { "premise": "Я уверен, что вместе мы победим. Да, парламентское большинство думает иначе.", "hypothesis": "Вместе мы проиграем.", "verb": "думать", "negation": "no_negation", "idx": 10, "label": -1 } ``` #### PARus - **Size of downloaded dataset files:** 0.06 MB - **Size of the generated dataset:** 0.20 MB - **Total amount of disk used:** 0.245 MB An example of 'train'/'dev' looks as follows ``` { "premise": "Женщина чинила кран.", "choice1": "Кран подтекал.", "choice2": "Кран был выключен.", "question": "cause", "idx": 10, "label": 0 } ``` An example of 'test' looks as follows ``` { "premise": "Ребятам было страшно.", "choice1": "Их вожатый рассказал им историю про призрака.", "choice2": "Они жарили маршмеллоу на костре.", "question": "cause", "idx": 10, "label": -1 } ``` #### MuSeRC - **Size of downloaded dataset files:** 1.26 MB - **Size of the generated dataset:** 59.77 MB - **Total amount of disk used:** 61.87 MB An example of 'train'/'dev' looks as follows ``` { "paragraph": "(1) Но люди не могут существовать без природы, поэтому в парке стояли железобетонные скамейки — деревянные моментально ломали. (2) В парке бегали ребятишки, водилась шпана, которая развлекалась игрой в карты, пьянкой, драками, «иногда насмерть». (3) «Имали они тут и девок...» (4) Верховодил шпаной Артемка-мыло, с вспененной белой головой. (5) Людочка сколько ни пыталась усмирить лохмотья на буйной голове Артемки, ничего у неё не получалось. (6) Его «кудри, издали напоминавшие мыльную пену, изблизя оказались что липкие рожки из вокзальной столовой — сварили их, бросили комком в пустую тарелку, так они, слипшиеся, неподъёмно и лежали. (7) Да и не ради причёски приходил парень к Людочке. (8) Как только её руки становились занятыми ножницами и расчёской, Артемка начинал хватать её за разные места. (9) Людочка сначала увёртывалась от хватких рук Артемки, а когда не помогло, стукнула его машинкой по голове и пробила до крови, пришлось лить йод на голову «ухажористого человека». (10) Артемка заулюлюкал и со свистом стал ловить воздух. (11) С тех пор «домогания свои хулиганские прекратил», более того, шпане повелел Людочку не трогать.", "question": "Как развлекались в парке ребята?", "answer": "Развлекались игрой в карты, пьянкой, драками, снимали они тут и девок.", "idx": { "paragraph": 0, "question": 2, "answer": 10 }, "label": 1 } ``` An example of 'test' looks as follows ``` { "paragraph": "\"(1) Издательство Viking Press совместно с компанией TradeMobile выпустят мобильное приложение, посвященное Анне Франк, передает The Daily Telegraph. (2) Программа будет включать в себя фрагменты из дневника Анны, озвученные британской актрисой Хеленой Бонэм Картер. (3) Помимо этого, в приложение войдут фотографии и видеозаписи, документы из архива Фонда Анны Франк, план здания в Амстердаме, где Анна с семьей скрывались от нацистов, и факсимильные копии страниц дневника. (4) Приложение, которое получит название Anne Frank App, выйдет 18 октября. (5) Интерфейс программы будет англоязычным. (6) На каких платформах будет доступно Anne Frank App, не уточняется. Анна Франк родилась в Германии в 1929 году. (7) Когда в стране начались гонения на евреев, Анна с семьей перебрались в Нидерланды. (8) С 1942 года члены семьи Франк и еще несколько человек скрывались от нацистов в потайных комнатах дома в Амстердаме, который занимала компания отца Анны. (9) В 1944 году группу по доносу обнаружили гестаповцы. (10) Обитатели \"Убежища\" (так Анна называла дом в дневнике) были отправлены в концлагеря; выжить удалось только отцу девочки Отто Франку. (11) Находясь в \"Убежище\", Анна вела дневник, в котором описывала свою жизнь и жизнь своих близких. (12) После ареста книгу с записями сохранила подруга семьи Франк и впоследствии передала ее отцу Анны. (13) Дневник был впервые опубликован в 1947 году. (14) Сейчас он переведен более чем на 60 языков.\"", "question": "Какая информация войдет в новой мобильное приложение?", "answer": "Видеозаписи Анны Франк.", "idx": { "paragraph": 0, "question": 2, "answer": 10 }, "label": -1 } ``` #### TERRa - **Size of downloaded dataset files:** 0.93 MB - **Size of the generated dataset:** 3.44 MB - **Total amount of disk used:** 4.39 MB An example of 'train'/'dev' looks as follows ``` { "premise": "Музей, расположенный в Королевских воротах, меняет экспозицию. На смену выставке, рассказывающей об истории ворот и их реставрации, придет «Аптека трех королей». Как рассказали в музее, посетители попадут в традиционный интерьер аптеки.", "hypothesis": "Музей закроется навсегда.", "idx": 10, "label": 1 } ``` An example of 'test' looks as follows ``` { "premise": "Маршрутка полыхала несколько минут. Свидетели утверждают, что приезду пожарных салон «Газели» выгорел полностью. К счастью, пассажиров внутри не было, а водитель успел выскочить из кабины.", "hypothesis": "Маршрутка выгорела.", "idx": 10, "label": -1 } ``` #### RUSSE - **Size of downloaded dataset files:** 3.88 MB - **Size of the generated dataset:** 20.97 MB - **Total amount of disk used:** 25.17 MB An example of 'train'/'dev' looks as follows ``` { "word": "дух", "sentence1": "Завертелась в доме веселая коловерть: праздничный стол, праздничный дух, шумные разговоры", "sentence2": "Вижу: духи собралися / Средь белеющих равнин. // Бесконечны, безобразны, / В мутной месяца игре / Закружились бесы разны, / Будто листья в ноябре", "start1": 68, "start2": 6, "end1": 72, "end2": 11, "gold_sense1": 3, "gold_sense2": 4, "idx": 10, "label": 0 } ``` An example of 'test' looks as follows ``` { "word": "доска", "sentence1": "На 40-й день после трагедии в переходе была установлена мемориальная доска, надпись на которой гласит: «В память о погибших и пострадавших от террористического акта 8 августа 2000 года».", "sentence2": "Фото с 36-летним миллиардером привлекло сеть его необычной фигурой при стойке на доске и кремом на лице.", "start1": 69, "start2": 81, "end1": 73, "end2": 85, "gold_sense1": -1, "gold_sense2": -1, "idx": 10, "label": -1 } ``` #### RWSD - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.29 MB - **Total amount of disk used:** 0.320 MB An example of 'train'/'dev' looks as follows ``` { "text": "Женя поблагодарила Сашу за помощь, которую она оказала.", "span1_index": 0, "span2_index": 6, "span1_text": "Женя", "span2_text": "она оказала", "idx": 10, "label": 0 } ``` An example of 'test' looks as follows ``` { "text": "Мод и Дора видели, как через прерию несутся поезда, из двигателей тянулись клубы черного дыма. Ревущие звуки их моторов и дикие, яростные свистки можно было услышать издалека. Лошади убежали, когда они увидели приближающийся поезд.", "span1_index": 22, "span2_index": 30, "span1_text": "свистки", "span2_text": "они увидели", "idx": 10, "label": -1 } ``` #### DaNetQA - **Size of downloaded dataset files:** 1.36 MB - **Size of the generated dataset:** 4.82 MB - **Total amount of disk used:** 5.9 MB An example of 'train'/'dev' looks as follows ``` { "question": "Вреден ли алкоголь на первых неделях беременности?", "passage": "А Бакингем-Хоуз и её коллеги суммировали последствия, найденные в обзорных статьях ранее. Частые случаи задержки роста плода, результатом чего является укороченный средний срок беременности и сниженный вес при рождении. По сравнению с нормальными детьми, дети 3-4-недельного возраста демонстрируют «менее оптимальную» двигательную активность, рефлексы, и ориентацию в пространстве, а дети 4-6 лет показывают низкий уровень работы нейроповеденческих функций, внимания, эмоциональной экспрессии, и развития речи и языка. Величина этих влияний часто небольшая, частично в связи с независимыми переменными: включая употребление во время беременности алкоголя/табака, а также факторы среды . У детей школьного возраста проблемы с устойчивым вниманием и контролем своего поведения, а также незначительные с ростом, познавательными и языковыми способностями.", "idx": 10, "label": 1 } ``` An example of 'test' looks as follows ``` { "question": "Вредна ли жесткая вода?", "passage": "Различают временную жёсткость, обусловленную гидрокарбонатами кальция и магния Са2; Mg2, и постоянную жёсткость, вызванную присутствием других солей, не выделяющихся при кипячении воды: в основном, сульфатов и хлоридов Са и Mg. Жёсткая вода при умывании сушит кожу, в ней плохо образуется пена при использовании мыла. Использование жёсткой воды вызывает появление осадка на стенках котлов, в трубах и т. п. В то же время, использование слишком мягкой воды может приводить к коррозии труб, так как, в этом случае отсутствует кислотно-щелочная буферность, которую обеспечивает гидрокарбонатная жёсткость. Потребление жёсткой или мягкой воды обычно не является опасным для здоровья, однако есть данные о том, что высокая жёсткость способствует образованию мочевых камней, а низкая — незначительно увеличивает риск сердечно-сосудистых заболеваний. Вкус природной питьевой воды, например, воды родников, обусловлен именно присутствием солей жёсткости.", "idx": 100, "label": -1 } ``` #### RuCoS - **Size of downloaded dataset files:** 56.62 MB - **Size of the generated dataset:** 202.38 MB - **Total amount of disk used:** 261.10 MB An example of 'train'/'dev' looks as follows ``` { "passage": "В Абхазии 24 августа на досрочных выборах выбирают нового президента. Кто бы ни стал победителем, возможности его будут ограничены, говорят эксперты, опрошенные DW. В Абхазии 24 августа проходят досрочные выборы президента не признанной международным сообществом республики. Толчком к их проведению стали массовые протесты в конце мая 2014 года, в результате которых со своего поста был вынужден уйти действующий президент Абхазии Александр Анкваб. Эксперты называют среди наиболее перспективных кандидатов находящегося в оппозиции политика Рауля Хаджимбу, экс-главу службы безопасности Аслана Бжанию и генерала Мираба Кишмарию, исполняющего обязанности министра обороны. У кого больше шансов\n\"Ставки делаются на победу Хаджимбы.\n@highlight\nВ Швеции задержаны двое граждан РФ в связи с нападением на чеченского блогера\n@highlight\nТуризм в эпоху коронавируса: куда поехать? И ехать ли вообще?\n@highlight\nКомментарий: Россия накануне эпидемии - виноватые назначены заранее", "query": "Несмотря на то, что Кремль вложил много денег как в @placeholder, так и в Южную Осетию, об экономическом восстановлении данных регионов говорить не приходится, считает Хальбах: \"Многие по-прежнему живут в полуразрушенных домах и временных жилищах\".", "entities": [ "DW.", "Абхазии ", "Александр Анкваб.", "Аслана Бжанию ", "Мираба Кишмарию,", "РФ ", "Рауля Хаджимбу,", "Россия ", "Хаджимбы.", "Швеции " ], "answers": [ "Абхазии" ], "idx": { "passage": 500, "query": 500 } } ``` An example of 'test' looks as follows ``` { "passage": "Почему и как изменится курс белорусского рубля? Какие инструменты следует предпочесть населению, чтобы сохранить сбережения, DW рассказали финансовые аналитики Беларуси. На последних валютных торгах БВФБ 2015 года в среду, 30 декабря, курс белорусского рубля к доллару - 18569, к евро - 20300, к российскому рублю - 255. В 2016 году белорусскому рублю пророчат падение как минимум на 12 процентов к корзине валют, к которой привязан его курс. А чтобы избежать потерь, белорусам советуют диверсифицировать инвестиционные портфели. Чем обусловлены прогнозные изменения котировок белорусского рубля, и какие финансовые инструменты стоит предпочесть, чтобы минимизировать риск потерь?\n@highlight\nВ Германии за сутки выявлено более 100 новых заражений коронавирусом\n@highlight\nРыночные цены на нефть рухнули из-за провала переговоров ОПЕК+\n@highlight\nВ Италии за сутки произошел резкий скачок смертей от COVID-19", "query": "Последнее, убежден аналитик, инструмент для узкого круга профессиональных инвесторов, культуры следить за финансовым состоянием предприятий - такой, чтобы играть на рынке корпоративных облигаций, - в @placeholder пока нет.", "entities": [ "DW ", "Беларуси.", "Германии ", "Италии ", "ОПЕК+" ], "answers": [], "idx": { "passage": 500, "query": 500 } } ``` ### Data Fields #### LiDiRus - `idx`: an `int32` feature - `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1) - `sentence1`: a `string` feature - `sentence2`: a `string` feature - `knowledge`: a `string` feature with possible values `''`, `'World knowledge'`, `'Common sense'` - `lexical-semantics`: a `string` feature - `logic`: a `string` feature - `predicate-argument-structure`: a `string` feature #### RCB - `idx`: an `int32` feature - `label`: a classification label, with possible values `entailment` (0), `contradiction` (1), `neutral` (2) - `premise`: a `string` feature - `hypothesis`: a `string` feature - `verb`: a `string` feature - `negation`: a `string` feature with possible values `'no_negation'`, `'negation'`, `''`, `'double_negation'` #### PARus - `idx`: an `int32` feature - `label`: a classification label, with possible values `choice1` (0), `choice2` (1) - `premise`: a `string` feature - `choice1`: a `string` feature - `choice2`: a `string` feature - `question`: a `string` feature with possible values `'cause'`, `'effect'` #### MuSeRC - `idx`: an `int32` feature - `label` : a classification label, with possible values `false` (0) , `true` (1) (does the provided `answer` contain a factual response to the `question`) - `paragraph`: a `string` feature - `question`: a `string` feature - `answer`: a `string` feature #### TERRa - `idx`: an `int32` feature - `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1) - `premise`: a `string` feature - `hypothesis`: a `string` feature #### RUSSE - `idx`: an `int32` feature - `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given `word` used in the same sense in both sentences) - `word`: a `string` feature - `sentence1`: a `string` feature - `sentence2`: a `string` feature - `gold_sense1`: an `int32` feature - `gold_sense2`: an `int32` feature - `start1`: an `int32` feature - `start2`: an `int32` feature - `end1`: an `int32` feature - `end2`: an `int32` feature #### RWSD - `idx`: an `int32` feature - `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given spans are coreferential) - `text`: a `string` feature - `span1_index`: an `int32` feature - `span2_index`: an `int32` feature - `span1_text`: a `string` feature - `span2_text`: a `string` feature #### DaNetQA - `idx`: an `int32` feature - `label` : a classification label, with possible values `false` (0), `true` (1) (yes/no answer to the `question` found in the `passage`) - `question`: a `string` feature - `passage`: a `string` feature #### RuCoS - `idx`: an `int32` feature - `passage`: a `string` feature - `query`: a `string` feature - `entities`: a `list of strings` feature - `answers`: a `list of strings` feature [More Information Needed] ### Data Splits #### LiDiRus | |test| |---|---:| |LiDiRus|1104| #### RCB | |train|validation|test| |----|---:|----:|---:| |RCB|438|220|438| #### PARus | |train|validation|test| |----|---:|----:|---:| |PARus|400|100|500| #### MuSeRC | |train|validation|test| |----|---:|----:|---:| |MuSeRC|500|100|322| #### TERRa | |train|validation|test| |----|---:|----:|---:| |TERRa|2616|307|3198| #### RUSSE | |train|validation|test| |----|---:|----:|---:| |RUSSE|19845|8508|18892| #### RWSD | |train|validation|test| |----|---:|----:|---:| |RWSD|606|204|154| #### DaNetQA | |train|validation|test| |----|---:|----:|---:| |DaNetQA|1749|821|805| #### RuCoS | |train|validation|test| |----|---:|----:|---:| |RuCoS|72193|7577|7257| ## 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 All our datasets are published by MIT License. ### Citation Information ``` @article{shavrina2020russiansuperglue, title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark}, author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova, Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and Evlampiev, Andrey}, journal={arXiv preprint arXiv:2010.15925}, year={2020} } @misc{fenogenova2022russian, title={Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models}, author={Alena Fenogenova and Maria Tikhonova and Vladislav Mikhailov and Tatiana Shavrina and Anton Emelyanov and Denis Shevelev and Alexandr Kukushkin and Valentin Malykh and Ekaterina Artemova}, year={2022}, eprint={2202.07791}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@slowwavesleep](https://github.com/slowwavesleep) for adding this dataset.
open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1
2023-09-22T13:40:02.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
null
0
994
--- pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [togethercomputer/GPT-JT-6B-v1](https://huggingface.co/togethercomputer/GPT-JT-6B-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T13:39:48.520000](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1/blob/main/results_2023-09-22T13-39-48.520000.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00041946308724832214,\n\ \ \"em_stderr\": 0.00020969854707829363,\n \"f1\": 0.04423657718120805,\n\ \ \"f1_stderr\": 0.0011409456494249344,\n \"acc\": 0.3324266847298275,\n\ \ \"acc_stderr\": 0.00819810174632109\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.00041946308724832214,\n \"em_stderr\": 0.00020969854707829363,\n\ \ \"f1\": 0.04423657718120805,\n \"f1_stderr\": 0.0011409456494249344\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \ \ \"acc_stderr\": 0.0030152942428909413\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6527229676400947,\n \"acc_stderr\": 0.013380909249751237\n\ \ }\n}\n```" repo_url: https://huggingface.co/togethercomputer/GPT-JT-6B-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|arc:challenge|25_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T15:44:05.719684.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T13_39_48.520000 path: - '**/details_harness|drop|3_2023-09-22T13-39-48.520000.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T13-39-48.520000.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T13_39_48.520000 path: - '**/details_harness|gsm8k|5_2023-09-22T13-39-48.520000.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T13-39-48.520000.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hellaswag|10_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T15_44_05.719684 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:44:05.719684.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:44:05.719684.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T13_39_48.520000 path: - '**/details_harness|winogrande|5_2023-09-22T13-39-48.520000.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T13-39-48.520000.parquet' - config_name: results data_files: - split: 2023_07_19T15_44_05.719684 path: - results_2023-07-19T15:44:05.719684.parquet - split: 2023_09_22T13_39_48.520000 path: - results_2023-09-22T13-39-48.520000.parquet - split: latest path: - results_2023-09-22T13-39-48.520000.parquet --- # Dataset Card for Evaluation run of togethercomputer/GPT-JT-6B-v1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/togethercomputer/GPT-JT-6B-v1 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [togethercomputer/GPT-JT-6B-v1](https://huggingface.co/togethercomputer/GPT-JT-6B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T13:39:48.520000](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1/blob/main/results_2023-09-22T13-39-48.520000.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.00041946308724832214, "em_stderr": 0.00020969854707829363, "f1": 0.04423657718120805, "f1_stderr": 0.0011409456494249344, "acc": 0.3324266847298275, "acc_stderr": 0.00819810174632109 }, "harness|drop|3": { "em": 0.00041946308724832214, "em_stderr": 0.00020969854707829363, "f1": 0.04423657718120805, "f1_stderr": 0.0011409456494249344 }, "harness|gsm8k|5": { "acc": 0.012130401819560273, "acc_stderr": 0.0030152942428909413 }, "harness|winogrande|5": { "acc": 0.6527229676400947, "acc_stderr": 0.013380909249751237 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
HuggingFaceH4/testing_codealpaca_small
2023-04-12T21:57:24.000Z
[ "region:us" ]
HuggingFaceH4
null
null
null
3
990
--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 31503 num_examples: 100 - name: test num_bytes: 29802 num_examples: 100 download_size: 44006 dataset_size: 61305 --- # Dataset Card for "testing_codealpaca_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vwxyzjn/lm-human-preferences
2023-09-01T02:02:15.000Z
[ "license:mit", "region:us" ]
vwxyzjn
null
null
null
0
990
--- license: mit ---
open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
2023-09-28T15:50:12.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
null
0
989
--- pretty_name: Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.018246644295302015,\n\ \ \"em_stderr\": 0.0013706682452812888,\n \"f1\": 0.0714765100671141,\n\ \ \"f1_stderr\": 0.0018411955158404013,\n \"acc\": 0.32543219642729987,\n\ \ \"acc_stderr\": 0.007862138879264232\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812888,\n\ \ \"f1\": 0.0714765100671141,\n \"f1_stderr\": 0.0018411955158404013\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022545044\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6440410418310971,\n \"acc_stderr\": 0.013456740656273959\n\ \ }\n}\n```" repo_url: https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_28T15_50_00.560199 path: - '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_28T15_50_00.560199 path: - '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T14_47_41.742069 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_28T15_50_00.560199 path: - '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet' - config_name: results data_files: - split: 2023_07_19T14_47_41.742069 path: - results_2023-07-19T14:47:41.742069.parquet - split: 2023_09_28T15_50_00.560199 path: - results_2023-09-28T15-50-00.560199.parquet - split: latest path: - results_2023-09-28T15-50-00.560199.parquet --- # Dataset Card for Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.018246644295302015, "em_stderr": 0.0013706682452812888, "f1": 0.0714765100671141, "f1_stderr": 0.0018411955158404013, "acc": 0.32543219642729987, "acc_stderr": 0.007862138879264232 }, "harness|drop|3": { "em": 0.018246644295302015, "em_stderr": 0.0013706682452812888, "f1": 0.0714765100671141, "f1_stderr": 0.0018411955158404013 }, "harness|gsm8k|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022545044 }, "harness|winogrande|5": { "acc": 0.6440410418310971, "acc_stderr": 0.013456740656273959 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
hatexplain
2023-01-25T14:31:48.000Z
[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "hate-speech-detection", "arxiv:2012.10289", "arxiv:1703.04009", "arxiv:1908.11049", "arxiv:1812.01693", "region:us" ]
null
Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based.
@misc{mathew2020hatexplain, title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee}, year={2020}, eprint={2012.10289}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
5
988
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: hatexplain pretty_name: hatexplain tags: - hate-speech-detection dataset_info: features: - name: id dtype: string - name: annotators sequence: - name: label dtype: class_label: names: '0': hatespeech '1': normal '2': offensive - name: annotator_id dtype: int32 - name: target sequence: string - name: rationales sequence: sequence: int32 - name: post_tokens sequence: string config_name: plain_text splits: - name: train num_bytes: 7114730 num_examples: 15383 - name: validation num_bytes: 884940 num_examples: 1922 - name: test num_bytes: 884784 num_examples: 1924 download_size: 12848091 dataset_size: 8884454 --- # Dataset Card for hatexplain ## 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:** [Needs More Information] - **Repository:** https://github.com/punyajoy/HateXplain/ - **Paper:** https://arxiv.org/abs/2012.10289 - **Leaderboard:** [Needs More Information] - **Point of Contact:** Punyajoy Saha (punyajoys@iitkgp.ac.in) ### Dataset Summary Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labeling decision (as hate, offensive or normal) is based. WARNING: This dataset contains content that are offensive and/or hateful in nature. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The language supported is English. ## Dataset Structure ### Data Instances Sample Entry: ``` { "id": "24198545_gab", "annotators": [ { "label": 0, # hatespeech "annotator_id": 4, "target": ["African"] }, { "label": 0, # hatespeech "annotator_id": 3, "target": ["African"] }, { "label": 2, # offensive "annotator_id": 5, "target": ["African"] } ], "rationales":[ [0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] ], "post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"] } } ``` ### Data Fields :small_blue_diamond:post_id : Unique id for each post<br/> :small_blue_diamond:annotators : The list of annotations from each annotator<br/> :small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: `hatespeech` (0), `normal` (1) or `offensive` (2)<br/> :small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator<br/> :small_blue_diamond:annotators[target] : A list of target community present in the post<br/> :small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"<br/> :small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated<br/> ### Data Splits [Post_id_divisions](https://github.com/hate-alert/HateXplain/blob/master/Data/post_id_divisions.json) has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1. ## Dataset Creation ### Curation Rationale The existing hate speech datasets do not provide human rationale which could justify the human reasoning behind their annotation process. This dataset allows researchers to move a step in this direction. The dataset provides token-level annotatoins for the annotation decision. ### Source Data We collected the data from Twitter and Gab. #### Initial Data Collection and Normalization We combined the lexicon set provided by [Davidson 2017](https://arxiv.org/abs/1703.04009), [Ousidhoum 2019](https://arxiv.org/abs/1908.11049), and [Mathew 2019](https://arxiv.org/abs/1812.01693) to generate a single lexicon. We do not consider reposts and remove duplicates. We also ensure that the posts do not contain links, pictures, or videos as they indicate additional information that mightnot be available to the annotators. However, we do not exclude the emojis from the text as they might carry importantinformation for the hate and offensive speech labeling task. #### Who are the source language producers? The dataset is human generated using Amazon Mechanical Turk (AMT). ### Annotations #### Annotation process Each post in our dataset contains three types of annotations. First, whether the text is a hate speech, offensive speech, or normal. Second, the target communities in the text. Third, if the text is considered as hate speech, or offensive by majority of the annotators, we further ask the annotators to annotate parts of the text, which are words orphrases that could be a potential reason for the given annotation. Before starting the annotation task, workers are explicitly warned that the annotation task displays some hateful or offensive content. We prepare instructions for workers that clearly explain the goal of the annotation task, how to annotate spans and also include a definition for each category. We provide multiple examples with classification, target community and span annotations to help the annotators understand the task. #### Who are the annotators? To ensure high quality dataset, we use built-in MTurk qualification requirements, namely the HITApproval Rate(95%) for all Requesters’ HITs and the Number of HITs Approved(5,000) requirements. Pilot annotation: In the pilot task, each annotator was provided with 20 posts and they were required to do the hate/offensive speech classification as well as identify the target community (if any). In order to have a clear understanding of the task, they were provided with multiple examples along with explanations for the labelling process. The main purpose of the pilot task was to shortlist those annotators who were able to do the classification accurately. We also collected feedback from annotators to improve the main annotation task. A total of 621 annotators took part in the pilot task. Out of these, 253 were selected for the main task. Main annotation: After the pilot annotation, once we had ascertained the quality of the annotators, we started with the main annotation task. In each round, we would select a batch of around 200 posts. Each post was annotated by three annotators, then majority voting was applied to decide the final label. The final dataset is composed of 9,055 posts from Twitter and 11,093 posts from Gab. The Krippendorff's alpha for the inter-annotator agreement is 0.46 which is higher than other hate speech datasets. ### Personal and Sensitive Information The posts were anonymized by replacing the usernames with <user> token. ## Considerations for Using the Data ### Social Impact of Dataset The dataset could prove beneficial to develop models which are more explainable and less biased. ### Discussion of Biases [Needs More Information] ### Other Known Limitations The dataset has some limitations. First is the lack of external context. The dataset lacks any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Another issue is the focus on English language and lack of multilingual hate speech. ## Additional Information ### Dataset Curators Binny Mathew - IIT Kharagpur, India Punyajoy Saha - IIT Kharagpur, India Seid Muhie Yimam - Universit ̈at Hamburg, Germany Chris Biemann - Universit ̈at Hamburg, Germany Pawan Goyal - IIT Kharagpur, India Animesh Mukherjee - IIT Kharagpur, India ### Licensing Information MIT License ### Citation Information ```bibtex @article{mathew2020hatexplain, title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee}, year={2021}, conference={AAAI conference on artificial intelligence} } ### Contributions Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset.
BeIR/climate-fever
2022-10-23T06:04:48.000Z
[ "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" ]
BeIR
null
null
null
1
988
--- 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.
indonlp/NusaX-senti
2023-01-24T17:02:06.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ace", "language:ban", "language:bjn", "language:bug", "language:en", "language:id", "language:jv", "language:mad", "language:min", "language:nij", "language:su", "language:bbc", "license:cc-by-sa-4.0", "arxiv:2205.15960", "region:us" ]
indonlp
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
@misc{winata2022nusax, title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages}, author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya, Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony, Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo, Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau, Jey Han and Sennrich, Rico and Ruder, Sebastian}, year={2022}, eprint={2205.15960}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
3
986
--- pretty_name: NusaX-senti annotations_creators: - expert-generated language_creators: - expert-generated license: - cc-by-sa-4.0 multilinguality: - multilingual language: - ace - ban - bjn - bug - en - id - jv - mad - min - nij - su - bbc size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification dataset_info: features: - name: id dtype: string - name: text dtype: string - name: lang dtype: string - name: label dtype: class_label: names: 0: negative 1: neutral 2: positive --- # Dataset Card for NusaX-Senti ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [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) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment) - **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960) - **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment) ### Dataset Summary NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English. ### Supported Tasks and Leaderboards - Sentiment analysis for Indonesian languages ### Languages - ace: acehnese, - ban: balinese, - bjn: banjarese, - bug: buginese, - eng: english, - ind: indonesian, - jav: javanese, - mad: madurese, - min: minangkabau, - nij: ngaju, - sun: sundanese, - bbc: toba_batak, ## Dataset Creation ### Curation Rationale There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia. ### Source Data #### Initial Data Collection and Normalization NusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers. #### Who are the source language producers? The data was produced by humans (native speakers). ### Annotations #### Annotation process NusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages. #### Who are the annotators? Native speakers of both Indonesian and the corresponding languages. Annotators were compensated based on the number of translated samples. ### Personal and Sensitive Information Personal information is removed. ## 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 NusaX is created from review text. These data sources may contain some bias. ### Other Known Limitations No other known limitations ## Additional Information ### Licensing Information CC-BY-SA 4.0. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Please contact authors for any information on the dataset. ### Citation Information ``` @misc{winata2022nusax, title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages}, author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya, Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony, Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo, Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau, Jey Han and Sennrich, Rico and Ruder, Sebastian}, year={2022}, eprint={2205.15960}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
Graphcore/vqa
2022-10-25T08:41:02.000Z
[ "language:en", "license:cc-by-4.0", "region:us" ]
Graphcore
VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.
@inproceedings{antol2015vqa, title={Vqa: Visual question answering}, author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi}, booktitle={Proceedings of the IEEE international conference on computer vision}, pages={2425--2433}, year={2015} }
null
1
981
--- language: - en license: - cc-by-4.0 ---
alexandrainst/scandi-qa
2023-01-16T13:51:25.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:mkqa", "source_datasets:natural_questions", "language:da", "language:sv", "language:no", "license:cc-by-sa-4.0", "region:us" ]
alexandrainst
ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish languages. All samples come from the Natural Questions (NQ) dataset, which is a large question answering dataset from Google searches. The Scandinavian questions and answers come from the MKQA dataset, where 10,000 NQ samples were manually translated into, among others, Danish, Norwegian, and Swedish. However, this did not include a translated context, hindering the training of extractive question answering models. We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long answers" from the NQ dataset, being the paragraph in which the answer was found, or otherwise we extract the context by locating the paragraphs which have the largest cosine similarity to the question, and which contains the desired answer. Further, many answers in the MKQA dataset were "language normalised": for instance, all date answers were converted to the format "YYYY-MM-DD", meaning that in most cases these answers are not appearing in any paragraphs. We solve this by extending the MKQA answers with plausible "answer candidates", being slight perturbations or translations of the answer. With the contexts extracted, we translated these to Danish, Swedish and Norwegian using the DeepL translation service for Danish and Swedish, and the Google Translation service for Norwegian. After translation we ensured that the Scandinavian answers do indeed occur in the translated contexts. As we are filtering the MKQA samples at both the "merging stage" and the "translation stage", we are not able to fully convert the 10,000 samples to the Scandinavian languages, and instead get roughly 8,000 samples per language. These have further been split into a training, validation and test split, with the former two containing roughly 750 samples. The splits have been created in such a way that the proportion of samples without an answer is roughly the same in each split.
# @InProceedings{huggingface:dataset, # title = {ScandiQA: A Scandinavian Question Answering Dataset}, # author={Dan Saattrup Nielsen}, # year={2022} # } #
null
7
980
--- pretty_name: ScandiQA language: - da - sv - no license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - mkqa - natural_questions task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for ScandiQA ## Dataset Description - **Repository:** <https://github.com/alexandrainst/scandi-qa> - **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk) - **Size of downloaded dataset files:** 69 MB - **Size of the generated dataset:** 67 MB - **Total amount of disk used:** 136 MB ### Dataset Summary ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish languages. All samples come from the Natural Questions (NQ) dataset, which is a large question answering dataset from Google searches. The Scandinavian questions and answers come from the MKQA dataset, where 10,000 NQ samples were manually translated into, among others, Danish, Norwegian, and Swedish. However, this did not include a translated context, hindering the training of extractive question answering models. We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long answers" from the NQ dataset, being the paragraph in which the answer was found, or otherwise we extract the context by locating the paragraphs which have the largest cosine similarity to the question, and which contains the desired answer. Further, many answers in the MKQA dataset were "language normalised": for instance, all date answers were converted to the format "YYYY-MM-DD", meaning that in most cases these answers are not appearing in any paragraphs. We solve this by extending the MKQA answers with plausible "answer candidates", being slight perturbations or translations of the answer. With the contexts extracted, we translated these to Danish, Swedish and Norwegian using the [DeepL translation service](https://www.deepl.com/pro-api?cta=header-pro-api) for Danish and Swedish, and the [Google Translation service](https://cloud.google.com/translate/docs/reference/rest/) for Norwegian. After translation we ensured that the Scandinavian answers do indeed occur in the translated contexts. As we are filtering the MKQA samples at both the "merging stage" and the "translation stage", we are not able to fully convert the 10,000 samples to the Scandinavian languages, and instead get roughly 8,000 samples per language. These have further been split into a training, validation and test split, with the latter two containing roughly 750 samples. The splits have been created in such a way that the proportion of samples without an answer is roughly the same in each split. ### Supported Tasks and Leaderboards Training machine learning models for extractive question answering is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`), Swedish (`sv`) and Norwegian (`no`). ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 69 MB - **Size of the generated dataset:** 67 MB - **Total amount of disk used:** 136 MB An example from the `train` split of the `da` subset looks as follows. ``` { 'example_id': 123, 'question': 'Er dette en test?', 'answer': 'Dette er en test', 'answer_start': 0, 'context': 'Dette er en testkontekst.', 'answer_en': 'This is a test', 'answer_start_en': 0, 'context_en': "This is a test context.", 'title_en': 'Train test' } ``` ### Data Fields The data fields are the same among all splits. - `example_id`: an `int64` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `answer_start`: an `int64` feature. - `context`: a `string` feature. - `answer_en`: a `string` feature. - `answer_start_en`: an `int64` feature. - `context_en`: a `string` feature. - `title_en`: a `string` feature. ### Data Splits | name | train | validation | test | |----------|------:|-----------:|-----:| | da | 6311 | 749 | 750 | | sv | 6299 | 750 | 749 | | no | 6314 | 749 | 750 | ## Dataset Creation ### Curation Rationale The Scandinavian languages does not have any gold standard question answering dataset. This is not quite gold standard, but the fact both the questions and answers are all manually translated, it is a solid silver standard dataset. ### Source Data The original data was collected from the [MKQA](https://github.com/apple/ml-mkqa/) and [Natural Questions](https://ai.google.com/research/NaturalQuestions) datasets from Apple and Google, respectively. ## Additional Information ### Dataset Curators [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra Institute](https://alexandra.dk/) curated this dataset. ### Licensing Information The dataset is licensed under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).
fujiki/oasst1-89k-ja-reformat-v1
2023-10-07T16:36:18.000Z
[ "license:apache-2.0", "region:us" ]
fujiki
null
null
null
0
979
--- license: apache-2.0 dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: instructions sequence: string - name: responses sequence: string splits: - name: train num_bytes: 58992730 num_examples: 33919 download_size: 21655251 dataset_size: 58992730 ---
darentang/sroie
2021-12-09T15:11:29.000Z
[ "region:us" ]
darentang
https://arxiv.org/abs/2103.10213
@article{2019, title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction}, url={http://dx.doi.org/10.1109/ICDAR.2019.00244}, DOI={10.1109/icdar.2019.00244}, journal={2019 International Conference on Document Analysis and Recognition (ICDAR)}, publisher={IEEE}, author={Huang, Zheng and Chen, Kai and He, Jianhua and Bai, Xiang and Karatzas, Dimosthenis and Lu, Shijian and Jawahar, C. V.}, year={2019}, month={Sep} }
null
1
978
Entry not found
Tevatron/wikipedia-trivia
2021-09-13T23:34:51.000Z
[ "region:us" ]
Tevatron
null
@inproceedings{karpukhin-etal-2020-dense, title = "Dense Passage Retrieval for Open-Domain Question Answering", author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", 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://www.aclweb.org/anthology/2020.emnlp-main.550", doi = "10.18653/v1/2020.emnlp-main.550", pages = "6769--6781", }
null
1
977
Entry not found
gpt3mix/sst2
2021-05-18T08:59:33.000Z
[ "region:us" ]
gpt3mix
null
null
null
0
975
Entry not found
hans
2023-04-05T10:06:58.000Z
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "arxiv:1902.01007", "region:us" ]
null
The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.
@article{DBLP:journals/corr/abs-1902-01007, author = {R. Thomas McCoy and Ellie Pavlick and Tal Linzen}, title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference}, journal = {CoRR}, volume = {abs/1902.01007}, year = {2019}, url = {http://arxiv.org/abs/1902.01007}, archivePrefix = {arXiv}, eprint = {1902.01007}, timestamp = {Tue, 21 May 2019 18:03:36 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
null
3
970
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: hans pretty_name: Heuristic Analysis for NLI Systems dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': non-entailment - name: parse_premise dtype: string - name: parse_hypothesis dtype: string - name: binary_parse_premise dtype: string - name: binary_parse_hypothesis dtype: string - name: heuristic dtype: string - name: subcase dtype: string - name: template dtype: string config_name: plain_text splits: - name: train num_bytes: 15916371 num_examples: 30000 - name: validation num_bytes: 15893137 num_examples: 30000 download_size: 30947358 dataset_size: 31809508 --- # Dataset Card for "hans" ## 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/tommccoy1/hans](https://github.com/tommccoy1/hans) - **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:** 30.94 MB - **Size of the generated dataset:** 31.81 MB - **Total amount of disk used:** 62.76 MB ### Dataset Summary The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. ### 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 #### plain_text - **Size of downloaded dataset files:** 30.94 MB - **Size of the generated dataset:** 31.81 MB - **Total amount of disk used:** 62.76 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `non-entailment` (1). - `parse_premise`: a `string` feature. - `parse_hypothesis`: a `string` feature. - `binary_parse_premise`: a `string` feature. - `binary_parse_hypothesis`: a `string` feature. - `heuristic`: a `string` feature. - `subcase`: a `string` feature. - `template`: a `string` feature. ### Data Splits | name |train|validation| |----------|----:|---------:| |plain_text|30000| 30000| ## 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 ``` @article{DBLP:journals/corr/abs-1902-01007, author = {R. Thomas McCoy and Ellie Pavlick and Tal Linzen}, title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference}, journal = {CoRR}, volume = {abs/1902.01007}, year = {2019}, url = {http://arxiv.org/abs/1902.01007}, archivePrefix = {arXiv}, eprint = {1902.01007}, timestamp = {Tue, 21 May 2019 18:03:36 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
GabeHD/pokemon-type-captions
2022-10-23T04:40:59.000Z
[ "region:us" ]
GabeHD
null
null
null
3
970
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 19372532.0 num_examples: 898 download_size: 0 dataset_size: 19372532.0 --- # Dataset Card for Pokémon type captions Contains official artwork and type-specific caption for Pokémon #1-898 (Bulbasaur-Calyrex). Each Pokémon is represented once by the default form from [PokéAPI](https://pokeapi.co/) Each row contains `image` and `text` keys: - `image` is a 475x475 PIL jpg of the Pokémon's official artwork. - `text` is a label describing the Pokémon by its type(s) ## Attributions _Images and typing information pulled from [PokéAPI](https://pokeapi.co/)_ _Based on the [Lambda Labs Pokémon Blip Captions Dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)_
banghua/tldr_reward_model_labeled
2023-09-21T19:08:04.000Z
[ "region:us" ]
banghua
null
null
null
0
968
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 300444471.0 num_examples: 176163 download_size: 177215543 dataset_size: 300444471.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "tldr_reward_model_labeled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hendrycks/ethics
2023-04-19T18:55:00.000Z
[ "language:en", "license:mit", "AI Alignment", "arxiv:2008.02275", "region:us" ]
hendrycks
A benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality.
@article{hendrycks2020aligning, title={Aligning ai with shared human values}, author={Hendrycks, Dan and Burns, Collin and Basart, Steven and Critch, Andrew and Li, Jerry and Song, Dawn and Steinhardt, Jacob}, journal={arXiv preprint arXiv:2008.02275}, year={2020} }
null
6
965
--- license: mit language: en dataset_info: - config_name: default features: - name: label dtype: int64 - name: input dtype: string - config_name: commonsense features: - name: label dtype: int32 - name: input dtype: string splits: - name: train num_bytes: 14429921 num_examples: 13910 - name: validation num_bytes: 3148616 num_examples: 3885 - name: test num_bytes: 3863068 num_examples: 3964 download_size: 21625153 dataset_size: 21441605 - config_name: deontology features: - name: label dtype: int32 - name: scenario dtype: string - name: excuse dtype: string splits: - name: train num_bytes: 1854277 num_examples: 18164 - name: validation num_bytes: 369318 num_examples: 3596 - name: test num_bytes: 359268 num_examples: 3536 download_size: 2384007 dataset_size: 2582863 - config_name: justice features: - name: label dtype: int32 - name: scenario dtype: string splits: - name: train num_bytes: 2423889 num_examples: 21791 - name: validation num_bytes: 297935 num_examples: 2704 - name: test num_bytes: 228008 num_examples: 2052 download_size: 2837375 dataset_size: 2949832 - config_name: utilitarianism features: - name: baseline dtype: string - name: less_pleasant dtype: string splits: - name: train num_bytes: 2186713 num_examples: 13737 - name: validation num_bytes: 730391 num_examples: 4807 - name: test num_bytes: 668429 num_examples: 4271 download_size: 3466564 dataset_size: 3585533 - config_name: virtue features: - name: label dtype: int32 - name: scenario dtype: string splits: - name: train num_bytes: 2605021 num_examples: 28245 - name: validation num_bytes: 467254 num_examples: 4975 - name: test num_bytes: 452491 num_examples: 4780 download_size: 3364070 dataset_size: 3524766 tags: - AI Alignment --- # Dataset Card for ETHICS This is the data from [Aligning AI With Shared Human Values](https://arxiv.org/pdf/2008.02275) by Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt, published at ICLR 2021. For more information, see the [Github Repo](https://github.com/hendrycks/ethics). ## Dataset Summary This dataset provides ethics-based tasks for evaluating language models for AI alignment. ## Loading Data To load this data, you can use HuggingFace datasets and the dataloader script. ``` from datasets import load_dataset load_dataset("hendrycks/ethics", "commonsense") ``` Where `commonsense` is one of the following sections: commonsense, deontology, justice, utilitarianism, and virtue. ### Citation Information ``` @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```
esnli
2023-04-05T10:05:24.000Z
[ "language:en", "region:us" ]
null
The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to include human-annotated natural language explanations of the entailment relations.
@incollection{NIPS2018_8163, title = {e-SNLI: Natural Language Inference with Natural Language Explanations}, author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil}, booktitle = {Advances in Neural Information Processing Systems 31}, editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett}, pages = {9539--9549}, year = {2018}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf} }
null
15
964
--- language: - en paperswithcode_id: e-snli pretty_name: e-SNLI dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: explanation_1 dtype: string - name: explanation_2 dtype: string - name: explanation_3 dtype: string config_name: plain_text splits: - name: test num_bytes: 3387169 num_examples: 9824 - name: train num_bytes: 108024142 num_examples: 549367 - name: validation num_bytes: 3423725 num_examples: 9842 download_size: 204516010 dataset_size: 114835036 --- # Dataset Card for "esnli" ## 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/OanaMariaCamburu/e-SNLI](https://github.com/OanaMariaCamburu/e-SNLI) - **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:** 204.51 MB - **Size of the generated dataset:** 114.84 MB - **Total amount of disk used:** 319.35 MB ### Dataset Summary The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to include human-annotated natural language explanations of the entailment relations. ### 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 #### plain_text - **Size of downloaded dataset files:** 204.51 MB - **Size of the generated dataset:** 114.84 MB - **Total amount of disk used:** 319.35 MB An example of 'validation' looks as follows. ``` { "explanation_1": "A woman must be present to smile.", "explanation_2": "A woman smiling implies that she is present.", "explanation_3": "A smiling woman is also present.", "hypothesis": "A woman is present.", "label": 0, "premise": "A woman smiles at the child." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `explanation_1`: a `string` feature. - `explanation_2`: a `string` feature. - `explanation_3`: a `string` feature. ### Data Splits | name |train |validation|test| |----------|-----:|---------:|---:| |plain_text|549367| 9842|9824| ## 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 ``` @incollection{NIPS2018_8163, title = {e-SNLI: Natural Language Inference with Natural Language Explanations}, author = {Camburu, Oana-Maria and Rockt"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil}, booktitle = {Advances in Neural Information Processing Systems 31}, editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett}, pages = {9539--9549}, year = {2018}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
RikoteMaster/isear_for_llama2
2023-08-03T13:01:30.000Z
[ "region:us" ]
RikoteMaster
null
null
null
0
960
--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool - name: text dtype: string splits: - name: train num_bytes: 3715314 num_examples: 7499 - name: validation num_bytes: 645323 num_examples: 1324 - name: test num_bytes: 854222 num_examples: 1879 download_size: 567800 dataset_size: 5214859 --- # Dataset Card for "isear_for_llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kyujinpy/KOpen-platypus
2023-10-06T17:07:39.000Z
[ "size_categories:10K<n<100K", "language:en", "language:ko", "license:cc-by-4.0", "arxiv:2308.07317", "region:us" ]
kyujinpy
null
null
null
18
960
--- license: cc-by-4.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: train num_examples: 24926 language: - en - ko size_categories: - 10K<n<100K --- # KOpenPlatypus: Korean Translation dataset about Open-Platypus ## Korean Translation Method I use [DeepL-pro-API](https://www.deepl.com/ko/pro/change-plan?cta=header-pro#single) and selenium. It takes about 140h times. ## Korean Translation post-processing ![image](./typeA.png) ![image](./typeB.png) ![image](./typeE.png) ![image](./typeD.png) ![image](./typeC.png) And also, applying post-processing. See below lists. (*약 2000개 이상의 코드 관련 데이터를 수작업으로 수정함) 1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정 2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존 3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴 4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음) 5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정 6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역 7. `고유명사`는 최대한 유지함 - 95% 이상의 번역 오류는 전부 고친 것으로 생각됨. - 약 144h 정도 번역 작업을 진행함. (72h/72h; Translation/Post-processing) ## Introdcution This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%: | Dataset Name | License Type | |--------------------------------------------------------------|--------------| | [PRM800K](https://github.com/openai/prm800k) | MIT | | [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) | | [SciBench](https://github.com/mandyyyyii/scibench) | MIT | | [ReClor](https://whyu.me/reclor/) | Non-commercial | | [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT | | [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed | | [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other | | [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 | | [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0 | | [ARB](https://arb.duckai.org) | MIT | | [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | apache-2.0 | ## Data Contamination Check We've removed approximately 200 questions that appear in the Hugging Face benchmark test sets. Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information. ## Model Info Please see models at [`garage-bAInd`](https://huggingface.co/garage-bAInd). ## Training and filtering code Please see the [Platypus GitHub repo](https://github.com/arielnlee/Platypus). ## Citations ```bibtex @article{platypus2023, title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, booktitle={arXiv preprint arxiv:2308.07317}, year={2023} } ``` ```bibtex @article{lightman2023lets, title={Let's Verify Step by Step}, author={Lightman, Hunter and Kosaraju, Vineet and Burda, Yura and Edwards, Harri and Baker, Bowen and Lee, Teddy and Leike, Jan and Schulman, John and Sutskever, Ilya and Cobbe, Karl}, journal={preprint arXiv:2305.20050}, year={2023} } ``` ```bibtex @inproceedings{lu2022learn, title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan}, booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)}, year={2022} } ``` ```bibtex @misc{wang2023scibench, title={SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models}, author={Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun and Wei Wang}, year={2023}, arXiv eprint 2307.10635 } ``` ```bibtex @inproceedings{yu2020reclor, author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi}, title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning}, booktitle = {International Conference on Learning Representations (ICLR)}, month = {April}, year = {2020} } ``` ```bibtex @article{chen2023theoremqa, title={TheoremQA: A Theorem-driven Question Answering dataset}, author={Chen, Wenhu and Ming Yin, Max Ku, Elaine Wan, Xueguang Ma, Jianyu Xu, Tony Xia, Xinyi Wang, Pan Lu}, journal={preprint arXiv:2305.12524}, year={2023} } ``` ```bibtex @inproceedings{OpenBookQA2018, title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering}, author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal}, booktitle={EMNLP}, year={2018} } ``` ```bibtex @misc{sawada2023arb, title={ARB: Advanced Reasoning Benchmark for Large Language Models}, author={Tomohiro Sawada and Daniel Paleka and Alexander Havrilla and Pranav Tadepalli and Paula Vidas and Alexander Kranias and John J. Nay and Kshitij Gupta and Aran Komatsuzaki}, arXiv eprint 2307.13692, year={2023} } ```
ddrg/super_eurlex
2023-09-05T15:48:37.000Z
[ "license:mit", "region:us" ]
ddrg
Super-EURLEX dataset containing legal documents from multiple languages. The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html] With one split per language and sector, because the available features (metadata) differs for each sector. Therefore, each sample contains the content of a full legal document in up to 3 different formats. Those are raw HTML and cleaned HTML (if the HTML format was available on the EURLEX website during the scrapping process) and cleaned text. The cleaned text should be available for each sample and was extracted from HTML or PDF. 'Cleaned' HTML stands here for minor cleaning that was done to preserve to a large extent the necessary HTML information like table structures while removing unnecessary complexity which was introduced to the original documents due to actions like writing each sentence into a new object. Additionally, each sample contains metadata which was scrapped on the fly, this implies the following 2 things. First, not every sector contains the same metadata. Second, most metadata might be irrelevant for most use cases. In our minds the most interesting metadata is the celex-id which is used to identify the legal document at hand, but also contains a lot of information about the document see [https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf] as well as eurovoc- concepts, which are labels that define the content of the documents. Eurovoc-Concepts are, for example, only available for the sectors 1, 2, 3, 4, 5, 6, 9, C, and E. The Naming of most metadata is kept like it was on the eurlex website, except for converting it to lower case and replacing whitespaces with '_'.
null
0
957
--- license: mit ---
PygmalionAI/PIPPA
2023-09-07T03:07:55.000Z
[ "task_categories:conversational", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "not-for-all-audiences", "conversational", "roleplay", "custom-format", "a.", "arxiv:2308.05884", "region:us" ]
PygmalionAI
Personal Interaction Pairs between People and AI (PIPPA) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project.
@misc{gosling2023pippa, title={PIPPA: A Partially Synthetic Conversational Dataset}, author={Tear Gosling and Alpin Dale and Yinhe Zheng}, year={2023}, eprint={2308.05884}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
96
953
--- license: apache-2.0 task_categories: - conversational language: - en tags: - not-for-all-audiences - conversational - roleplay - custom-format - a. pretty_name: PIPPA - Personal Interaction Pairs Between People and AI size_categories: - 10K<n<100K viewer: false --- # PIPPA - Personal Interaction Pairs between People and AI It's been a long time coming, but we're proud to finally release the public portion of our conversational dataset to the public. **Personal Interaction Pairs between People and AI** (**PIPPA**) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project. This dataset is a subset of what we have received - it consists only of the valid conversational logs in which the submitter gave consent to redistribute to the public. Furthermore, we have done our best to redact or modify any personal information that could potentially be found within PIPPA. If you have found something within PIPPA which has not been redacted properly, please contact us via. email at `teargosling@pygmalion.chat` or `alpindale@pygmalion.chat` and we'll take care of it for you. You may contact us for any other purpose as well, including yelling at us for when the next model will be released. **⚠️ CAUTION: PIPPA contains conversations, themes and scenarios which can be considered "not safe for work" (NSFW) and/or heavily disturbing in nature. Models trained purely with PIPPA may have the tendency to generate X-rated output. You have been warned.** ## Dataset Summary PIPPA consists of just a little more than 1 million lines of dialogue spread out over 26,000 conversations between users of the popular chatbot website "Character.AI" and its large language model, obtained through a large community effort taking place over the course of several months. Tallying shows that over 1,000 unique personas simulating both real and fictional characters are represented within the dataset, allowing PIPPA and LLMs fine-tuned on it to adapt to many different roleplay domains. The dataset is represented with a JSONL file, with a singular JSON snippet representing one entire conversation. Every snippet contains the following pieces of data: - `submission_timestamp`: The Unix timestamp of when this particular conversation was submitted to the project, in milliseconds. - `categories`: The categories assigned to the character on the Character.AI website, if any were assigned. If no categories were assigned, it will be `null` - `bot_id`: The unique ID assigned to the specific character which the user was conversing with on the website. - `bot_name`: The name of the character. - `bot_greeting`: The introductory line of the character to the user. This is always the first utterance of dialogue in a conversation. - `bot_definitions`: Contains whatever was typed in the **Definitions** field in the character creator on the website. This usually consists of one or more example conversations between the user and the character designed to steer the model towards emulating the persona correctly. Bot definitions required a separate effort to gather, and thus may not be present for a specific persona - if this is the case, an empty string is provided. Because the defintions were written on Character.AI, this field usually follows Character.AI's unique formatting and should be preprocessed before feeding into any model - please see **Appendix A** of the paper for further details. - `bot_description`: Contains whatever was typed in the **Description** field in the character creator on the website. It usually consists of a few sentences which gives a brief overview of the character and any important details about them. - `conversation`: The conversation between the user and the model. This is represented as a list of dictionaries, each dictionary representing a single utterance and containing two key-value pairs: `message`, referring to the utterance itself and `is_human`, which designates whether the dialogue was generated by the user or the LLM. For further information about PIPPA, please refer to our [published paper](https://arxiv.org/abs/2308.05884) or contact us at the emails listed above. ## Files We publish PIPPA in multiple variants, each a singular JSONL file: - **pippa.jsonl**: The original dataset, almost exactly as submitted to us (barring any modifications resulting from the redaction of personally identifiable information). - **pippa_deduped.jsonl**: The 'cleaned' version of PIPPA, with duplicate conversations as well as any conversation with less than three turns removed from the dataset. **We recommend using this file.** - **pippa_metharme.jsonl**: A version of deduped PIPPA which is formatted in a similar way to our [Metharme instructional models](https://huggingface.co/PygmalionAI/metharme-13b), useful as an example to demonstrate how to properly format the PIPPA dataset. If you are using HuggingFace's `datasets` library, you can choose the file you wish to use by specifying the name of it (without extension) as an argument, like so: `dataset = load_dataset("PygmalionAI/PIPPA", 'pippa_deduped')`. The default value is `pippa_deduped`. Thank you for your patience, everyone! ## Citation If you're using our dataset, please consider citing our work: ```bibtex @misc{gosling2023pippa, title={PIPPA: A Partially Synthetic Conversational Dataset}, author={Tear Gosling and Alpin Dale and Yinhe Zheng}, year={2023}, eprint={2308.05884}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ___ Any relationship between the name of this dataset and any public personas is entirely and totally coincidential.
miracl/miracl-corpus
2023-01-05T17:28:26.000Z
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "multilinguality:multilingual", "language:ar", "language:bn", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:hi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "language:te", "language:th", "language:zh", "license:apache-2.0", "arxiv:2210.09984", "region:us" ]
miracl
null
null
null
12
950
--- annotations_creators: - expert-generated language: - ar - bn - en - es - fa - fi - fr - hi - id - ja - ko - ru - sw - te - th - zh multilinguality: - multilingual pretty_name: MIRACL-corpus size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # Dataset Card for MIRACL Corpus ## Dataset Description * **Homepage:** http://miracl.ai * **Repository:** https://github.com/project-miracl/miracl * **Paper:** https://arxiv.org/abs/2210.09984 MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later. The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. ## Dataset Structure Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus: ``` { "docid": "39#0", "title": "Albedo", "text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)." } ``` The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from. The collection can be loaded using: ``` lang='ar' # or any of the 16 languages miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train'] for doc in miracl_corpus: docid = doc['docid'] title = doc['title'] text = doc['text'] ``` ## Dataset Statistics and Links The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps. | Language | # of Passages | # of Articles | Links | Raw Wiki Dump | |:----------------|--------------:|--------------:|:------|:------| | Arabic (ar) | 2,061,414 | 656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2) | Bengali (bn) | 297,265 | 63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2) | English (en) | 32,893,221 | 5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2) | Spanish (es) | 10,373,953 | 1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2) | Persian (fa) | 2,207,172 | 857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2) | Finnish (fi) | 1,883,509 | 447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2) | French (fr) | 14,636,953 | 2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2) | Hindi (hi) | 506,264 | 148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2) | Indonesian (id) | 1,446,315 | 446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2) | Japanese (ja) | 6,953,614 | 1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2) | Korean (ko) | 1,486,752 | 437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2) | Russian (ru) | 9,543,918 | 1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2) | Swahili (sw) | 131,924 | 47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2) | Telugu (te) | 518,079 | 66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2) | Thai (th) | 542,166 | 128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2) | Chinese (zh) | 4,934,368 | 1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)
nlphuji/flickr_1k_test_image_text_retrieval
2023-01-14T19:54:08.000Z
[ "region:us" ]
nlphuji
null
null
null
0
950
# Flickr30k (1K test set) Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006) Homepage: https://shannon.cs.illinois.edu/DenotationGraph/ 1K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip Bibtex: ``` @article{young2014image, title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions}, author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia}, journal={Transactions of the Association for Computational Linguistics}, volume={2}, pages={67--78}, year={2014}, publisher={MIT Press} } ```
wmt18
2023-04-05T13:44:00.000Z
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|opus_paracrawl", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:et", "language:fi", "language:kk", "language:ru", "language:tr", "language:zh", "license:unknown", "region:us" ]
null
null
@InProceedings{bojar-EtAl:2018:WMT1, author = {Bojar, Ond\v{r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} }
null
3
943
--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - et - fi - kk - ru - tr - zh license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|news_commentary - extended|opus_paracrawl - extended|setimes - extended|un_multi task_categories: - translation task_ids: [] pretty_name: WMT18 paperswithcode_id: wmt-2018 dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 1461016186 num_examples: 11046024 - name: validation num_bytes: 674430 num_examples: 3005 - name: test num_bytes: 696229 num_examples: 2983 download_size: 2030359086 dataset_size: 1462386845 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 8187552108 num_examples: 42271874 - name: validation num_bytes: 729519 num_examples: 3004 - name: test num_bytes: 757649 num_examples: 2998 download_size: 3808612335 dataset_size: 8189039276 - config_name: et-en features: - name: translation dtype: translation: languages: - et - en splits: - name: train num_bytes: 647992667 num_examples: 2175873 - name: validation num_bytes: 459398 num_examples: 2000 - name: test num_bytes: 489394 num_examples: 2000 download_size: 524534404 dataset_size: 648941459 - config_name: fi-en features: - name: translation dtype: translation: languages: - fi - en splits: - name: train num_bytes: 857171881 num_examples: 3280600 - name: validation num_bytes: 1388828 num_examples: 6004 - name: test num_bytes: 691841 num_examples: 3000 download_size: 491874780 dataset_size: 859252550 - config_name: kk-en features: - name: translation dtype: translation: languages: - kk - en splits: - name: train - name: validation - name: test download_size: 0 dataset_size: 0 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 13665367647 num_examples: 36858512 - name: validation num_bytes: 1040195 num_examples: 3001 - name: test num_bytes: 1085596 num_examples: 3000 download_size: 4195144356 dataset_size: 13667493438 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: train num_bytes: 60416617 num_examples: 205756 - name: validation num_bytes: 752773 num_examples: 3007 - name: test num_bytes: 770313 num_examples: 3000 download_size: 62263061 dataset_size: 61939703 - config_name: zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 5536169801 num_examples: 25160346 - name: validation num_bytes: 540347 num_examples: 2001 - name: test num_bytes: 1107522 num_examples: 3981 download_size: 2259428767 dataset_size: 5537817670 --- # Dataset Card for "wmt18" ## 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.statmt.org/wmt18/translation-task.html](http://www.statmt.org/wmt18/translation-task.html) - **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:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt18", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### 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 #### cs-en - **Size of downloaded dataset files:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name | train |validation|test| |-----|-------:|---------:|---:| |cs-en|11046024| 3005|2983| ## 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{bojar-EtAl:2018:WMT1, author = {Bojar, Ond {r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
SetFit/sst2
2021-12-25T06:16:15.000Z
[ "region:us" ]
SetFit
null
null
null
3
943
# Stanford Sentiment Treebank - Binary [Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/) with 2 labels: negative, positive Splits are from: [https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data](https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data) Training data is on sentence level, not on phrase level!
news_commentary
2022-11-03T16:47:41.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ar", "language:cs", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:ja", "language:nl", "language:pt", "language:ru", "language:zh", "license:unknown", "region:us" ]
null
A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html 12 languages, 63 bitexts total number of files: 61,928 total number of tokens: 49.66M total number of sentence fragments: 1.93M
@InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
null
21
939
--- annotations_creators: - found language_creators: - found language: - ar - cs - de - en - es - fr - it - ja - nl - pt - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: NewsCommentary dataset_info: - config_name: ar-cs features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - cs splits: - name: train num_bytes: 51546460 num_examples: 52128 download_size: 16242918 dataset_size: 51546460 - config_name: ar-de features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 69681419 num_examples: 68916 download_size: 21446768 dataset_size: 69681419 - config_name: cs-de features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - de splits: - name: train num_bytes: 57470799 num_examples: 172706 download_size: 21623462 dataset_size: 57470799 - config_name: ar-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 80655273 num_examples: 83187 download_size: 24714354 dataset_size: 80655273 - config_name: cs-en features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 54487874 num_examples: 177278 download_size: 20636368 dataset_size: 54487874 - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 73085451 num_examples: 223153 download_size: 26694093 dataset_size: 73085451 - config_name: ar-es features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 79255985 num_examples: 78074 download_size: 24027435 dataset_size: 79255985 - config_name: cs-es features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - es splits: - name: train num_bytes: 56794825 num_examples: 170489 download_size: 20994380 dataset_size: 56794825 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 74708740 num_examples: 209839 download_size: 26653320 dataset_size: 74708740 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 78600789 num_examples: 238872 download_size: 28106064 dataset_size: 78600789 - config_name: ar-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 71035061 num_examples: 69157 download_size: 21465481 dataset_size: 71035061 - config_name: cs-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - fr splits: - name: train num_bytes: 50364837 num_examples: 148578 download_size: 18483528 dataset_size: 50364837 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 67083899 num_examples: 185442 download_size: 23779967 dataset_size: 67083899 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 70340014 num_examples: 209479 download_size: 24982452 dataset_size: 70340014 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 71025933 num_examples: 195241 download_size: 24693126 dataset_size: 71025933 - config_name: ar-it features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - it splits: - name: train num_bytes: 17413450 num_examples: 17227 download_size: 5186438 dataset_size: 17413450 - config_name: cs-it features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - it splits: - name: train num_bytes: 10441845 num_examples: 30547 download_size: 3813656 dataset_size: 10441845 - config_name: de-it features: - name: id dtype: string - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 13993454 num_examples: 38961 download_size: 4933419 dataset_size: 13993454 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 14213972 num_examples: 40009 download_size: 4960768 dataset_size: 14213972 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 15139636 num_examples: 41497 download_size: 5215173 dataset_size: 15139636 - config_name: fr-it features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - it splits: - name: train num_bytes: 14216079 num_examples: 38485 download_size: 4867267 dataset_size: 14216079 - config_name: ar-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ja splits: - name: train num_bytes: 661992 num_examples: 569 download_size: 206664 dataset_size: 661992 - config_name: cs-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ja splits: - name: train num_bytes: 487902 num_examples: 622 download_size: 184374 dataset_size: 487902 - config_name: de-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ja splits: - name: train num_bytes: 465575 num_examples: 582 download_size: 171371 dataset_size: 465575 - config_name: en-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 485484 num_examples: 637 download_size: 178451 dataset_size: 485484 - config_name: es-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ja splits: - name: train num_bytes: 484463 num_examples: 602 download_size: 175281 dataset_size: 484463 - config_name: fr-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ja splits: - name: train num_bytes: 418188 num_examples: 519 download_size: 151400 dataset_size: 418188 - config_name: ar-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - nl splits: - name: train num_bytes: 9054134 num_examples: 9047 download_size: 2765542 dataset_size: 9054134 - config_name: cs-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - nl splits: - name: train num_bytes: 5860976 num_examples: 17358 download_size: 2174494 dataset_size: 5860976 - config_name: de-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 7645565 num_examples: 21439 download_size: 2757414 dataset_size: 7645565 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 7316599 num_examples: 19399 download_size: 2575916 dataset_size: 7316599 - config_name: es-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - nl splits: - name: train num_bytes: 7560123 num_examples: 21012 download_size: 2674557 dataset_size: 7560123 - config_name: fr-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - nl splits: - name: train num_bytes: 7603503 num_examples: 20898 download_size: 2659946 dataset_size: 7603503 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 5380912 num_examples: 15428 download_size: 1899094 dataset_size: 5380912 - config_name: ar-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - pt splits: - name: train num_bytes: 11340074 num_examples: 11433 download_size: 3504173 dataset_size: 11340074 - config_name: cs-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - pt splits: - name: train num_bytes: 6183725 num_examples: 18356 download_size: 2310039 dataset_size: 6183725 - config_name: de-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 7699083 num_examples: 21884 download_size: 2794173 dataset_size: 7699083 - config_name: en-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 9238819 num_examples: 25929 download_size: 3310748 dataset_size: 9238819 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 9195685 num_examples: 25551 download_size: 3278814 dataset_size: 9195685 - config_name: fr-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pt splits: - name: train num_bytes: 9261169 num_examples: 25642 download_size: 3254925 dataset_size: 9261169 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 3988570 num_examples: 11407 download_size: 1397344 dataset_size: 3988570 - config_name: nl-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - pt splits: - name: train num_bytes: 3612339 num_examples: 10598 download_size: 1290715 dataset_size: 3612339 - config_name: ar-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 105804303 num_examples: 84455 download_size: 28643600 dataset_size: 105804303 - config_name: cs-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ru splits: - name: train num_bytes: 71185695 num_examples: 161133 download_size: 21917168 dataset_size: 71185695 - config_name: de-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 81812014 num_examples: 175905 download_size: 24610973 dataset_size: 81812014 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 83282480 num_examples: 190104 download_size: 24849511 dataset_size: 83282480 - config_name: es-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 84345850 num_examples: 180217 download_size: 24883942 dataset_size: 84345850 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 75967253 num_examples: 160740 download_size: 22385777 dataset_size: 75967253 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 12915073 num_examples: 27267 download_size: 3781318 dataset_size: 12915073 - config_name: ja-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - ru splits: - name: train num_bytes: 596166 num_examples: 586 download_size: 184791 dataset_size: 596166 - config_name: nl-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - ru splits: - name: train num_bytes: 8933805 num_examples: 19112 download_size: 2662250 dataset_size: 8933805 - config_name: pt-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - ru splits: - name: train num_bytes: 8645475 num_examples: 18458 download_size: 2584012 dataset_size: 8645475 - config_name: ar-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 65483204 num_examples: 66021 download_size: 21625859 dataset_size: 65483204 - config_name: cs-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - zh splits: - name: train num_bytes: 29971192 num_examples: 45424 download_size: 12495392 dataset_size: 29971192 - config_name: de-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 39044704 num_examples: 59020 download_size: 15773631 dataset_size: 39044704 - config_name: en-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 44596087 num_examples: 69206 download_size: 18101984 dataset_size: 44596087 - config_name: es-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 43940013 num_examples: 65424 download_size: 17424938 dataset_size: 43940013 - config_name: fr-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 40144071 num_examples: 59060 download_size: 15817862 dataset_size: 40144071 - config_name: it-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - it - zh splits: - name: train num_bytes: 9676756 num_examples: 14652 download_size: 3799012 dataset_size: 9676756 - config_name: ja-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - zh splits: - name: train num_bytes: 462685 num_examples: 570 download_size: 181924 dataset_size: 462685 - config_name: nl-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - zh splits: - name: train num_bytes: 5509070 num_examples: 8433 download_size: 2218937 dataset_size: 5509070 - config_name: pt-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - zh splits: - name: train num_bytes: 7152774 num_examples: 10873 download_size: 2889296 dataset_size: 7152774 - config_name: ru-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 43112824 num_examples: 47687 download_size: 14225498 dataset_size: 43112824 --- # Dataset Card for NewsCommentary ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/News-Commentary.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [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.
SetFit/enron_spam
2022-01-16T18:12:43.000Z
[ "region:us" ]
SetFit
null
null
null
7
939
This is a version of the [Enron Spam Email Dataset](https://github.com/MWiechmann/enron_spam_data), containing emails (subject + message) and a label whether it is spam or ham.
Matthijs/snacks
2022-04-12T14:26:59.000Z
[ "task_categories:image-classification", "license:cc-by-4.0", "region:us" ]
Matthijs
null
@article{OpenImages2, title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.}, author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Kamali, Shahab and Malloci, Matteo and Pont-Tuset, Jordi and Veit, Andreas and Belongie, Serge and Gomes, Victor and Gupta, Abhinav and Sun, Chen and Chechik, Gal and Cai, David and Feng, Zheyun and Narayanan, Dhyanesh and Murphy, Kevin}, journal={Dataset available from https://storage.googleapis.com/openimages/web/index.html}, year={2017} }
null
6
938
--- pretty_name: Snacks task_categories: - image-classification - computer-vision license: cc-by-4.0 --- # Dataset Card for Snacks ## Dataset Summary This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-by-tutorials/v2.0). The images were taken from the [Google Open Images dataset](https://storage.googleapis.com/openimages/web/index.html), release 2017_11. ## Dataset Structure Number of images in the train/validation/test splits: ```nohighlight train 4838 val 955 test 952 total 6745 ``` Total images in each category: ```nohighlight apple 350 banana 350 cake 349 candy 349 carrot 349 cookie 349 doughnut 350 grape 350 hot dog 350 ice cream 350 juice 350 muffin 348 orange 349 pineapple 340 popcorn 260 pretzel 204 salad 350 strawberry 348 waffle 350 watermelon 350 ``` To save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed. ### Data Splits Train, Test, Validation ## Licensing Information Just like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. The images are listed as having a [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) license. The annotations are licensed by Google Inc. under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. The **credits.csv** file contains the original URL, author information and license for each image.
Multimodal-Fatima/VizWiz
2023-03-07T01:26:12.000Z
[ "region:us" ]
Multimodal-Fatima
null
null
null
1
935
Entry not found
oscar-corpus/OSCAR-2201
2023-05-30T07:48:15.000Z
[ "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" ]
oscar-corpus
The Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.\
@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} }
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--- 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.
mteb/mtop_intent
2022-09-27T19:10:23.000Z
[ "language:de", "language:en", "language:es", "language:fr", "language:hi", "language:th", "region:us" ]
mteb
null
null
null
2
932
--- language: - de - en - es - fr - hi - th ---
mattmdjaga/human_parsing_dataset
2023-09-11T09:07:44.000Z
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "size_categories:10K<n<100K", "region:us" ]
mattmdjaga
null
null
null
10
932
--- size_categories: - 10K<n<100K task_categories: - image-segmentation task_ids: - semantic-segmentation dataset_info: features: - name: image dtype: image - name: mask dtype: image splits: - name: train num_bytes: 5892290030.116 num_examples: 17706 download_size: 5893438158 dataset_size: 5892290030.116 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Human parsing data (ATR) ## 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 This dataset has 17,706 images and mask pairs. It is just a copy of [Deep Human Parsing](https://github.com/lemondan/HumanParsing-Dataset) ATR dataset. The mask labels are: "0": "Background", "1": "Hat", "2": "Hair", "3": "Sunglasses", "4": "Upper-clothes", "5": "Skirt", "6": "Pants", "7": "Dress", "8": "Belt", "9": "Left-shoe", "10": "Right-shoe", "11": "Face", "12": "Left-leg", "13": "Right-leg", "14": "Left-arm", "15": "Right-arm", "16": "Bag", "17": "Scarf" ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions ```bibtex @ARTICLE{ATR, author={Xiaodan Liang and Si Liu and Xiaohui Shen and Jianchao Yang and Luoqi Liu and Jian Dong and Liang Lin and Shuicheng Yan}, journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={Deep Human Parsing with Active Template Regression}, year={2015}, volume={37}, number={12}, pages={2402-2414}, doi={10.1109/TPAMI.2015.2408360}, ISSN={0162-8828}, month={Dec}} @InProceedings{CO-CNN, author={Xiaodan Liang and Chunyan Xu and Xiaohui Shen and Jianchao Yang and Si Liu and Jinhui Tang and Liang Lin and Shuicheng Yan}, journal ={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={ICCV}, year={2015}, } ```
quac
2023-01-25T14:43:01.000Z
[ "task_categories:question-answering", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|wikipedia", "language:en", "license:mit", "arxiv:1808.07036", "region:us" ]
null
Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
@inproceedings{choi-etal-2018-quac, title = "QUAC: Question answering in context", abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.", author = "Eunsol Choi and He He and Mohit Iyyer and Mark Yatskar and Yih, {Wen Tau} and Yejin Choi and Percy Liang and Luke Zettlemoyer", year = "2018", language = "English (US)", series = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018", publisher = "Association for Computational Linguistics", pages = "2174--2184", editor = "Ellen Riloff and David Chiang and Julia Hockenmaier and Jun'ichi Tsujii", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018", note = "2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 ; Conference date: 31-10-2018 Through 04-11-2018", }
null
13
930
--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|wikipedia task_categories: - question-answering - text-generation - fill-mask task_ids: - dialogue-modeling - extractive-qa paperswithcode_id: quac pretty_name: Question Answering in Context dataset_info: features: - name: dialogue_id dtype: string - name: wikipedia_page_title dtype: string - name: background dtype: string - name: section_title dtype: string - name: context dtype: string - name: turn_ids sequence: string - name: questions sequence: string - name: followups sequence: class_label: names: '0': y '1': n '2': m - name: yesnos sequence: class_label: names: '0': y '1': n '2': x - name: answers sequence: - name: texts sequence: string - name: answer_starts sequence: int32 - name: orig_answers struct: - name: texts sequence: string - name: answer_starts sequence: int32 config_name: plain_text splits: - name: train num_bytes: 58174754 num_examples: 11567 - name: validation num_bytes: 7375938 num_examples: 1000 download_size: 77043986 dataset_size: 65550692 --- # Dataset Card for Question Answering in Context ## 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:** [QuAC](https://quac.ai/) - **Paper:** [QuAC: Question Answering in Context](https://arxiv.org/abs/1808.07036) - **Leaderboard:** [QuAC's leaderboard](https://quac.ai/) - **Point of Contact:** [Google group](https://groups.google.com/forum/#!forum/quac_ai) ### Dataset Summary Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context. ### Supported Tasks and Leaderboards The core problem involves predicting a text span to answer a question about a Wikipedia section (extractive question answering). Since QuAC questions include a dialog component, each instance includes a “dialog history” of questions and answers asked in the dialog prior to the given question, along with some additional metadata. Authors provided [an official evaluation script](https://s3.amazonaws.com/my89public/quac/scorer.py) for evaluation. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A validation examples looks like this (one entry per dialogue): ``` { 'dialogue_id': 'C_6abd2040a75d47168a9e4cca9ca3fed5_0', 'wikipedia_page_title': 'Satchel Paige', 'background': 'Leroy Robert "Satchel" Paige (July 7, 1906 - June 8, 1982) was an American Negro league baseball and Major League Baseball (MLB) pitcher who became a legend in his own lifetime by being known as perhaps the best pitcher in baseball history, by his longevity in the game, and by attracting record crowds wherever he pitched. Paige was a right-handed pitcher, and at age 42 in 1948, he was the oldest major league rookie while playing for the Cleveland Indians. He played with the St. Louis Browns until age 47, and represented them in the All-Star Game in 1952 and 1953.', 'section_title': 'Chattanooga and Birmingham: 1926-29', 'context': 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month, of which Paige would collect $50 with the rest going to his mother. He also agreed to pay Lula Paige a $200 advance, and she agreed to the contract. The local newspapers--the Chattanooga News and Chattanooga Times--recognized from the beginning that Paige was special. In April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers. Part way through the 1927 season, Paige\'s contract was sold to the Birmingham Black Barons of the major Negro National League (NNL). According to Paige\'s first memoir, his contract was for $450 per month, but in his second he said it was for $275. Pitching for the Black Barons, Paige threw hard but was wild and awkward. In his first big game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray. Murray then charged the mound and Paige raced for the dugout, but Murray flung his bat and struck Paige above the hip. The police were summoned, and the headline of the Birmingham Reporter proclaimed a "Near Riot." Paige improved and matured as a pitcher with help from his teammates, Sam Streeter and Harry Salmon, and his manager, Bill Gatewood. He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings. Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (Several sources credit his 1929 strikeout total as the all-time single-season record for the Negro leagues, though there is variation among the sources about the exact number of strikeouts.) On April 29 of that season he recorded 17 strikeouts in a game against the Cuban Stars, which exceeded what was then the major league record of 16 held by Noodles Hahn and Rube Waddell. Six days later he struck out 18 Nashville Elite Giants, a number that was tied in the white majors by Bob Feller in 1938. Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut. CANNOTANSWER', 'turn_ids': ['C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#0', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#1', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#2', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#3', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#4', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#5', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#6', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#7'], 'questions': ['what did he do in Chattanooga', 'how did he discover him', 'what position did he play', 'how did they help him', 'when did he go to Birmingham', 'how did he feel about this', 'how did he do with this team', 'What made him leave the team'], 'followups': [0, 2, 0, 1, 0, 1, 0, 1], 'yesnos': [2, 2, 2, 2, 2, 2, 2, 2] 'answers': { 'answer_starts': [ [480, 39, 0, 67, 39], [2300, 2300, 2300], [848, 1023, 848, 848, 1298], [2300, 2300, 2300, 2300, 2300], [600, 600, 600, 634, 600], [2300, 2300, 2300], [939, 1431, 848, 848, 1514], [2106, 2106, 2165] ], 'texts': [ ['April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers.', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige', 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League.', 'manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ['Pitching for the Black Barons,', 'fastball', 'Pitching for', 'Pitching', 'Paige improved and matured as a pitcher with help from his teammates,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ["Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Paige's contract was sold to the Birmingham Black Barons of the major Negro National League (NNL", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons"], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'], ['game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray.', 'He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. ('], ['Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs', 'Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd,', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.'] ] }, 'orig_answers': { 'answer_starts': [39, 2300, 1298, 2300, 600, 2300, 1514, 2165], 'texts': ['Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'CANNOTANSWER', 'Paige improved and matured as a pitcher with help from his teammates,', 'CANNOTANSWER', "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", 'CANNOTANSWER', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.'] }, } ``` ### Data Fields - `dialogue_id`: ID of the dialogue. - `wikipedia_page_title`: title of the Wikipedia page. - `background`: first paragraph of the main Wikipedia article. - `section_tile`: Wikipedia section title. - `context`: Wikipedia section text. - `turn_ids`: list of identification of dialogue turns. One list of ids per dialogue. - `questions`: list of questions in the dialogue. One list of questions per dialogue. - `followups`: list of followup actions in the dialogue. One list of followups per dialogue. `y`: follow, `m`: maybe follow yp, `n`: don't follow up. - `yesnos`: list of yes/no in the dialogue. One list of yes/nos per dialogue. `y`: yes, `n`: no, `x`: neither. - `answers`: dictionary of answers to the questions (validation step of data collection) - `answer_starts`: list of list of starting offsets. For training, list of single element lists (one answer per question). - `texts`: list of list of span texts answering questions. For training, list of single element lists (one answer per question). - `orig_answers`: dictionary of original answers (the ones provided by the teacher in the dialogue) - `answer_starts`: list of starting offsets - `texts`: list of span texts answering questions. ### Data Splits QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog contains between four and twelve questions. The dataset comes with a train/dev split such that there is no overlap in sections across splits. Furthermore, the dev and test sets only include one dialog per section, in contrast to the training set which can have multiple dialogs per section. Dev and test instances come with five reference answers instead of just one as in the training set; we obtain the extra references to improve the reliability of our evaluations, as questions can have multiple valid answer spans. The test set is not publicly available; instead, researchers must submit their models to the [leaderboard](http://quac.ai), which will run the model on our hidden test set. The training set contains 83,568 questions (11,567 dialogues), while 7,354 (1,000) and 7,353 (1,002) separate questions are reserved for the dev and test set respectively. ## Dataset Creation ### Curation Rationale Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Source Data Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Initial Data Collection and Normalization Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Who are the source language producers? Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Annotations Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Annotation process Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. #### Who are the annotators? Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Personal and Sensitive Information Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ## Considerations for Using the Data ### Social Impact of Dataset Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Discussion of Biases Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Other Known Limitations Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ## Additional Information ### Dataset Curators Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset. ### Licensing Information The dataset is distributed under the MIT license. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{choi-etal-2018-quac, title = "{Q}u{AC}: Question Answering in Context", author = "Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1241", doi = "10.18653/v1/D18-1241", pages = "2174--2184", abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at \url{http://quac.ai}.", } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
SetFit/20_newsgroups
2022-02-03T08:27:00.000Z
[ "region:us" ]
SetFit
null
null
null
5
930
This is a version of the [20 newsgroups dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) that is provided in Scikit-learn. From the Scikit-learn docs: > The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a messages posted before and after a specific date. We followed the [recommended practice](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#filtering-text-for-more-realistic-training) to remove headers, signature blocks, and quotations from each news article.
HuggingFaceH4/oasst1_en
2023-06-06T13:54:52.000Z
[ "license:apache-2.0", "region:us" ]
HuggingFaceH4
null
null
null
24
928
--- license: apache-2.0 dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_ift num_bytes: 30802170.224582057 num_examples: 19111 - name: test_ift num_bytes: 3423358.775417942 num_examples: 2124 download_size: 18666186 dataset_size: 34225529.0 --- # Dataset Card for `oasst1_en` This dataset is a processed version of [OpenAssistant's `oasst1` dataset](https://huggingface.co/datasets/OpenAssistant/oasst1) to: * Filter all conversations for English. * Group all conversation trees such that each row in the dataset corresponds to a single conversation. See the `create_dataset.py` script in this repo for the processing details. ## Splits | Split | Description | Size | | :--- | :--- | :--- | | `train` | The full training split | 19034 | | `test` | The full test split | 2115 |
ccdv/govreport-summarization
2022-10-24T20:32:47.000Z
[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "conditional-text-generation", "arxiv:2104.02112", "region:us" ]
ccdv
GovReport dataset for summarization. From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al. See: https://arxiv.org/pdf/2104.02112.pdf See: https://github.com/luyang-huang96/LongDocSum
@misc{huang2021efficient, title={Efficient Attentions for Long Document Summarization}, author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, year={2021}, eprint={2104.02112}, archivePrefix={arXiv}, primaryClass={cs.CL} } }
null
9
926
--- language: - en multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation --- # GovReport dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/govreport-summarization": ("report", "summary") ``` ### Data Fields - `id`: paper id - `report`: a string containing the body of the report - `summary`: a string containing the summary of the report ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts with a RoBERTa tokenizer. | Dataset Split | Number of Instances | Avg. tokens | | ------------- | --------------------|:----------------------| | Train | 17,517 | < 9,000 / < 500 | | Validation | 973 | < 9,000 / < 500 | | Test | 973 | < 9,000 / < 500 | # Cite original article ``` @misc{huang2021efficient, title={Efficient Attentions for Long Document Summarization}, author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, year={2021}, eprint={2104.02112}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
mteb/biorxiv-clustering-s2s
2022-09-27T19:15:35.000Z
[ "language:en", "region:us" ]
mteb
null
null
null
0
919
--- language: - en ---
nelorth/oxford-flowers
2022-12-11T02:38:31.000Z
[ "task_categories:image-classification", "task_categories:unconditional-image-generation", "source_datasets:https://www.robots.ox.ac.uk/~vgg/data/flowers", "license:unknown", "flowers", "oxford", "region:us" ]
nelorth
null
null
null
6
916
--- pretty_name: Oxford Flowers Dataset source_datasets: https://www.robots.ox.ac.uk/~vgg/data/flowers tags: - flowers - oxford task_categories: - image-classification - unconditional-image-generation license: - unknown dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1' '1': '10' '2': '100' '3': '101' '4': '102' '5': '11' '6': '12' '7': '13' '8': '14' '9': '15' '10': '16' '11': '17' '12': '18' '13': '19' '14': '2' '15': '20' '16': '21' '17': '22' '18': '23' '19': '24' '20': '25' '21': '26' '22': '27' '23': '28' '24': '29' '25': '3' '26': '30' '27': '31' '28': '32' '29': '33' '30': '34' '31': '35' '32': '36' '33': '37' '34': '38' '35': '39' '36': '4' '37': '40' '38': '41' '39': '42' '40': '43' '41': '44' '42': '45' '43': '46' '44': '47' '45': '48' '46': '49' '47': '5' '48': '50' '49': '51' '50': '52' '51': '53' '52': '54' '53': '55' '54': '56' '55': '57' '56': '58' '57': '59' '58': '6' '59': '60' '60': '61' '61': '62' '62': '63' '63': '64' '64': '65' '65': '66' '66': '67' '67': '68' '68': '69' '69': '7' '70': '70' '71': '71' '72': '72' '73': '73' '74': '74' '75': '75' '76': '76' '77': '77' '78': '78' '79': '79' '80': '8' '81': '80' '82': '81' '83': '82' '84': '83' '85': '84' '86': '85' '87': '86' '88': '87' '89': '88' '90': '89' '91': '9' '92': '90' '93': '91' '94': '92' '95': '93' '96': '94' '97': '95' '98': '96' '99': '97' '100': '98' '101': '99' splits: - name: train num_bytes: 308119477.446 num_examples: 7169 - name: test num_bytes: 43247670.14 num_examples: 1020 download_size: 346597973 dataset_size: 351367147.58599997 --- # Dataset Card for "oxford-flowers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huggan/flowers-102-categories
2022-04-04T17:21:42.000Z
[ "region:us" ]
huggan
null
null
null
4
908
Entry not found
wyzelabs/RuleRecommendation
2023-09-15T19:26:50.000Z
[ "license:cc-by-nc-nd-4.0", "IoT", "Smart Home", "Rule Recommendation", "Recommendation Systems", "region:us" ]
wyzelabs
null
null
null
8
907
--- license: cc-by-nc-nd-4.0 extra_gated_heading: >- Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions extra_gated_prompt: >- Please read the <a href="https://drive.google.com/uc?id=1eM3RQYeQUZeiIo8cqTgC3ixM17Vhd6QL" target="_blank">Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions</a> carefully. In order to gain access to the data and take part in the Wyze Rule Recommendation challenge, you must first read and consent to these terms and conditions. extra_gated_fields: Name: text Affiliation: text Email: text I have read and agree to the Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions: checkbox tags: - IoT - Smart Home - Rule Recommendation - Recommendation Systems pretty_name: Wyze Rule Recommendation Dataset --- # Wyze Rule Recommendation Dataset <img src="https://drive.google.com/uc?id=1TrgrQk8mWcwseDhP5htvJya8bThGUw1Y" alt="WRR" width="100%"/> <!--- ## Dataset Description - **Paper:TBA** - **Leaderboard:TBA** - **Point of Contact:** ---> ## Dataset Summary The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups. The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation systems for smart home automation. As smart devices proliferate in homes, automating their interactions becomes increasingly complex. Rules recommend how a user's devices could be connected to work together automatically, like a motion sensor triggering a camera to record. But with users having different devices, manually configuring these rules is difficult. This dataset enables creating intelligent algorithms that automatically recommend customized rules tailored to each user's specific smart home setup. By training machine learning models on the diverse real-world data of over 1 million rules from 300,000 Wyze users, researchers can build personalized recommendation systems. These would simplify and enhance automation for end users by suggesting rules that connect their devices in useful ways, while respecting their privacy. The Wyze Rule dataset provides the large-scale and varied data needed to make such personalized, private rule recommendation a reality. The key features of this dataset are: - Over 1 million automation rules governing how users' smart devices interact - Rules are highly personalized based on each user's specific devices and needs - 16 distinct device types like cameras, sensors, lights etc. - There are 44 different trigger states and 46 different action by various devices. - 1,641 unique trigger-action device and state (trigger_device + trigger_state + action + action_device) pairs capturing diverse automation logics - Non-IID distribution among users makes it suitable for federated learning - Allows development of personalized rule recommendation systems while preserving user privacy - Enables benchmarking different algorithms on large-scale real-world data Overall, the Wyze Rule dataset bridges the gap between rule recommendation research and practical applications, facilitating the creation of intelligent home automation systems. Its scale, diversity, and focus on individual users' needs make it a valuable resource for advancing personalized recommendation techniques. ## Dataset Structure The Wyze Rule dataset contains two main CSV files - one for the rules and one for the devices owned by each user. Each rule has attributes like user ID, trigger device, trigger state, action device, and action. For example, a rule could be: user 123, contact sensor, "open", light bulb, "turn on". This captures the trigger condition and the action to take. The device file maps user IDs to the specific devices owned by each user. This is key because automating different device setups requires different valid rules. With 16 device types and 1641 trigger-action state and device pairs, the rules reflect a user's customized needs. Each user can have multiple instances of a device type, like several motion sensors. The non-IID distribution of rules among 300,000 users with varying device combinations makes this dataset uniquely suitable for developing personalized federated learning algorithms for rule recommendation. By separating rules into triggers and actions, the data structure provides flexibility lacking in user-item matrices that treat rules as single items. Overall, the real-world granularity enables personalized automation. ### Data Fields The main two files of this dataset, rules and devices, have the following fields: 1. Rule Dataset: This dataset contains data related to the rules that govern the behavior of Wyze smart home devices. Each row represents a single rule and contains various attributes describing the rule. The attributes of this file are as follows: + `user_id` (int): A unique integer identifier for the user associated with the rule. This identifier has been anonymized and does not contain any information related to the Wyze users. + `trigger_device` (str): The model of the device that triggers the rule when a specific condition is met. It may be a Wyze smart home device such as a sensor or a camera. + `trigger_device_id` (int): A unique integer identifier for the trigger device. + `trigger_state` (str): The state or condition that needs to be met on the trigger device for the rule to be activated. It may represent values such as "on," "off," "motion detected," or "sensor open." + `trigger_state_id` (int): A unique integer identifier for the trigger state. + `action` (str): The action to be executed on the action device when the rule is triggered. It may include values like "power on," "power off," "start recording," or "change brightness." + `action_id` (int): A unique integer identifier for the action. + `action_device` (str): The model of the device that performs an action when the rule is triggered. It is a Wyze smart home device such as a light or a camera. + `action_device_id` (int): A unique integer identifier for the action device. + `rule` (str): The combination of 4 ids as follows: `trigger_device_id`\_\_`trigger_state_id`\_\_`action_id`\_\_`action_device_id` 3. Device Dataset: This file contains data related to the devices owned by users. Each row represents a single device and contains information about the device model and its association with a specific user. There are a number of devices in this dataset that are not used in any rules by users, and hence, are not present in the rule dataset. The attributes of this dataset are as follows: + `user_id` (int): A unique integer identifier for the user associated with the device. + `device_id` (int): A unique integer identifier for the device. + `device_model` (str): The model or type of the device owned by the user. It represents various Wyze smart home devices such as a camera, a sensor, or a switch There are a total of 16 different device types included in this dataset as follows: 1. `Camera` 2. `ClimateSensor` 3. `Cloud` 4. `ContactSensor` 5. `Irrigation` 6. `LeakSensor` 7. `Light` 8. `LightStrip` 9. `Lock` 10. `MeshLight` 11. `MotionSensor` 12. `OutdoorPlug` 13. `Plug` 14. `RobotVacuum` 15. `Switch` 16. `Thermostat` ### Data Splits We have two public splits, which are `train` and `test`. The `train` split contains all the available rules set by the users in the dataset, as well as their device list. In the `test` dataset, for each user in this dataset, we have omitted one rule at random. The goal of building recommendation system is to recommend that omitted rule with high probability. The ground truth for this dataset will be released after the Wyze Rule Recommendation challenge has finished. ### Personal and Sensitive Information Protecting user privacy was a top priority when creating the Wyze Rule dataset. Any personally identifiable information or sensitive data that could reveal users' identities has been meticulously obscured. The user IDs have been anonymized into random numeric values, removing any links to actual Wyze users. The rules simply capture abstract triggers and actions for automation using generic device types. By only retaining high-level functionality while erasing all personal attributes, the Wyze Rule dataset enables developing personalized recommendation algorithms without compromising user privacy. Researchers can leverage this rich real-world data to advance the field of automation systems significantly while ensuring ethical data practices. The dataset creators' commitment to protecting users' privacy will help propel innovation responsibly. ## Considerations for Using the Data This data is mainly released for the [Wyze Rule Recommendation Challenge](https://huggingface.co/spaces/competitions/wyze-rule-recommendation). ### Licensing Information This dataset is licensed by cc-by-nc-nd-4.0, which prohibits commercial use, distribution, modification, and reproduction of the data without permission from the copyright holder. ### Citation Information TBA
mteb/reddit-clustering
2022-09-27T19:13:31.000Z
[ "language:en", "region:us" ]
mteb
null
null
null
0
894
--- language: - en ---
dongyoung4091/hh-generated_flan_t5_rx_xl_all
2023-09-03T02:17:32.000Z
[ "region:us" ]
dongyoung4091
null
null
null
0
894
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: model_A dtype: float64 - name: model_B dtype: float64 - name: external_rm1 dtype: float64 - name: external_rm2 dtype: float64 - name: RM_enough-detail dtype: float64 - name: RM_fail-to-consider-context dtype: float64 - name: RM_readability dtype: float64 - name: zeroshot_helpfulness dtype: float64 - name: zeroshot_specificity dtype: float64 - name: zeroshot_intent dtype: float64 - name: zeroshot_factuality dtype: float64 - name: zeroshot_easy-to-understand dtype: float64 - name: zeroshot_relevance dtype: float64 - name: zeroshot_readability dtype: float64 - name: zeroshot_enough-detail dtype: float64 - name: 'zeroshot_biased:' dtype: float64 - name: zeroshot_fail-to-consider-individual-preferences dtype: float64 - name: zeroshot_repetetive dtype: float64 - name: zeroshot_fail-to-consider-context dtype: float64 - name: zeroshot_too-long dtype: float64 splits: - name: train num_bytes: 7769957 num_examples: 25600 download_size: 3659087 dataset_size: 7769957 --- # Dataset Card for "hh-generated_flan_t5_rx_xl_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xquad_r
2023-06-01T14:59:54.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|squad", "source_datasets:extended|xquad", "language:ar", "language:de", "language:el", "language:en", "language:es", "language:hi", "language:ru", "language:th", "language:tr", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:2004.05484", "region:us" ]
null
XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.
@article{roy2020lareqa, title={LAReQA: Language-agnostic answer retrieval from a multilingual pool}, author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei}, journal={arXiv preprint arXiv:2004.05484}, year={2020} }
null
2
893
--- annotations_creators: - expert-generated language_creators: - found language: - ar - de - el - en - es - hi - ru - th - tr - vi - zh license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - extended|squad - extended|xquad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: xquad-r pretty_name: LAReQA dataset_info: - config_name: ar features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1722799 num_examples: 1190 download_size: 17863417 dataset_size: 1722799 - config_name: de features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1283301 num_examples: 1190 download_size: 17863417 dataset_size: 1283301 - config_name: zh features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 984241 num_examples: 1190 download_size: 17863417 dataset_size: 984241 - config_name: vi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1477239 num_examples: 1190 download_size: 17863417 dataset_size: 1477239 - config_name: en features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1116123 num_examples: 1190 download_size: 17863417 dataset_size: 1116123 - config_name: es features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1273499 num_examples: 1190 download_size: 17863417 dataset_size: 1273499 - config_name: hi features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2682975 num_examples: 1190 download_size: 17863417 dataset_size: 2682975 - config_name: el features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2206690 num_examples: 1190 download_size: 17863417 dataset_size: 2206690 - config_name: th features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2854959 num_examples: 1190 download_size: 17863417 dataset_size: 2854959 - config_name: tr features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1210763 num_examples: 1190 download_size: 17863417 dataset_size: 1210763 - config_name: ru features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 2136990 num_examples: 1190 download_size: 17863417 dataset_size: 2136990 config_names: - ar - de - el - en - es - hi - ru - th - tr - vi - zh --- # 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:** [LAReQA](https://github.com/google-research-datasets/lareqa) - **Repository:** [XQuAD-R](https://github.com/google-research-datasets/lareqa) - **Paper:** [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://arxiv.org/pdf/2004.05484.pdf) - **Point of Contact:** [Noah Constant](mailto:nconstant@google.com) ### Dataset Summary XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset can be found with the following languages: * Arabic: `xquad-r/ar.json` * German: `xquad-r/de.json` * Greek: `xquad-r/el.json` * English: `xquad-r/en.json` * Spanish: `xquad-r/es.json` * Hindi: `xquad-r/hi.json` * Russian: `xquad-r/ru.json` * Thai: `xquad-r/th.json` * Turkish: `xquad-r/tr.json` * Vietnamese: `xquad-r/vi.json` * Chinese: `xquad-r/zh.json` ## Dataset Structure [More Information Needed] ### Data Instances An example from `en` config: ``` {'id': '56beb4343aeaaa14008c925b', 'context': "The Panthers defense gave up just 308 points, ranking sixth in the league, while also leading the NFL in interceptions with 24 and boasting four Pro Bowl selections. Pro Bowl defensive tackle Kawann Short led the team in sacks with 11, while also forcing three fumbles and recovering two. Fellow lineman Mario Addison added 6½ sacks. The Panthers line also featured veteran defensive end Jared Allen, a 5-time pro bowler who was the NFL's active career sack leader with 136, along with defensive end Kony Ealy, who had 5 sacks in just 9 starts. Behind them, two of the Panthers three starting linebackers were also selected to play in the Pro Bowl: Thomas Davis and Luke Kuechly. Davis compiled 5½ sacks, four forced fumbles, and four interceptions, while Kuechly led the team in tackles (118) forced two fumbles, and intercepted four passes of his own. Carolina's secondary featured Pro Bowl safety Kurt Coleman, who led the team with a career high seven interceptions, while also racking up 88 tackles and Pro Bowl cornerback Josh Norman, who developed into a shutdown corner during the season and had four interceptions, two of which were returned for touchdowns.", 'question': 'How many points did the Panthers defense surrender?', 'answers': {'text': ['308'], 'answer_start': [34]}} ``` ### Data Fields - `id` (`str`): Unique ID for the context-question pair. - `context` (`str`): Context for the question. - `question` (`str`): Question. - `answers` (`dict`): Answers with the following keys: - `text` (`list` of `str`): Texts of the answers. - `answer_start` (`list` of `int`): Start positions for every answer text. ### Data Splits The number of questions and candidate sentences for each language for XQuAD-R is shown in the table below: | | XQuAD-R | | |-----|-----------|------------| | | questions | candidates | | ar | 1190 | 1222 | | de | 1190 | 1276 | | el | 1190 | 1234 | | en | 1190 | 1180 | | es | 1190 | 1215 | | hi | 1190 | 1244 | | ru | 1190 | 1219 | | th | 1190 | 852 | | tr | 1190 | 1167 | | vi | 1190 | 1209 | | zh | 1190 | 1196 | ## Dataset Creation [More Information Needed] ### 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 [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators The dataset was initially created by Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips and Yinfei Yang, during work done at Google Research. ### Licensing Information XQuAD-R is distributed under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/legalcode). ### Citation Information ``` @article{roy2020lareqa, title={LAReQA: Language-agnostic answer retrieval from a multilingual pool}, author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei}, journal={arXiv preprint arXiv:2004.05484}, year={2020} } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.
Muennighoff/xP3x-sample
2023-09-18T13:51:06.000Z
[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100M<n<1B", "language:af", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:br", "language:bs", "language:ca", "language:ch", "language:cs", "language:cv", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fo", "language:fr", "language:fy", "language:ga", "language:gd", "language:gl", "language:gn", "language:he", "language:hi", "language:hr", "language:hu", "language:hy", "language:ia", "language:id", "language:ie", "language:io", "language:is", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:km", "language:ko", "language:ku", "language:kw", "language:la", "language:lb", "language:lt", "language:lv", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:nb", "language:nl", "language:nn", "language:no", "language:oc", "language:pl", "language:pt", "language:qu", "language:rn", "language:ro", "language:ru", "language:sh", "language:sl", "language:sq", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tk", "language:tl", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vi", "language:vo", "language:yi", "language:zh", "language:ace", "language:acm", "language:acq", "language:aeb", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "language:ars", "language:ary", "language:arz", "language:as", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:ba", "language:bm", "language:ban", "language:bem", "language:bho", "language:bjn", "language:bo", "language:bug", "language:ceb", "language:cjk", "language:ckb", "language:crh", "language:dik", "language:dyu", "language:dz", "language:ee", "language:fj", "language:fon", "language:fur", "language:fuv", "language:gaz", "language:gu", "language:ht", "language:ha", "language:hne", "language:ig", "language:ilo", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:kbp", "language:kea", "language:khk", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:lo", "language:lij", "language:li", "language:ln", "language:lmo", "language:ltg", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:mar", "language:min", "language:mni", "language:mos", "language:npi", "language:nso", "language:nus", "language:ny", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:prs", "language:quy", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:sc", "language:ss", "language:su", "language:swh", "language:szl", "language:taq", "language:tg", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tum", "language:tw", "language:tzm", "language:umb", "language:uzn", "language:vec", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zsm", "language:zu", "license:apache-2.0", "region:us" ]
Muennighoff
A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities.
@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
1
888
--- annotations_creators: - expert-generated - crowdsourced language: - af - ar - az - be - bg - bn - br - bs - ca - ch - cs - cv - cy - da - de - el - en - eo - es - et - eu - fa - fi - fo - fr - fy - ga - gd - gl - gn - he - hi - hr - hu - hy - ia - id - ie - io - is - it - ja - jv - ka - kk - km - ko - ku - kw - la - lb - lt - lv - mi - mk - ml - mn - mr - ms - mt - my - nb - nl - nn - 'no' - oc - pl - pt - qu - rn - ro - ru - sh - sl - sq - sr - sv - sw - ta - te - th - tk - tl - tr - tt - ug - uk - ur - uz - vi - vo - yi - zh - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu programming_language: - Java - Python - Jupyter-Notebook license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3x size_categories: - 100M<n<1B task_categories: - other --- Can be loaded via e.g.: ```python from datasets import load_dataset d = load_dataset("Muennighoff/xP3x-sample", "apps") ``` 1,000 rows from random languages and splits of xP3x for each of the multilingual datasets represented in [xP3x](https://huggingface.co/datasets/Muennighoff/xP3x).
eugenesiow/Div2k
2022-10-21T04:01:10.000Z
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:other", "other-image-super-resolution", "region:us" ]
eugenesiow
DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and CVPR 2018) and @ PIRM (ECCV 2018)
@InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} }
null
2
885
--- annotations_creators: - machine-generated language_creators: - found language: [] license: - other multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: Div2k tags: - other-image-super-resolution --- # Dataset Card for Div2k ## 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**: https://data.vision.ee.ethz.ch/cvl/DIV2K/ - **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k - **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 ### Dataset Summary DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents. The DIV2K dataset is divided into: - train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors - validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts. Install with `pip`: ```bash pip install datasets super-image ``` Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library: ```python from datasets import load_dataset from super_image import EdsrModel from super_image.data import EvalDataset, EvalMetrics dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation') eval_dataset = EvalDataset(dataset) model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) EvalMetrics().evaluate(model, eval_dataset) ``` ### Supported Tasks and Leaderboards The dataset is commonly used for training and evaluation of the `image-super-resolution` task. Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for: - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2) - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3) - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4) - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8) ### Languages Not applicable. ## Dataset Structure ### Data Instances An example of `train` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png" } ``` ### Data Fields The data fields are the same among all splits. - `hr`: a `string` to the path of the High Resolution (HR) `.png` image. - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image. ### Data Splits | name |train |validation| |-------|-----:|---:| |bicubic_x2|800|100| |bicubic_x3|800|100| |bicubic_x4|800|100| |bicubic_x8|800|100| |unknown_x2|800|100| |unknown_x3|800|100| |unknown_x4|800|100| |realistic_mild_x4|800|100| |realistic_difficult_x4|800|100| |realistic_wild_x4|800|100| ## Dataset Creation ### Curation Rationale Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section. ### Source Data #### Initial Data Collection and Normalization **Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of 12 pixels on both axes. Most of the crawled images were originally above 20M pixels. The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions (like blur and color shifts). **Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr, Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments (cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions. **Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings. The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of bicubic, ANR and A+ methods. Only the 800 train and 100 validation images are included in this dataset. #### Who are the source language producers? The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality, to the diversity of sources (sites and cameras), to the image contents and to the copyrights. ### Annotations #### Annotation process No annotations. #### Who are the annotators? No annotators. ### Personal and Sensitive Information All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ## 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 - **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/) ### Licensing Information Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ### Citation Information ```bibtex @InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
visheratin/laion-coco-nllb
2023-09-20T04:00:48.000Z
[ "task_categories:image-to-text", "task_categories:translation", "size_categories:100K<n<1M", "language:ace", "language:acm", "language:acq", "language:aeb", "language:af", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "language:ar", "language:ars", "language:ary", "language:arz", "language:as", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:ba", "language:bm", "language:ban", "language:be", "language:bem", "language:bn", "language:bho", "language:bjn", "language:bo", "language:bs", "language:bug", "language:bg", "language:ca", "language:ceb", "language:cs", "language:cjk", "language:ckb", "language:crh", "language:cy", "language:da", "language:de", "language:dik", "language:dyu", "language:dz", "language:el", "language:en", "language:eo", "language:et", "language:eu", "language:ee", "language:fo", "language:fj", "language:fi", "language:fon", "language:fr", "language:fur", "language:fuv", "language:gaz", "language:gd", "language:ga", "language:gl", "language:gn", "language:gu", "language:ht", "language:ha", "language:he", "language:hi", "language:hne", "language:hr", "language:hu", "language:hy", "language:ig", "language:ilo", "language:id", "language:is", "language:it", "language:jv", "language:ja", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:ka", "language:kk", "language:kbp", "language:kea", "language:khk", "language:km", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:ko", "language:lo", "language:lij", "language:li", "language:ln", "language:lt", "language:lmo", "language:ltg", "language:lb", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:ml", "language:mar", "language:min", "language:mk", "language:mt", "language:mni", "language:mos", "language:mi", "language:my", "language:nl", "language:nn", "language:nb", "language:npi", "language:nso", "language:nus", "language:ny", "language:oc", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:pl", "language:pt", "language:prs", "language:quy", "language:ro", "language:rn", "language:ru", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:es", "language:sc", "language:sr", "language:ss", "language:su", "language:sv", "language:swh", "language:szl", "language:ta", "language:taq", "language:tt", "language:te", "language:tg", "language:tl", "language:th", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tk", "language:tum", "language:tr", "language:tw", "language:tzm", "language:ug", "language:uk", "language:umb", "language:ur", "language:uzn", "language:vec", "language:vi", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zh", "language:zsm", "language:zu", "license:cc-by-nc-4.0", "arxiv:2309.01859", "doi:10.57967/hf/1006", "region:us" ]
visheratin
null
null
null
12
885
--- language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: cc-by-nc-4.0 size_categories: - 100K<n<1M task_categories: - image-to-text - translation pretty_name: LAION-COCO translated to 200 languages dataset_info: features: - name: id dtype: string - name: url dtype: string - name: eng_caption dtype: string - name: captions sequence: sequence: string - name: score dtype: float64 splits: - name: test num_bytes: 289987047 num_examples: 15937 - name: train num_bytes: 3659435447 num_examples: 200687 download_size: 2512641787 dataset_size: 3949422494 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- # LAION COCO translated into 200 languages This dataset contains the samples of the [LAION-COCO](https://huggingface.co/datasets/laion/laion-coco) dataset translated to 200 languages using the largest [NLLB-200 model](https://huggingface.co/facebook/nllb-200-3.3B) (3.3B parameters). ## Fields description 1. `id` - unique ID of the image. 2. `url` - original URL of the image from the LAION-COCO dataset. 3. `eng_caption` - original English caption from the LAION-COCO dataset. 4. `captions` - a list of captions translated to the languages from the Flores 200 dataset. Every item in the list is a list where the first element is a BCP-47 language code, and the second one is a caption in this language. The list of all language codes for the Flores 200 dataset can be found [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200). 5. `score` - aesthetic score generated using [LAION aesthetic predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor/). The images in the dataset have the score of 4.5+. ## Images The dataset was filtered to contain only working image URLs. However, the availability may change in the future. Because of that, all images from this dataset are available at [https://nllb-data.com/](https://nllb-data.com/). To get the image, use the following format: ``` https://nllb-data.com/{id}.jpg ``` ## Paper The dataset was used to train the models in the paper: "[NLLB-CLIP - train performant multilingual image retrieval model on a budget](https://arxiv.org/abs/2309.01859)".
WizardLM/WizardLM_evol_instruct_70k
2023-08-24T03:59:32.000Z
[ "arxiv:2308.09583", "arxiv:2304.12244", "arxiv:2306.08568", "region:us" ]
WizardLM
null
null
null
106
882
This is the training data of WizardLM. ## News - 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models. - 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**. - 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM. - 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM. | Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License| | ----- |------| ---- |------|-------| ----- | ----- | | WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>| <font size=4> | <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>WizardEval</sup> | <sup>HumanEval</sup> | <sup>License</sup>| | ----- |------| ---- |------|-------| ----- | ----- | ----- | | <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> | <sup>101.4% </sup>|<sup>36.6 pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> | | <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | <sup>99.3% </sup> |<sup>25.0 pass@1</sup>| <sup>Non-commercial</sup>| | <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | <sup>97.8% </sup> | <sup>37.8 pass@1</sup>| <sup>Non-commercial</sup> | | <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | <sup>89.1% </sup> |<sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>| | <sup>WizardLM-7B-V1.0 </sup>| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | <sup>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>| | <sup>WizardCoder-15B-V1.0</sup> | <sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a></sup> | <sup>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a></sup> | || |<sup> 57.3 pass@1 </sup> | <sup> <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a></sup> | </font>
mariosasko/test_multi_dir_dataset
2022-02-25T17:58:58.000Z
[ "region:us" ]
mariosasko
null
null
null
0
879
Entry not found
tasksource/oasst1_pairwise_rlhf_reward
2023-07-04T17:47:46.000Z
[ "language:en", "language:es", "language:ru", "language:de", "language:pl", "language:th", "language:vi", "language:sv", "language:bn", "language:da", "language:he", "language:it", "language:fa", "language:sk", "language:id", "language:nb", "language:el", "language:nl", "language:hu", "language:eu", "language:zh", "language:eo", "language:ja", "language:ca", "language:cs", "language:bg", "language:fi", "language:pt", "language:tr", "language:ro", "language:ar", "language:uk", "language:gl", "language:fr", "language:ko", "region:us" ]
tasksource
null
null
null
19
877
--- dataset_info: features: - name: lang dtype: string - name: parent_id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 40736437 num_examples: 17966 - name: validation num_bytes: 2152443 num_examples: 952 download_size: 22371458 dataset_size: 42888880 language: - en - es - ru - de - pl - th - vi - sv - bn - da - he - it - fa - sk - id - nb - el - nl - hu - eu - zh - eo - ja - ca - cs - bg - fi - pt - tr - ro - ar - uk - gl - fr - ko --- # Dataset Card for "oasst1_pairwise_rlhf_reward" [OASST1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1) preprocessed for reward modeling: ```python import pandas as pd from datasets import load_dataset,concatenate_datasets, Dataset, DatasetDict import numpy as np dataset = load_dataset("OpenAssistant/oasst1") df=concatenate_datasets(list(dataset.values())).to_pandas() m2t=df.set_index("message_id")['text'].to_dict() m2r=df.set_index("message_id")['role'].to_dict() m2p=df.set_index('message_id')['parent_id'].to_dict() m2history=dict() # message id to unrolled history for k,v in m2p.items(): history=[k] while history[-1] in m2p: history+=[m2p[history[-1]]] m2history[k]="\n".join([f"{m2r[m]}: {m2t[m]}" for m in history[::-1] if m]) d=dict() for split in "train","validation": df=dataset[split].to_pandas() df['prompt']=df.parent_id.map(lambda x: m2history.get(x,'')) df=df[~df['rank'].isna()] def agg(x): x=list(x) return [x[0],x[-1]] df=df.groupby(['prompt',"parent_id",'lang'])[['text','rank']].agg(agg).reset_index() df=df[df['rank'].map(lambda x:len(set(x))>1)] df['chosen'] = df.apply(lambda x:x['text'][np.argmin(x['rank'])],axis=1) df['rejected'] = df.apply(lambda x:x['text'][np.argmax(x['rank'])],axis=1) d[split]=Dataset.from_pandas(df[['lang','parent_id','prompt','chosen','rejected']],preserve_index=False) DatasetDict(d).push_to_hub('tasksource/oasst1_pairwise_rlhf_reward') ```
empathetic_dialogues
2023-04-05T10:05:17.000Z
[ "task_categories:conversational", "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "arxiv:1811.00207", "region:us" ]
null
PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
@inproceedings{rashkin2019towards, title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset}, author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau}, booktitle = {ACL}, year = {2019}, }
null
52
874
--- annotations_creators: - crowdsourced language: - en language_creators: - crowdsourced license: - cc-by-nc-4.0 multilinguality: - monolingual pretty_name: EmpatheticDialogues size_categories: - 10K<n<100K source_datasets: - original task_categories: - conversational - question-answering task_ids: - dialogue-generation - open-domain-qa paperswithcode_id: empatheticdialogues dataset_info: features: - name: conv_id dtype: string - name: utterance_idx dtype: int32 - name: context dtype: string - name: prompt dtype: string - name: speaker_idx dtype: int32 - name: utterance dtype: string - name: selfeval dtype: string - name: tags dtype: string splits: - name: test num_bytes: 3011332 num_examples: 10943 - name: train num_bytes: 19040509 num_examples: 76673 - name: validation num_bytes: 3077481 num_examples: 12030 download_size: 28022709 dataset_size: 25129322 --- # Dataset Card for "empathetic_dialogues" ## 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/facebookresearch/EmpatheticDialogues](https://github.com/facebookresearch/EmpatheticDialogues) - **Repository:** https://github.com/facebookresearch/EmpatheticDialogues - **Paper:** [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://arxiv.org/abs/1811.00207) - **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:** 28.02 MB - **Size of the generated dataset:** 25.13 MB - **Total amount of disk used:** 53.15 MB ### Dataset Summary PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset ### 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:** 28.02 MB - **Size of the generated dataset:** 25.13 MB - **Total amount of disk used:** 53.15 MB An example of 'train' looks as follows. ``` { "context": "sentimental", "conv_id": "hit:0_conv:1", "prompt": "I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.", "selfeval": "5|5|5_2|2|5", "speaker_idx": 1, "tags": "", "utterance": "I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world.", "utterance_idx": 1 } ``` ### Data Fields The data fields are the same among all splits. #### default - `conv_id`: a `string` feature. - `utterance_idx`: a `int32` feature. - `context`: a `string` feature. - `prompt`: a `string` feature. - `speaker_idx`: a `int32` feature. - `utterance`: a `string` feature. - `selfeval`: a `string` feature. - `tags`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|76673| 12030|10943| ## 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 Creative Commons [Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{rashkin-etal-2019-towards, title = "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset", author = "Rashkin, Hannah and Smith, Eric Michael and Li, Margaret and Boureau, Y-Lan", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1534", doi = "10.18653/v1/P19-1534", pages = "5370--5381", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
facebook/multilingual_librispeech
2023-02-13T11:33:31.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:nl", "language:fr", "language:it", "language:es", "language:pt", "language:pl", "license:cc-by-4.0", "arxiv:2012.03411", "region:us" ]
facebook
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages: English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
@article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} }
null
27
865
--- pretty_name: MultiLingual LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - nl - fr - it - es - pt - pl license: - cc-by-4.0 multilinguality: - multilingual paperswithcode_id: multilingual-librispeech size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition --- # Dataset Card for MultiLingual LibriSpeech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - **Repository:** [Needs More Information] - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer) ### Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) print(next(iter(mls))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'file': '10900_6473_000030.flac', 'audio': {'path': '10900_6473_000030.flac', 'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ..., 4.27246094e-04, 5.49316406e-04, 4.57763672e-04]), 'sampling_rate': 16000}, 'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n', 'speaker_id': 10900, 'chapter_id': 6473, 'id': '10900_6473_000030'} ``` ### Data Fields - file: A filename .flac format. - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits | | Train | Train.9h | Train.1h | Dev | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | german | 469942 | 2194 | 241 | 3469 | 3394 | | dutch | 374287 | 2153 | 234 | 3095 | 3075 | | french | 258213 | 2167 | 241 | 2416 | 2426 | | spanish | 220701 | 2110 | 233 | 2408 | 2385 | | italian | 59623 | 2173 | 240 | 1248 | 1262 | | portuguese | 37533 | 2116 | 236 | 826 | 871 | | polish | 25043 | 2173 | 238 | 512 | 520 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
lj_speech
2022-11-03T16:16:34.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unlicense", "region:us" ]
null
This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```
@misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 }
null
8
863
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unlicense multilinguality: - monolingual paperswithcode_id: ljspeech pretty_name: LJ Speech size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] train-eval-index: - config: main task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train col_mapping: file: path text: text metrics: - type: wer name: WER - type: cer name: CER dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 22050 - name: file dtype: string - name: text dtype: string - name: normalized_text dtype: string config_name: main splits: - name: train num_bytes: 4667022 num_examples: 13100 download_size: 2748572632 dataset_size: 4667022 --- # Dataset Card for lj_speech ## 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:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) - **Repository:** [N/A] - **Paper:** [N/A] - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech) - **Point of Contact:** [Keith Ito](mailto:kito@kito.us) ### Dataset Summary This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). - `other:automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text. The most common ASR evaluation metric is the word error rate (WER). - `other:text-to-speech`: A TTS model is given a written text in natural language and asked to generate a speech audio file. A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech ### Languages The transcriptions and audio are in English. ## Dataset Structure ### Data Instances A data point comprises the path to the audio file, called `file` and its transcription, called `text`. A normalized version of the text is also provided. ``` { 'id': 'LJ002-0026', 'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 22050}, 'text': 'in the three years between 1813 and 1816,' 'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,', } ``` Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields - id: unique id of the data sample. - file: a path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits The dataset is not pre-split. Some statistics: - Total Clips: 13,100 - Total Words: 225,715 - Total Characters: 1,308,678 - Total Duration: 23:55:17 - Mean Clip Duration: 6.57 sec - Min Clip Duration: 1.11 sec - Max Clip Duration: 10.10 sec - Mean Words per Clip: 17.23 - Distinct Words: 13,821 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization This dataset consists of excerpts from the following works: - Morris, William, et al. Arts and Crafts Essays. 1893. - Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884. - Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42. - Harland, Marion. Marion Harland's Cookery for Beginners. 1893. - Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910. - Banks, Edgar J. The Seven Wonders of the Ancient World. 1916. - President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964. Some details about normalization: - The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8) - 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être"). - The following abbreviations appear in the text. They may be expanded as follows: | Abbreviation | Expansion | |--------------|-----------| | Mr. | Mister | | Mrs. | Misess (*) | | Dr. | Doctor | | No. | Number | | St. | Saint | | Co. | Company | | Jr. | Junior | | Maj. | Major | | Gen. | General | | Drs. | Doctors | | Rev. | Reverend | | Lt. | Lieutenant | | Hon. | Honorable | | Sgt. | Sergeant | | Capt. | Captain | | Esq. | Esquire | | Ltd. | Limited | | Col. | Colonel | | Ft. | Fort | (*) there's no standard expansion for "Mrs." #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process - The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always. - The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators? Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations - The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding. ## Additional Information ### Dataset Curators The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information Public Domain ([LibriVox](https://librivox.org/pages/public-domain/)) ### Citation Information ``` @misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 } ``` ### Contributions Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
Multimodal-Fatima/StanfordCars_train
2023-06-12T06:26:48.000Z
[ "region:us" ]
Multimodal-Fatima
null
null
null
0
862
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': am general hummer suv 2000 '1': acura rl sedan 2012 '2': acura tl sedan 2012 '3': acura tl type-s 2008 '4': acura tsx sedan 2012 '5': acura integra type r 2001 '6': acura zdx hatchback 2012 '7': aston martin v8 vantage convertible 2012 '8': aston martin v8 vantage coupe 2012 '9': aston martin virage convertible 2012 '10': aston martin virage coupe 2012 '11': audi rs 4 convertible 2008 '12': audi a5 coupe 2012 '13': audi tts coupe 2012 '14': audi r8 coupe 2012 '15': audi v8 sedan 1994 '16': audi 100 sedan 1994 '17': audi 100 wagon 1994 '18': audi tt hatchback 2011 '19': audi s6 sedan 2011 '20': audi s5 convertible 2012 '21': audi s5 coupe 2012 '22': audi s4 sedan 2012 '23': audi s4 sedan 2007 '24': audi tt rs coupe 2012 '25': bmw activehybrid 5 sedan 2012 '26': bmw 1 series convertible 2012 '27': bmw 1 series coupe 2012 '28': bmw 3 series sedan 2012 '29': bmw 3 series wagon 2012 '30': bmw 6 series convertible 2007 '31': bmw x5 suv 2007 '32': bmw x6 suv 2012 '33': bmw m3 coupe 2012 '34': bmw m5 sedan 2010 '35': bmw m6 convertible 2010 '36': bmw x3 suv 2012 '37': bmw z4 convertible 2012 '38': bentley continental supersports conv. convertible 2012 '39': bentley arnage sedan 2009 '40': bentley mulsanne sedan 2011 '41': bentley continental gt coupe 2012 '42': bentley continental gt coupe 2007 '43': bentley continental flying spur sedan 2007 '44': bugatti veyron 16.4 convertible 2009 '45': bugatti veyron 16.4 coupe 2009 '46': buick regal gs 2012 '47': buick rainier suv 2007 '48': buick verano sedan 2012 '49': buick enclave suv 2012 '50': cadillac cts-v sedan 2012 '51': cadillac srx suv 2012 '52': cadillac escalade ext crew cab 2007 '53': chevrolet silverado 1500 hybrid crew cab 2012 '54': chevrolet corvette convertible 2012 '55': chevrolet corvette zr1 2012 '56': chevrolet corvette ron fellows edition z06 2007 '57': chevrolet traverse suv 2012 '58': chevrolet camaro convertible 2012 '59': chevrolet hhr ss 2010 '60': chevrolet impala sedan 2007 '61': chevrolet tahoe hybrid suv 2012 '62': chevrolet sonic sedan 2012 '63': chevrolet express cargo van 2007 '64': chevrolet avalanche crew cab 2012 '65': chevrolet cobalt ss 2010 '66': chevrolet malibu hybrid sedan 2010 '67': chevrolet trailblazer ss 2009 '68': chevrolet silverado 2500hd regular cab 2012 '69': chevrolet silverado 1500 classic extended cab 2007 '70': chevrolet express van 2007 '71': chevrolet monte carlo coupe 2007 '72': chevrolet malibu sedan 2007 '73': chevrolet silverado 1500 extended cab 2012 '74': chevrolet silverado 1500 regular cab 2012 '75': chrysler aspen suv 2009 '76': chrysler sebring convertible 2010 '77': chrysler town and country minivan 2012 '78': chrysler 300 srt-8 2010 '79': chrysler crossfire convertible 2008 '80': chrysler pt cruiser convertible 2008 '81': daewoo nubira wagon 2002 '82': dodge caliber wagon 2012 '83': dodge caliber wagon 2007 '84': dodge caravan minivan 1997 '85': dodge ram pickup 3500 crew cab 2010 '86': dodge ram pickup 3500 quad cab 2009 '87': dodge sprinter cargo van 2009 '88': dodge journey suv 2012 '89': dodge dakota crew cab 2010 '90': dodge dakota club cab 2007 '91': dodge magnum wagon 2008 '92': dodge challenger srt8 2011 '93': dodge durango suv 2012 '94': dodge durango suv 2007 '95': dodge charger sedan 2012 '96': dodge charger srt-8 2009 '97': eagle talon hatchback 1998 '98': fiat 500 abarth 2012 '99': fiat 500 convertible 2012 '100': ferrari ff coupe 2012 '101': ferrari california convertible 2012 '102': ferrari 458 italia convertible 2012 '103': ferrari 458 italia coupe 2012 '104': fisker karma sedan 2012 '105': ford f-450 super duty crew cab 2012 '106': ford mustang convertible 2007 '107': ford freestar minivan 2007 '108': ford expedition el suv 2009 '109': ford edge suv 2012 '110': ford ranger supercab 2011 '111': ford gt coupe 2006 '112': ford f-150 regular cab 2012 '113': ford f-150 regular cab 2007 '114': ford focus sedan 2007 '115': ford e-series wagon van 2012 '116': ford fiesta sedan 2012 '117': gmc terrain suv 2012 '118': gmc savana van 2012 '119': gmc yukon hybrid suv 2012 '120': gmc acadia suv 2012 '121': gmc canyon extended cab 2012 '122': geo metro convertible 1993 '123': hummer h3t crew cab 2010 '124': hummer h2 sut crew cab 2009 '125': honda odyssey minivan 2012 '126': honda odyssey minivan 2007 '127': honda accord coupe 2012 '128': honda accord sedan 2012 '129': hyundai veloster hatchback 2012 '130': hyundai santa fe suv 2012 '131': hyundai tucson suv 2012 '132': hyundai veracruz suv 2012 '133': hyundai sonata hybrid sedan 2012 '134': hyundai elantra sedan 2007 '135': hyundai accent sedan 2012 '136': hyundai genesis sedan 2012 '137': hyundai sonata sedan 2012 '138': hyundai elantra touring hatchback 2012 '139': hyundai azera sedan 2012 '140': infiniti g coupe ipl 2012 '141': infiniti qx56 suv 2011 '142': isuzu ascender suv 2008 '143': jaguar xk xkr 2012 '144': jeep patriot suv 2012 '145': jeep wrangler suv 2012 '146': jeep liberty suv 2012 '147': jeep grand cherokee suv 2012 '148': jeep compass suv 2012 '149': lamborghini reventon coupe 2008 '150': lamborghini aventador coupe 2012 '151': lamborghini gallardo lp 570-4 superleggera 2012 '152': lamborghini diablo coupe 2001 '153': land rover range rover suv 2012 '154': land rover lr2 suv 2012 '155': lincoln town car sedan 2011 '156': mini cooper roadster convertible 2012 '157': maybach landaulet convertible 2012 '158': mazda tribute suv 2011 '159': mclaren mp4-12c coupe 2012 '160': mercedes-benz 300-class convertible 1993 '161': mercedes-benz c-class sedan 2012 '162': mercedes-benz sl-class coupe 2009 '163': mercedes-benz e-class sedan 2012 '164': mercedes-benz s-class sedan 2012 '165': mercedes-benz sprinter van 2012 '166': mitsubishi lancer sedan 2012 '167': nissan leaf hatchback 2012 '168': nissan nv passenger van 2012 '169': nissan juke hatchback 2012 '170': nissan 240sx coupe 1998 '171': plymouth neon coupe 1999 '172': porsche panamera sedan 2012 '173': ram c/v cargo van minivan 2012 '174': rolls-royce phantom drophead coupe convertible 2012 '175': rolls-royce ghost sedan 2012 '176': rolls-royce phantom sedan 2012 '177': scion xd hatchback 2012 '178': spyker c8 convertible 2009 '179': spyker c8 coupe 2009 '180': suzuki aerio sedan 2007 '181': suzuki kizashi sedan 2012 '182': suzuki sx4 hatchback 2012 '183': suzuki sx4 sedan 2012 '184': tesla model s sedan 2012 '185': toyota sequoia suv 2012 '186': toyota camry sedan 2012 '187': toyota corolla sedan 2012 '188': toyota 4runner suv 2012 '189': volkswagen golf hatchback 2012 '190': volkswagen golf hatchback 1991 '191': volkswagen beetle hatchback 2012 '192': volvo c30 hatchback 2012 '193': volvo 240 sedan 1993 '194': volvo xc90 suv 2007 '195': smart fortwo convertible 2012 - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: blip_caption_beam_5 dtype: string - name: Attributes_ViT_L_14_text_davinci_003_full sequence: string - name: Attributes_ViT_L_14_text_davinci_003_stanfordcars sequence: string - name: clip_tags_ViT_L_14_with_openai_classes sequence: string - name: clip_tags_ViT_L_14_wo_openai_classes sequence: string - name: clip_tags_ViT_L_14_simple_specific dtype: string - name: clip_tags_ViT_L_14_ensemble_specific dtype: string - name: clip_tags_ViT_B_16_simple_specific dtype: string - name: clip_tags_ViT_B_16_ensemble_specific dtype: string - name: clip_tags_ViT_B_32_ensemble_specific dtype: string - name: Attributes_ViT_B_16_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_LAION_ViT_H_14_2B_simple_specific dtype: string - name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific dtype: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string splits: - name: train num_bytes: 1016273762.0 num_examples: 8144 download_size: 991440998 dataset_size: 1016273762.0 --- # Dataset Card for "StanfordCars_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
natural_questions
2023-04-05T13:35:01.000Z
[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "region:us" ]
null
The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
@article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} }
null
21
861
--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: natural-questions pretty_name: Natural Questions dataset_info: features: - name: id dtype: string - name: document struct: - name: title dtype: string - name: url dtype: string - name: html dtype: string - name: tokens sequence: - name: token dtype: string - name: is_html dtype: bool - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: short_answers sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' - name: long_answer_candidates sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: top_label dtype: bool splits: - name: train num_bytes: 97445142568 num_examples: 307373 - name: validation num_bytes: 2353975312 num_examples: 7830 download_size: 45069199013 dataset_size: 99799117880 --- # Dataset Card for Natural Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset) - **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions) - **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/) - **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:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions) ### Languages en ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB An example of 'train' looks as follows. This is a toy example. ``` { "id": "797803103760793766", "document": { "title": "Google", "url": "http://www.wikipedia.org/Google", "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>", "tokens":[ {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True}, {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False}, {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False}, {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False}, {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True}, {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True}, {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False}, {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False}, {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False}, {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False}, {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False}, {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False}, {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False}, {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True}, {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True}, {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False}, {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True}, {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True}, {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False}, {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True}, {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True}, {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True} ], }, "question" :{ "text": "who founded google", "tokens": ["who", "founded", "google"] }, "long_answer_candidates": [ {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True}, {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False}, {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False}, {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False} ], "annotations": [{ "id": "6782080525527814293", "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0}, "short_answers": [ {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"}, {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"} ], "yes_no_answer": -1 }] } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `document` a dictionary feature containing: - `title`: a `string` feature. - `url`: a `string` feature. - `html`: a `string` feature. - `tokens`: a dictionary feature containing: - `token`: a `string` feature. - `is_html`: a `bool` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `question`: a dictionary feature containing: - `text`: a `string` feature. - `tokens`: a `list` of `string` features. - `long_answer_candidates`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `top_level`: a `bool` feature. - `annotations`: a dictionary feature containing: - `id`: a `string` feature. - `long_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `candidate_index`: a `int64` feature. - `short_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `text`: a `string` feature. - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1). ### Data Splits | name | train | validation | |---------|-------:|-----------:| | default | 307373 | 7830 | | dev | N/A | 7830 | ## 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 [Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
bigcode/the-stack-smol-xl
2023-02-10T17:22:38.000Z
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:code", "region:us" ]
bigcode
null
null
null
3
861
--- annotations_creators: [] language_creators: - crowdsourced language: ["code"] multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- ## Dataset Description A small subset of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, with 87 programming languages, each has 10,000 random samples from the original dataset. ## Languages The dataset contains 87 programming languages: ```` 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' ````` ## Dataset Structure ```python # to load go: from datasets import load_dataset load_dataset("bigcode/the-stack-smol-xl", data_dir="data/go") ```
DFKI-SLT/cross_ner
2023-01-19T09:17:38.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|conll2003", "language:en", "cross domain", "ai", "news", "music", "literature", "politics", "science", "arxiv:2012.04373", "region:us" ]
DFKI-SLT
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains. For details, see the paper: [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
@article{liu2020crossner, title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung}, year={2020}, eprint={2012.04373}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
0
860
--- annotations_creators: - expert-generated language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: CrossNER is a cross-domain dataset for named entity recognition size_categories: - 10K<n<100K source_datasets: - extended|conll2003 tags: - cross domain - ai - news - music - literature - politics - science task_categories: - token-classification task_ids: - named-entity-recognition dataset_info: - config_name: ai features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 65080 num_examples: 100 - name: validation num_bytes: 189453 num_examples: 350 - name: test num_bytes: 225691 num_examples: 431 download_size: 289173 dataset_size: 480224 - config_name: literature features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 63181 num_examples: 100 - name: validation num_bytes: 244076 num_examples: 400 - name: test num_bytes: 270092 num_examples: 416 download_size: 334380 dataset_size: 577349 - config_name: music features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 65077 num_examples: 100 - name: validation num_bytes: 259702 num_examples: 380 - name: test num_bytes: 327195 num_examples: 465 download_size: 414065 dataset_size: 651974 - config_name: conll2003 features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 3561081 num_examples: 14041 - name: validation num_bytes: 891431 num_examples: 3250 - name: test num_bytes: 811470 num_examples: 3453 download_size: 2694794 dataset_size: 5263982 - config_name: politics features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 143507 num_examples: 200 - name: validation num_bytes: 422760 num_examples: 541 - name: test num_bytes: 472690 num_examples: 651 download_size: 724168 dataset_size: 1038957 - config_name: science features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-academicjournal '2': I-academicjournal '3': B-album '4': I-album '5': B-algorithm '6': I-algorithm '7': B-astronomicalobject '8': I-astronomicalobject '9': B-award '10': I-award '11': B-band '12': I-band '13': B-book '14': I-book '15': B-chemicalcompound '16': I-chemicalcompound '17': B-chemicalelement '18': I-chemicalelement '19': B-conference '20': I-conference '21': B-country '22': I-country '23': B-discipline '24': I-discipline '25': B-election '26': I-election '27': B-enzyme '28': I-enzyme '29': B-event '30': I-event '31': B-field '32': I-field '33': B-literarygenre '34': I-literarygenre '35': B-location '36': I-location '37': B-magazine '38': I-magazine '39': B-metrics '40': I-metrics '41': B-misc '42': I-misc '43': B-musicalartist '44': I-musicalartist '45': B-musicalinstrument '46': I-musicalinstrument '47': B-musicgenre '48': I-musicgenre '49': B-organisation '50': I-organisation '51': B-person '52': I-person '53': B-poem '54': I-poem '55': B-politicalparty '56': I-politicalparty '57': B-politician '58': I-politician '59': B-product '60': I-product '61': B-programlang '62': I-programlang '63': B-protein '64': I-protein '65': B-researcher '66': I-researcher '67': B-scientist '68': I-scientist '69': B-song '70': I-song '71': B-task '72': I-task '73': B-theory '74': I-theory '75': B-university '76': I-university '77': B-writer '78': I-writer splits: - name: train num_bytes: 121928 num_examples: 200 - name: validation num_bytes: 276118 num_examples: 450 - name: test num_bytes: 334181 num_examples: 543 download_size: 485191 dataset_size: 732227 --- # Dataset Card for CrossRE ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [CrossNER](https://github.com/zliucr/CrossNER) - **Paper:** [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373) ### Dataset Summary CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains. For details, see the paper: [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373) ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The language data in CrossNER is in English (BCP-47 en) ## Dataset Structure ### Data Instances #### conll2003 - **Size of downloaded dataset files:** 2.69 MB - **Size of the generated dataset:** 5.26 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], "ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0] } ``` #### politics - **Size of downloaded dataset files:** 0.72 MB - **Size of the generated dataset:** 1.04 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."], "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0] } ``` #### science - **Size of downloaded dataset files:** 0.49 MB - **Size of the generated dataset:** 0.73 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], "ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ``` #### music - **Size of downloaded dataset files:** 0.41 MB - **Size of the generated dataset:** 0.65 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], "ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0] } ``` #### literature - **Size of downloaded dataset files:** 0.33 MB - **Size of the generated dataset:** 0.58 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."], "ner_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0] } ``` #### ai - **Size of downloaded dataset files:** 0.29 MB - **Size of the generated dataset:** 0.48 MB An example of 'train' looks as follows: ```json { "id": "0", "tokens": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."], "ner_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ``` ### Data Fields The data fields are the same among all splits. - `id`: the instance id of this sentence, a `string` feature. - `tokens`: the list of tokens of this sentence, a `list` of `string` features. - `ner_tags`: the list of entity tags, a `list` of classification labels. ```json {"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78} ``` ### Data Splits | | Train | Dev | Test | |--------------|--------|-------|-------| | conll2003 | 14,987 | 3,466 | 3,684 | | politics | 200 | 541 | 651 | | science | 200 | 450 | 543 | | music | 100 | 380 | 456 | | literature | 100 | 400 | 416 | | ai | 100 | 350 | 431 | ## 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 ``` @article{liu2020crossner, title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung}, year={2020}, eprint={2012.04373}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
d0rj/curation-corpus
2023-06-13T13:25:32.000Z
[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "news", "summarization", "region:us" ]
d0rj
null
null
null
0
859
--- dataset_info: features: - name: title dtype: string - name: summary dtype: string - name: url dtype: string - name: date dtype: string - name: article_content dtype: string splits: - name: train num_bytes: 127948910 num_examples: 30455 download_size: 76620775 dataset_size: 127948910 license: cc-by-4.0 task_categories: - summarization multilinguality: - monolingual language: - en source_datasets: - original tags: - news - summarization pretty_name: Curation Corpus for Abstractive Text Summarisation paperswithcode_id: curation-corpus size_categories: - 10K<n<100K --- # curation-corpus ## Dataset Description - **Homepage:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus) - **Repository:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus) ## Source Data from [this official repo](https://github.com/CurationCorp/curation-corpus) with downloaded news articles content. ## Citation ``` @misc{curationcorpusbase:2020, title={Curation Corpus Base}, author={Curation}, year={2020} } ```
ambig_qa
2022-11-03T16:31:34.000Z
[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|natural_questions", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "arxiv:2004.10645", "region:us" ]
null
AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity. We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
@inproceedings{ min2020ambigqa, title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions }, author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke }, booktitle={ EMNLP }, year={2020} }
null
2
854
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|natural_questions - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: ambigqa pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions' dataset_info: - config_name: light features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string splits: - name: train num_bytes: 2739732 num_examples: 10036 - name: validation num_bytes: 805808 num_examples: 2002 download_size: 19700900 dataset_size: 3545540 - config_name: full features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string - name: viewed_doc_titles sequence: string - name: used_queries sequence: - name: query dtype: string - name: results sequence: - name: title dtype: string - name: snippet dtype: string - name: nq_answer sequence: string - name: nq_doc_title dtype: string splits: - name: train num_bytes: 43538733 num_examples: 10036 - name: validation num_bytes: 15383368 num_examples: 2002 download_size: 19700900 dataset_size: 58922101 --- # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions ## 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://nlp.cs.washington.edu/ambigqa/) - [**Repository:**](https://github.com/shmsw25/AmbigQA) - [**Paper:**](https://arxiv.org/pdf/2004.10645.pdf) ### Dataset Summary AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity. We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs. ### Supported Tasks and Leaderboards `question-answering` ### Languages English ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` {'annotations': {'answer': [[]], 'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']], 'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?', 'When did the Simpsons first air as a half-hour prime time show?']}], 'type': ['multipleQAs']}, 'id': '-4469503464110108318', 'nq_answer': ['December 17 , 1989'], 'nq_doc_title': 'The Simpsons', 'question': 'When did the simpsons first air on television?', 'used_queries': {'query': ['When did the simpsons first air on television?'], 'results': [{'snippet': ['The <b>Simpsons</b> is an American animated <b>television</b> sitcom starring the animated \nSimpson family, ... Since its <b>debut</b> on December 17, 1989, the show <b>has</b> \nbroadcast 673 episodes and its 30th season started ... The <b>Simpsons first</b> season \n<b>was</b> the Fox network&#39;s <b>first TV</b> series to rank among a season&#39;s top 30 highest-\nrated shows.', 'The <b>Simpsons</b> is an American animated sitcom created by Matt Groening for the \nFox ... Since its <b>debut</b> on December 17, 1989, 674 episodes of The <b>Simpsons</b> \nhave been broadcast. ... When producer James L. Brooks <b>was</b> working on the \n<b>television</b> variety show The Tracey Ullman Show, he decided to include small \nanimated&nbsp;...', '... in shorts from The Tracey Ullman Show as their <b>television debut</b> in 1987. The \n<b>Simpsons</b> shorts are a series of animated shorts that <b>aired</b> as a recurring \nsegment on Fox variety <b>television</b> series The Tracey ... The final short to <b>air was</b> &quot;\n<b>TV Simpsons</b>&quot;, originally airing on May 14, 1989. The <b>Simpsons</b> later debuted on\n&nbsp;...', 'The <b>first</b> season of the American animated <b>television</b> series The <b>Simpsons</b> \noriginally <b>aired</b> on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special &quot;<b>Simpsons</b> Roasting on an Open Fire\n&quot;. The executive producers for the <b>first</b> production season <b>were</b> Matt Groening,&nbsp;...', 'The <b>Simpsons</b> is an American animated <b>television</b> sitcom created by Matt \nGroening for the Fox ... Since its <b>debut</b> on December 17, 1989, The <b>Simpsons</b> \n<b>has</b> broadcast 674 episodes. The show holds several American <b>television</b> \nlongevity&nbsp;...', 'The opening sequence of the American animated <b>television</b> series The <b>Simpsons</b> \nis among the most popular opening sequences in <b>television</b> and is accompanied \nby one of <b>television&#39;s</b> most recognizable theme songs. The <b>first</b> episode to use \nthis intro <b>was</b> the series&#39; second episode &quot;Bart the ... <b>was</b> the <b>first</b> episode of The \n<b>Simpsons</b> to <b>air</b> in 720p high-definition <b>television</b>,&nbsp;...', '&quot;<b>Simpsons</b> Roasting on an Open Fire&quot;, titled onscreen as &quot;The <b>Simpsons</b> \nChristmas Special&quot;, is the premiere episode of the American animated <b>TV</b> series \nThe <b>Simpsons</b>, ... The show <b>was</b> originally intended to <b>debut</b> earlier in 1989 with &quot;\nSome Enchanted Evening&quot;, but due to animation problems with that episode, the \nshow&nbsp;...', '&quot;Stark Raving Dad&quot; is the <b>first</b> episode of the third season of the American \nanimated <b>television</b> series The <b>Simpsons</b>. It <b>first aired</b> on the Fox network in the \nUnited States on September 19, 1991. ... The <b>Simpsons was</b> the second highest \nrated show on Fox the week it <b>aired</b>, behind Married... with Children. &quot;Stark \nRaving Dad,&quot;&nbsp;...', 'The <b>Simpsons</b>&#39; twentieth season <b>aired</b> on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime <b>television</b> series in terms of total number ... It <b>was</b> the <b>first</b>-\never episode of the show to <b>air</b> in Europe before being seen in the United States.', 'The animated <b>TV</b> show The <b>Simpsons</b> is an American English language \nanimated sitcom which ... The <b>Simpsons was</b> dubbed for the <b>first</b> time in Punjabi \nand <b>aired</b> on Geo <b>TV</b> in Pakistan. The name of the localised Punjabi version is \nTedi Sim&nbsp;...'], 'title': ['History of The Simpsons', 'The Simpsons', 'The Simpsons shorts', 'The Simpsons (season 1)', 'List of The Simpsons episodes', 'The Simpsons opening sequence', 'Simpsons Roasting on an Open Fire', 'Stark Raving Dad', 'The Simpsons (season 20)', 'Non-English versions of The Simpsons']}]}, 'viewed_doc_titles': ['The Simpsons']} ``` ### Data Fields Full ``` {'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None), 'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None), 'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'nq_doc_title': Value(dtype='string', id=None)} ``` In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. ``` for example in train_light_dataset: for i,t in enumerate(example['annotations']['type']): if t =='singleAnswer': # use the example['annotations']['answer'][i] # example['annotations']['qaPairs'][i] - > is [] print(example['annotations']['answer'][i]) else: # use the example['annotations']['qaPairs'][i] # example['annotations']['answer'][i] - > is [] print(example['annotations']['qaPairs'][i]) ``` please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. Light version only has `id`, `question`, `annotations` fields ### Data Splits - train: 10036 - validation: 2002 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - Wikipedia - NQ-open: ``` @article{ kwiatkowski2019natural, title={ Natural questions: a benchmark for question answering research}, author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others }, journal={ Transactions of the Association for Computational Linguistics }, year={ 2019 } } ``` #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) ### Citation Information ``` @inproceedings{ min2020ambigqa, title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions }, author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke }, booktitle={ EMNLP }, year={2020} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
ybelkada/football-dataset
2023-01-17T11:47:41.000Z
[ "region:us" ]
ybelkada
null
null
null
0
852
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2073622.0 num_examples: 6 download_size: 2074835 dataset_size: 2073622.0 --- # Dataset Card for "football-dataset" Dummy dataset of 6 football players with a caption that can be used to fine-tune any Image Captioning model.
THUDM/ImageRewardDB
2023-06-21T06:36:29.000Z
[ "task_categories:text-to-image", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "arxiv:2304.05977", "region:us" ]
THUDM
ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and annotator training, optimizing labeling experience, and ensuring quality validation. \
@misc{xu2023imagereward, title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong}, year={2023}, eprint={2304.05977}, archivePrefix={arXiv}, primaryClass={cs.CV} }
null
16
850
--- license: apache-2.0 task_categories: - text-to-image language: - en pretty_name: ImageReward Dataset size_categories: - 100K<n<1M --- # ImageRewardDB ## Dataset Description - **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB** - **Repository: https://github.com/THUDM/ImageReward** - **Paper: https://arxiv.org/abs/2304.05977** ### Dataset Summary ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB). Notice: All images in ImageRewardDB are collected from DiffusionDB, and in addition, we gathered together images corresponding to the same prompt. ### Languages The text in the dataset is all in English. ### Four Subsets Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs. For all subsets, the validation and test splits remain the same. The validation split(1.10GB) contains 412 prompts and 2.6K images(7.32K pairs) and the test(1.16GB) split contains 466 prompts and 2.7K images(7.23K pairs). The information on the train split in different scales is as follows: |Subset|Num of Pairs|Num of Images|Num of Prompts|Size| |:--|--:|--:|--:|--:| |ImageRewardDB 1K|17.6K|6.2K|1K|2.7GB| |ImageRewardDB 2K|35.5K|12.5K|2K|5.5GB| |ImageRewardDB 4K|71.0K|25.1K|4K|10.8GB| |ImageRewardDB 8K|141.1K|49.9K|8K|20.9GB| ## Dataset Structure All the data in this repository is stored in a well-organized way. The 62.6K images in ImageRewardDB are split into several folders, stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, their corresponding images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation. The file structure is as follows: ``` # ImageRewardDB ./ ├── images │   ├── train │   │   ├── train_1 │   │   │ ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp │   │   │ ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp │   │   │ ├── [...] │   │   │ └── train_1.json │   │   ├── train_2 │   │   ├── train_3 │   │   ├── [...] │   │   └── train_32 │   ├── validation │ │ └── [...] │   └── test │ └── [...] ├── metadata-train.parquet ├── metadata-validation.parquet └── metadata-test.parquet ``` The sub-folders have the name of {split_name}_{part_id}, and the JSON file has the same name as the sub-folder. Each image is a lossless WebP file and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier). ### Data Instances For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json. ```json { "image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp", "prompt_id": "000864-0061", "prompt": "painting of a holy woman, decorated, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k ", "classification": "People", "image_amount_in_total": 9, "rank": 5, "overall_rating": 4, "image_text_alignment_rating": 3, "fidelity_rating": 4 } ``` ### Data Fields * image: The image object * prompt_id: The id of the corresponding prompt * prompt: The text of the corresponding prompt * classification: The classification of the corresponding prompt * image_amount_in_total: Total amount of images related to the prompt * rank: The relative rank of the image in all related images * overall_rating: The overall score of this image * image_text_alignment_rating: The score of how well the generated image matches the given text * fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have ### Data Splits As we mentioned above, all scales of the subsets we provided have three splits of "train", "validation", and "test". And all the subsets share the same validation and test splits. ### Dataset Metadata We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to help you access and comprehend ImageRewardDB without downloading the Zip files. All the tables share the same schema, and each row refers to an image. The schema is shown below, and actually, the JSON files we mentioned above share the same schema: |Column|Type|Description| |:---|:---|:---| |`image_path`|`string`|The relative path of the image in the repository.| |`prompt_id`|`string`|The id of the corresponding prompt.| |`prompt`|`string`|The text of the corresponding prompt.| |`classification`|`string`| The classification of the corresponding prompt.| |`image_amount_in_total`|`int`| Total amount of images related to the prompt.| |`rank`|`int`| The relative rank of the image in all related images.| |`overall_rating`|`int`| The overall score of this image. |`image_text_alignment_rating`|`int`|The score of how well the generated image matches the given text.| |`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.| Below is an example row from metadata-train.parquet. |image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating| |:---|:---|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---|:---|:---|:---|:---|:---| |images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, ...|Outdoor Scenes|8|3|6|6|6| ## Loading ImageRewardDB You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB. As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following: ```python from datasets import load_dataset # Load the 1K-scale dataset dataset = load_dataset("THUDM/ImageRewardDB", "1k") # Load the 2K-scale dataset dataset = load_dataset("THUDM/ImageRewardDB", "2k") # Load the 4K-scale dataset dataset = load_dataset("THUDM/ImageRewardDB", "4K") # Load the 8K-scale dataset dataset = load_dataset("THUDM/ImageRewardDB", "8k") ``` ## Additional Information ### Licensing Information The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE). ### Citation Information ``` @misc{xu2023imagereward, title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong}, year={2023}, eprint={2304.05977}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
mlabonne/guanaco-llama2
2023-07-26T14:49:17.000Z
[ "region:us" ]
mlabonne
null
null
null
7
849
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15409089 num_examples: 9846 - name: test num_bytes: 815811 num_examples: 518 download_size: 9461517 dataset_size: 16224900 --- # Guanaco: Lazy Llama 2 Formatting This is the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 model in a Google Colab.
theblackcat102/evol-codealpaca-v1
2023-09-07T11:42:00.000Z
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "code", "region:us" ]
theblackcat102
null
null
null
65
848
--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - code size_categories: - 100K<n<1M --- ## Evolved codealpaca Updates: * 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response Median sequence length : 471 We employed a methodology similar to that of [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314. The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the [evol-dataset](https://github.com/theblackcat102/evol-dataset) package, as we strive to develop improved future augmentation strategies. We used a total of [10 strategies](https://github.com/theblackcat102/evol-dataset/tree/main/evolinstruct/instructions) to augment the [HuggingFaceH4/CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) dataset and create our own. It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese. A Chinese code evol version is now available here : [theblackcat102/evol-code-zh](https://huggingface.co/datasets/theblackcat102/evol-code-zh) ## Comparison to existing dataset Comparing to [nickrosh/Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), evol-codealpaca-v1 has longer instruction and output conversation ![](./48f1b380-dc0b-4b0b-9b97-3cc5aa619655.png) ### Citation If you use this dataset to finetune any LLMs just cite wizard coder ``` @misc{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang}, year={2023}, } ```
squad_adversarial
2022-11-18T21:47:43.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|squad", "language:en", "license:mit", "region:us" ]
null
Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph: AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model. AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.
@inproceedings{jia-liang-2017-adversarial, title = "Adversarial Examples for Evaluating Reading Comprehension Systems", author = "Jia, Robin and Liang, Percy", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D17-1215", doi = "10.18653/v1/D17-1215", pages = "2021--2031", abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", }
null
5
847
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: null pretty_name: '''Adversarial Examples for SQuAD''' dataset_info: - config_name: squad_adversarial features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: AddSent num_bytes: 3803551 num_examples: 3560 - name: AddOneSent num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 5668318 - config_name: AddSent features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 3803551 num_examples: 3560 download_size: 5994513 dataset_size: 3803551 - config_name: AddOneSent features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 1864767 --- # Dataset Card for 'Adversarial Examples for SQuAD' ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [**Homepage**](https://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d/) - [**Repository**](https://github.com/robinjia/adversarial-squad/) - [**Paper**](https://www.aclweb.org/anthology/D17-1215/) ### Dataset Summary Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. ### Supported Tasks and Leaderboards `question-answering`, `adversarial attack` ### Languages English ## Dataset Structure Follows the standart SQuAD format. ### Data Instances An example from the data set looks as follows: ```py {'answers': {'answer_start': [334, 334, 334], 'text': ['February 7, 2016', 'February 7', 'February 7, 2016']}, 'context': 'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50. The Champ Bowl was played on August 18th,1991.', 'id': '56bea9923aeaaa14008c91bb-high-conf-turk2', 'question': 'What day was the Super Bowl played on?', 'title': 'Super_Bowl_50'} ``` `id` field is formed like: [original_squad_id]-[annotator_id] ### Data Fields ```py {'id': Value(dtype='string', id=None), # id of example (same as SQuAD) OR SQuAD-id-[annotator_id] for adversarially modified examples 'title': Value(dtype='string', id=None), # title of document the context is from (same as SQuAD) 'context': Value(dtype='string', id=None), # the context (same as SQuAD) +adversarially added sentence 'question': Value(dtype='string', id=None), # the question (same as SQuAD) 'answers': Sequence(feature={'text': Value(dtype='string', id=None), # the answer (same as SQuAD) 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) # the answer_start index (same as SQuAD) } ``` ### Data Splits - AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way. - AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way. Number of Q&A pairs - AddSent : 3560 - AddOneSent: 1787 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data SQuAD dev set (+with adversarial sentences added) #### 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 [MIT License](https://github.com/robinjia/adversarial-squad/blob/master/LICENSE) ### Citation Information ``` @inproceedings{jia-liang-2017-adversarial, title = "Adversarial Examples for Evaluating Reading Comprehension Systems", author = "Jia, Robin and Liang, Percy", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D17-1215", doi = "10.18653/v1/D17-1215", pages = "2021--2031", abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
BeIR/fiqa
2022-10-23T06:00:28.000Z
[ "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" ]
BeIR
null
null
null
3
844
--- 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.
qanastek/MASSIVE
2022-12-23T21:28:08.000Z
[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:af", "language:am", "language:ar", "language:az", "language:bn", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:hy", "language:id", "language:is", "language:it", "language:ja", "language:jv", "language:ka", "language:km", "language:kn", "language:ko", "language:lv", "language:ml", "language:mn", "language:ms", "language:my", "language:nb", "language:nl", "language:pl", "language:pt", "language:ro", "language:ru", "language:sl", "language:sq", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:zh", "arxiv:2204.08582", "region:us" ]
qanastek
MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
@misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", 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.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." }
null
16
830
--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - af - am - ar - az - bn - cy - da - de - el - en - es - fa - fi - fr - he - hi - hu - hy - id - is - it - ja - jv - ka - km - kn - ko - lv - ml - mn - ms - my - nb - nl - pl - pt - ro - ru - sl - sq - sv - sw - ta - te - th - tl - tr - ur - vi - zh - zh multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification - named-entity-recognition pretty_name: MASSIVE language_bcp47: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW --- # MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [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) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/alexa/massive - **Repository:** https://github.com/alexa/massive - **Paper:** https://arxiv.org/abs/2204.08582 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview - **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues) ### Dataset Summary MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | Name | Lang | Utt/Lang | Domains | Intents | Slots | |:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| | MASSIVE | 51 | 19,521 | 18 | 60 | 55 | | SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | | NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | | Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | | ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | | MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | | Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | | Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | | Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | | Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | | Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | | Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | | Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | | Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | ### Supported Tasks and Leaderboards The dataset can be used to train a model for `natural-language-understanding` (NLU) : - `intent-classification` - `multi-class-classification` - `natural-language-understanding` ### Languages The corpora consists of parallel sentences from 51 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/MASSIVE", "en-US", split='train') print(dataset) print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```json { "id": "1", "locale": "fr-FR", "partition": "train", "scenario": 16, "intent": 48, "utt": "réveille-moi à neuf heures du matin le vendredi", "annot_utt": "réveille-moi à [time : neuf heures du matin] le [date : vendredi]", "tokens": [ "réveille-moi", "à", "neuf", "heures", "du", "matin", "le", "vendredi" ], "ner_tags": [0, 0, 71, 6, 6, 6, 0, 14], "worker_id": "22", "slot_method": { "slot": ["time", "date"], "method": ["translation", "translation"] }, "judgments": { "worker_id": ["11", "22", "0"], "intent_score": [2, 1, 1], "slots_score": [1, 1, 1], "grammar_score": [3, 4, 4], "spelling_score": [2, 2, 2], "language_identification": ["target", "target", "target"] } } ``` ### Data Fields (taken from Alexa Github) `id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. `locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. `partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). `scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance `intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` `utt`: the raw utterance text without annotations `annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` `worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. `slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). `judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. ```plain intent_score : "Does the sentence match the intent?" 0: No 1: Yes 2: It is a reasonable interpretation of the goal slots_score : "Do all these terms match the categories in square brackets?" 0: No 1: Yes 2: There are no words in square brackets (utterance without a slot) grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" 0: Completely unnatural (nonsensical, cannot be understood at all) 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) 3: Good enough (easily understood and sounds almost natural in your language) 4: Perfect (sounds natural in your language) spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." 0: There are more than 2 spelling errors 1: There are 1-2 spelling errors 2: All words are spelled correctly language_identification : "The following sentence contains words in the following languages (check all that apply)" 1: target 2: english 3: other 4: target & english 5: target & other 6: english & other 7: target & english & other ``` ### Data Splits |Language|Train|Dev|Test| |:---:|:---:|:---:|:---:| |af-ZA|11514|2033|2974| |am-ET|11514|2033|2974| |ar-SA|11514|2033|2974| |az-AZ|11514|2033|2974| |bn-BD|11514|2033|2974| |cy-GB|11514|2033|2974| |da-DK|11514|2033|2974| |de-DE|11514|2033|2974| |el-GR|11514|2033|2974| |en-US|11514|2033|2974| |es-ES|11514|2033|2974| |fa-IR|11514|2033|2974| |fi-FI|11514|2033|2974| |fr-FR|11514|2033|2974| |he-IL|11514|2033|2974| |hi-IN|11514|2033|2974| |hu-HU|11514|2033|2974| |hy-AM|11514|2033|2974| |id-ID|11514|2033|2974| |is-IS|11514|2033|2974| |it-IT|11514|2033|2974| |ja-JP|11514|2033|2974| |jv-ID|11514|2033|2974| |ka-GE|11514|2033|2974| |km-KH|11514|2033|2974| |kn-IN|11514|2033|2974| |ko-KR|11514|2033|2974| |lv-LV|11514|2033|2974| |ml-IN|11514|2033|2974| |mn-MN|11514|2033|2974| |ms-MY|11514|2033|2974| |my-MM|11514|2033|2974| |nb-NO|11514|2033|2974| |nl-NL|11514|2033|2974| |pl-PL|11514|2033|2974| |pt-PT|11514|2033|2974| |ro-RO|11514|2033|2974| |ru-RU|11514|2033|2974| |sl-SL|11514|2033|2974| |sq-AL|11514|2033|2974| |sv-SE|11514|2033|2974| |sw-KE|11514|2033|2974| |ta-IN|11514|2033|2974| |te-IN|11514|2033|2974| |th-TH|11514|2033|2974| |tl-PH|11514|2033|2974| |tr-TR|11514|2033|2974| |ur-PK|11514|2033|2974| |vi-VN|11514|2033|2974| |zh-CN|11514|2033|2974| |zh-TW|11514|2033|2974| ## Dataset Creation ### Source Data #### Who are the source language producers? The corpus has been produced and uploaded by Amazon Alexa. ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. __SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. __Hugging Face__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain Copyright Amazon.com Inc. or its affiliates. Attribution 4.0 International ======================================================================= Creative Commons Corporation ("Creative Commons") is not a law firm and does not provide legal services or legal advice. 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Creative Commons may be contacted at creativecommons.org. ``` ### Citation Information Please cite the following paper when using this dataset. ```latex @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", 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.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." } ```
fusing/instructpix2pix-1000-samples
2023-02-23T07:08:49.000Z
[ "region:us" ]
fusing
null
null
null
4
830
--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 416880759.0 num_examples: 1000 download_size: 416899514 dataset_size: 416880759.0 --- # Dataset Card for "instructpix2pix-1000-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The dataset was created using the code from [this repository](https://github.com/sayakpaul/instruct-pix2pix-dataset).
jojo0217/korean_rlhf_dataset
2023-09-25T08:36:04.000Z
[ "task_categories:text-generation", "language:ko", "license:apache-2.0", "region:us" ]
jojo0217
null
null
null
7
829
--- license: apache-2.0 task_categories: - text-generation language: - ko --- 성균관대학교 산학협력프로젝트 과정에서 한국어 llm 모델 SFT 학습을 위해 구축한 데이터셋 입니다. 2023-09-25 오픈 어시스턴트 data에서 오픈 어시스턴트를 포함하는 데이터 삭제 -> 답변에 오픈 어시스턴트라고 하는 경우가 나오기 때문 또한 스탠포드 대학 번역 데이터에서 번역 과정 오류로 input에 입력없음 과 같이 추가된 부분 삭제 그리고 \<unk\> 등으로 gpt 상에서 번역 오류가 난 것들을 삭제 *** 자연스러움을 위해 stanford alpaca data, oig_chip2를 ChatGPT3.5 turbo 16k를 이용하여 새롭게 전처리 과정을 거쳤습니다. https://github.com/JoJo0217/rlhf_korean_dataset/tree/main 여기에서 자세한 설명을 볼 수 있으며 데이터의 구성은 다음과 같습니다. *** 데이터 구성 |데이터 종류|개수|url| |:---|---:|---:| |koalpaca v1.1|21155|https://github.com/Beomi/KoAlpaca| |stanford alpaca|51374|https://huggingface.co/datasets/tatsu-lab/alpaca| |dolly|15009|https://huggingface.co/datasets/nlpai-lab/databricks-dolly-15k-ko| |openassistant|9651|https://huggingface.co/datasets/nlpai-lab/openassistant-guanaco-ko| |oig_chip2|10000|https://huggingface.co/datasets/0-hero/OIG-small-chip2| |총합|107189||
lbox/lbox_open
2022-11-09T06:41:26.000Z
[ "license:cc-by-nc-4.0", "region:us" ]
lbox
null
null
null
2
827
--- 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/)
aharley/rvl_cdip
2023-05-02T09:06:16.000Z
[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|iit_cdip", "language:en", "license:other", "arxiv:1502.07058", "region:us" ]
aharley
The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.
@inproceedings{harley2015icdar, title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval}, author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis}, booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}}, year = {2015} }
null
28
827
--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|iit_cdip task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: rvl-cdip pretty_name: RVL-CDIP viewer: false dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': letter '1': form '2': email '3': handwritten '4': advertisement '5': scientific report '6': scientific publication '7': specification '8': file folder '9': news article '10': budget '11': invoice '12': presentation '13': questionnaire '14': resume '15': memo splits: - name: train num_bytes: 38816373360 num_examples: 320000 - name: test num_bytes: 4863300853 num_examples: 40000 - name: validation num_bytes: 4868685208 num_examples: 40000 download_size: 38779484559 dataset_size: 48548359421 --- # Dataset Card for RVL-CDIP ## 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:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/) - **Repository:** - **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058) - **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip) - **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu) ### Dataset Summary The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip). ### Languages All the classes and documents use English as their primary language. ## Dataset Structure ### Data Instances A sample from the training set is provided below : ``` { 'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>, 'label': 15 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing a document. - `label`: an `int` classification label. <details> <summary>Class Label Mappings</summary> ```json { "0": "letter", "1": "form", "2": "email", "3": "handwritten", "4": "advertisement", "5": "scientific report", "6": "scientific publication", "7": "specification", "8": "file folder", "9": "news article", "10": "budget", "11": "invoice", "12": "presentation", "13": "questionnaire", "14": "resume", "15": "memo" } ``` </details> ### Data Splits | |train|test|validation| |----------|----:|----:|---------:| |# of examples|320000|40000|40000| The dataset was split in proportions similar to those of ImageNet. - 320000 images were used for training, - 40000 images for validation, and - 40000 images for testing. ## Dataset Creation ### Curation Rationale From the paper: > This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis. ### Source Data #### Initial Data Collection and Normalization The same as in the IIT-CDIP collection. #### Who are the source language producers? The same as in the IIT-CDIP collection. ### Annotations #### Annotation process The same as in the IIT-CDIP collection. #### Who are the annotators? The same as in the IIT-CDIP collection. ### 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 was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis. ### Licensing Information RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/). ### Citation Information ```bibtex @inproceedings{harley2015icdar, title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval}, author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis}, booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}}, year = {2015} } ``` ### Contributions Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.
allenai/scirepeval_test
2022-10-21T20:54:57.000Z
[ "region:us" ]
allenai
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
@InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2021} }
null
0
826
Entry not found
TREC-AToMiC/AToMiC-Texts-v0.2.1
2023-05-04T18:58:43.000Z
[ "region:us" ]
TREC-AToMiC
null
null
null
2
826
--- dataset_info: features: - name: text_id dtype: string - name: page_url dtype: string - name: page_title dtype: string - name: section_title dtype: string - name: context_page_description dtype: string - name: context_section_description dtype: string - name: media sequence: string - name: hierachy sequence: string - name: category sequence: string - name: source_id dtype: string splits: - name: train num_bytes: 20393084595 num_examples: 10134744 download_size: 7192298025 dataset_size: 20393084595 --- # Dataset Card for "AToMiC-Texts-v0.2.updated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
leemeng/jcommonsenseqa-v1.1
2023-04-28T08:13:50.000Z
[ "license:cc-by-4.0", "region:us" ]
leemeng
null
null
null
1
825
--- license: cc-by-4.0 dataset_info: features: - name: q_id dtype: int64 - name: question dtype: string - name: choice0 dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: choice3 dtype: string - name: choice4 dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1183829 num_examples: 8939 - name: validation num_bytes: 148293 num_examples: 1119 download_size: 887894 dataset_size: 1332122 ---
universal_morphologies
2023-06-08T09:28:28.000Z
[ "task_categories:token-classification", "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "size_categories:n<1K", "source_datasets:original", "language:ady", "language:ang", "language:ar", "language:arn", "language:ast", "language:az", "language:ba", "language:be", "language:bg", "language:bn", "language:bo", "language:br", "language:ca", "language:ckb", "language:crh", "language:cs", "language:csb", "language:cu", "language:cy", "language:da", "language:de", "language:dsb", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fo", "language:fr", "language:frm", "language:fro", "language:frr", "language:fur", "language:fy", "language:ga", "language:gal", "language:gd", "language:gmh", "language:gml", "language:got", "language:grc", "language:gv", "language:hai", "language:he", "language:hi", "language:hu", "language:hy", "language:is", "language:it", "language:izh", "language:ka", "language:kbd", "language:kjh", "language:kk", "language:kl", "language:klr", "language:kmr", "language:kn", "language:krl", "language:kw", "language:la", "language:liv", "language:lld", "language:lt", "language:lud", "language:lv", "language:mk", "language:mt", "language:mwf", "language:nap", "language:nb", "language:nds", "language:nl", "language:nn", "language:nv", "language:oc", "language:olo", "language:osx", "language:pl", "language:ps", "language:pt", "language:qu", "language:ro", "language:ru", "language:sa", "language:sga", "language:sh", "language:sl", "language:sme", "language:sq", "language:sv", "language:swc", "language:syc", "language:te", "language:tg", "language:tk", "language:tr", "language:tt", "language:uk", "language:ur", "language:uz", "language:vec", "language:vep", "language:vot", "language:xcl", "language:xno", "language:yi", "language:zu", "license:cc-by-sa-3.0", "morphology", "region:us" ]
null
The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described in Sylak-Glassman (2016).
@article{sylak2016composition, title={The composition and use of the universal morphological feature schema (unimorph schema)}, author={Sylak-Glassman, John}, journal={Johns Hopkins University}, year={2016} }
null
13
824
--- annotations_creators: - expert-generated language_creators: - found language: - ady - ang - ar - arn - ast - az - ba - be - bg - bn - bo - br - ca - ckb - crh - cs - csb - cu - cy - da - de - dsb - el - en - es - et - eu - fa - fi - fo - fr - frm - fro - frr - fur - fy - ga - gal - gd - gmh - gml - got - grc - gv - hai - he - hi - hu - hy - is - it - izh - ka - kbd - kjh - kk - kl - klr - kmr - kn - krl - kw - la - liv - lld - lt - lud - lv - mk - mt - mwf - nap - nb - nds - nl - nn - nv - oc - olo - osx - pl - ps - pt - qu - ro - ru - sa - sga - sh - sl - sme - sq - sv - swc - syc - te - tg - tk - tr - tt - uk - ur - uz - vec - vep - vot - xcl - xno - yi - zu license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - token-classification - text-classification task_ids: - multi-class-classification - multi-label-classification paperswithcode_id: null pretty_name: UniversalMorphologies tags: - morphology dataset_info: - config_name: ady features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3428235 num_examples: 1666 download_size: 1008487 dataset_size: 3428235 - config_name: ang features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6569844 num_examples: 1867 download_size: 1435972 dataset_size: 6569844 - config_name: ara features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 24388295 num_examples: 4134 download_size: 7155824 dataset_size: 24388295 - config_name: arn features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 124050 num_examples: 26 download_size: 20823 dataset_size: 124050 - config_name: ast features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 4913008 num_examples: 436 download_size: 1175901 dataset_size: 4913008 - config_name: aze features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1248687 num_examples: 340 download_size: 276306 dataset_size: 1248687 - config_name: bak features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1984657 num_examples: 1084 download_size: 494758 dataset_size: 1984657 - config_name: bel features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2626405 num_examples: 1027 download_size: 739537 dataset_size: 2626405 - config_name: ben features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 746181 num_examples: 136 download_size: 251991 dataset_size: 746181 - config_name: bod features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 880074 num_examples: 1335 download_size: 197523 dataset_size: 880074 - config_name: bre features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 387583 num_examples: 44 download_size: 82159 dataset_size: 387583 - config_name: bul features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9589915 num_examples: 2468 download_size: 3074574 dataset_size: 9589915 - config_name: cat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12988492 num_examples: 1547 download_size: 2902458 dataset_size: 12988492 - config_name: ces features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 21056640 num_examples: 5125 download_size: 4875288 dataset_size: 21056640 - config_name: chu features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 628237 num_examples: 152 download_size: 149081 dataset_size: 628237 - config_name: ckb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3843267 num_examples: 274 download_size: 914302 dataset_size: 3843267 - config_name: cor features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 83434 num_examples: 9 download_size: 17408 dataset_size: 83434 - config_name: crh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1154595 num_examples: 1230 download_size: 186325 dataset_size: 1154595 - config_name: csb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 82172 num_examples: 37 download_size: 14259 dataset_size: 82172 - config_name: cym features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1748431 num_examples: 183 download_size: 374501 dataset_size: 1748431 - config_name: dan features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 4204551 num_examples: 3193 download_size: 845939 dataset_size: 4204551 - config_name: deu features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 28436466 num_examples: 15060 download_size: 5966618 dataset_size: 28436466 - config_name: dsb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2985168 num_examples: 994 download_size: 536096 dataset_size: 2985168 - config_name: ell features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 34112450 num_examples: 11906 download_size: 11222248 dataset_size: 34112450 - config_name: eng features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 18455909 num_examples: 22765 download_size: 3285554 dataset_size: 18455909 - config_name: est features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6125879 num_examples: 886 download_size: 1397385 dataset_size: 6125879 - config_name: eus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2444247 num_examples: 26 download_size: 876480 dataset_size: 2444247 - config_name: fao features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7117926 num_examples: 3077 download_size: 1450065 dataset_size: 7117926 - config_name: fas features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6382709 num_examples: 273 download_size: 2104724 dataset_size: 6382709 - config_name: fin features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: '1' num_bytes: 331855860 num_examples: 46152 - name: '2' num_bytes: 81091817 num_examples: 11491 download_size: 109324828 dataset_size: 412947677 - config_name: fra features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 58747699 num_examples: 7535 download_size: 13404983 dataset_size: 58747699 - config_name: frm features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6015940 num_examples: 603 download_size: 1441122 dataset_size: 6015940 - config_name: fro features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 20260793 num_examples: 1700 download_size: 4945582 dataset_size: 20260793 - config_name: frr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 526898 num_examples: 51 download_size: 112236 dataset_size: 526898 - config_name: fry features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 222067 num_examples: 85 download_size: 38227 dataset_size: 222067 - config_name: fur features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1282374 num_examples: 168 download_size: 258793 dataset_size: 1282374 - config_name: gal features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5844604 num_examples: 486 download_size: 1259120 dataset_size: 5844604 - config_name: gla features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 126847 num_examples: 73 download_size: 25025 dataset_size: 126847 - config_name: gle features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 17065939 num_examples: 7464 download_size: 3853188 dataset_size: 17065939 - config_name: glv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7523 num_examples: 1 download_size: 401 dataset_size: 7523 - config_name: gmh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 114677 num_examples: 29 download_size: 20851 dataset_size: 114677 - config_name: gml features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 233831 num_examples: 52 download_size: 47151 dataset_size: 233831 - config_name: got features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train download_size: 2 dataset_size: 0 - config_name: grc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6779867 num_examples: 2431 download_size: 2057514 dataset_size: 6779867 - config_name: hai features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1166240 num_examples: 41 download_size: 329817 dataset_size: 1166240 - config_name: hbs features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 132933961 num_examples: 24419 download_size: 32194142 dataset_size: 132933961 - config_name: heb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2211208 num_examples: 510 download_size: 498065 dataset_size: 2211208 - config_name: hin features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 10083004 num_examples: 258 download_size: 3994359 dataset_size: 10083004 - config_name: hun features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 83517327 num_examples: 14892 download_size: 19544319 dataset_size: 83517327 - config_name: hye features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 56537127 num_examples: 7033 download_size: 17810316 dataset_size: 56537127 - config_name: isl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12120572 num_examples: 4775 download_size: 2472980 dataset_size: 12120572 - config_name: ita features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 81905203 num_examples: 10009 download_size: 19801423 dataset_size: 81905203 - config_name: izh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 170094 num_examples: 50 download_size: 28558 dataset_size: 170094 - config_name: kal features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 60434 num_examples: 23 download_size: 9795 dataset_size: 60434 - config_name: kan features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1052294 num_examples: 159 download_size: 318512 dataset_size: 1052294 - config_name: kat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12532540 num_examples: 3782 download_size: 4678979 dataset_size: 12532540 - config_name: kaz features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 62519 num_examples: 26 download_size: 14228 dataset_size: 62519 - config_name: kbd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 511406 num_examples: 250 download_size: 133788 dataset_size: 511406 - config_name: kjh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 193741 num_examples: 75 download_size: 44907 dataset_size: 193741 - config_name: klr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 28909688 num_examples: 591 download_size: 7561829 dataset_size: 28909688 - config_name: kmr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 35504487 num_examples: 15083 download_size: 8592722 dataset_size: 35504487 - config_name: krl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 106475 num_examples: 20 download_size: 19024 dataset_size: 106475 - config_name: lat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 81932667 num_examples: 17214 download_size: 19567252 dataset_size: 81932667 - config_name: lav features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 21219584 num_examples: 7548 download_size: 5048680 dataset_size: 21219584 - config_name: lit features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5287268 num_examples: 1458 download_size: 1191554 dataset_size: 5287268 - config_name: liv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 642166 num_examples: 203 download_size: 141467 dataset_size: 642166 - config_name: lld features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1240257 num_examples: 180 download_size: 278592 dataset_size: 1240257 - config_name: lud features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: mikhailovskoye num_bytes: 11361 num_examples: 2 - name: new_written num_bytes: 35132 num_examples: 94 - name: southern_ludian_svjatozero num_bytes: 57276 num_examples: 71 download_size: 14697 dataset_size: 103769 - config_name: mkd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 27800390 num_examples: 10313 download_size: 8157589 dataset_size: 27800390 - config_name: mlt features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 604577 num_examples: 112 download_size: 124584 dataset_size: 604577 - config_name: mwf features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 172890 num_examples: 29 download_size: 25077 dataset_size: 172890 - config_name: nap features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 293699 num_examples: 40 download_size: 64163 dataset_size: 293699 - config_name: nav features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2051393 num_examples: 674 download_size: 523673 dataset_size: 2051393 - config_name: nds features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train download_size: 2 dataset_size: 0 - config_name: nld features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 8813867 num_examples: 4993 download_size: 1874427 dataset_size: 8813867 - config_name: nno features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2704566 num_examples: 4689 download_size: 420695 dataset_size: 2704566 - config_name: nob features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3359706 num_examples: 5527 download_size: 544432 dataset_size: 3359706 - config_name: oci features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1327716 num_examples: 174 download_size: 276611 dataset_size: 1327716 - config_name: olo features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: kotkozero num_bytes: 7682 num_examples: 5 - name: new_written num_bytes: 11158424 num_examples: 15293 - name: syamozero num_bytes: 6379 num_examples: 2 - name: vedlozero num_bytes: 6120 num_examples: 1 - name: vidlitsa num_bytes: 54363 num_examples: 3 download_size: 2130154 dataset_size: 11232968 - config_name: osx features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3500590 num_examples: 863 download_size: 759997 dataset_size: 3500590 - config_name: pol features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 30855235 num_examples: 10185 download_size: 6666266 dataset_size: 30855235 - config_name: por features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 48530106 num_examples: 4001 download_size: 10982524 dataset_size: 48530106 - config_name: pus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1176421 num_examples: 395 download_size: 297043 dataset_size: 1176421 - config_name: que features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 27823298 num_examples: 1006 download_size: 6742890 dataset_size: 27823298 - config_name: ron features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 13187957 num_examples: 4405 download_size: 2990521 dataset_size: 13187957 - config_name: rus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 77484460 num_examples: 28068 download_size: 25151401 dataset_size: 77484460 - config_name: san features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5500001 num_examples: 917 download_size: 1788739 dataset_size: 5500001 - config_name: sga features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 190479 num_examples: 49 download_size: 43469 dataset_size: 190479 - config_name: slv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9071547 num_examples: 2535 download_size: 1911039 dataset_size: 9071547 - config_name: sme features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9764653 num_examples: 2103 download_size: 2050015 dataset_size: 9764653 - config_name: spa features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 61472202 num_examples: 5460 download_size: 14386131 dataset_size: 61472202 - config_name: sqi features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5422400 num_examples: 589 download_size: 1261468 dataset_size: 5422400 - config_name: swc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1694529 num_examples: 100 download_size: 414624 dataset_size: 1694529 - config_name: swe features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12897827 num_examples: 10553 download_size: 2709960 dataset_size: 12897827 - config_name: syc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 553392 num_examples: 160 download_size: 130000 dataset_size: 553392 - config_name: tat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1203356 num_examples: 1283 download_size: 194277 dataset_size: 1203356 - config_name: tel features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 285769 num_examples: 127 download_size: 95069 dataset_size: 285769 - config_name: tgk features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 25276 num_examples: 75 download_size: 2366 dataset_size: 25276 - config_name: tuk features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 127712 num_examples: 68 download_size: 20540 dataset_size: 127712 - config_name: tur features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 44723850 num_examples: 3579 download_size: 11552946 dataset_size: 44723850 - config_name: ukr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3299187 num_examples: 1493 download_size: 870660 dataset_size: 3299187 - config_name: urd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2197237 num_examples: 182 download_size: 685613 dataset_size: 2197237 - config_name: uzb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 196802 num_examples: 15 download_size: 41921 dataset_size: 196802 - config_name: vec features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2892987 num_examples: 368 download_size: 615931 dataset_size: 2892987 - config_name: vep features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: central_eastern num_bytes: 500981 num_examples: 65 - name: central_western num_bytes: 2527618 num_examples: 111 - name: new_written num_bytes: 79899484 num_examples: 9304 - name: northern num_bytes: 175242 num_examples: 21 - name: southern num_bytes: 206289 num_examples: 17 download_size: 20131151 dataset_size: 83309614 - config_name: vot features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 217663 num_examples: 55 download_size: 37179 dataset_size: 217663 - config_name: xcl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 16856327 num_examples: 4300 download_size: 4950513 dataset_size: 16856327 - config_name: xno features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 48938 num_examples: 5 download_size: 9641 dataset_size: 48938 - config_name: yid features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1409582 num_examples: 803 download_size: 429391 dataset_size: 1409582 - config_name: zul features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7152507 num_examples: 566 download_size: 1581402 dataset_size: 7152507 config_names: - ady - ang - ara - arn - ast - aze - bak - bel - ben - bod - bre - bul - cat - ces - chu - ckb - cor - crh - csb - cym - dan - deu - dsb - ell - eng - est - eus - fao - fas - fin - fra - frm - fro - frr - fry - fur - gal - gla - gle - glv - gmh - gml - got - grc - hai - hbs - heb - hin - hun - hye - isl - ita - izh - kal - kan - kat - kaz - kbd - kjh - klr - kmr - krl - lat - lav - lit - liv - lld - lud - mkd - mlt - mwf - nap - nav - nds - nld - nno - nob - oci - olo - osx - pol - por - pus - que - ron - rus - san - sga - slv - sme - spa - sqi - swc - swe - syc - tat - tel - tgk - tuk - tur - ukr - urd - uzb - vec - vep - vot - xcl - xno - yid - zul --- # 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:** [UniMorph Homepage](https://unimorph.github.io/) - **Repository:** [List of UniMorph repositories](https://github.com/unimorph) - **Paper:** [The Composition and Use of the Universal Morphological Feature Schema (UniMorph Schema)](https://unimorph.github.io/doc/unimorph-schema.pdf) - **Point of Contact:** [Arya McCarthy](mailto:arya@jhu.edu) ### Dataset Summary The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described in Sylak-Glassman (2016). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The current version of the UniMorph dataset covers 110 languages. ## Dataset Structure ### Data Instances Each data instance comprises of a lemma and a set of possible realizations with morphological and meaning annotations. For example: ``` {'forms': {'Aktionsart': [[], [], [], [], []], 'Animacy': [[], [], [], [], []], ... 'Finiteness': [[], [], [], [1], []], ... 'Number': [[], [], [0], [], []], 'Other': [[], [], [], [], []], 'Part_Of_Speech': [[7], [10], [7], [7], [10]], ... 'Tense': [[1], [1], [0], [], [0]], ... 'word': ['ablated', 'ablated', 'ablates', 'ablate', 'ablating']}, 'lemma': 'ablate'} ``` ### Data Fields Each instance in the dataset has the following fields: - `lemma`: the common lemma for all all_forms - `forms`: all annotated forms for this lemma, with: - `word`: the full word form - [`category`]: a categorical variable denoting one or several tags in a category (several to represent composite tags, originally denoted with `A+B`). The full list of categories and possible tags for each can be found [here](https://github.com/unimorph/unimorph.github.io/blob/master/unimorph-schema-json/dimensions-to-features.json) ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.
mteb/amazon_polarity
2022-09-27T19:11:44.000Z
[ "language:en", "region:us" ]
mteb
null
null
null
0
824
--- language: - en ---
codah
2023-01-25T14:28:20.000Z
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
null
The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
@inproceedings{chen2019codah, title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense}, author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug}, booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP}, pages={63--69}, year={2019} }
null
4
822
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: codah pretty_name: COmmonsense Dataset Adversarially-authored by Humans dataset_info: - config_name: codah features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 571208 num_examples: 2776 download_size: 485130 dataset_size: 571208 - config_name: fold_0 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 344912 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 112109 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_1 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 340990 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 116031 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_2 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342293 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114728 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_3 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114211 num_examples: 556 - name: test num_bytes: 114177 num_examples: 555 download_size: 485130 dataset_size: 571232 - config_name: fold_4 features: - name: id dtype: int32 - name: question_category dtype: class_label: names: '0': Idioms '1': Reference '2': Polysemy '3': Negation '4': Quantitative '5': Others - name: question_propmt dtype: string - name: candidate_answers sequence: string - name: correct_answer_idx dtype: int32 splits: - name: train num_bytes: 342844 num_examples: 1665 - name: validation num_bytes: 114177 num_examples: 555 - name: test num_bytes: 114211 num_examples: 556 download_size: 485130 dataset_size: 571232 --- # Dataset Card for COmmonsense Dataset Adversarially-authored by Humans ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
mlsum
2023-06-01T14:59:54.000Z
[ "task_categories:summarization", "task_categories:translation", "task_categories:text-classification", "task_ids:news-articles-summarization", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:extended|cnn_dailymail", "source_datasets:original", "language:de", "language:es", "language:fr", "language:ru", "language:tr", "license:other", "region:us" ]
null
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
@article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} }
null
24
822
--- annotations_creators: - found language_creators: - found language: - de - es - fr - ru - tr license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - extended|cnn_dailymail - original task_categories: - summarization - translation - text-classification task_ids: - news-articles-summarization - multi-class-classification - multi-label-classification - topic-classification paperswithcode_id: mlsum pretty_name: MLSUM dataset_info: - config_name: de features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 846959840 num_examples: 220887 - name: validation num_bytes: 47119541 num_examples: 11394 - name: test num_bytes: 46847612 num_examples: 10701 download_size: 1005814154 dataset_size: 940926993 - config_name: es features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1214558302 num_examples: 266367 - name: validation num_bytes: 50643400 num_examples: 10358 - name: test num_bytes: 71263665 num_examples: 13920 download_size: 1456211154 dataset_size: 1336465367 - config_name: fr features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1471965014 num_examples: 392902 - name: validation num_bytes: 70413212 num_examples: 16059 - name: test num_bytes: 69660288 num_examples: 15828 download_size: 1849565564 dataset_size: 1612038514 - config_name: ru features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 257389497 num_examples: 25556 - name: validation num_bytes: 9128497 num_examples: 750 - name: test num_bytes: 9656398 num_examples: 757 download_size: 766226107 dataset_size: 276174392 - config_name: tu features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 641622783 num_examples: 249277 - name: validation num_bytes: 25530661 num_examples: 11565 - name: test num_bytes: 27830212 num_examples: 12775 download_size: 942308960 dataset_size: 694983656 config_names: - de - es - fr - ru - tu --- # Dataset Card for MLSUM ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** []() - **Repository:** https://github.com/recitalAI/MLSUM - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/ - **Point of Contact:** [email](thomas@recital.ai) - **Size of downloaded dataset files:** 1.83 GB - **Size of the generated dataset:** 4.86 GB - **Total amount of disk used:** 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. ### 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 #### de - **Size of downloaded dataset files:** 346.58 MB - **Size of the generated dataset:** 940.93 MB - **Total amount of disk used:** 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - **Size of downloaded dataset files:** 513.31 MB - **Size of the generated dataset:** 1.34 GB - **Total amount of disk used:** 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - **Size of downloaded dataset files:** 619.99 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 276.17 MB - **Total amount of disk used:** 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - **Size of downloaded dataset files:** 247.50 MB - **Size of the generated dataset:** 694.99 MB - **Total amount of disk used:** 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits |name|train |validation|test | |----|-----:|---------:|----:| |de |220887| 11394|10701| |es |266367| 10358|13920| |fr |392902| 16059|15828| |ru | 25556| 750| 757| |tu |249277| 11565|12775| ## 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 Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
roszcz/maestro-v1-sustain
2023-04-23T13:35:49.000Z
[ "region:us" ]
roszcz
null
null
null
0
818
--- dataset_info: features: - name: notes struct: - name: duration sequence: float64 - name: end sequence: float64 - name: pitch sequence: int64 - name: start sequence: float64 - name: velocity sequence: int64 - name: composer dtype: string - name: title dtype: string - name: year dtype: int64 - name: midi_filename dtype: string splits: - name: test num_bytes: 29686362 num_examples: 177 - name: validation num_bytes: 25599834 num_examples: 137 - name: train num_bytes: 226534277 num_examples: 962 download_size: 87287914 dataset_size: 281820473 --- # Dataset Card for "maestro-v1-sustain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/autotree_snnxor_n15_l1_10
2023-09-18T21:51:32.000Z
[ "region:us" ]
yzhuang
null
null
null
0
817
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 236440000 num_examples: 10000 - name: validation num_bytes: 236440000 num_examples: 10000 - name: test num_bytes: 236440000 num_examples: 10000 download_size: 432260994 dataset_size: 709320000 --- # Dataset Card for "autotree_snnxor_n15_l1_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mattymchen/celeba-hq
2023-04-26T05:56:53.000Z
[ "region:us" ]
mattymchen
null
null
null
0
816
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 2731627350.0 num_examples: 28000 - name: validation num_bytes: 197550788.0 num_examples: 2000 download_size: 2762109745 dataset_size: 2929178138.0 --- # Dataset Card for "celeba-hq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jeanlee/kmhas_korean_hate_speech
2022-11-28T16:26:56.000Z
[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ko", "license:cc-by-sa-4.0", "K-MHaS", "Korean NLP", "Hate Speech Detection", "Dataset", "Coling2022", "arxiv:2208.10684", "region:us" ]
jeanlee
The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class. The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are selected in order to reflect the social and historical context.
@inproceedings{lee-etal-2022-k, title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment", author = "Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.311", pages = "3530--3538", abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.", }
null
9
813
--- annotations_creators: - crowdsourced language: - ko language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'K-MHaS' size_categories: - 100K<n<1M source_datasets: - original tags: - K-MHaS - Korean NLP - Hate Speech Detection - Dataset - Coling2022 task_categories: - text-classification task_ids: - multi-label-classification - hate-speech-detection paperswithcode_id: korean-multi-label-hate-speech-dataset dataset_info: features: - name: text dtype: string - name: label sequence: class_label: names: 0: origin 1: physical 2: politics 3: profanity 4: age 5: gender 6: race 7: religion 8: not_hate_speech splits: - name: train num_bytes: 6845463 num_examples: 78977 - name: validation num_bytes: 748899 num_examples: 8776 - name: test num_bytes: 1902352 num_examples: 21939 download_size: 9496714 dataset_size: 109692 --- # Dataset Card for K-MHaS ## 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) ## Sample Code <a href="https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="base"/></a> ## Dataset Description - **Homepage:** [K-MHaS](https://github.com/adlnlp/K-MHaS) - **Repository:** [Korean Multi-label Hate Speech Dataset](https://github.com/adlnlp/K-MHaS) - **Paper:** [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://arxiv.org/abs/2208.10684) - **Point of Contact:** [Caren Han](caren.han@sydney.edu.au) - **Sample code:** [Colab](https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing) ### Dataset Summary The Korean Multi-label Hate Speech Dataset, **K-MHaS**, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about [**K-MHaS**](https://aclanthology.org/2022.coling-1.311), published at COLING 2022. ### Supported Tasks and Leaderboards Hate Speech Detection * `binary classification` (labels: `Hate Speech`, `Not Hate Speech`) * `multi-label classification`: (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`, `Not Hate Speech`) For the multi-label classification, a `Hate Speech` class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the `Politics` class is chosen, due to a significant influence on the style of Korean hate speech. ### Languages Korean ## Dataset Structure ### Data Instances The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. The label numbers matching in both English and Korean is in the data fields section. ```python {'text':'수꼴틀딱시키들이 다 디져야 나라가 똑바로 될것같다..답이 없는 종자들ㅠ' 'label': [2, 3, 4] } ``` ### Data Fields * `text`: utterance from Korean online news comment. * `label`: the label numbers matching with 8 fine-grained hate speech classes and `not hate speech` class are follows. * `0`: `Origin`(`출신차별`) hate speech based on place of origin or identity; * `1`: `Physical`(`외모차별`) hate speech based on physical appearance (e.g. body, face) or disability; * `2`: `Politics`(`정치성향차별`) hate speech based on political stance; * `3`: `Profanity`(`혐오욕설`) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category; * `4`: `Age`(`연령차별`) hate speech based on age; * `5`: `Gender`(`성차별`) hate speech based on gender or sexual orientation (e.g. woman, homosexual); * `6`: `Race`(`인종차별`) hate speech based on ethnicity; * `7`: `Religion`(`종교차별`) hate speech based on religion; * `8`: `Not Hate Speech`(`해당사항없음`). ### Data Splits In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion. ## Dataset Creation ### Curation Rationale We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech. ### Source Data #### Initial Data Collection and Normalization Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper [K-MHaS](https://aclanthology.org/2022.coling-1.311) published at COLING2020. #### Who are the source language producers? The language producers are users who left the comments on the Korean online news platform between 2018 and 2020. ### Annotations #### Annotation process We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper [K-MHaS](https://aclanthology.org/2022.coling-1.311). #### Who are the annotators? Five native speakers were recruited for manual annotation in both the preliminary and main rounds. ### Personal and Sensitive Information This datasets contains examples of hateful language, however, has no personal information. ## Considerations for Using the Data ### Social Impact of Dataset We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech. ### Discussion of Biases All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo. ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0). ### Citation Information ``` @inproceedings{lee-etal-2022-k, title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment", author = "Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.311", pages = "3530--3538", abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.", } ``` ### Contributions The contributors of the work are: - [Jean Lee](https://jeanlee-ai.github.io/) (The University of Sydney) - [Taejun Lim](https://github.com/taezun) (The University of Sydney) - [Heejun Lee](https://bigwaveai.com/) (BigWave AI) - [Bogeun Jo](https://bigwaveai.com/) (BigWave AI) - Yangsok Kim (Keimyung University) - Heegeun Yoon (National Information Society Agency) - [Soyeon Caren Han](https://drcarenhan.github.io/) (The University of Western Australia and The University of Sydney)
Jackmin108/c4-en-validation
2023-08-18T22:00:10.000Z
[ "region:us" ]
Jackmin108
null
null
null
0
809
--- configs: - config_name: default data_files: - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string splits: - name: validation num_bytes: 825766822 num_examples: 364608 download_size: 509605306 dataset_size: 825766822 --- # Dataset Card for "c4-en-validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Tevatron/msmarco-passage
2023-07-18T07:34:33.000Z
[ "region:us" ]
Tevatron
null
@misc{bajaj2018ms, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song and Alina Stoica and Saurabh Tiwary and Tong Wang}, year={2018}, eprint={1611.09268}, archivePrefix={arXiv}, primaryClass={cs.CL} }
null
3
808
Entry not found
kumapo/JAQKET
2023-10-09T06:44:28.000Z
[ "task_categories:multiple-choice", "task_categories:question-answering", "language:ja", "license:cc-by-sa-4.0", "region:us" ]
kumapo
JAQKET: JApanese Questions on Knowledge of EnTitie
@InProceedings{Kurihara_nlp2020, author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也", title = "JAQKET: クイズを題材にした日本語 QA データセットの構築", booktitle = "言語処理学会第26回年次大会", year = "2020", url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf" note= "in Japanese"
null
0
804
--- license: cc-by-sa-4.0 task_categories: - multiple-choice - question-answering language: - ja --- # Dataset Card for JAQKET This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset). Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull request](https://github.com/kumapo/JAQKET-dataset/pulls). ## Dataset Description - **Homepage:** https://sites.google.com/view/project-aio/dataset - **Repository:** https://github.com/kumapo/JAQKET-dataset ### Dataset Summary From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf): > 本研究では，日本における質問応答/機械読解研究の促進を目的として，研究者が容易に利用可能な日本語のオープンドメイン QA タスクのデータセット「JAQKET」1を構築する. > 作成するデータセットは，既存研究 [7] に倣い，Wikipedia2 の記事名を答えとした，日本語のオープンドメイン QA タスクのデータセットである. ### Supported Tasks #### JAQKET v1.0 From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf): > 本研究で扱う日本語オープンドメイン QA タスクを定義する.本研究では，クイズの問題文に対して複数(数個から数十個程度)の解答の選択肢が与られ，その選択肢から正解を一つ選択するという択一問題を取り扱う. #### JAQKET v2.0 From [the homepage](https://sites.google.com/view/project-aio/competition2): > 問題として与えられるのはクイズの問題文のみです．その問題文から解答となる文字列を解答として返すシステムを構築してもらいます． ### Languages The language data in JAQKET is in Japanese. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: #### JAQKET v1.0 ```python from datasets import load_dataset dataset = load_dataset("kumapo/JAQKET", name="v1.0") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'], # num_rows: 13061 # }) # validation: Dataset({ # features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'], # num_rows: 271 # }) # }) ``` An example of the JAQKET v1.0 dataset looks as follows: ```json { "qid": "QA20QBIK-0002", "question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?", "answer_entity": "ササ", "answer_candidates": [ "ササ", "チシマザサ", "クマザサ", "アダン", "チガヤ", "アセビ", "ススキ", "ホオノキ", "マテバシイ", "ヤマフジ", "ウツギ", "タムシバ", "ミズキ", "アキタブキ", "トベラ", "クヌギ", "ネズミモチ", "ヒシ", "コブシ", "オオウバユリ" ], "qtype": "なに〜" } ``` ```json { "qid": "QA20QBIK-0026", "question": "北海道の中心に位置することから「北海道のへそ」と名乗る、ラベンダーで有名な都市はどこ?", "answer_entity": "富良野市", "answer_candidates": [ "富良野市", "滝川市", "北見市", "芦別市", "中富良野町", "名寄市", "網走市", "美瑛町", "南富良野町", "岩見沢市", "美唄市", "上富良野町", "倶知安町", "小樽市", "歌志内市", "旭川市", "ニセコ町", "北斗市", "稚内市", "帯広市" ], "qtype": "どこ" } ``` #### JAQKET v2.0 ```python from datasets import load_dataset dataset = load_dataset("kumapo/JAQKET", name="v2.0") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['qid', 'question', 'answers', 'ctxs'], # num_rows: 2154 # }) # validation: Dataset({ # features: ['qid', 'question', 'answers', 'ctxs'], # num_rows: 1164 # }) # }) ``` An example of the JAQKET v2.0 dataset looks as follows: ```json { "qid": "QA20QBIK-0002", "competition": "第1回AI王", "timestamp": "2020/01/27", "section": "開発データ問題 (dev1)", "number": "2", "original_question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉？", "original_answer": "ササ", "original_additional_info": "", "question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?", "answers" :["ササ"] } ``` ## Additional Information ### Citation Information ```bibtex @InProceedings{Kurihara_nlp2020, author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也", title = "JAQKET: クイズを題材にした日本語 QA データセットの構築", booktitle = "言語処理学会第26回年次大会", year = "2020", url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf" note= "in Japanese"} ```
nlphuji/winogavil
2022-11-26T19:56:27.000Z
[ "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "commonsense-reasoning", "visual-reasoning", "arxiv:2207.12576", "region:us" ]
nlphuji
WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more.
@article{bitton2022winogavil, title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models}, author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2207.12576}, year={2022} }
null
0
803
--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: winogavil pretty_name: WinoGAViL size_categories: - 10K<n<100K source_datasets: - original tags: - commonsense-reasoning - visual-reasoning task_ids: [] extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files." --- # Dataset Card for WinoGAViL - [Dataset Description](#dataset-description) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more. - **Homepage:** https://winogavil.github.io/ - **Colab** https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi - **Repository:** https://github.com/WinoGAViL/WinoGAViL-experiments/ - **Paper:** https://arxiv.org/abs/2207.12576 - **Leaderboard:** https://winogavil.github.io/leaderboard - **Point of Contact:** winogavil@gmail.com; yonatanbitton1@gmail.com ### Supported Tasks and Leaderboards https://winogavil.github.io/leaderboard. https://paperswithcode.com/dataset/winogavil. ## Colab notebook code for Winogavil evaluation with CLIP https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi ### Languages English. ## Dataset Structure ### Data Fields candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates. cue (string): pogonophile - the generated cue. associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user. score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model. num_associations (int64): 3 - The number of images selected as associative with the cue. num_candidates (int64): 6 - the number of total candidates. solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance. solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance ID (int64): 367 - association ID. ### Data Splits There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set. There are different number of candidates, which creates different difficulty levels: -- With 5 candidates, random model expected score is 38%. -- With 6 candidates, random model expected score is 34%. -- With 10 candidates, random model expected score is 24%. -- With 12 candidates, random model expected score is 19%. <details> <summary>Why random chance for success with 5 candidates is 38%?</summary> It is a binomial distribution probability calculation. Assuming N=5 candidates, and K=2 associations, there could be three events: (1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0. (2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.6*0.33 = 0.198. (3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=2, the expected score is 0+0.198+0.1 = 0.298. To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3. Same goes for (2) and (3). Now we can perform the same calculation with K=3 associations. Assuming N=5 candidates, and K=3 associations, there could be four events: (4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0. (5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.3*0.2 = 0.06. (6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.6*5 = 0.3. (7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46. Taking the average of 0.298 and 0.46 we reach 0.379. Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6). </details> ## Dataset Creation Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. ### Annotations #### Annotation process We paid Amazon Mechanical Turk Workers to play our game. ## Considerations for Using the Data All associations were obtained with human annotators. ### Licensing Information CC-By 4.0 ### Citation Information @article{bitton2022winogavil, title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models}, author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2207.12576}, year={2022}
shibing624/medical
2023-06-02T07:03:41.000Z
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:zh", "language:en", "license:apache-2.0", "text-generation", "region:us" ]
shibing624
纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。
null
null
134
803
--- license: apache-2.0 language: - zh - en tags: - text-generation pretty_name: medical task_categories: - text-generation size_categories: - 1M<n<10M --- # Dataset Card for medical 中文医疗数据集 - LLM Supervised Finetuning repository: https://github.com/shibing624/textgen - MeidcalGPT repository: https://github.com/shibing624/MedicalGPT ## Dataset Description medical is a Chinese Medical dataset. 医疗数据集，可用于医疗领域大模型训练。 ``` tree medical |-- finetune # 监督微调数据集，可用于SFT和RLHF | |-- test_en_1.json | |-- test_zh_0.json | |-- train_en_1.json | |-- train_zh_0.json | |-- valid_en_1.json | `-- valid_zh_0.json |-- medical.py # hf dataset 数据展示用 |-- pretrain # 二次预训练数据集 | |-- medical_book_zh.json | |-- test_encyclopedia.json | |-- train_encyclopedia.json | `-- valid_encyclopedia.json |-- README.md `-- reward # 奖励模型数据集 |-- test.json |-- train.json `-- valid.json ``` ### Original Dataset Summary #### pretrain - train_encyclopedia.json: 共36万条，来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers，形成 text 文本字段，语句通顺，用于预训练注入医疗知识。 - medical_book_zh.json: 共8475条，来自医疗教材的文本数据，来源：https://github.com/jind11/MedQA， 原始数据集：[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ，只对长段落切分为2048字的小段落了。 #### finetune - train_zh_0.json: 共195万条，来自1）中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据， 有79万条；2）在线医疗百科 huatuo_encyclopedia_qa ，有36万条；3）医疗知识图谱 huatuo_knowledge_graph_qa，有79万条。三部分合并，共195万条。 - train_en_1.json：共11万条，来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)，合并了HealthCareMagic-100k、GenMedGPT-5k 数据集，共11万条。 #### reward - train.json 共4000条，问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问，`response_chosen`来自该数据集的医生答复， `response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。 ### Supported Tasks and Leaderboards 中文医疗对话模型 The dataset designed for medical task training pretrained language models. ### Languages The data are in Chinese. ## Dataset Structure ### Data Instances An example of "train" looks as follows: head pretrain/train_encyclopedia.json ```json {"text": "怀孕后嘴巴很淡怎么办？有孕妇在怀孕之后，发现自己嘴巴比较淡，出现这种情况的原因其实也非常的复杂，首先和妊娠反应有直接的关系，这是一种正常的情况，另外有些孕妇平常不注意口腔卫生，舌苔比较厚或者自己有了一些消化系统方面的疾病，这就要求人们必须要及时的进行处理。女性在怀孕之后，身体就会出现一些明显的变化，首先人们月经会停止，另外也会有恶心、呕吐等一些妊娠反应，不过这些都是正常的。有些孕妇发现自己在怀孕之后，口味发生了很大的变化，嘴巴变得非常的淡。其实这也和激素变化有直接的关系，可能是妊娠反应所致，在怀孕期间，因为受到体内激素水平的变化，所以就会有肠胃系统的改变，人们可能会出现食欲不振，消化不良等症状表现，也有一些孕妇会发现自己嘴巴没有味道，会有口苦的症状，而这也是正常的孕期反应，人们在平常要多喝一些水，多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致，孕妇嘴巴里面没有味道，很有可能是舌苔比较重、舌苔过厚导致的，这样就会影响到味蕾对味道的敏感度，不仅嘴巴里面没有味道，甚至有些孕妇在说话的时候也会有口臭，这就要求人们在每天早上漱口的时候，必须要用牙刷刷舌苔开始，不要清理的特别深，以免人们会有呕吐，慢慢习惯之后再往深一点的清洗，一般2到3天就会得到改善。嘴巴感到比较淡，其实也和脾胃虚寒有直接的关系，消化系统疾病，内分泌疾病，营养不良等，但有可能导致舌头部位因为味蕾的敏感度下降，产生口淡之感，患者会有食欲不振的表现，发现病症及时就诊治疗。"} ``` head finetune/train_zh_0.json ```json {"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意：同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定，请参看药品随带的说明书或向医生询问。口服。一次50～100mg（1-2片），3次/日，或遵医嘱。"} ``` head finetune/train_en_1.json ```json {"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."} ``` head reward/train.json ```json {"question": "孩子感统失调，学习不行怎么办？，孩子说话晚，走路晚，现在小学二年级，学习跟不上，理解力差，运动协调性差，家里很着急，不知怎么办。", "response_chosen": "病情分析：你好!孩子说话晚，走路也晚，很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围，那就是说孩子的智商是有问题的，也应考虑与大脑发育不好有关。指导意见：人的大脑在头一年发育最快，可塑性最强，在头三年可塑性还是可以的，超过三岁再进行训练，效果就不怎么好了。建议再给孩子做一做智力测试，如果孩子的智商还是在临界范围，那就要考虑让孩子去特殊学校进行康复训练，而不是继续在普通小学就读，否则对孩子来说，就是强人所难了。希望自己的孩子能聪明，这是每个家长都会有的心愿，但如果孩子自身的条件就是不能跟上同龄孩子，那家长也要面对这个事实的，对吗？医生询问：", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度；同时可以进行物理治疗、康复训练等辅助治疗方法。"} ``` ### Data Fields #### 预训练数据集 pretrain 字段解释： - text: 文本 #### 指令微调数据集 finetune 字段解释： - instruction: 指令 - input：问题（可为空） - output：答复 #### 奖励模型数据集 reward 字段解释： - question: 问题 - response_chosen: 优质回答 - response_rejected: 低质回答 ### Data Splits ``` > wc -l medical/*/* 500 medical/finetune/test_en_1.json 500 medical/finetune/test_zh_0.json 116617 medical/finetune/train_en_1.json 1949972 medical/finetune/train_zh_0.json 500 medical/finetune/valid_en_1.json 500 medical/finetune/valid_zh_0.json 8475 medical/pretrain/medical_book_zh.json 500 medical/pretrain/test_encyclopedia.json 361420 medical/pretrain/train_encyclopedia.json 500 medical/pretrain/valid_encyclopedia.json 100 medical/reward/test.json 3800 medical/reward/train.json 100 medical/reward/valid.json 2443484 total ``` ### Licensing Information The dataset is available under the Apache 2.0. ### Citation Information - https://github.com/Toyhom/Chinese-medical-dialogue-data - https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md - https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa - https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa - https://github.com/Kent0n-Li/ChatDoctor 附上几个优质的reward model dataset: - https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise - https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus - https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12 - https://huggingface.co/datasets/Dahoas/rm-static ### Contributions [shibing624](https://github.com/shibing624) 整理并上传
DFKI-SLT/few-nerd
2023-06-21T09:59:09.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-sa-4.0", "structure-prediction", "region:us" ]
DFKI-SLT
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).
@inproceedings{ding2021few, title={Few-NERD: A Few-Shot Named Entity Recognition Dataset}, author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan}, booktitle={ACL-IJCNLP}, year={2021} }
null
12
802
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|wikipedia task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: few-nerd pretty_name: Few-NERD tags: - structure-prediction --- # Dataset Card for "Few-NERD" ## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) - **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD) - **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/) - **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) ### Dataset Summary This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/. Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)). NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered. For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/. ### Supported Tasks and Leaderboards - **Tasks:** Named Entity Recognition, Few-shot NER - **Leaderboards:** - https://ningding97.github.io/fewnerd/ - named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter ### Languages English ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** - `super`: 14.6 MB - `intra`: 11.4 MB - `inter`: 11.5 MB - **Size of the generated dataset:** - `super`: 116.9 MB - `intra`: 106.2 MB - `inter`: 106.2 MB - **Total amount of disk used:** 366.8 MB An example of 'train' looks as follows. ```json { 'id': '1', 'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0] } ``` ### Data Fields The data fields are the same among all splits. - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8) - `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ... ### Data Splits | Task | Train | Dev | Test | | ----- | ------ | ----- | ---- | | SUP | 131767 | 18824 | 37648 | | INTRA | 99519 | 19358 | 44059 | | INTER | 130112 | 18817 | 14007 | ## 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 [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{ding-etal-2021-nerd, title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset", author = "Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan", 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.248", doi = "10.18653/v1/2021.acl-long.248", pages = "3198--3213", } ``` ### Contributions
result-kand2-sdxl-wuerst-karlo/46328984
2023-09-14T18:58:10.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
null
0
795
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 209 num_examples: 10 download_size: 1390 dataset_size: 209 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "46328984" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
edarchimbaud/perimeter-stocks
2023-10-10T15:00:20.000Z
[ "region:us" ]
edarchimbaud
null
null
null
0
793
--- dataset_info: features: - name: symbol dtype: string - name: security dtype: string - name: gics_sector dtype: string - name: gics_sub_industry dtype: string splits: - name: train num_bytes: 112249 num_examples: 1500 download_size: 43983 dataset_size: 112249 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "perimeter-stocks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sms_spam
2023-01-25T14:44:29.000Z
[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:crowdsourced", "annotations_creators:found", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-nus-sms-corpus", "language:en", "license:unknown", "region:us" ]
null
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
@inproceedings{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", }
null
12
788
--- annotations_creators: - crowdsourced - found language_creators: - crowdsourced - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-nus-sms-corpus task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: sms-spam-collection-data-set pretty_name: SMS Spam Collection Data Set dataset_info: features: - name: sms dtype: string - name: label dtype: class_label: names: '0': ham '1': spam config_name: plain_text splits: - name: train num_bytes: 521756 num_examples: 5574 download_size: 203415 dataset_size: 521756 train-eval-index: - config: plain_text task: text-classification task_id: binary_classification splits: train_split: train col_mapping: sms: text label: 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 [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://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection - **Repository:** - **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011. - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - sms: the sms message - label: indicating if the sms message is ham or spam, ham means it is not spam ### 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{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", } ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.
liwu/MNBVC
2023-10-09T01:24:55.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:zh", "license:mit", "region:us" ]
liwu
MNBVC: Massive Never-ending BT Vast Chinese corpus
\
null
256
788
--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://mnbvc.253874.net/ - **Repository:** https://github.com/esbatmop/MNBVC - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### 数据集介绍 中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。 可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自知乎的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 ## 数据格式 目前MNBVC数据集包含如下[几类数据](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)： ### 文本数据 文本数据使用如下格式组织： ```json { "文件名": datasets.Value("string"), "是否待查文件": datasets.Value("bool"), "是否重复文件": datasets.Value("bool"), "文件大小": datasets.Value("int32"), "simhash": datasets.Value("uint64"), "最长段落长度": datasets.Value("int32"), "段落数": datasets.Value("int32"), "去重段落数": datasets.Value("int32"), "低质量段落数": datasets.Value("int32"), "段落": [ datasets.Features( { "行号": datasets.Value("int32"), "是否重复": datasets.Value("bool"), "是否跨文件重复": datasets.Value("bool"), "md5": datasets.Value("string"), "内容": datasets.Value("string"), } ) ] } ``` ### 问答数据 问答数据使用如下格式组织： ```json { "id": datasets.Value("int32"), "问": datasets.Value("string"), "答": datasets.Value("string"), "来源": datasets.Value("string"), "元数据": { "create_time": datasets.Value("string"), "问题明细": datasets.Value("string"), "回答明细": datasets.Value("string"), "扩展字段": datasets.Value("string"), } } ``` 项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) for adding this dataset.
Francesco/animals-ij5d2
2023-03-30T09:30:09.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]
Francesco
null
null
null
4
787
--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': animals '1': cat '2': chicken '3': cow '4': dog '5': fox '6': goat '7': horse '8': person '9': racoon '10': skunk annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: animals-ij5d2 tags: - rf100 --- # Dataset Card for animals-ij5d2 ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/animals-ij5d2 - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary animals-ij5d2 ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/animals-ij5d2 ### Citation Information ``` @misc{ animals-ij5d2, title = { animals ij5d2 Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/animals-ij5d2 } }, url = { https://universe.roboflow.com/object-detection/animals-ij5d2 }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
code_x_glue_ct_code_to_text
2023-06-01T14:59:54.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:other-programming-languages", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "language:en", "license:c-uda", "code-to-text", "region:us" ]
null
The dataset we use comes from CodeSearchNet and we filter the dataset as the following: - Remove examples that codes cannot be parsed into an abstract syntax tree. - Remove examples that #tokens of documents is < 3 or >256 - Remove examples that documents contain special tokens (e.g. <img ...> or https:...) - Remove examples that documents are not English.
@article{husain2019codesearchnet, title={Codesearchnet challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }
null
35
785
--- annotations_creators: - found language_creators: - found language: - code - en license: - c-uda multilinguality: - other-programming-languages size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] pretty_name: CodeXGlueCtCodeToText tags: - code-to-text dataset_info: - config_name: go features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 342244027 num_examples: 167288 - name: validation num_bytes: 13721912 num_examples: 7325 - name: test num_bytes: 16328458 num_examples: 8122 download_size: 499922799 dataset_size: 372294397 - config_name: java features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 452554719 num_examples: 164923 - name: validation num_bytes: 13366396 num_examples: 5183 - name: test num_bytes: 29080857 num_examples: 10955 download_size: 1072966017 dataset_size: 495001972 - config_name: javascript features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 160860743 num_examples: 58025 - name: validation num_bytes: 10337396 num_examples: 3885 - name: test num_bytes: 10190765 num_examples: 3291 download_size: 1677110214 dataset_size: 181388904 - config_name: php features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 614655799 num_examples: 241241 - name: validation num_bytes: 33283149 num_examples: 12982 - name: test num_bytes: 35375097 num_examples: 14014 download_size: 864290912 dataset_size: 683314045 - config_name: python features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 813664500 num_examples: 251820 - name: validation num_bytes: 46888668 num_examples: 13914 - name: test num_bytes: 50659792 num_examples: 14918 download_size: 953306861 dataset_size: 911212960 - config_name: ruby features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 51956595 num_examples: 24927 - name: validation num_bytes: 2821089 num_examples: 1400 - name: test num_bytes: 2671603 num_examples: 1261 download_size: 124154892 dataset_size: 57449287 config_names: - go - java - javascript - php - python - ruby --- # Dataset Card for "code_x_glue_ct_code_to_text" ## 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-Text/code-to-text ### Dataset Summary CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text The dataset we use comes from CodeSearchNet and we filter the dataset as the following: - Remove examples that codes cannot be parsed into an abstract syntax tree. - Remove examples that #tokens of documents is < 3 or >256 - Remove examples that documents contain special tokens (e.g. <img ...> or https:...) - Remove examples that documents are not English. ### Supported Tasks and Leaderboards - `machine-translation`: The dataset can be used to train a model for automatically generating **English** docstrings for code. ### Languages - Go **programming** language - Java **programming** language - Javascript **programming** language - PHP **programming** language - Python **programming** language - Ruby **programming** language - English **natural** language ## Dataset Structure ### Data Instances #### go An example of 'test' looks as follows. ``` { "code": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "code_tokens": ["func", "NewSTM", "(", "c", "*", "v3", ".", "Client", ",", "apply", "func", "(", "STM", ")", "error", ",", "so", "...", "stmOption", ")", "(", "*", "v3", ".", "TxnResponse", ",", "error", ")", "{", "opts", ":=", "&", "stmOptions", "{", "ctx", ":", "c", ".", "Ctx", "(", ")", "}", "\n", "for", "_", ",", "f", ":=", "range", "so", "{", "f", "(", "opts", ")", "\n", "}", "\n", "if", "len", "(", "opts", ".", "prefetch", ")", "!=", "0", "{", "f", ":=", "apply", "\n", "apply", "=", "func", "(", "s", "STM", ")", "error", "{", "s", ".", "Get", "(", "opts", ".", "prefetch", "...", ")", "\n", "return", "f", "(", "s", ")", "\n", "}", "\n", "}", "\n", "return", "runSTM", "(", "mkSTM", "(", "c", ",", "opts", ")", ",", "apply", ")", "\n", "}"], "docstring": "// NewSTM initiates a new STM instance, using serializable snapshot isolation by default.", "docstring_tokens": ["NewSTM", "initiates", "a", "new", "STM", "instance", "using", "serializable", "snapshot", "isolation", "by", "default", "."], "func_name": "NewSTM", "id": 0, "language": "go", "original_string": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}", "path": "clientv3/concurrency/stm.go", "repo": "etcd-io/etcd", "sha": "616592d9ba993e3fe9798eef581316016df98906", "url": "https://github.com/etcd-io/etcd/blob/616592d9ba993e3fe9798eef581316016df98906/clientv3/concurrency/stm.go#L89-L102" } ``` #### java An example of 'test' looks as follows. ``` { "code": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "code_tokens": ["protected", "final", "void", "fastPathOrderedEmit", "(", "U", "value", ",", "boolean", "delayError", ",", "Disposable", "disposable", ")", "{", "final", "Observer", "<", "?", "super", "V", ">", "observer", "=", "downstream", ";", "final", "SimplePlainQueue", "<", "U", ">", "q", "=", "queue", ";", "if", "(", "wip", ".", "get", "(", ")", "==", "0", "&&", "wip", ".", "compareAndSet", "(", "0", ",", "1", ")", ")", "{", "if", "(", "q", ".", "isEmpty", "(", ")", ")", "{", "accept", "(", "observer", ",", "value", ")", ";", "if", "(", "leave", "(", "-", "1", ")", "==", "0", ")", "{", "return", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "if", "(", "!", "enter", "(", ")", ")", "{", "return", ";", "}", "}", "QueueDrainHelper", ".", "drainLoop", "(", "q", ",", "observer", ",", "delayError", ",", "disposable", ",", "this", ")", ";", "}"], "docstring": "Makes sure the fast-path emits in order.\n@param value the value to emit or queue up\n@param delayError if true, errors are delayed until the source has terminated\n@param disposable the resource to dispose if the drain terminates", "docstring_tokens": ["Makes", "sure", "the", "fast", "-", "path", "emits", "in", "order", "."], "func_name": "QueueDrainObserver.fastPathOrderedEmit", "id": 0, "language": "java", "original_string": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }", "path": "src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java", "repo": "ReactiveX/RxJava", "sha": "ac84182aa2bd866b53e01c8e3fe99683b882c60e", "url": "https://github.com/ReactiveX/RxJava/blob/ac84182aa2bd866b53e01c8e3fe99683b882c60e/src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java#L88-L108" } ``` #### javascript An example of 'test' looks as follows. ``` { "code": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "code_tokens": ["function", "createInstance", "(", "defaultConfig", ")", "{", "var", "context", "=", "new", "Axios", "(", "defaultConfig", ")", ";", "var", "instance", "=", "bind", "(", "Axios", ".", "prototype", ".", "request", ",", "context", ")", ";", "// Copy axios.prototype to instance", "utils", ".", "extend", "(", "instance", ",", "Axios", ".", "prototype", ",", "context", ")", ";", "// Copy context to instance", "utils", ".", "extend", "(", "instance", ",", "context", ")", ";", "return", "instance", ";", "}"], "docstring": "Create an instance of Axios\n\n@param {Object} defaultConfig The default config for the instance\n@return {Axios} A new instance of Axios", "docstring_tokens": ["Create", "an", "instance", "of", "Axios"], "func_name": "createInstance", "id": 0, "language": "javascript", "original_string": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}", "path": "lib/axios.js", "repo": "axios/axios", "sha": "92d231387fe2092f8736bc1746d4caa766b675f5", "url": "https://github.com/axios/axios/blob/92d231387fe2092f8736bc1746d4caa766b675f5/lib/axios.js#L15-L26" } ``` #### php An example of 'train' looks as follows. ``` { "code": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "code_tokens": ["public", "static", "function", "build", "(", "$", "serviceAddress", ",", "$", "restConfigPath", ",", "array", "$", "config", "=", "[", "]", ")", "{", "$", "config", "+=", "[", "'httpHandler'", "=>", "null", ",", "]", ";", "list", "(", "$", "baseUri", ",", "$", "port", ")", "=", "self", "::", "normalizeServiceAddress", "(", "$", "serviceAddress", ")", ";", "$", "requestBuilder", "=", "new", "RequestBuilder", "(", "\"$baseUri:$port\"", ",", "$", "restConfigPath", ")", ";", "$", "httpHandler", "=", "$", "config", "[", "'httpHandler'", "]", "?", ":", "self", "::", "buildHttpHandlerAsync", "(", ")", ";", "return", "new", "RestTransport", "(", "$", "requestBuilder", ",", "$", "httpHandler", ")", ";", "}"], "docstring": "Builds a RestTransport.\n\n@param string $serviceAddress\nThe address of the API remote host, for example \"example.googleapis.com\".\n@param string $restConfigPath\nPath to rest config file.\n@param array $config {\nConfig options used to construct the gRPC transport.\n\n@type callable $httpHandler A handler used to deliver PSR-7 requests.\n}\n@return RestTransport\n@throws ValidationException", "docstring_tokens": ["Builds", "a", "RestTransport", "."], "func_name": "RestTransport.build", "id": 0, "language": "php", "original_string": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }", "path": "src/Transport/RestTransport.php", "repo": "googleapis/gax-php", "sha": "48387fb818c6882296710a2302a0aa973b99afb2", "url": "https://github.com/googleapis/gax-php/blob/48387fb818c6882296710a2302a0aa973b99afb2/src/Transport/RestTransport.php#L85-L94" } ``` #### python An example of 'validation' looks as follows. ``` { "code": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "code_tokens": ["def", "save_act", "(", "self", ",", "path", "=", "None", ")", ":", "if", "path", "is", "None", ":", "path", "=", "os", ".", "path", ".", "join", "(", "logger", ".", "get_dir", "(", ")", ",", "\"model.pkl\"", ")", "with", "tempfile", ".", "TemporaryDirectory", "(", ")", "as", "td", ":", "save_variables", "(", "os", ".", "path", ".", "join", "(", "td", ",", "\"model\"", ")", ")", "arc_name", "=", "os", ".", "path", ".", "join", "(", "td", ",", "\"packed.zip\"", ")", "with", "zipfile", ".", "ZipFile", "(", "arc_name", ",", "'w'", ")", "as", "zipf", ":", "for", "root", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "td", ")", ":", "for", "fname", "in", "files", ":", "file_path", "=", "os", ".", "path", ".", "join", "(", "root", ",", "fname", ")", "if", "file_path", "!=", "arc_name", ":", "zipf", ".", "write", "(", "file_path", ",", "os", ".", "path", ".", "relpath", "(", "file_path", ",", "td", ")", ")", "with", "open", "(", "arc_name", ",", "\"rb\"", ")", "as", "f", ":", "model_data", "=", "f", ".", "read", "(", ")", "with", "open", "(", "path", ",", "\"wb\"", ")", "as", "f", ":", "cloudpickle", ".", "dump", "(", "(", "model_data", ",", "self", ".", "_act_params", ")", ",", "f", ")"], "docstring": "Save model to a pickle located at `path`", "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"], "func_name": "ActWrapper.save_act", "id": 0, "language": "python", "original_string": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)", "path": "baselines/deepq/deepq.py", "repo": "openai/baselines", "sha": "3301089b48c42b87b396e246ea3f56fa4bfc9678", "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72" } ``` #### ruby An example of 'train' looks as follows. ``` { "code": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "code_tokens": ["def", "render_body", "(", "context", ",", "options", ")", "if", "options", ".", "key?", "(", ":partial", ")", "[", "render_partial", "(", "context", ",", "options", ")", "]", "else", "StreamingTemplateRenderer", ".", "new", "(", "@lookup_context", ")", ".", "render", "(", "context", ",", "options", ")", "end", "end"], "docstring": "Render but returns a valid Rack body. If fibers are defined, we return\n a streaming body that renders the template piece by piece.\n\n Note that partials are not supported to be rendered with streaming,\n so in such cases, we just wrap them in an array.", "docstring_tokens": ["Render", "but", "returns", "a", "valid", "Rack", "body", ".", "If", "fibers", "are", "defined", "we", "return", "a", "streaming", "body", "that", "renders", "the", "template", "piece", "by", "piece", "."], "func_name": "ActionView.Renderer.render_body", "id": 0, "language": "ruby", "original_string": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end", "path": "actionview/lib/action_view/renderer/renderer.rb", "repo": "rails/rails", "sha": "85a8bc644be69908f05740a5886ec19cd3679df5", "url": "https://github.com/rails/rails/blob/85a8bc644be69908f05740a5886ec19cd3679df5/actionview/lib/action_view/renderer/renderer.rb#L38-L44" } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### go, java, javascript, php, python, ruby | field name | type | description | |----------------|----------------|-----------------------------------------------------------------------------------| |id |int32 | Index of the sample | |repo |string | repo: the owner/repo | |path |string | path: the full path to the original file | |func_name |string | func_name: the function or method name | |original_string |string | original_string: the raw string before tokenization or parsing | |language |string | language: the programming language name | |code |string | code/function: the part of the original_string that is code | |code_tokens |Sequence[string]| code_tokens/function_tokens: tokenized version of code | |docstring |string | docstring: the top-level comment or docstring, if it exists in the original string| |docstring_tokens|Sequence[string]| docstring_tokens: tokenized version of docstring | |sha |string | sha of the file | |url |string | url of the file | ### Data Splits | name |train |validation|test | |----------|-----:|---------:|----:| |go |167288| 7325| 8122| |java |164923| 5183|10955| |javascript| 58025| 3885| 3291| |php |241241| 12982|14014| |python |251820| 13914|14918| |ruby | 24927| 1400| 1261| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Data from CodeSearchNet Challenge dataset. [More Information Needed] #### Who are the source language producers? Software Engineering developers. ### 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{husain2019codesearchnet, title={Codesearchnet challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
cmrc2018
2023-04-05T09:42:31.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:zh", "license:cc-by-sa-4.0", "region:us" ]
null
A Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context.
@inproceedings{cui-emnlp2019-cmrc2018, title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension}, author = {Cui, Yiming and Liu, Ting and Che, Wanxiang and Xiao, Li and Chen, Zhipeng and Ma, Wentao and Wang, Shijin and Hu, Guoping}, booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, month = {nov}, year = {2019}, address = {Hong Kong, China}, publisher = {Association for Computational Linguistics}, url = {https://www.aclweb.org/anthology/D19-1600}, doi = {10.18653/v1/D19-1600}, pages = {5886--5891}}
null
13
783
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - zh license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: cmrc-2018 pretty_name: Chinese Machine Reading Comprehension 2018 dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: test num_bytes: 1606931 num_examples: 1002 download_size: 11508117 dataset_size: 22298850 --- # Dataset Card for "cmrc2018" ## 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/ymcui/cmrc2018](https://github.com/ymcui/cmrc2018) - **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:** 11.50 MB - **Size of the generated dataset:** 22.31 MB - **Total amount of disk used:** 33.83 MB ### Dataset Summary A Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. ### 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:** 11.50 MB - **Size of the generated dataset:** 22.31 MB - **Total amount of disk used:** 33.83 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [11, 11], "text": ["光荣和ω-force", "光荣和ω-force"] }, "context": "\"《战国无双3》（）是由光荣和ω-force开发的战国无双系列的正统第三续作。本作以三大故事为主轴，分别是以武田信玄等人为主的《关东三国志》，织田信长等人为主的《战国三杰》，石田三成等人为主的《关原的年轻武者》，丰富游戏内的剧情。此部份专门介绍角色，欲知武...", "id": "DEV_0_QUERY_0", "question": "《战国无双3》是由哪两个公司合作开发的？" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | test | | ------- | ----: | ---------: | ---: | | default | 10142 | 3219 | 1002 | ## 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{cui-emnlp2019-cmrc2018, title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension", author = "Cui, Yiming and Liu, Ting and Che, Wanxiang and Xiao, Li and Chen, Zhipeng and Ma, Wentao and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1600", doi = "10.18653/v1/D19-1600", pages = "5886--5891", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
result-kand2-sdxl-wuerst-karlo/b5ddd948
2023-09-15T04:06:31.000Z
[ "region:us" ]
result-kand2-sdxl-wuerst-karlo
null
null
null
0
783
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 205 num_examples: 10 download_size: 1388 dataset_size: 205 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b5ddd948" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/pile-duped-pythia-random-sampled
2023-08-25T08:07:30.000Z
[ "region:us" ]
EleutherAI
null
null
null
1
782
--- 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: 915501044 dataset_size: 1020000000 --- # Dataset Card for "pile-duped-pythia-random-sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)