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etalab-ia/piaf
etalab-ia
2022-11-03T16:31:15Z
311
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[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:fr", "license:mit", "region:us" ]
2022-11-03T16:31:15Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - fr language_bcp47: - fr-FR license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: null pretty_name: Piaf dataset_info: 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 config_name: plain_text splits: - name: train num_bytes: 3332905 num_examples: 3835 download_size: 1370384 dataset_size: 3332905 --- # Dataset Card for Piaf ## 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://piaf.etalab.studio](https://piaf.etalab.studio) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB ### Dataset Summary Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. ### 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:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB An example of 'train' looks as follows. ``` { "answers": { "answer_start": [0], "text": ["Voici"] }, "context": "Voici le contexte du premier paragraphe du deuxième article.", "id": "p140295460356960", "question": "Suis-je la troisième question ?", "title": "Jakob Böhme" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `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 | train | |------------|------:| | plain_text | 3835 | ## 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{keraron-EtAl:2020:LREC, author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo}, title = {Project PIAF: Building a Native French Question-Answering Dataset}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5483--5492}, abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.}, url = {https://www.aclweb.org/anthology/2020.lrec-1.673} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset.
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sofc_materials_articles
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2023-03-09T10:44:46Z
311
7
null
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:token-classification", "task_categories:text-classification", "task_ids:named-entity-recognition", "task_ids:slot-filling", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2006.03039", "region:us" ]
2023-03-09T10:44:46Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-generation - fill-mask - token-classification - text-classification task_ids: - named-entity-recognition - slot-filling - topic-classification pretty_name: SofcMaterialsArticles dataset_info: features: - name: text dtype: string - name: sentence_offsets sequence: - name: begin_char_offset dtype: int64 - name: end_char_offset dtype: int64 - name: sentences sequence: string - name: sentence_labels sequence: int64 - name: token_offsets sequence: - name: offsets sequence: - name: begin_char_offset dtype: int64 - name: end_char_offset dtype: int64 - name: tokens sequence: sequence: string - name: entity_labels sequence: sequence: class_label: names: '0': B-DEVICE '1': B-EXPERIMENT '2': B-MATERIAL '3': B-VALUE '4': I-DEVICE '5': I-EXPERIMENT '6': I-MATERIAL '7': I-VALUE '8': O - name: slot_labels sequence: sequence: class_label: names: '0': B-anode_material '1': B-cathode_material '2': B-conductivity '3': B-current_density '4': B-degradation_rate '5': B-device '6': B-electrolyte_material '7': B-experiment_evoking_word '8': B-fuel_used '9': B-interlayer_material '10': B-interconnect_material '11': B-open_circuit_voltage '12': B-power_density '13': B-resistance '14': B-support_material '15': B-thickness '16': B-time_of_operation '17': B-voltage '18': B-working_temperature '19': I-anode_material '20': I-cathode_material '21': I-conductivity '22': I-current_density '23': I-degradation_rate '24': I-device '25': I-electrolyte_material '26': I-experiment_evoking_word '27': I-fuel_used '28': I-interlayer_material '29': I-interconnect_material '30': I-open_circuit_voltage '31': I-power_density '32': I-resistance '33': I-support_material '34': I-thickness '35': I-time_of_operation '36': I-voltage '37': I-working_temperature '38': O - name: links sequence: - name: relation_label dtype: class_label: names: '0': coreference '1': experiment_variation '2': same_experiment '3': thickness - name: start_span_id dtype: int64 - name: end_span_id dtype: int64 - name: slots sequence: - name: frame_participant_label dtype: class_label: names: '0': anode_material '1': cathode_material '2': current_density '3': degradation_rate '4': device '5': electrolyte_material '6': fuel_used '7': interlayer_material '8': open_circuit_voltage '9': power_density '10': resistance '11': support_material '12': time_of_operation '13': voltage '14': working_temperature - name: slot_id dtype: int64 - name: spans sequence: - name: span_id dtype: int64 - name: entity_label dtype: class_label: names: '0': '' '1': DEVICE '2': MATERIAL '3': VALUE - name: sentence_id dtype: int64 - name: experiment_mention_type dtype: class_label: names: '0': '' '1': current_exp '2': future_work '3': general_info '4': previous_work - name: begin_char_offset dtype: int64 - name: end_char_offset dtype: int64 - name: experiments sequence: - name: experiment_id dtype: int64 - name: span_id dtype: int64 - name: slots sequence: - name: frame_participant_label dtype: class_label: names: '0': anode_material '1': cathode_material '2': current_density '3': degradation_rate '4': conductivity '5': device '6': electrolyte_material '7': fuel_used '8': interlayer_material '9': open_circuit_voltage '10': power_density '11': resistance '12': support_material '13': time_of_operation '14': voltage '15': working_temperature - name: slot_id dtype: int64 splits: - name: train num_bytes: 7402373 num_examples: 26 - name: test num_bytes: 2650700 num_examples: 11 - name: validation num_bytes: 1993857 num_examples: 8 download_size: 3733137 dataset_size: 12046930 --- # Dataset Card for SofcMaterialsArticles ## 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:** [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources) - **Repository:** [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources) - **Paper:** [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://arxiv.org/abs/2006.03039) - **Leaderboard:** - **Point of Contact:** [Annemarie Friedrich](annemarie.friedrich@de.bosch.com) ### Dataset Summary > The SOFC-Exp corpus contains 45 scientific publications about solid oxide fuel cells (SOFCs), published between 2013 and 2019 as open-access articles all with a CC-BY license. The dataset was manually annotated by domain experts with the following information: > > * Mentions of relevant experiments have been marked using a graph structure corresponding to instances of an Experiment frame (similar to the ones used in FrameNet.) We assume that an Experiment frame is introduced to the discourse by mentions of words such as report, test or measure (also called the frame-evoking elements). The nodes corresponding to the respective tokens are the heads of the graphs representing the Experiment frame. > * The Experiment frame related to SOFC-Experiments defines a set of 16 possible participant slots. Participants are annotated as dependents of links between the frame-evoking element and the participant node. > * In addition, we provide coarse-grained entity/concept types for all frame participants, i.e, MATERIAL, VALUE or DEVICE. Note that this annotation has not been performed on the full texts but only on sentences containing information about relevant experiments, and a few sentences in addition. In the paper, we run experiments for both tasks only on the set of sentences marked as experiment-describing in the gold standard, which is admittedly a slightly simplified setting. Entity types are only partially annotated on other sentences. Slot filling could of course also be evaluated in a fully automatic setting with automatic experiment sentence detection as a first step. ### Supported Tasks and Leaderboards - `topic-classification`: The dataset can be used to train a model for topic-classification, to identify sentences that mention SOFC-related experiments. - `named-entity-recognition`: The dataset can be used to train a named entity recognition model to detect `MATERIAL`, `VALUE`, `DEVICE`, and `EXPERIMENT` entities. - `slot-filling`: The slot-filling task is approached as fine-grained entity-typing-in-context, assuming that each sentence represents a single experiment frame. Sequence tagging architectures are utilized for tagging the tokens of each experiment-describing sentence with the set of slot types. The paper experiments with BiLSTM architectures with `BERT`- and `SciBERT`- generated token embeddings, as well as with `BERT` and `SciBERT` directly for the modeling task. A simple CRF architecture is used as a baseline for sequence-tagging tasks. Implementations of the transformer-based architectures can be found in the `huggingface/transformers` library: [BERT](https://huggingface.co/bert-base-uncased), [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) ### Languages This corpus is in English. ## Dataset Structure ### Data Instances As each example is a full text of an academic paper, plus annotations, a json formatted example is space-prohibitive for this README. ### Data Fields - `text`: The full text of the paper - `sentence_offsets`: Start and end character offsets for each sentence in the text. - `begin_char_offset`: a `int64` feature. - `end_char_offset`: a `int64` feature. - `sentences`: A sequence of the sentences in the text (using `sentence_offsets`) - `sentence_labels`: Sequence of binary labels for whether a sentence contains information of interest. - `token_offsets`: Sequence of sequences containing start and end character offsets for each token in each sentence in the text. - `offsets`: a dictionary feature containing: - `begin_char_offset`: a `int64` feature. - `end_char_offset`: a `int64` feature. - `tokens`: Sequence of sequences containing the tokens for each sentence in the text. - `feature`: a `string` feature. - `entity_labels`: a dictionary feature containing: - `feature`: a classification label, with possible values including `B-DEVICE`, `B-EXPERIMENT`, `B-MATERIAL`, `B-VALUE`, `I-DEVICE`. - `slot_labels`: a dictionary feature containing: - `feature`: a classification label, with possible values including `B-anode_material`, `B-cathode_material`, `B-conductivity`, `B-current_density`, `B-degradation_rate`. - `links`: a dictionary feature containing: - `relation_label`: a classification label, with possible values including `coreference`, `experiment_variation`, `same_experiment`, `thickness`. - `start_span_id`: a `int64` feature. - `end_span_id`: a `int64` feature. - `slots`: a dictionary feature containing: - `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `device`. - `slot_id`: a `int64` feature. - `spans`: a dictionary feature containing: - `span_id`: a `int64` feature. - `entity_label`: a classification label, with possible values including ``, `DEVICE`, `MATERIAL`, `VALUE`. - `sentence_id`: a `int64` feature. - `experiment_mention_type`: a classification label, with possible values including ``, `current_exp`, `future_work`, `general_info`, `previous_work`. - `begin_char_offset`: a `int64` feature. - `end_char_offset`: a `int64` feature. - `experiments`: a dictionary feature containing: - `experiment_id`: a `int64` feature. - `span_id`: a `int64` feature. - `slots`: a dictionary feature containing: - `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `conductivity`. - `slot_id`: a `int64` feature. Very detailed information for each of the fields can be found in the [corpus file formats section](https://github.com/boschresearch/sofc-exp_textmining_resources#corpus-file-formats) of the associated dataset repo ### Data Splits This dataset consists of three splits: | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | Input Examples | 26 | 8 | 11 | The authors propose the experimental setting of using the training data in a 5-fold cross validation setting for development and tuning, and finally applying tte model(s) to the independent test set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The corpus consists of 45 open-access scientific publications about SOFCs and related research, annotated by domain experts. ### Annotations #### Annotation process For manual annotation, the authors use the InCeption annotation tool (Klie et al., 2018). #### 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 The manual annotations created for the SOFC-Exp corpus are licensed under a [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @misc{friedrich2020sofcexp, title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain}, author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange}, year={2020}, eprint={2006.03039}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset.
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ttc4900
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2023-01-25T14:54:33Z
311
2
null
[ "task_categories:text-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:tr", "license:unknown", "news-category-classification", "region:us" ]
2023-01-25T14:54:33Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - tr license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: TTC4900 - A Benchmark Data for Turkish Text Categorization tags: - news-category-classification dataset_info: features: - name: category dtype: class_label: names: '0': siyaset '1': dunya '2': ekonomi '3': kultur '4': saglik '5': spor '6': teknoloji - name: text dtype: string config_name: ttc4900 splits: - name: train num_bytes: 10640831 num_examples: 4900 download_size: 10627541 dataset_size: 10640831 --- # Dataset Card for TTC4900: A Benchmark Data for Turkish Text Categorization ## 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:** [TTC4900 Homepage](https://www.kaggle.com/savasy/ttc4900) - **Repository:** [TTC4900 Repository](https://github.com/savasy/TurkishTextClassification) - **Paper:** [A Comparison of Different Approaches to Document Representation in Turkish Language](https://dergipark.org.tr/en/pub/sdufenbed/issue/38975/456349) - **Point of Contact:** [Savaş Yıldırım](mailto:savasy@gmail.com) ### Dataset Summary The data set is taken from [kemik group](http://www.kemik.yildiz.edu.tr/) The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth. We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study ["A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014"](https://link.springer.com/chapter/10.1007/978-3-642-54903-8_36) If you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows: - A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018 - A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018 - A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A text classification dataset with 7 different news category. Here is an example from the dataset: ``` { "category": 0, # politics/siyaset "text": "paris teki infaz imralı ile başlayan sürece bir darbe mi elif_çakır ın sunduğu söz_bitmeden in bugünkü konuğu gazeteci melih altınok oldu programdan satıbaşları imralı ile görüşmeler hangi aşamada bundan sonra ne olacak hangi kesimler sürece engel oluyor psikolojik mayınlar neler türk solu bu dönemde evrensel sorumluluğunu yerine getirebiliyor mu elif_çakır sordu melih altınok söz_bitmeden de yanıtladı elif_çakır pkk nın silahsızlandırılmasına yönelik olarak öcalan ile görüşme sonrası 3 kadının infazı enteresan çünkü kurucu isimlerden birisi sen nasıl okudun bu infazı melih altınok herkesin ciddi anlamda şüpheleri var şu an yürüttüğümüz herşey bir delile dayanmadığı için komple teorisinden ibaret kalacak ama şöyle bir durum var imralı görüşmelerin ilk defa bir siyasi iktidar tarafından açıkça söylendiği bir dönem ardından geliyor bu sürecin gerçekleşmemesini isteyen kesimler yaptırmıştır dedi" } ``` ### Data Fields - **category** : Indicates to which category the news text belongs. (Such as "politics", "world", "economy", "culture", "health", "sports", "technology".) - **text** : Contains the text of the news. ### Data Splits It is not divided into Train set and Test set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth. #### Who are the source language producers? Turkish online news sites. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by [Savaş Yıldırım](https://github.com/savasy) ### Licensing Information [More Information Needed] ### Citation Information ``` @article{doi:10.5505/pajes.2018.15931, author = {Yıldırım, Savaş and Yıldız, Tuğba}, title = {A comparative analysis of text classification for Turkish language}, journal = {Pamukkale Univ Muh Bilim Derg}, volume = {24}, number = {5}, pages = {879-886}, year = {2018}, doi = {10.5505/pajes.2018.15931}, note ={doi: 10.5505/pajes.2018.15931}, URL = {https://dx.doi.org/10.5505/pajes.2018.15931}, eprint = {https://dx.doi.org/10.5505/pajes.2018.15931} } ``` ### Contributions Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.
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yoruba_wordsim353
null
2022-11-03T16:07:49Z
311
0
null
[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:yo", "license:unknown", "region:us" ]
2022-11-03T16:07:49Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en - yo license: - unknown multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - semantic-similarity-scoring paperswithcode_id: null pretty_name: Wordsim-353 In Yorùbá (YorubaWordsim353) dataset_info: features: - name: english1 dtype: string - name: english2 dtype: string - name: yoruba1 dtype: string - name: yoruba2 dtype: string - name: similarity dtype: float32 splits: - name: test num_bytes: 19299 num_examples: 353 download_size: 17039 dataset_size: 19299 --- # Dataset Card for wordsim-353 in Yorùbá (yoruba_wordsim353) ## 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/ajesujoba/YorubaTwi-Embedding - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Leaderboard:** - - **Point of Contact:** Jesujoba Alabi ( jesujobaoluwadara.alabi (at) dfki.de ) and David Adelani ( didelani (at) lsv.uni-saarland.de ) ### Dataset Summary A translation of the word pair similarity dataset wordsim-353 to Yorùbá. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Yorùbá. ### Data Fields - `english1`: the first word of the pair; the original English word - `english2`: the second word of the pair; the original English word - `yoruba1`: the first word of the pair; translation to Yorùbá - `yoruba2`: the second word of the pair; translation to Yorùbá - `similarity`: similarity rating according to the English dataset ### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.
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null
null
null
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null
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Aisha/BAAD16
Aisha
2022-10-22T05:31:54Z
311
0
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:bn", "license:cc-by-4.0", "arxiv:2001.05316", "region:us" ]
2022-10-22T05:31:54Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found - crowdsourced - expert-generated language_creators: - found - crowdsourced language: - bn license: - cc-by-4.0 multilinguality: - monolingual pretty_name: 'BAAD16: Bangla Authorship Attribution Dataset (16 Authors)' source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification --- ## Description **BAAD16** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by the authors of [this paper](https://arxiv.org/abs/2001.05316). It was created by scraping text from an online Bangla e-library using custom web crawler and contains literary works of various famous Bangla writers. It contains novels, stories, series, and other works of 16 authors. Each sample document is created with 750 words. The dataset is imbalanced and resembles real-world scenarios more closely, where not all the authors will have a large number of sample texts. The following table gives more details about the dataset. | Author Name | Number of Samples | Word Count | Unique Word | --- | --- | --- | --- | | zahir rayhan | 185 | 138k | 20k |nazrul | 223 | 167k | 33k |manik bandhopaddhay | 469 | 351k | 44k |nihar ronjon gupta | 476 | 357k | 43k |bongkim | 562 | 421k | 62k |tarashonkor | 775 | 581k | 84k |shottojit roy | 849 | 636k | 67k |shordindu | 888 | 666k | 84k |toslima nasrin | 931 | 698k | 76k |shirshendu | 1048 | 786k | 69k |zafar iqbal | 1100 | 825k | 53k |robindronath | 1259 | 944k | 89k |shorotchandra | 1312 | 984k | 78k |shomresh | 1408 | 1056k|69k |shunil gongopaddhay | 1963 | 1472k|109k |humayun ahmed | 4518 | 3388k |161k **Total**| 17,966|13,474,500 | 590,660 **Average**|1,122.875|842,156.25| 71,822.25 ## Citation If you use this dataset, please cite the paper [Authorship Attribution in Bangla literature using Character-level CNN](https://ieeexplore.ieee.org/abstract/document/9038560/). [Archive link](https://arxiv.org/abs/2001.05316). ``` @inproceedings{BAAD16Dataset, title={Authorship Attribution in Bangla literature using Character-level CNN}, author={Khatun, Aisha and Rahman, Anisur and Islam, Md Saiful and others}, booktitle={2019 22nd International Conference on Computer and Information Technology (ICCIT)}, pages={1--5}, year={2019}, organization={IEEE} doi={10.1109/ICCIT48885.2019.9038560} } ``` This dataset is also available in Mendeley: [BAAD16 dataset](https://data.mendeley.com/datasets/6d9jrkgtvv/4). Always make sure to use the latest version of the dataset. Cite the dataset directly by: ``` @misc{BAAD6Dataset, author = {Khatun, Aisha and Rahman, Anisur and Islam, Md. Saiful}, title = {BAAD16: Bangla Authorship Attribution Dataset}, year={2019}, doi = {10.17632/6d9jrkgtvv.4}, howpublished= {\url{https://data.mendeley.com/datasets/6d9jrkgtvv/4}} } ```
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Aisha/BAAD6
Aisha
2022-10-22T05:30:28Z
311
0
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:bn", "license:cc-by-4.0", "region:us" ]
2022-10-22T05:30:28Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found - crowdsourced - expert-generated language_creators: - found - crowdsourced language: - bn license: - cc-by-4.0 multilinguality: - monolingual pretty_name: 'BAAD6: Bangla Authorship Attribution Dataset (6 Authors)' size_categories: - unknown source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification --- ## Description **BAAD6** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by Hemayet et al [[1]](https://ieeexplore.ieee.org/document/8631977). The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below. | Author | Samples | Word count | Unique word | | ------ | ------ | ------ | ------ | |fe|350|357k|53k| | ij | 350 | 391k | 72k | mk | 350 | 377k | 47k | rn | 350 | 231k | 50k | hm | 350 | 555k | 72k | rg | 350 | 391k | 58k **Total** | 2,100 | 2,304,338 | 230,075 **Average** | 350 | 384,056.33 | 59,006.67 ## Citation If you use this dataset, please cite the paper [A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature](https://ieeexplore.ieee.org/document/8631977). ``` @INPROCEEDINGS{BAAD6Dataset, author={Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Islam, Md. Saiful}, booktitle={2018 21st International Conference of Computer and Information Technology (ICCIT)}, title={A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature}, year={2018}, volume={}, number={}, pages={1-6}, doi={10.1109/ICCITECHN.2018.8631977} } ``` This dataset is also available in Mendeley: [BAAD6 dataset](https://data.mendeley.com/datasets/w9wkd7g43f/5). Always make sure to use the latest version of the dataset. Cite the dataset directly by: ``` @misc{BAAD6Dataset, author = {Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Khatun, Aisha and Islam, Md. Saiful}, title = {BAAD6: Bangla Authorship Attribution Dataset}, year={2018}, doi = {10.17632/w9wkd7g43f.5}, howpublished= {\url{https://data.mendeley.com/datasets/w9wkd7g43f/5}} } ```
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adorkin/extended_tweet_emojis
adorkin
2023-02-07T12:18:57Z
311
1
null
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "region:us" ]
2023-02-07T12:18:57Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- task_categories: - text-classification language: - en size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is comprised of `emoji` and `emotion` subsets of [tweet_eval](https://huggingface.co/datasets/tweet_eval). The motivation is that the original `emoji` subset essentially contains only positive/neutral emojis, while `emotion` subset contains a varied array of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the `emotion` subset and mix it together with the `emoji` subset. ### Supported Tasks and Leaderboards Similar to tweet eval the expected usage is text classification. ### Languages Only English is present in the dataset. ## 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 Refer to [tweet_eval](https://huggingface.co/datasets/tweet_eval). No additional data was added. #### Annotation process Same as tweet eval. #### Who are the annotators? Same as tweet eval. ### Personal and Sensitive Information Same as tweet eval. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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Atsushi/fungi_diagnostic_chars_comparison_japanese
Atsushi
2023-10-08T21:35:23Z
311
0
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ja", "license:cc-by-4.0", "region:us" ]
2023-10-08T21:35:23Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - other language: - ja license: - cc-by-4.0 multilinguality: - monolingual source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification size_categories: - 100K<n<1M --- fungi_diagnostic_chars_comparison_japanese 大菌輪「識別形質まとめ」データセット 最終更新日:2023/10/9(R3-11401まで) ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。 その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。 本データセットは、抽出された識別形質の一覧に、「色/color」、「形状/shape」などのカテゴリを半自動的に付与して集積したものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 ## 関連データセット 「論文3行まとめ」 [Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese) 「Trait Circusデータセット」(統制形質) [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * No … 各識別文を一意のIDで区別するために、各R3IDにおいてナンバリングしたものです。 * comparison_source … 比較元の分類群(学名)です。 * comparison_target … 比較先の分類群(学名)です。 * sentence … 識別文です。全て日本語です。 * label …半自動的に付与されたカテゴリです(人手で修正していますが、ダブルチェックは行っていないので誤分類もあると思います)。以下の25のカテゴリが存在します。 * サイズ/size * 分子系統解析/molecular_phylogenetic_analysis * 形状/shape * 色/color * 地理的分布/geographical_distribution * 生息環境/habitat * 表面性状/surface_characteristics * 構造/structure * 有無/presence * 形態全般/general_morphology * 位置/position * 二次代謝産物/secondary_metabolite * 呈色反応/chemical_reaction * 数量/amount * 発達/development * 生理学的形質/physiological_characters * 分類/classification * 資化・発酵能/assimilation_and_fermentation * 質感/texture * 味・臭い/taste_and_smell * 病害・病原性関連/disease_and_pathogenecity * 全般/general_characters * 耐性・感受性/resistance_and_susceptibility * 栄養摂取様式/nutrition_style * 未分類/unclassified * common_or_different … 共通する形質は「1」、異なる形質は「0」です。 * data_source … 各情報の 出典(文献)のURLです。
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NYTK/HuSST
NYTK
2023-03-27T09:54:13Z
311
1
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:bsd-2-clause", "region:us" ]
2023-03-27T09:54:13Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found - expert-generated language: - hu license: - bsd-2-clause multilinguality: - monolingual size_categories: - unknown source_datasets: - extended|other task_categories: - text-classification task_ids: - sentiment-classification - sentiment-scoring - text-scoring pretty_name: HuSST --- # Dataset Card for HuSST ## 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) - [Language](#language) - [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:** [HuSST dataset](https://github.com/nytud/HuSST) - **Paper:** - **Leaderboard:** - **Point of Contact:** [lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu) ### Dataset Summary This is the dataset card for the Hungarian version of the Stanford Sentiment Treebank. This dataset which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu). The corpus was created by translating and re-annotating the original SST (Roemmele et al., 2011). ### Supported Tasks and Leaderboards 'sentiment classification' 'sentiment scoring' ### Language The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU. ## Dataset Structure ### Data Instances For each instance, there is an id, a sentence and a sentiment label. An example: ``` { "Sent_id": "dev_0", "Sent": "Nos, a Jason elment Manhattanbe és a Pokolba kapcsán, azt hiszem, az elkerülhetetlen folytatások ötletlistájáról kihúzhatunk egy űrállomást 2455-ben (hé, ne lődd le a poént).", "Label": "neutral" } ``` ### Data Fields - Sent_id: unique id of the instances; - Sent: the sentence, translation of an instance of the SST dataset; - Label: "negative", "neutral", or "positive". ### Data Splits HuSST has 3 splits: *train*, *validation* and *test*. | Dataset split | Number of instances in the split | |---------------|----------------------------------| | train | 9344 | | validation | 1168 | | test | 1168 | The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment). ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data is a translation of the content of the SST dataset (only the whole sentences were used). Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator. ### Annotations #### Annotation process The translated sentences were annotated by three human annotators with one of the following labels: negative, neutral and positive. Each sentence was then curated by a fourth annotator (the 'curator'). The final label is the decision of the curator based on the three labels of the annotators. #### Who are the annotators? The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background. ## Additional Information ### Licensing Information ### Citation Information If you use this resource or any part of its documentation, please refer to: Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. pp. 431–446. ``` @inproceedings{ligetinagy2022hulu, title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából}, author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Vadász, T.}, booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia}, year={2022}, pages = {431--446} } ``` and to: Socher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642. ``` @inproceedings{socher-etal-2013-recursive, title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1170", pages = "1631--1642", } ``` ### Contributions Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.
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RohanAiLab/persian_daily_news
RohanAiLab
2022-10-21T16:13:09Z
311
0
null
[ "source_datasets:original", "language:fa", "region:us" ]
2022-10-21T16:13:09Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- pretty_name: Persian Daily News language: - fa source_datasets: - original task_categories: - Summarization - sequence-modeling --- # Persian Daily News # Dataset Summary persian_daily_news is a collection of 2 million of unique news articles with the headline for each article. dataset can be used in abstractive summarization and paraphrasing tasks. This effort is part of a bigger perspective to have several datasets in Persian language(and other low resources languages) for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html) # Description As discussed before, this dataset contains 2M news articles. Each article has these two attributes: text and summary. Here is a sample of dataset: ``` text: به گزارش گروه بین الملل ، خبرگزاری رسمی قطر اعلام کرد، بعد از امضای موافقتنامه همکاری نظامی بین قطر و روسیه این امکان فراهم شده است تا نظامیان قطری برای تکمیل آموزش‌های نظامی خود عازم روسیه شده و در آنجا تعلیم ببینند.در چارچوب این قرارداد که امروز یک شنبه توسط سرتیپ ستاد عبدالعزیز صالح السلیطی رییس هییت همکاری‌های بین المللی نظامی قطر و سرلشکر ویکتور جوریمیکین رییس اداره عمومی نیروی انسانی وزارت دفاع روسیه به امضا رسید، روابط نظامی بین دوحه و مسکو در زمینه موسسات آموزش‌های نظامی شاهد توسه قابل توجهی خواهد شد.به نوشته این خبرگزاری روابط قطر و روسیه در حال گسترش بوده و به سوی شکل‌گیری مشارکت راهبردی در تمامی زمینه‌ها پیش می‌رود. summary: از این پس نظامیان قطری برای آموزش عازم روسیه شده و در موسسات آموزش نظامی این کشور تعلیم خواهند دید. ``` # Citation ``` rohanailab@gmail.com title={persian_daily_news}, author={Saied Alimoradi}, year={2021} } ```
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RohanAiLab/persian_news_dataset
RohanAiLab
2022-10-21T16:13:59Z
311
1
null
[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:multi-class-classification", "source_datasets:original", "language:fa", "region:us" ]
2022-10-21T16:13:59Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- pretty_name: persian_news_datset language: - fa source_datasets: - original task_categories: - text-classification - sequence-modeling task_ids: - language-modeling - multi-class-classification --- # Persian_News_Dataset # Dataset Summary persian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,... This effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html) # Description As discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset: ``` text :سه‌شنبه شب از دور برگشت مرحله نیمه‌نهایی لیگ قهرمانان اروپا، منچسترسیتی در ورزشگاه «اتحاد» میزبان پاری‌سن‌ژرمن بود و با ارائه نمایشی حساب شده و تحسین برانگیز به پیروزی دو بر صفر دست یافت.بازی رفت در پاریس با برتری دو بر یک سیتی به اتمام رسیده بود و با این اوصاف تیم تحت هدایت «پپ گواردیولا» در مجموع با پیروزی چهار بر یک، راهی فینال شد.بارش برف موجب سفیدپوش شدن زمین شده بود و همین امر بر عملکرد تیم‌ها تاثیر گذاشت. دیدار در حالی آغاز به کار کرد که «امباپه» ستاره پاریسی‌ها که به تازگی از مصدومیت رهایی پیدا کرده است، نیمکت‌نشین بود.بازی با حملات میهمان آغاز شد و در دقیقه هفتم داور هلندی با تصمیمی عجیب اعتقاد داشت توپ به دست «زینچنکو» مدافع سیتی برخورد کرده و نقطه پنالتی را نشان داد، اما با استفاده از سیستم کمک داور ویدئویی، پنالتی پس گرفته شد. سیتی خیلی زود به هدفش رسید و در دقیقه ۱۰ حرکت عالی او و پاس به «دی‌بروین» موجب شد تا توپ در یک رفت و برگشت به «ریاض محرز» رسیده و این بازیکن الجزایری گل نخست بازی را برای میزبان به ارمغان آورد.در دقیقه ۱۶ ضربه سر «مارکینیوش» مدافع پیش‌تاخته پاری‌سن‌ژرمن با بدشانسی به تیرک دروازه سیتی برخورد کرد.در ادامه برای دقایقی، بازیکنان در میانه میدان خطاهای متعددی انجام دادند و این امر موجب ایجاد چند درگیری شد.هرچند نماینده فرانسه درپی جبران مافات بود اما برنامه‌ای برای رسیدن به این مهم نداشت تا نیمه نخست با همین یک گل همراه شود.در نیمه دوم هم حملات پاریسی‌ها سودی نداشت و در طرف مقابل منچسترسیتی، بازی بسیار هوشمندانه‌ای ارائه کرد.در دقیقه ۶۲ و در ضد حمله‌ای برق آسا، «فیل فودن» با پاسی عالی توپ را به «ریاض محرز» رساند تا این بازیکن گل دوم خود و تیمش را ثبت کرده و سند صعود سیتی به فینال را امضا کند.در دقیقه ۶۸ «آنخل دی‌ماریا» وینگر آرژانتینی تیم پاری‌سن‌ژرمن پس از درگیری با «فرناندینو» با کارت قرمز داور از زمین اخراج شد تا کار تیمش تمام شود.در این بازی پاری‌سن‌ژرمن با تفکرات «پوچتینو»، طراحی حملات خود را به «نیمار» سپرده بود اما این بازیکن مطرح برزیلی با حرکات انفرادی بیش از از اندازه، عملکرد خوبی نداشت و حملات تیمش را خراب کرد.در نهایت بازی با پیروزی سیتی همراه شد و مالکان ثروتمند منچسترسیتی به آرزوی خود رسیده و پس از سال‌ها سرمایه‌گذاری به دیدار نهایی رسیدند. این اولین حضور سیتی در فینال لیگ قهرمانان اروپا است.چهارشنبه شب در دیگر دیدار دور برگشت نیمه‌نهایی، چلسی انگلیس در ورزشگاه «استمفورد بریج» شهر لندن پذیرای رئال‌مادرید اسپانیا است. بازی رفت با تساوی یک بر یک به اتمام رسید title:آرزوی سیتی برآورده شد؛ صعود شاگردان «گواردیولا» به فینال category:ورزش ``` # Citation ``` rohanailab@gmail.com title={persian_news_dataset}, author={Saied Alimoradi}, year={2021} } ```
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benjaminbeilharz/better_daily_dialog
benjaminbeilharz
2022-01-22T18:03:59Z
311
2
null
[ "region:us" ]
2022-01-22T18:03:59Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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giganticode/java-cmpx
giganticode
2022-07-01T20:33:03Z
311
0
null
[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:unknown", "license:mit", "region:us" ]
2022-07-01T20:33:03Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - java license: - mit multilinguality: - monolingual pretty_name: - java-cmpx size_categories: - unknown source_datasets: [] task_categories: - text-classification task_ids: - multi-class-classification ---
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huggingartists/eminem
huggingartists
2022-10-25T09:29:07Z
311
0
null
[ "language:en", "huggingartists", "lyrics", "region:us" ]
2022-10-25T09:29:07Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/eminem" ## 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 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) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **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 the generated dataset:** 8.291956 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/c7367126e7e6ebc13fcea9d4efca0204.1000x1000x1.jpg&#39;)"> </div> </div> <a href="https://huggingface.co/huggingartists/eminem"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Eminem</div> <a href="https://genius.com/artists/eminem"> <div style="text-align: center; font-size: 14px;">@eminem</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/eminem). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/eminem") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |1285| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/eminem") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## 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{huggingartists, author={Aleksey Korshuk} year=2022 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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tner/ontonotes5
tner
2022-07-18T00:43:55Z
311
3
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]
2022-07-18T00:43:55Z
2022-07-16T11:07:45.000Z
2022-07-16T11:07:45
--- language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Ontonotes5 --- # Dataset Card for "tner/ontonotes5" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://aclanthology.org/N06-2015/](https://aclanthology.org/N06-2015/) - **Dataset:** Ontonotes5 - **Domain:** News - **Number of Entity:** 8 ### Dataset Summary Ontonotes5 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `CARDINAL`, `DATE`, `PERSON`, `NORP`, `GPE`, `LAW`, `PERCENT`, `ORDINAL`, `MONEY`, `WORK_OF_ART`, `FAC`, `TIME`, `QUANTITY`, `PRODUCT`, `LANGUAGE`, `ORG`, `LOC`, `EVENT` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 11, 12, 12, 12, 12, 0, 0, 7, 0, 0, 0, 0, 0], 'tokens': ['``', 'It', "'s", 'very', 'costly', 'and', 'time', '-', 'consuming', ',', "''", 'says', 'Phil', 'Rosen', ',', 'a', 'partner', 'in', 'Fleet', '&', 'Leasing', 'Management', 'Inc.', ',', 'a', 'Boston', 'car', '-', 'leasing', 'company', '.'] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/onotonotes5/raw/main/dataset/label.json). ```python { "O": 0, "B-CARDINAL": 1, "B-DATE": 2, "I-DATE": 3, "B-PERSON": 4, "I-PERSON": 5, "B-NORP": 6, "B-GPE": 7, "I-GPE": 8, "B-LAW": 9, "I-LAW": 10, "B-ORG": 11, "I-ORG": 12, "B-PERCENT": 13, "I-PERCENT": 14, "B-ORDINAL": 15, "B-MONEY": 16, "I-MONEY": 17, "B-WORK_OF_ART": 18, "I-WORK_OF_ART": 19, "B-FAC": 20, "B-TIME": 21, "I-CARDINAL": 22, "B-LOC": 23, "B-QUANTITY": 24, "I-QUANTITY": 25, "I-NORP": 26, "I-LOC": 27, "B-PRODUCT": 28, "I-TIME": 29, "B-EVENT": 30, "I-EVENT": 31, "I-FAC": 32, "B-LANGUAGE": 33, "I-PRODUCT": 34, "I-ORDINAL": 35, "I-LANGUAGE": 36 } ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |ontonotes5|59924| 8528|8262| ### Citation Information ``` @inproceedings{hovy-etal-2006-ontonotes, title = "{O}nto{N}otes: The 90{\%} Solution", author = "Hovy, Eduard and Marcus, Mitchell and Palmer, Martha and Ramshaw, Lance and Weischedel, Ralph", booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers", month = jun, year = "2006", address = "New York City, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N06-2015", pages = "57--60", } ```
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kejian/ACL-ARC
kejian
2023-11-02T04:51:48Z
311
0
null
[ "region:us" ]
2023-11-02T04:51:48Z
2023-11-02T04:51:45.000Z
2023-11-02T04:51:45
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string - name: citing_paper_id dtype: string - name: cited_paper_id dtype: string - name: citing_paper_year dtype: int64 - name: cited_paper_year dtype: int64 - name: citing_paper_title dtype: string - name: cited_paper_title dtype: string - name: cited_author_ids sequence: string - name: citing_author_ids dtype: 'null' - name: extended_context dtype: string - name: section_number dtype: int64 - name: section_title dtype: 'null' - name: intent dtype: string - name: cite_marker_offset sequence: int64 - name: sents_before list: list: - name: index dtype: int64 - name: word dtype: string - name: lemma dtype: string - name: after dtype: string - name: pos dtype: string - name: characterOffsetEnd dtype: int64 - name: segment_span sequence: int64 - name: characterOffsetBegin dtype: int64 - name: originalText dtype: string - name: ArgType dtype: string - name: before dtype: string - name: is_root dtype: bool - name: tense dtype: string - name: has_aux dtype: bool - name: is_pass dtype: bool - name: sents_after list: list: - name: index dtype: int64 - name: word dtype: string - name: lemma dtype: string - name: after dtype: string - name: pos dtype: string - name: characterOffsetEnd dtype: int64 - name: segment_span sequence: int64 - name: characterOffsetBegin dtype: int64 - name: originalText dtype: string - name: ArgType dtype: string - name: before dtype: string - name: is_root dtype: bool - name: tense dtype: string - name: is_pass dtype: bool - name: has_aux dtype: bool - name: cleaned_cite_text dtype: string - name: citation_id dtype: string - name: citation_excerpt_index dtype: int64 - name: section_name dtype: string splits: - name: train num_bytes: 32094179 num_examples: 1688 - name: test num_bytes: 2705971 num_examples: 139 - name: validation num_bytes: 2095387 num_examples: 114 download_size: 6517047 dataset_size: 36895537 --- # Dataset Card for "ACL-ARC" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia
vwxyzjn
2023-11-02T19:19:03Z
311
0
null
[ "region:us" ]
2023-11-02T19:19:03Z
2023-11-02T19:18:32.000Z
2023-11-02T19:18:32
--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 splits: - name: train num_bytes: 886229543 num_examples: 116722 - name: validation num_bytes: 48966797 num_examples: 6447 - name: test num_bytes: 49800881 num_examples: 6553 download_size: 338995010 dataset_size: 984997221 --- # Dataset Card for "summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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y22ma/LLAVA-Test
y22ma
2023-11-10T01:24:15Z
311
0
null
[ "region:us" ]
2023-11-10T01:24:15Z
2023-11-10T01:12:06.000Z
2023-11-10T01:12:06
Entry not found
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bnl_newspapers
null
2023-01-25T14:27:26Z
310
1
null
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ar", "language:da", "language:de", "language:fi", "language:fr", "language:lb", "language:nl", "language:pt", "license:cc0-1.0", "region:us" ]
2023-01-25T14:27:26Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - ar - da - de - fi - fr - lb - nl - pt license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling pretty_name: BnL Historical Newspapers dataset_info: features: - name: id dtype: string - name: source dtype: string - name: url dtype: string - name: title dtype: string - name: ispartof dtype: string - name: text dtype: string - name: pub_date dtype: timestamp[s] - name: publisher dtype: string - name: language dtype: string - name: article_type dtype: class_label: names: '0': ADVERTISEMENT_SECTION '1': BIBLIOGRAPHY '2': CHAPTER '3': INDEX '4': CONTRIBUTION '5': TABLE_OF_CONTENTS '6': WEATHER '7': SHIPPING '8': SECTION '9': ARTICLE '10': TITLE_SECTION '11': DEATH_NOTICE '12': SUPPLEMENT '13': TABLE '14': ADVERTISEMENT '15': CHART_DIAGRAM '16': ILLUSTRATION '17': ISSUE - name: extent dtype: int32 config_name: processed splits: - name: train num_bytes: 1611620156 num_examples: 537558 download_size: 1224029060 dataset_size: 1611620156 --- # Dataset Card for BnL Historical Newspapers ## 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.bnl.lu/data/historical-newspapers/ - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** opendata@bnl.etat.lu ### Dataset Summary The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL: > processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large number of small, easy to use XML files formatted using Dublin Core. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure The dataset currently contains a single configuration. ### Data Instances An example instance from the datasets: ``` python {'id': 'https://persist.lu/ark:/70795/wx8r4c/articles/DTL47', 'article_type': 8, 'extent': 49, 'ispartof': 'Luxemburger Wort', 'pub_date': datetime.datetime(1853, 3, 23, 0, 0), 'publisher': 'Verl. der St-Paulus-Druckerei', 'source': 'newspaper/luxwort/1853-03-23', 'text': 'Asien. Eine neue Nedcrland-Post ist angekommen mil Nachrichten aus Calcutta bis zum 5. Febr.; Vom» vay, 12. Febr. ; Nangun und HongKong, 13. Jan. Die durch die letzte Post gebrachle Nachricht, der König von Ava sei durch seinen Bruder enlhronl worden, wird bestätigt. (K. Z.) Verantwortl. Herausgeber, F. Schümann.', 'title': 'Asien.', 'url': 'http://www.eluxemburgensia.lu/webclient/DeliveryManager?pid=209701#panel:pp|issue:209701|article:DTL47', 'language': 'de' } ``` ### Data Fields - 'id': This is a unique and persistent identifier using ARK. - 'article_type': The type of the exported data, possible values ('ADVERTISEMENT_SECTION', 'BIBLIOGRAPHY', 'CHAPTER', 'INDEX', 'CONTRIBUTION', 'TABLE_OF_CONTENTS', 'WEATHER', 'SHIPPING', 'SECTION', 'ARTICLE', 'TITLE_SECTION', 'DEATH_NOTICE', 'SUPPLEMENT', 'TABLE', 'ADVERTISEMENT', 'CHART_DIAGRAM', 'ILLUSTRATION', 'ISSUE') - 'extent': The number of words in the text field - 'ispartof: The complete title of the source document e.g. “Luxemburger Wort”. - 'pub_date': The publishing date of the document e.g “1848-12-15” - 'publisher':The publisher of the document e.g. “Verl. der St-Paulus-Druckerei”. - 'source': Describes the source of the document. For example <dc:source>newspaper/luxwort/1848-12-15</dc:source> means that this article comes from the newspaper “luxwort” (ID for Luxemburger Wort) issued on 15.12.1848. - 'text': The full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines. - 'title': The main title of the article, section, advertisement, etc. - 'url': The link to the BnLViewer on eluxemburgensia.lu to view the resource online. - 'language': The language of the text, possible values ('ar', 'da', 'de', 'fi', 'fr', 'lb', 'nl', 'pt') ### Data Splits This dataset contains a single split `train`. ## 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 ``` @misc{bnl_newspapers, title={Historical Newspapers}, url={https://data.bnl.lu/data/historical-newspapers/}, author={ Bibliothèque nationale du Luxembourg}, ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
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msr_genomics_kbcomp
null
2023-01-25T14:40:48Z
310
0
null
[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "genomics-knowledge-base-bompletion", "region:us" ]
2023-01-25T14:40:48Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] pretty_name: MsrGenomicsKbcomp tags: - genomics-knowledge-base-bompletion dataset_info: features: - name: GENE1 dtype: string - name: relation dtype: class_label: names: '0': Positive_regulation '1': Negative_regulation '2': Family - name: GENE2 dtype: string splits: - name: train num_bytes: 256789 num_examples: 12160 - name: test num_bytes: 58116 num_examples: 2784 - name: validation num_bytes: 27457 num_examples: 1315 download_size: 0 dataset_size: 342362 --- # 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:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791) - **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset) - **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/) - **Point of Contact:** [Kristina Toutanova](mailto:kristout@google.com) ### Dataset Summary The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure NCI-PID-PubMed Genomics Knowledge Base Completion Dataset This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". FILE FORMAT DETAILS The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in. The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). The format is: | GENE1 | relation | GENE2 | Example: ABL1 Positive_regulation CDK2 The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). ### Data Instances [More Information Needed] ### Data Fields The format is: | GENE1 | relation | GENE2 | ### Data Splits [More Information Needed] ## 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 Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{toutanova-etal-2016-compositional, title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text", author = "Toutanova, Kristina and Lin, Victoria and Yih, Wen-tau and Poon, Hoifung and Quirk, Chris", booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2016", address = "Berlin, Germany", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P16-1136", doi = "10.18653/v1/P16-1136", pages = "1434--1444", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.
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onestop_qa
null
2023-01-25T14:42:12Z
310
4
onestopqa
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "source_datasets:extended|onestop_english", "language:en", "license:cc-by-sa-4.0", "arxiv:2004.14797", "region:us" ]
2023-01-25T14:42:12Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original - extended|onestop_english task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: onestopqa pretty_name: OneStopQA language_bcp47: - en-US dataset_info: features: - name: title dtype: string - name: paragraph dtype: string - name: level dtype: class_label: names: '0': Adv '1': Int '2': Ele - name: question dtype: string - name: paragraph_index dtype: int32 - name: answers sequence: string length: 4 - name: a_span sequence: int32 - name: d_span sequence: int32 splits: - name: train num_bytes: 1423090 num_examples: 1458 download_size: 118173 dataset_size: 1423090 --- # Dataset Card for OneStopQA ## 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) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [OneStopQA repository](https://github.com/berzak/onestop-qa) - **Repository:** [OneStopQA repository](https://github.com/berzak/onestop-qa) - **Paper:** [STARC: Structured Annotations for Reading Comprehension](https://arxiv.org/abs/2004.14797) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English (`en-US`). The original Guardian articles were manually converted from British to American English. ## Dataset Structure ### Data Instances An example of instance looks as follows. ```json { "title": "101-Year-Old Bottle Message", "paragraph": "Angela Erdmann never knew her grandfather. He died in 1946, six years before she was born. But, on Tuesday 8th April, 2014, she described the extraordinary moment when she received a message in a bottle, 101 years after he had lobbed it into the Baltic Sea. Thought to be the world’s oldest message in a bottle, it was presented to Erdmann by the museum that is now exhibiting it in Germany.", "paragraph_index": 1, "level": "Adv", "question": "How did Angela Erdmann find out about the bottle?", "answers": ["A museum told her that they had it", "She coincidentally saw it at the museum where it was held", "She found it in her basement on April 28th, 2014", "A friend told her about it"], "a_span": [56, 70], "d_span": [16, 34] } ``` Where, | Answer | Description | Textual Span | |--------|------------------------------------------------------------|-----------------| | a | Correct answer. | Critical Span | | b | Incorrect answer. A miscomprehension of the critical span. | Critical Span | | c | Incorrect answer. Refers to an additional span. | Distractor Span | | d | Incorrect answer. Has no textual support. | - | The order of the answers in the `answers` list corresponds to the order of the answers in the table. ### Data Fields - `title`: A `string` feature. The article title. - `paragraph`: A `string` feature. The paragraph from the article. - `paragraph_index`: An `int` feature. Corresponds to the paragraph index in the article. - `question`: A `string` feature. The given question. - `answers`: A list of `string` feature containing the four possible answers. - `a_span`: A list of start and end indices (inclusive) of the critical span. - `d_span`: A list of start and end indices (inclusive) of the distractor span. *Span indices are according to word positions after whitespace tokenization. **In the rare case where a span is spread over multiple sections, the span list will contain multiple instances of start and stop indices in the format: [start_1, stop_1, start_2, stop_2,...]. ### Data Splits Articles: 30 Paragraphs: 162 Questions: 486 Question-Paragraph Level pairs: 1,458 No preconfigured split is currently provided. ## 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 The annotation and piloting process of the dataset is described in Appendix A in [STARC: Structured Annotations for Reading Comprehension](https://aclanthology.org/2020.acl-main.507.pdf). #### 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 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. ### Citation Information [STARC: Structured Annotations for Reading Comprehension](http://people.csail.mit.edu/berzak/papers/acl2020.pdf) ``` @inproceedings{starc2020, author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger}, title = {STARC: Structured Annotations for Reading Comprehension}, booktitle = {ACL}, year = {2020}, publisher = {Association for Computational Linguistics} } ``` ### Contributions Thanks to [@scaperex](https://github.com/scaperex) for adding this dataset.
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twi_wordsim353
null
2022-11-03T16:07:57Z
310
1
null
[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:tw", "license:unknown", "region:us" ]
2022-11-03T16:07:57Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en - tw license: - unknown multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - semantic-similarity-scoring paperswithcode_id: null pretty_name: Yorùbá Wordsim-353 dataset_info: features: - name: twi1 dtype: string - name: twi2 dtype: string - name: similarity dtype: float32 splits: - name: test num_bytes: 7285 num_examples: 274 download_size: 6141 dataset_size: 7285 --- # Dataset Card for Yorùbá Wordsim-353 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** -https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Leaderboard:** - - **Point of Contact:** [Kwabena Amponsah-Kaakyire](mailto:s8kwampo@stud.uni-saarland.de) ### Dataset Summary A translation of the word pair similarity dataset wordsim-353 to Twi. However, only 274 (out of 353) pairs of words were translated ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Twi (ISO 639-1: tw) ## Dataset Structure ### Data Instances An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Twi. ### Data Fields - `twi1`: the first word of the pair; translation to Twi - `twi2`: the second word of the pair; translation to Twi - `similarity`: similarity rating according to the English dataset ### Data Splits Only the test data is available ## 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{alabi-etal-2020-massive, title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi", author = "Alabi, Jesujoba and Amponsah-Kaakyire, Kwabena and Adelani, David and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.335", pages = "2754--2762", abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.
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null
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abdusah/masc_dev
abdusah
2022-07-01T15:28:05Z
310
0
null
[ "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language:ar", "license:cc-by-nc-4.0", "region:us" ]
2022-07-01T15:28:05Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar license: - cc-by-nc-4.0 multilinguality: [] paperswithcode_id: [] pretty_name: 'MASC' size_categories: source_datasets: [] task_categories: [] task_ids: [] --- # Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS ## 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://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus - **Repository:** - **Paper:** https://dx.doi.org/10.21227/e1qb-jv46 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Multi-dialect Arabic ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields #### masc_dev - speech - sampling_rate - target_text (label) ### Data Splits #### masc_dev - train: 100 - test: 40 ## 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 Note: this is a small development set for testing. ### Dataset Curators [More Information Needed] ### Licensing Information CC 4.0 ### Citation Information [More Information Needed] ### Contributions Mohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, "MASC: Massive Arabic Speech Corpus", IEEE Dataport, doi: https://dx.doi.org/10.21227/e1qb-jv46.
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allegro/klej-psc
allegro
2022-10-26T09:01:54Z
310
0
null
[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:5K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0", "paraphrase-classification", "region:us" ]
2022-10-26T09:01:54Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 5K - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: Polish Summaries Corpus tags: - paraphrase-classification --- # klej-psc ## Description The Polish Summaries Corpus (PSC) is a dataset of summaries for 569 news articles. The human annotators created five extractive summaries for each article by choosing approximately 5% of the original text. A different annotator created each summary. The subset of 154 articles was also supplemented with additional five abstractive summaries each, i.e., not created from the fragments of the original article. In huggingface version of this dataset, summaries of the same article are used as positive pairs, and the most similar summaries of different articles are sampled as negatives. ## Tasks (input, output, and metrics) The task is to predict whether the extract text and summary are similar. Based on PSC, we formulate a text-similarity task. We generate the positive pairs (i.e., referring to the same article) using only those news articles with both extractive and abstractive summaries. We match each extractive summary with two least similar abstractive ones of the same article. To create negative pairs, we follow a similar procedure. We find two most similar abstractive summaries for each extractive summary, but from different articles. **Input** (*'extract_text'*, *'summary_text'* columns): extract text and summary text sentences **Output** (*'label'* column): label: 1 indicates summary is similar, 0 means that it is not similar **Domain**: News articles **Measurements**: F1-Score **Example**: Input: `Mit o potopie jest prastary, sięga czasów, gdy topniał lodowiec. Na skutek tego wydarzenia w dziejach planety, poziom mórz i oceanów podniósł się o kilkadziesiąt metrów. Potop polodowcowy z całą, naukową pewnością, miał miejsce, ale najprawdopodobniej został przez ludzkość przegapiony. I oto pojawiła się w tej sprawie kolejna glosa. Jej autorami są amerykańscy geofizycy.` ; `Dwójka amerykańskich geofizyków przedstawiła swój scenariusz pochodzenia mitu o potopie. Przed 7500 laty do będącego jeszcze jeziorem Morza Czarnego wlały się wezbrane wskutek topnienia lodowców wody Morza Śródziemnego. Geofizycy twierdzą, że dzięki temu rozkwitło rolnictwo, bo ludzie musieli migrować i szerzyć rolniczy tryb życia. Środowiska naukowe twierdzą jednak, że potop był tylko jednym z czynników ekspansji rolnictwa.` Input (translated by DeepL): `The myth of the Flood is ancient, dating back to the time when the glacier melted. As a result of this event in the history of the planet, the level of the seas and oceans rose by several tens of meters. The post-glacial flood with all, scientific certainty, took place, but was most likely missed by mankind. And here is another gloss on the matter. Its authors are American geophysicists.` ; `Two American geophysicists presented their scenario of the origin of the Flood myth. 7500 years ago, the waters of the Mediterranean Sea flooded into the Black Sea, which was still a lake, due to the melting of glaciers. Geophysicists claim that this made agriculture flourish because people had to migrate and spread their agricultural lifestyle. However, the scientific community argues that the Flood was only one factor in the expansion of agriculture.` Output: `1` (summary is similar) ## Data splits | Subset | Cardinality | | ----------- | ----------: | | train | 4302 | | val | 0 | | test | 1078 | ## Class distribution | Class | train | validation | test | |:------------|--------:|-------------:|-------:| | not similar | 0.705 | - | 0.696 | | similar | 0.295 | - | 0.304 | ## Citation ``` @inproceedings{ogro:kop:14:lrec, title={The {P}olish {S}ummaries {C}orpus}, author={Ogrodniczuk, Maciej and Kope{'c}, Mateusz}, booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014", year = "2014", } ``` ## License ``` Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0) ``` ## Links [HuggingFace](https://huggingface.co/datasets/allegro/klej-psc) [Source](http://zil.ipipan.waw.pl/PolishSummariesCorpus) [Paper](https://aclanthology.org/L14-1145/) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("allegro/klej-psc") pprint(dataset['train'][100]) #{'extract_text': 'Nowe prawo energetyczne jest zagrożeniem dla małych ' # 'producentów energii ze źródeł odnawialnych. Sytuacja się ' # 'pogarsza wdobie urynkowienia energii. zniosło preferencje ' # 'wprowadzone dla energetyki wodnej. UE zamierza podwoić ' # 'udział takich źródeł energetyki jak woda, wiatr, słońce do ' # '2010 r.W Polsce 1-1,5 proc. zużycia energii wytwarza się ze ' # 'źródeł odnawialnych. W krajach Unii udział ten wynosi ' # 'średnio 5,6 proc.', # 'label': 1, # 'summary_text': 'W Polsce w niewielkim stopniu wykorzystuje się elektrownie ' # 'wodne oraz inne sposoby tworzenia energii ze źródeł ' # 'odnawialnych. Podczas gdy w innych krajach europejskich jest ' # 'to średnio 5,6 % w Polsce jest to 1-1,5 %. Powodem jest ' # 'niska opłacalność posiadania tego typu elektrowni-zakład ' # 'energetyczny płaci ok. 17 gr. za 1kWh, podczas gdy ' # 'wybudowanie takiej elektrowni kosztuje ok. 100 tyś. zł.'} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("allegro/klej-psc") dataset = dataset.class_encode_column("label") references = dataset["test"]["label"] # generate random predictions predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] acc = load_metric("accuracy") f1 = load_metric("f1") acc_score = acc.compute(predictions=predictions, references=references) f1_score = f1.compute(predictions=predictions, references=references, average="macro") pprint(acc_score) pprint(f1_score) # {'accuracy': 0.18588469184890655} # {'f1': 0.17511412402843068} ```
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eugenesiow/Urban100
eugenesiow
2022-10-21T03:58:53Z
310
0
null
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:cc-by-4.0", "other-image-super-resolution", "region:us" ]
2022-10-21T03:58:53Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - found language: [] license: - cc-by-4.0 multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: Urban100 tags: - other-image-super-resolution --- # Dataset Card for Urban100 ## 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://github.com/jbhuang0604/SelfExSR - **Repository**: https://huggingface.co/datasets/eugenesiow/Urban100 - **Paper**: https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 ### Dataset Summary The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. It was first published by [Huang et al. (2015)](https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html) in the paper "Single Image Super-Resolution From Transformed Self-Exemplars". 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/Urban100', '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 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 `validation` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_HR/img_001.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_LR_x2/img_001.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 |validation| |-------|---:| |bicubic_x2|100| |bicubic_x3|100| |bicubic_x4|100| ## Dataset Creation ### Curation Rationale The authors have created Urban100 containing 100 HR images with a variety of real-world structures. ### Source Data #### Initial Data Collection and Normalization The authors constructed this dataset using images from Flickr (under CC license) using keywords such as urban, city, architecture, and structure. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process No annotations. #### Who are the annotators? No annotators. ### 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 - **Original Authors**: [Huang et al. (2015)](https://github.com/jbhuang0604/SelfExSR) ### Licensing Information The dataset provided uses images from Flikr under the CC (CC-BY-4.0) license. ### Citation Information ```bibtex @InProceedings{Huang_2015_CVPR, author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra}, title = {Single Image Super-Resolution From Transformed Self-Exemplars}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2015} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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mozilla-foundation/common_voice_12_0
mozilla-foundation
2023-11-17T18:09:06Z
310
12
common-voice
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:extended|common_voice", "language:ab", "language:ar", "language:as", "language:ast", "language:az", "language:ba", "language:bas", "language:be", "language:bg", "language:bn", "language:br", "language:ca", "language:ckb", "language:cnh", "language:cs", "language:cv", "language:cy", "language:da", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:gl", "language:gn", "language:ha", "language:hi", "language:hsb", "language:hu", "language:ia", "language:id", "language:ig", "language:it", "language:ja", "language:ka", "language:kab", "language:kk", "language:kmr", "language:ko", "language:ky", "language:lg", "language:lt", "language:lv", "language:mdf", "language:mhr", "language:mk", "language:ml", "language:mn", "language:mr", "language:mrj", "language:mt", "language:myv", "language:nl", "language:oc", "language:or", "language:pl", "language:pt", "language:quy", "language:ro", "language:ru", "language:rw", "language:sah", "language:sat", "language:sc", "language:sk", "language:skr", "language:sl", "language:sr", "language:sw", "language:ta", "language:th", "language:ti", "language:tig", "language:tok", "language:tr", "language:tt", "language:tw", "language:ug", "language:uk", "language:ur", "language:uz", "language:vi", "language:vot", "language:yo", "language:yue", "language:rm", "language:zh", "language:sv", "language:pa", "language:nn", "language:ne", "language:nan", "language:hy", "language:ga", "language:fy", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]
2023-11-17T18:09:06Z
2023-03-12T17:28:02.000Z
2023-03-12T17:28:02
--- pretty_name: Common Voice Corpus 12.0 annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ab - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - el - en - eo - es - et - eu - fa - fi - fr - gl - gn - ha - hi - hsb - hu - ia - id - ig - it - ja - ka - kab - kk - kmr - ko - ky - lg - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nl - oc - or - pl - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sr - sw - ta - th - ti - tig - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yo - yue - rm - zh - sv - pa - nn - ne - nan - hy - ga - fy language_bcp47: - fy-NL - ga-IE - hy-AM - nan-tw - ne-NP - nn-NO - pa-IN - rm-sursilv - rm-vallader - sv-SE - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10M<n<100M source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition paperswithcode_id: common-voice extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset." --- # Dataset Card for Common Voice Corpus 12.0 ## 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:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Vaibhav Srivastav](mailto:vaibhav@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 26119 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 17127 validated hours in 104 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## 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 Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi): ```python from datasets import load_dataset cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", 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 cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train", streaming=True) print(next(iter(cv_12))) ``` *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 cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train") batch_sampler = BatchSampler(RandomSampler(cv_12), batch_size=32, drop_last=False) dataloader = DataLoader(cv_12, batch_sampler=batch_sampler) ``` ### Streaming ```python from datasets import load_dataset from torch.utils.data import DataLoader cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train") dataloader = DataLoader(cv_12, 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 Common Voice 12 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 and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_12_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```
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ehartford/wizard_vicuna_70k_unfiltered
ehartford
2023-05-16T00:43:23Z
310
119
null
[ "license:apache-2.0", "region:us" ]
2023-05-16T00:43:23Z
2023-05-07T05:12:54.000Z
2023-05-07T05:12:54
--- license: apache-2.0 --- This dataset is the wizard_vicuna dataset junelee/wizard_vicuna_70k, removing conversations with alignment. 34598 conversations remain. inspired by https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered All credit to anon8231489123 I basically took his scripts and applied them to this new dataset.
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2022-11-18T19:58:15Z
309
7
dialogre
[ "task_categories:other", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "relation-extraction", "arxiv:2004.08056", "region:us" ]
2022-11-18T19:58:15Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - other - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: dialogre pretty_name: DialogRE tags: - relation-extraction dataset_info: features: - name: dialog sequence: string - name: relation_data sequence: - name: x dtype: string - name: y dtype: string - name: x_type dtype: string - name: y_type dtype: string - name: r sequence: string - name: rid sequence: int32 - name: t sequence: string config_name: dialog_re splits: - name: train num_bytes: 1520940 num_examples: 1073 - name: test num_bytes: 472306 num_examples: 357 - name: validation num_bytes: 490580 num_examples: 358 download_size: 3816234 dataset_size: 2483826 --- # Dataset Card for [DialogRE] ## 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:** [DialogRE Homepage](https://dataset.org/dialogre/) - **Repository:** [DialogRE Repository](https://github.com/nlpdata/dialogre) - **Paper:** [Arxiv](https://arxiv.org/abs/2004.08056v1) - **Point of Contact:** [dialogre@dataset.org](mailto:dialogre@dataset.org) ### Dataset Summary The DialogRE dataset is the first human-annotated dialogue-based relation extraction (RE) dataset, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. DialogRE can also act as a platform for studying cross-sentence RE as most facts span multiple sentences. Specifically, the dataset annotate all occurrences of 36 possible relation types that exist between pairs of arguments in the 1,788 dialogues originating from the complete transcripts of Friends (in English). ### Supported Tasks and Leaderboards * `other-other-relation-extraction`: The dataset can be used to train a model for Relation Extraction, which consists of the prediction of relation between two arguments that appear in a dialogue. Success on this task is typically measured by achieving a *high* [F1 Score](https://huggingface.co/metrics/f1). ### Languages The dialogues in the dataset is in English originating from the transcripts of Friends. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A typical data point consists of a dialogue between speakers as a list of sentences. This is followed by the annotations of the relations between the entities in the dialog. An example from the DialogRE train set looks as follows: ``` {'dialog': ["Speaker 1: It's been an hour and not one of my classmates has shown up! I tell you, when I actually die some people are gonna get seriously haunted!", 'Speaker 2: There you go! Someone came!', "Speaker 1: Ok, ok! I'm gonna go hide! Oh, this is so exciting, my first mourner!", 'Speaker 3: Hi, glad you could come.', 'Speaker 2: Please, come in.', "Speaker 4: Hi, you're Chandler Bing, right? I'm Tom Gordon, I was in your class.", 'Speaker 2: Oh yes, yes... let me... take your coat.', "Speaker 4: Thanks... uh... I'm so sorry about Ross, it's...", 'Speaker 2: At least he died doing what he loved... watching blimps.', 'Speaker 1: Who is he?', 'Speaker 2: Some guy, Tom Gordon.', "Speaker 1: I don't remember him, but then again I touched so many lives.", 'Speaker 3: So, did you know Ross well?', "Speaker 4: Oh, actually I barely knew him. Yeah, I came because I heard Chandler's news. D'you know if he's seeing anyone?", 'Speaker 3: Yes, he is. Me.', 'Speaker 4: What? You... You... Oh! Can I ask you a personal question? Ho-how do you shave your beard so close?', "Speaker 2: Ok Tommy, that's enough mourning for you! Here we go, bye bye!!", 'Speaker 4: Hey, listen. Call me.', 'Speaker 2: Ok!'], 'relation_data': {'r': [['per:alternate_names'], ['per:alumni'], ['per:alternate_names'], ['per:alumni', 'per:positive_impression'], ['per:alternate_names'], ['unanswerable']], 'rid': [[30], [4], [30], [4, 1], [30], [37]], 't': [[''], [''], [''], ['', 'call me'], [''], ['']], 'x': ['Speaker 2', 'Speaker 2', 'Speaker 4', 'Speaker 4', 'Speaker 4', 'Speaker 1'], 'x_type': ['PER', 'PER', 'PER', 'PER', 'PER', 'PER'], 'y': ['Chandler Bing', 'Speaker 4', 'Tom Gordon', 'Speaker 2', 'Tommy', 'Tommy'], 'y_type': ['PER', 'PER', 'PER', 'PER', 'PER', 'PER']}} ``` ### Data Fields * `dialog` * List of dialog spoken between the speakers * List of annotations per dialog per argument * `x` : First entity * `y` : Second entity * `x_type` : Type of the first entity * `y_type`: Type of the second entity * `r` : List of relations * `rid`: List of relation IDs * `t`: List of relation Trigger words ### Data Splits The data is split into a training, validation and test set as per the original dataset split. | | train | validation | test | | --------------------- |-------:|------------:|------:| | Input dialog examples | 1073 | 358 | 357 | ## 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 DialogRE dataset is intended for non-commercial research purpose only ### Citation Information ``` @inproceedings{yu2020dialogue, title={Dialogue-Based Relation Extraction}, author={Yu, Dian and Sun, Kai and Cardie, Claire and Yu, Dong}, booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020}, url={https://arxiv.org/abs/2004.08056v1} } ``` ### Contributions Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset.
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ehealth_kd
null
2023-01-25T14:29:46Z
309
1
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:es", "license:cc-by-nc-sa-4.0", "relation-prediction", "region:us" ]
2023-01-25T14:29:46Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - es license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: eHealth-KD tags: - relation-prediction dataset_info: features: - name: sentence dtype: string - name: entities list: - name: ent_id dtype: string - name: ent_text dtype: string - name: ent_label dtype: class_label: names: '0': Concept '1': Action '2': Predicate '3': Reference - name: start_character dtype: int32 - name: end_character dtype: int32 - name: relations list: - name: rel_id dtype: string - name: rel_label dtype: class_label: names: '0': is-a '1': same-as '2': has-property '3': part-of '4': causes '5': entails '6': in-time '7': in-place '8': in-context '9': subject '10': target '11': domain '12': arg - name: arg1 dtype: string - name: arg2 dtype: string config_name: ehealth_kd splits: - name: train num_bytes: 425713 num_examples: 800 - name: validation num_bytes: 108154 num_examples: 199 - name: test num_bytes: 47314 num_examples: 100 download_size: 565900 dataset_size: 581181 --- # Dataset Card for eHealth-KD ## 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:** [eHealth-KD homepage](https://knowledge-learning.github.io/ehealthkd-2020/) - **Repository:** [eHealth-KD repository](https://github.com/knowledge-learning/ehealthkd-2020) - **Paper:** [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf) - **Leaderboard:** [eHealth-KD Challenge 2020 official results](https://knowledge-learning.github.io/ehealthkd-2020/results) - **Point of Contact:** [Yoan Gutiérrez Vázquez](mailto:ygutierrez@dlsi.ua.es) (Organization Committee), [María Grandury](mailto:yacine@huggingface.co) (Dataset Submitter) ### Dataset Summary Dataset of the eHealth-KD Challenge at IberLEF 2020. It is designed for the identification of semantic entities and relations in Spanish health documents. ### Supported Tasks and Leaderboards The eHealth-KD challenge proposes two computational subtasks: - `named-entity-recognition`: Given a sentence of an eHealth document written in Spanish, the goal of this subtask is to identify all the entities and their types. - `relation-prediction`: The purpose of this subtask is to recognise all relevant semantic relationships between the entities recognised. For an analysis of the most successful approaches of this challenge, read the [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf). ### Languages The text in the dataset is in Spanish (BCP-47 code: `es`). ## Dataset Structure ### Data Instances The first example of the eHeatlh-KD Corpus train set looks as follows: ``` { 'sentence': 'En la leucemia linfocítica crónica, hay demasiados linfocitos, un tipo de glóbulos blancos.', 'entities': { [ 'ent_id: 'T1', 'ent_text': 'leucemia linfocítica crónica', 'ent_label': 0, 'start_character': 6, 'end_character': 34 ], [ 'ent_id: 'T2', 'ent_text': 'linfocitos', 'ent_label': 0, 'start_character': 51, 'end_character': 61 ], [ 'ent_id: 'T3', 'ent_text': 'glóbulos blancos', 'ent_label': 0, 'start_character': 74, 'end_character': 90 ] }, relations: { [ 'rel_id: 'R0' 'rel_label': 0, 'arg1': T2 'arg2': T3 ], [ 'rel_id': 'R1' 'rel_label': 5, 'arg1': T1, 'arg2': T2 ] } } ``` ### Data Fields - `sentence`: sentence of an eHealth document written in Spanish - `entities`: list of entities identified in the sentence - `ent_id`: entity identifier (`T`+ a number) - `ent_text`: entity, can consist of one or more complete words (i.e., not a prefix or a suffix of a word), and will never include any surrounding punctuation symbols, parenthesis, etc. - `ent_label`: type of entity (`Concept`, `Action`, `Predicate` or `Reference`) - `start_character`: position of the first character of the entity - `end_character`: position of the last character of the entity - `relations`: list of semantic relationships between the entities recognised - `rel_id`: relation identifier (`R` + a number) - `rel_label`: type of relation, can be a general relation (`is-a`, `same-as`, `has-property`, `part-of`, `causes`, `entails`), a contextual relation (`in-time`, `in-place`, `in-context`) an action role (`subject`, `target`) or a predicate role (`domain`, `arg`). - `arg1`: ID of the first entity of the relation - `arg2`: ID of the second entity of the relation For more information about the types of entities and relations, click [here](https://knowledge-learning.github.io/ehealthkd-2020/tasks). ### Data Splits The data is split into a training, validation and test set. The split sizes are as follow: | | Train | Val | Test | | ----- | ------ | ----- | ---- | | eHealth-KD 2020 | 800 | 199 | 100 | In the challenge there are 4 different scenarios for testing. The test data of this dataset corresponds to the third scenario. More information about the testing data [here](https://github.com/knowledge-learning/ehealthkd-2020/tree/master/data/testing). ## Dataset Creation ### Curation Rationale The vast amount of clinical text available online has motivated the development of automatic knowledge discovery systems that can analyse this data and discover relevant facts. The eHealth Knowledge Discovery (eHealth-KD) challenge, in its third edition, leverages a semantic model of human language that encodes the most common expressions of factual knowledge, via a set of four general-purpose entity types and thirteen semantic relations among them. The challenge proposes the design of systems that can automatically annotate entities and relations in clinical text in the Spanish language. ### Source Data #### Initial Data Collection and Normalization As in the previous edition, the corpus for eHealth-KD 2020 has been extracted from MedlinePlus sources. This platform freely provides large health textual data from which we have made a selection for constituting the eHealth-KD corpus. The selection has been made by sampling specific XML files from the collection available in the [Medline website](https://medlineplus.gov/xml.html). ``` “MedlinePlus is the National Institutes of Health’s Website for patients and their families and friends. Produced by the National Library of Medicine, the world’s largest medical library, it brings you information about diseases, conditions, and wellness issues in language you can understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.” ``` These files contain several entries related to health and medicine topics and have been processed to remove all XML markup to extract the textual content. Only Spanish language items were considered. Once cleaned, each individual item was converted to a plain text document, and some further post-processing is applied to remove unwanted sentences, such as headers, footers and similar elements, and to flatten HTML lists into plain sentences. #### Who are the source language producers? As in the previous edition, the corpus for eHealth-KD 2020 was extracted from [MedlinePlus](https://medlineplus.gov/xml.html) sources. ### Annotations #### Annotation process Once the MedlinePlus files were cleaned, they were manually tagged using [BRAT](http://brat.nlplab.org/) by a group of annotators. After tagging, a post-processing was applied to BRAT’s output files (ANN format) to obtain the output files in the formats needed for the challenge. #### Who are the annotators? The data was manually tagged. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset "The eHealth-KD 2020 proposes –as the previous editions– modeling the human language in a scenario in which Spanish electronic health documents could be machine-readable from a semantic point of view. With this task, we expect to encourage the development of software technologies to automatically extract a large variety of knowledge from eHealth documents written in the Spanish Language." ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators #### Organization Committee | Name | Email | Institution | |:---------------------------------------:|:---------------------:|:-----------------------------:| | Yoan Gutiérrez Vázquez (contact person) | ygutierrez@dlsi.ua.es | University of Alicante, Spain | | Suilan Estévez Velarde | sestevez@matcom.uh.cu | University of Havana, Cuba | | Alejandro Piad Morffis | apiad@matcom.uh.cu | University of Havana, Cuba | | Yudivián Almeida Cruz | yudy@matcom.uh.cu | University of Havana, Cuba | | Andrés Montoyo Guijarro | montoyo@dlsi.ua.es | University of Alicante, Spain | | Rafael Muñoz Guillena | rafael@dlsi.ua.es | University of Alicante, Spain | #### Funding This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, IUII, Generalitat Valenciana, Spanish Government, Ministerio de Educación, Cultura y Deporte through the projects SIIA (PROMETEU/2018/089) and LIVINGLANG (RTI2018-094653-B-C22). ### Licensing Information This dataset is under the Attribution-NonCommercial-ShareAlike 4.0 International [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). To accept the distribution terms, please fill in the following [form](https://forms.gle/pUJutSDq2FYLwNWQA). ### Citation Information In the following link you can find the [preliminar bibtexts of the systems’ working-notes](https://knowledge-learning.github.io/ehealthkd-2020/shared/eHealth-KD_2020_bibtexts.zip). In addition, to cite the eHealth-KD challenge you can use the following preliminar bibtext: ``` @inproceedings{overview_ehealthkd2020, author = {Piad{-}Morffis, Alejandro and Guti{\'{e}}rrez, Yoan and Ca{\~{n}}izares-Diaz, Hian and Estevez{-}Velarde, Suilan and Almeida{-}Cruz, Yudivi{\'{a}}n and Mu{\~{n}}oz, Rafael and Montoyo, Andr{\'{e}}s}, title = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020}, booktitle = , year = {2020}, } ``` ### Contributions Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset.
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null
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eitb_parcc
null
2022-11-03T16:15:31Z
309
1
eitb-parcc
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:es", "language:eu", "license:unknown", "region:us" ]
2022-11-03T16:15:31Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - es - eu license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: eitb-parcc pretty_name: EiTB-ParCC dataset_info: features: - name: translation dtype: translation: languages: - es - eu config_name: es-eu splits: - name: train num_bytes: 139039398 num_examples: 637183 download_size: 57244346 dataset_size: 139039398 --- # 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:**[EiTB-ParCC: Parallel Corpus of Comparable New](http://opus.nlpl.eu/EiTB-ParCC.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary EiTB-ParCC: Parallel Corpus of Comparable News. A Basque-Spanish parallel corpus provided by \ Vicomtech (https://www.vicomtech.org), extracted from comparable news produced by the \ Basque public broadcasting group Euskal Irrati Telebista. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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hebrew_projectbenyehuda
null
2022-11-03T16:15:45Z
309
2
null
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:he", "license:mit", "region:us" ]
2022-11-03T16:15:45Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - he license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Hebrew Projectbenyehuda dataset_info: features: - name: id dtype: int32 - name: url dtype: string - name: title dtype: string - name: authors dtype: string - name: translators dtype: string - name: original_language dtype: string - name: genre dtype: string - name: source_edition dtype: string - name: text dtype: string splits: - name: train num_bytes: 318732537 num_examples: 10078 download_size: 317749152 dataset_size: 318732537 --- # Dataset Card for Hebrew Projectbenyehuda ## 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/projectbenyehuda/public_domain_dump - **Repository:** https://github.com/projectbenyehuda/public_domain_dump - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud), and in HTML files. The pseudocatalogue.csv file is a list of titles, authors, genres, and file paths, to help you process the dump. The Releases tab contains a downloadable ZIP archive of the full release. The git repo can be used to track individual file changes, or for incremenetal updates. In the ZIPs, each format (plaintext, plaintext stripped of diacritics, and HTML) has a ZIP file containing one directory per author, with all the author's works under that directory. To request changes or improvements to this dump, file an issue against this repository. All these works are in the public domain, so you are free to make any use of them, and do not need to ask for permission. If you would like to give credit, please credit "Project Ben-Yehuda volunteers", and include a link to the site. We'd also love to hear about the uses you've made of this dump, as it encourages us to keep producing the dump. E-mail us with a brief description (and links, if/as appropriate) of your re-use, at editor@benyehuda.org. There are 10078 files, 3181136 lines Data Annotation: ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hebrew ## Dataset Structure ### Data Instances Sample: ``` { 'id': 10, 'url': 'https://raw.githubusercontent.com/projectbenyehuda/public_domain_dump/master/txt/p23/m10.txt', 'title': 'חצי-נחמה', 'authors': 'אחד העם', 'translators': '', 'original_language': '', 'genre': 'מאמרים ומסות', 'source_edition': '', 'text': '\n\n\n\t\n\tחצי-נחמה\n\t\n\n\n\n1\n\nבין כל הצרות שנתחדשו עלינו בעת האחרונה תעשׂה ביחוד רושם מעציב בלב כל איש ישׂראל התחדשות ‘עלילת־הדם’. העלילה הנתעבה הזאת, בכל יָשנה, היתה ותהיה תמיד בעינינו כחדשה, ומימי הבינים ועד עתה תצטין בפעולתה החזקה על רוח עמנו, לא רק במקום המעשׂה, כי אם גם בארצות רחוקות שהגיעה אליהן השמועה.\n\nאמרתי: ‘על רוח עמנו’, כי אמנם רואה אני מקור החזיון הזה לא בסבּות חיצוניות, כי אם עמוק ברוח העם. בימי הבינים, שהיה כלל ישׂראל במקרים כאלה רגיל לחשוב עצמו כעומד במשפט ביחד עם אותם האומללים שעלה עליהם הגורל להיות כפּרותו, – יש מקום אמנם לראות בזה רק תוצאת הסכנה הגשמית הגדולה להכלל כולו, שהיתה כרוכה אז באמת בעקב כל עלילה כזו. גם לפני חמשים שנה, בימי מנוחה ושלוה, שעוררה עלילת דמשׂק רעש גדול כל־כך בארצות המערב, עדיין יש מקום לאמר, כי היתה בזה, להפך, יד הקנאה הגדולה לכבודם וזכויותיהם ששׂררה אז בלבות אחינו המערביים, אשר זה מעט יצאו מעבדות לחרות. אך בימינו אלה הרי מצד אחד אין הסכנה הגשמית גדולה עוד הרבה, ביחוד לקהלות רחוקות, ומצד אחר כבר הורגלנו לשמוע חרפתנו בקור רוח וקנאת כבודנו לא תאכלנו עוד, ואם בכל זאת גם עתה עודנו מתעוררים ומתנודדים בחזקה לשמע ‘עלילת־דם’, ורגש הכלל יתפרץ החוצה מכל עברים להשליך מעליו את החלאה הזאת, – אות הוא, כי לא הפחד ולא הכבוד החיצוני הם המניעים לזה, כי אם רוח העם הוא המרגיש פה את קלונו והוא זה המתעורר והמעורר; כי אעפ"י שבכל יתר הדברים כבר הביאונו צרותינו לאותו המצב שעליו אמר הנשׂיא החכם בימי קדם: ‘אין בשׂר המת מרגיש באיזמל’, – הנה פה אין ‘האיזמל’ חותך את ‘הבשׂר’ בלבד, כי אם עד הנפש יגע…\n\nאבל – ‘אין רע בלא טוב’, כלומר, בלא לקח טוב. גם הרע הגדול הזה שאנו עסוקים בו אינו ריק מלקח טוב, ואנחנו, אשר לא אדונים אנחנו לגורלנו וגם את הטוב גם את הרע נקבל מן החוץ שלא בטובתנו, ראוי לנו לבקש ברעותינו תמיד את התועלת הלמודית הצפונה בהן, והיתה לנו זאת, לפחות, חצי נחמה.\n\n\n\nאחד הכוחות היותר גדולים בחיי החברה הוא – ‘ההסכמה הכללית’. היו ימים שגם הפלוסופים ראו בהסכמה זו מופת נאמן על הדבר המוסכם ונתנו לה מקום בתוך שאר מופתיהם על מציאות האלהות. עתה אמנם יודעים הפלוסופים , שאין שקר ואין אולת אשר לא תוכל לבוא עליו ‘ההסכמה הכללית’, אם אך תנאי החיים נאותים לזה. אבל רק הפלוסופים יודעים זאת, ובעיני ההמון עוד גם עתה אין אַבטוֹריטט גדול מן ‘ההסכמה’: אם ‘כל העולם’ מאמינים שהדבר כן, בודאי כן הוא; ואם אני איני מבינו, אחרים מבינים; ואם אני רואה כעין סתירה לו, הרי ‘הכל’ רואים גם כן ואעפ"כ מאמינים, וכי חכם אני מכל העולם? – זה הוא בקירוב מהלך הרעיונות של האיש הפשוט, בדעת או בלי דעת ברורה, ומתוך כך הוא מסכים גם מצדו ונעשׂה בעצמו חלק מן ‘ההסכמה’.\n\nוכל־כך גדול כוח ‘ההסכמה’, עד שעל הרוב לא יוכל האדם למַלט נפשו מפעולתה גם כשהוא עצמו הוא ‘הדבר המוסכם’. אם ‘כל העולם’ אומרים על פלוני שגדול הוא בחכמה או ביראה, שיש בו מדה פלונית, טובה או רעה, – סופו להסכים לזה גם בעצמו, אע"פ שמתחלה לא מצא בנפשו אותו היתרון או החסרון שאחרים מיחסים לו. ולא זו בלבד אלא שההסכמה הזאת מצד ‘המוסכם’ עצמו פועלת מעט מעט על תכונת רוחו עד שמקרבתו באמת (או, לפחות, מולידה בו נטיה להתקרב) אל המצב ההוא שרואה בו ‘כל העולם’. על כן יזהירו הפדגוגים בצדק, לבלתי עורר את הילדים על מגרעותיהם המוסריות בראשית התפתחותן, וכל שכּן לבלתי יחס להם מגרעות שאין בהם, כי על ידי זה אפשר שנחזק בלבם את הראשונות ונוליד בם נטיה להאחרונות.\n\nואולם, הדבר מובן, כי ‘כל העולם’ אינו אחד לכל אחד. האדם רואה ‘עולמו’ רק באותה החברה שהוא חושב עצמו לחלק ממנה ורואה באישיה אנשים הקרובים לו מאיזה צד; אבל אין אדם חושב למאומה הסכמת אנשים שרוחם זרה לו לגמרי, שאינו מרגיש בנפשו שום יחס פנימי בינו ובינם. ככה אין האוֹרתוֹדוֹכּסים והמשׂכילים שלנו שׂמים לב כלל אלו להסכמתם של אלו, אף בדברים שאינם נוגעים לאמונה ודת, ושׂחקם ולעגם של אלו על אלו אינו עושׂה שום רושם בלבם של שניהם, לפי שכּל אחת משתי הכּתּות רואה את חברתה כאלו אינה. ואולם כשתנאי החיים מכריחים את בני הכתות השונות להמצא במשׂא ומתן תמידי זה עם זה והם מתרגלים לראות זה בזה קודם כל את האדם, – אז יתרחב ‘עולמם’ והשקפותיהם סובלות שנויים רבים על פי הסכמת ‘העולם’ במובנו החדש.\n\n\n\nלפיכך, בדורות שעברו, כשהיו אבותינו מאמינים בפשטו של ‘אתה בחרתנו’, לא היתה החרפּה שחרפום האומות פועלת כלל על טוהר נפשם פנימה. הם ידעו את ערכם ולא התפעלו עד מה מן ‘ההסכמה הכללית’ אשר מחוץ להם, בהיות כל חברת ‘המסכימים’ נחשבת בעיניהם למין מיוחד של בריות זרות להם ושונות מהם שנוי עצמי, בלי כל יחס וכל דמיון בינם ובינן. אז היה היהודי יכול לשמוע במנוחת לב כל המגרעות המוסריות והחטאים המעשׂיים שטפלה עליו הסכמת העמים, מבלי להרגיש בנפשו שום בושה או שפלוּת פנימית. כי מה לו ולמחשבות ‘הנכרים’ עליו ועל ערכּוֹ? לוּ רק יתנו לו לישב בשלוה! – אבל בדור הזה אין הדבר כן, עתה ‘עולמנו’ נתרחב הרבה, וההסכמה האירופּית פועלת עלינו בחזקה בכל ענפי החיים. ולפי שאין אנו מוציאים עוד את ‘הכל’ מן הכלל, לכן נתפעל בעל כרחנו ממה ש’הכל\' מוציאים אותנו מן הכלל, סופר אחד רוסי שאל באלו הימים בתמימוּת: אחר שכל העולם שׂונאים את היהודים, וכי אפשר לאמור, שכל העולם חייבים והיהודים זכאים? – ושאלה כזו מתגנבת עתה גם אל לב רבים מאחינו: וכי אפשר לאמור, שכל אותן התכונות הנשחתות והמעשׂים הרעים שכל העולם מיחס ליהודים אינם אלא ‘בדותא’?\n\nוהספק הזה, מכיון שנתעורר, מוצא לו מחיה בנקל באותם ההיקשים המוטעים ‘מן הפרט אל הכלל’ הרגילים מאד אצל המון בני האדם. הספור הידוע על דבר נוסע אחד, שבא לאחת הערים ונזדמן לאכסניא שהיה בה משרת כבד־פה, וכתב בפנקסו: בעיר פלונית משרתי האכסניות הם כבדי־פה, – הספור הזה מצייר בצורה של התוּל דרכי־ההגיון של ההמון ברוב משפטיו הכלליים. כל החזיונות הנראים באיזה דבר פרטי רגיל ההמון ליחס אל הכלל שהדבר ההוא מתחשב עליו לפי שמו התמידי, מבלי להתבונן, כי ‘פרט’ אחד יוכל להתחשב על ‘כללים’ רבים ביחד, כלומר, להיות שוּתף בתכוּנה אחת עם פרטיו של כלל אחד ובתכונה אחרת עם פרטיו של כלל אחר, בעוד שהשם הנקרא עליו מציין רק את התיחסותו לאחד הכללים באחד מצדדיו, לא בכולם. – על משפטים ממין זה תוכל להשען, וגם תשען באמת, ההסכמה הכללית ביחוסה אלינו: פלוני ופלוני הם יהודים לפי שמם ורמאים לפי תכוּנתם; שמע מינה, שהיהודים הם לפי תכונתם רמאים. ההגיון האמתי ישיב אמנם על זה, כי אף אם היו באמת כל היהודים בדורנו רמאים, אין מזה עוד ראיה, שהיהודים הם רמאים, כלומר, שתכוּנת הרמאוּת הנמצאת בכל יהודי נמצאת בו מצד התיחסותו אל הכלל ‘יהודים’ ולא מצד איזה כלל אחר (למשל, כלל ‘סוחרים’), שגם אליו מתיחס היהודי בתור פרט, ביחד עם אחרים אשר דבר אין להם עם הכלל ‘יהודים’. וכדי לברר הדבר, צריך לבדוֹק תחלה אותם ‘האחרים’ המשתתפים יחד עם היהודים בכללים אחרים. ורק אחר שנמצא על ידי בדיקה זו, שאין תכוּנת הרמאוּת מצויה בשום ‘כלל’ אחר המשותף ליהודים ולאחרים, – רק אז תהיה לנו צדקה לחרוץ משפט, כי היהדות היא אֵם הרמאוּת. – אבל, כאמור, אין דרכם של בני אדם להעמיק בהגיון, ואין אנו יכולים לדרוש כזאת גם מהמון בני עמנו. הם שומעים את המשפט החרוץ של ההסכמה הכללית ורואים עם זה, שרבים בקרבּנוּ כך הם באמת כמו שאומרת ההסכמה, ובזה די להם, והרי הם מתחילים להסכים גם בעצמם. וככה עוברות ‘תכוּנות היהודים’ כמטבע כשרה מיד ליד, מן ההסכמה החיצונית של העמים אל ההסכמה הפנימית בקרב עמנו, רק עם ההבדל הזה, שהעמים מונים את תכוּנותינו הרעות אחת לאחת בקול ענוֹת גבוּרה ולעג השאננים, ואנחנו עונים אחריהם מלה במלה בקול דממה דקה והצטדקות חלושה; הם ממשילים אותנו לכלי חרס, שאין לו תקנה אלא שבירה, ואנחנו ממשילים עצמנו לכלי מתכת, שאפשר לו בהגעלה ולבּוּן…\n\nהמצב הזה, אם יאריך ימים, יוכל לגרום לנו נזק מוסרי גדול. אין דבר מסוכּן לגוי ולאדם כהודאה על חטאים שאין בו. מי שחטא באמת, הרי שערי תשובה לא ננעלו, וברצונו הטוב יכול להסיר חלאתו מעליו. אבל מי שאחרים הביאוהו לחשוֹד עצמו במה שאין בו, איך יוכל להטהר בעיני עצמו? מצד אחד מאמין הוא לדברי האומרים לו: טול קורה מבין עיניך, ומצד אחר מרגיש הוא, שאינו יכול לטול את הקורה מבין עיניו, אחר שאינה באמת אלא בדמיון, והרי הוא במצב אותם המונומַנים הידועים, שמאיזו סבּה באו לידי אמונה, כי משׂא כבד תלוי להם בחוטמם מבלי שיוכלו להסירו. ולא עוד אלא שלפעמים תביא אמונה זו את האיש הפרטי להשתתף באותה המדה המגוּנה שלפי אמונתו היא קנין הכלל כולו, אעפ“י שהוא עצמו מצד פרטיותו אינו נוטה כלל לזה. אין ספק, למשל, כי בקרב העם שיצאו מתוכו אנשים כהרמב”ם נמצאים גם עתה בעלי דעה מיושבת ואוהבי סדר ושיטה בכל דבר, והם, בקחתם חלק בעבודת הצבּוּר, היו יכולים לתת בה את רוחם ולפעול גם על יתר העובדים. אבל מה נעשׂה, וכל גזרה ‘ההסכמה’, ששׂנאת הסדרים היא תכוּנה יהודית, וכבר הסכמנו גם אנחנו להסכמה זו (אעפ"י שעוד לא נתברר, אם התכוּנה הזאת, המצויה באמת בחלק גדול מעמנו, מתיחסת אל הכלל ‘יהודים’, או אולי – מה שיותר מתקבל על הלב – אל הכלל ‘חניכי־החדר’). ועל כן תרפינה ידי אוהבי הסדר, בהאמינם, כי אין עצה ואין תבונה נגד תכוּנת העם. ואם פטריוטים הם, יעקרו גם מלבם את האהבה לסדרים, המתנגדת לרוח עמם, ויעשׂו גם הם את מעשׂיהם כראוי ליהודים אמתיים…\n\n\n\nצריך איפוא לבקש איזה אמצעי, איך להוציא את עצמנו מתחת השפעת ‘ההסכמה הכללית’ בנוגע לתכוּנות ישׂראל וערכו המוסרי, כדי שלא נהיה בזויים בעיני עצמנו ולא נחשוב, שבאמת גרועים אנחנו מכל בני האדם תחת השמש, וכדי שלא נבוא עי"ז להיות ברבות הימים בפועל מה שאין אנו עתה אלא בדמיון.\n\nואת האמצעי הזה נותנת לנו ‘ההסכמה הכללית’ עצמה על ידי עלילת־הדם. העלילה הזאת היא היחידה בין כל רעותיה אשר בה לא תוכל ההסכמה להביא גם אותנו לידי ספק, אם באמת ‘כל העולם חייבים ואנחנו זכאים’, בהיותה מיוסדת כולה על שקר מוחלט ואין לה משען באיזה היקש מוטעה ‘מן הפרט על הכלל’. כל איש ישׂראל שנתחנך בתוך עמו יודע בבירור גמור, שאין בתוך כלל ישׂראל אף פרט אחד האוכל דם אדם לשם שמים. ואת הידיעה הברורה הזאת משגיאת ‘ההסכמה הכללית’, המתחדשת בלבנו מזמן לזמן על ידי התחדשות עלילת־הדם, צריכים אנו לשמור תמיד בזכרוננו, והיא תעזור לנו לעקור מלבנו את הנטיה להכּנע מפני האַבטוֹריטט של ‘כל העולם’ גם ביתר הדברים. יאמר כל העולם מה שיאמר על דבר פחיתוּת ערכּנוּ המוסרי, – אנחנו יודעים, כי ‘ההסכמה’ הזאת נשענת רק על הגיון המוני, בלי כל יסוד מדעי אמתּי. כי מי בא בסוד עמקי רוחנו וראה את ‘היהודי’ כמו שהוא מצד עצמו? מי שקל זה לעומת זה יהודים ושאינם יהודים הדומים אלו לאלו בכל יתר ‘הכללים’: סוחרים לעומת סוחרים, נרדפים לעומת נרדפים, רעבים לעומת רעבים וכו\'. – מי שקל כל אלה במאזני החכמה האמתּית ומצא את הכף מַכרעת לאחד הצדדים?\n\n‘וכי אפשר שכּל העולם חייבים והיהודים זכאים?’\n\nאפשר ואפשר, ועלילת־הדם תוכיח. פה הרי היהודים זכאים וטהורים כמלאכי השרת: יהודי ודם! היש שני הפכים גדולים מאלו? – ואף על פי כן…\n\n\n\nה\' תשרי תרנ"ג\n\n\n\n\n\n\nנדפס ב‘המליץ’ י“ד תשרי תרנ”ג. \xa0↩\n\n\n\n\n\n\n\n\n\n\nאת הטקסט לעיל הפיקו מתנדבי פרויקט בן־יהודה באינטרנט. הוא זמין תמיד בכתובת הבאה:https://benyehuda.org/read/10' } ``` ### Data Fields - `authors` - `genre` - `id` - `original_language` - `source_edition` - `text` - `title` - `translators` - `url` ### Data Splits | | train | |--------|------:| | corpus | 10078 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Researchers. ### 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 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ### Citation Information ``` @article{, author = {}, title = {Public domain texts from Project Ben-Yehuda}, journal = {}, url = {https://github.com/projectbenyehuda/public_domain_dump}, year = {2020}, } ``` ### Contributions Thanks to [@imvladikon](https://github.com/imvladikon) for adding this dataset.
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hippocorpus
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2022-11-03T16:15:25Z
309
3
null
[ "task_categories:text-classification", "task_ids:text-scoring", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "narrative-flow", "region:us" ]
2022-11-03T16:15:25Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: null pretty_name: hippocorpus tags: - narrative-flow dataset_info: features: - name: AssignmentId dtype: string - name: WorkTimeInSeconds dtype: string - name: WorkerId dtype: string - name: annotatorAge dtype: float32 - name: annotatorGender dtype: string - name: annotatorRace dtype: string - name: distracted dtype: float32 - name: draining dtype: float32 - name: frequency dtype: float32 - name: importance dtype: float32 - name: logTimeSinceEvent dtype: string - name: mainEvent dtype: string - name: memType dtype: string - name: mostSurprising dtype: string - name: openness dtype: string - name: recAgnPairId dtype: string - name: recImgPairId dtype: string - name: similarity dtype: string - name: similarityReason dtype: string - name: story dtype: string - name: stressful dtype: string - name: summary dtype: string - name: timeSinceEvent dtype: string splits: - name: train num_bytes: 7229795 num_examples: 6854 download_size: 0 dataset_size: 7229795 --- # 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:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318) - **Repository:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318) - **Paper:** [Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models](http://erichorvitz.com/cognitive_studies_narrative.pdf) - **Point of Contact:** [Eric Horvitz](mailto:horvitz@microsoft.com) ### Dataset Summary To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset can be found in English ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields This CSV file contains all the stories in Hippcorpus v2 (6854 stories) These are the columns in the file: - `AssignmentId`: Unique ID of this story - `WorkTimeInSeconds`: Time in seconds that it took the worker to do the entire HIT (reading instructions, storywriting, questions) - `WorkerId`: Unique ID of the worker (random string, not MTurk worker ID) - `annotatorAge`: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+ - `annotatorGender`: Gender of the worker - `annotatorRace`: Race/ethnicity of the worker - `distracted`: How distracted were you while writing your story? (5-point Likert) - `draining`: How taxing/draining was writing for you emotionally? (5-point Likert) - `frequency`: How often do you think about or talk about this event? (5-point Likert) - `importance`: How impactful, important, or personal is this story/this event to you? (5-point Likert) - `logTimeSinceEvent`: Log of time (days) since the recalled event happened - `mainEvent`: Short phrase describing the main event described - `memType`: Type of story (recalled, imagined, retold) - `mostSurprising`: Short phrase describing what the most surpring aspect of the story was - `openness`: Continuous variable representing the openness to experience of the worker - `recAgnPairId`: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs. - `recImgPairId`: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs. - `similarity`: How similar to your life does this event/story feel to you? (5-point Likert) - `similarityReason`: Free text annotation of similarity - `story`: Story about the imagined or recalled event (15-25 sentences) - `stressful`: How stressful was this writing task? (5-point Likert) - `summary`: Summary of the events in the story (1-3 sentences) - `timeSinceEvent`: Time (num. days) since the recalled event happened ### Data Splits [More Information Needed] ## 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 Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker, during work done at Microsoft Research. ### Licensing Information Hippocorpus is distributed under the [Open Use of Data Agreement v1.0](https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view). ### Citation Information ``` @inproceedings{sap-etal-2020-recollection, title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models", author = "Sap, Maarten and Horvitz, Eric and Choi, Yejin and Smith, Noah A. and Pennebaker, James", 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.178", doi = "10.18653/v1/2020.acl-main.178", pages = "1970--1978", abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.
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ilist
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2023-01-25T14:32:46Z
309
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[ "task_categories:text-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:awa", "language:bho", "language:bra", "language:hi", "language:mag", "license:cc-by-4.0", "language-identification", "region:us" ]
2023-01-25T14:32:46Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - awa - bho - bra - hi - mag license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: ilist tags: - language-identification dataset_info: features: - name: language_id dtype: class_label: names: '0': AWA '1': BRA '2': MAG '3': BHO '4': HIN - name: text dtype: string splits: - name: train num_bytes: 14362998 num_examples: 70351 - name: test num_bytes: 2146857 num_examples: 9692 - name: validation num_bytes: 2407643 num_examples: 10329 download_size: 18284850 dataset_size: 18917498 --- # Dataset Card for ilist ## 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/kmi-linguistics/vardial2018 - **Paper:** [Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign](https://aclanthology.org/W18-3901/) - **Leaderboard:** - **Point of Contact:** linguistics.kmi@gmail.com ### Dataset Summary This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family: Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri and Magahi. These languages form part of a continuum starting from Western Uttar Pradesh (Hindi and Braj Bhasha) to Eastern Uttar Pradesh (Awadhi and Bhojpuri) and the neighbouring Eastern state of Bihar (Bhojpuri and Magahi). For this task, participants were provided with a dataset of approximately 15,000 sentences in each language, mainly from the domain of literature, published over the web as well as in print. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Hindi, Braj Bhasha, Awadhi, Bhojpuri and Magahi ## Dataset Structure ### Data Instances ``` { "language_id": 4, "text": 'तभी बारिश हुई थी जिसका गीलापन इन मूर्तियों को इन तस्वीरों में एक अलग रूप देता है .' } ``` ### Data Fields - `text`: text which you want to classify - `language_id`: label for the text as an integer from 0 to 4 The language ids correspond to the following languages: "AWA", "BRA", "MAG", "BHO", "HIN". ### Data Splits | | train | valid | test | |----------------------|-------|-------|-------| | # of input sentences | 70351 | 9692 | 10329 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The data for this task was collected from both hard printed and digital sources. Printed materials were obtained from different institutions that promote these languages. We also gathered data from libraries, as well as from local literary and cultural groups. We collected printed stories, novels and essays in books, magazines, and newspapers. #### Initial Data Collection and Normalization We scanned the printed materials, then we performed OCR, and finally we asked native speakers of the respective languages to correct the OCR output. Since there are no specific OCR models available for these languages, we used the Google OCR for Hindi, part of the Drive API. Since all the languages used the Devanagari script, we expected the OCR to work reasonably well, and overall it did. We further managed to get some blogs in Magahi and Bhojpuri. #### 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 This work is licensed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/ ### Citation Information ``` @inproceedings{zampieri-etal-2018-language, title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign", author = {Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Ali, Ahmed and Shon, Suwon and Glass, James and Scherrer, Yves and Samard{\v{z}}i{\'c}, Tanja and Ljube{\v{s}}i{\'c}, Nikola and Tiedemann, J{\"o}rg and van der Lee, Chris and Grondelaers, Stefan and Oostdijk, Nelleke and Speelman, Dirk and van den Bosch, Antal and Kumar, Ritesh and Lahiri, Bornini and Jain, Mayank}, booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-3901", pages = "1--17", } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
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msr_zhen_translation_parity
null
2022-11-03T16:08:10Z
309
0
null
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|other-newstest2017", "language:en", "license:ms-pl", "region:us" ]
2022-11-03T16:08:10Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - expert-generated - machine-generated language: - en license: - ms-pl multilinguality: - monolingual - translation size_categories: - 1K<n<10K source_datasets: - extended|other-newstest2017 task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: MsrZhenTranslationParity dataset_info: features: - name: Reference-HT dtype: string - name: Reference-PE dtype: string - name: Combo-4 dtype: string - name: Combo-5 dtype: string - name: Combo-6 dtype: string - name: Online-A-1710 dtype: string splits: - name: train num_bytes: 1797033 num_examples: 2001 download_size: 0 dataset_size: 1797033 --- # Dataset Card for msr_zhen_translation_parity ## 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:** [Translator Human Parity Data](https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b) - **Repository:** - **Paper:** [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary > Human evaluation results and translation output for the Translator Human Parity Data release, > as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/ > The Translator Human Parity Data release contains all human evaluation results and translations > related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation", > published on March 14, 2018. We have released this data to > 1) allow external validation of our claim of having achieved human parity > 2) to foster future research by releasing two additional human references > for the Reference-WMT test set. > The dataset includes: 1) two new references for Chinese-English language pair of WMT17, one based on human translation from scratch (Reference-HT), the other based on human post-editing (Reference-PE); 2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6, as well as translation output from online machine translation service Online-A-1710, collected on October 16, 2017; The data package provided with the study also includes (but not parsed and provided as workable features of this dataset) all data points collected in human evaluation campaigns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset contains 6 extra English translations to Chinese-English language pair of WMT17. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields As mentioned in the summary, this dataset provides 6 extra English translations of Chinese-English language pair of WMT17. Data fields are named exactly like the associated paper for easier cross-referenceing. - `Reference-HT`: human translation from scrach. - `Reference-PE`: human post-editing. - `Combo-4`, `Combo-5`, `Combo-6`: three translations by research systems. - `Online-A-1710`: a translation from an anonymous online machine translation service. All data fields of a record are translations for the same Chinese source sentence. ### 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 Citation information is available at this link [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/) ### Contributions Thanks to [@leoxzhao](https://github.com/leoxzhao) for adding this dataset.
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2023-01-25T14:54:29Z
309
0
tsac
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:aeb", "license:lgpl-3.0", "region:us" ]
2023-01-25T14:54:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - aeb license: - lgpl-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: tsac pretty_name: Tunisian Sentiment Analysis Corpus dataset_info: features: - name: id dtype: string - name: sentence dtype: string - name: target dtype: class_label: names: '0': '1' '1': '-1' splits: - name: train num_bytes: 1020146 num_examples: 13669 - name: test num_bytes: 268504 num_examples: 3400 download_size: 963015 dataset_size: 1288650 --- # Dataset Card for Tunisian Sentiment Analysis Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** None - **Repository:** https://github.com/fbougares/TSAC - **Paper:** https://www.aclweb.org/anthology/W17-1307 - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** Salima Mdhaffar (firstname.lastname@univ-lemans.fr) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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DDSC/europarl
DDSC
2022-07-01T15:42:03Z
309
2
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0", "region:us" ]
2022-07-01T15:42:03Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - da license: - cc-by-4.0 multilinguality: - monolingual pretty_name: TwitterSent size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for DKHate ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Direct Download**: http://danlp-downloads.alexandra.dk/datasets/europarl.sentiment2.zip ### Dataset Summary This dataset consists of Danish data from the European Parliament that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a document and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The text content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 669 documents in the training split and 288 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @misc{europarl, title={EuroParl}, author={Alexandra Institute}, year={2020}, note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2}} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.
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null
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Narsil/conversational_dummy
Narsil
2021-08-19T08:51:34Z
309
0
null
[ "region:us" ]
2021-08-19T08:51:34Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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TristanBehrens/js-fakes-4bars
TristanBehrens
2022-01-03T15:53:23Z
309
9
null
[ "region:us" ]
2022-01-03T15:53:23Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
# JSFakes (Dr. Tristan Behrens). This is a tokenized version of the JS-Fakes dataset by Omar Peracha. The original dataset can be found here: [js-fakes.git](https://github.com/omarperacha/js-fakes.git) The representation is four tracks with four bars per track. ## Purpose. This dataset is a good starting point for Music Generation. You could train GPT-2 on the samples to compose music. ## Contact. Find me on [LinkedIn](https://www.linkedin.com/in/dr-tristan-behrens-734967a2/) and say hello. If you find and issue or have a feature request, please contact me. Please be so kind and like this dataset if you find it useful.
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gayanin/pubmed-gastro-paraphrasing
gayanin
2021-11-29T12:14:54Z
309
2
null
[ "region:us" ]
2021-11-29T12:14:54Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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tau/fs
tau
2022-02-03T16:05:28Z
309
0
null
[ "region:us" ]
2022-02-03T16:05:28Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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tals/vitaminc
tals
2022-07-01T19:58:42Z
309
4
null
[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:natural-language-inference", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:cc-by-sa-3.0", "region:us" ]
2022-07-01T19:58:42Z
2022-06-21T01:22:38.000Z
2022-06-21T01:22:38
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual pretty_name: VitaminC size_categories: - 100K<n<1M source_datasets: [] task_categories: - text-classification task_ids: - fact-checking - natural-language-inference --- # Details Fact Verification dataset created for [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`) based on Wikipedia edits (revisions). For more details see: https://github.com/TalSchuster/VitaminC When using this dataset, please cite the paper: # BibTeX entry and citation info ```bibtex @inproceedings{schuster-etal-2021-get, title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence", author = "Schuster, Tal and Fisch, Adam and Barzilay, Regina", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.52", doi = "10.18653/v1/2021.naacl-main.52", pages = "624--643", abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.", } ```
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emi429/humansleepproject-rr-pretraining
emi429
2023-11-08T18:46:04Z
309
0
null
[ "region:us" ]
2023-11-08T18:46:04Z
2023-11-06T19:48:00.000Z
2023-11-06T19:48:00
--- dataset_info: features: - name: rr_intervals sequence: float64 - name: patient_id dtype: string splits: - name: train num_bytes: 119094553 num_examples: 469 download_size: 19848443 dataset_size: 119094553 --- # Dataset Card for "humansleepproject-rr-pretraining" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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diplomacy_detection
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2023-01-25T14:29:25Z
308
0
null
[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
2023-01-25T14:29:25Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification pretty_name: HateOffensive dataset_info: features: - name: messages sequence: string - name: sender_labels sequence: class_label: names: '0': 'false' '1': 'true' - name: receiver_labels sequence: class_label: names: '0': 'false' '1': 'true' '2': noannotation - name: speakers sequence: class_label: names: '0': italy '1': turkey '2': russia '3': england '4': austria '5': germany '6': france - name: receivers sequence: class_label: names: '0': italy '1': turkey '2': russia '3': england '4': austria '5': germany '6': france - name: absolute_message_index sequence: int64 - name: relative_message_index sequence: int64 - name: seasons sequence: class_label: names: '0': spring '1': fall '2': winter '3': Spring '4': Fall '5': Winter - name: years sequence: class_label: names: '0': '1901' '1': '1902' '2': '1903' '3': '1904' '4': '1905' '5': '1906' '6': '1907' '7': '1908' '8': '1909' '9': '1910' '10': '1911' '11': '1912' '12': '1913' '13': '1914' '14': '1915' '15': '1916' '16': '1917' '17': '1918' - name: game_score sequence: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' '17': '17' '18': '18' - name: game_score_delta sequence: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' '17': '17' '18': '18' '19': '-1' '20': '-2' '21': '-3' '22': '-4' '23': '-5' '24': '-6' '25': '-7' '26': '-8' '27': '-9' '28': '-10' '29': '-11' '30': '-12' '31': '-13' '32': '-14' '33': '-15' '34': '-16' '35': '-17' '36': '-18' - name: players sequence: class_label: names: '0': italy '1': turkey '2': russia '3': england '4': austria '5': germany '6': france - name: game_id dtype: int64 splits: - name: validation num_bytes: 254344 num_examples: 21 - name: train num_bytes: 2539778 num_examples: 189 - name: test num_bytes: 506191 num_examples: 42 download_size: 3208706 dataset_size: 3300313 --- # Dataset Card for HateOffensive ## 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://sites.google.com/view/qanta/projects/diplomacy - **Repository** : https://github.com/DenisPeskov/2020_acl_diplomacy - **Paper** : http://users.umiacs.umd.edu/~jbg/docs/2020_acl_diplomacy.pdf - **Leaderboard** : - **Point of Contact** : ### Dataset Summary This dataset contains pairwise conversations annotated by the sender and the receiver for deception (and conversely truthfulness). The 17,289 messages are gathered from 12 games. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances ``` { "messages": ["Greetings Sultan!\n\nAs your neighbor I would like to propose an alliance! What are your views on the board so far?", "I think an alliance would be great! Perhaps a dmz in the Black Sea would be a good idea to solidify this alliance?\n\nAs for my views on the board, my first moves will be Western into the Balkans and Mediterranean Sea.", "Sounds good lets call a dmz in the black sea", "What's our move this year?", "I've been away from the game for a while", "Not sure yet, what are your thoughts?", "Well I'm pretty worried about Germany attacking me (and Austria to a lesser extent) so im headed west. It looks like Italy's landing a army in Syr this fall unless you can stop it", "That sounds good to me. I'll move to defend against Italy while you move west. If it's not too much too ask, I'd like to request that you withdraw your fleet from bla.", "Oh sorry missed the msg to move out of bl sea ill do that this turn. I did bring my army down into Armenia, To help you expel the Italian. It looks like Austria and Italy are working together. If we have a chance in the region you should probably use smy to protect con. We can't afford to lose con.", "I'll defend con from both ank and smy.", "Hey sorry for stabbing you earlier, it was an especially hard choice since Turkey is usually my country of choice. It's cool we got to do this study huh?"], "sender_labels": [false, true, false, true, true, true, true, true, true, true, true], "receiver_labels": [true, true, true, true, true, true, true, true, true, true, "NOANNOTATION"], "speakers": ["russia", "turkey", "russia", "russia", "russia", "turkey", "russia", "turkey", "russia", "turkey", "russia"], "receivers": ["turkey", "russia", "turkey", "turkey", "turkey", "russia", "turkey", "russia", "turkey", "russia", "turkey"], "absolute_message_index": [78, 107, 145, 370, 371, 374, 415, 420, 495, 497, 717], "relative_message_index": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "seasons": ["Spring", "Spring", "Spring", "Spring", "Spring", "Spring", "Fall", "Fall", "Spring", "Spring", "Fall"], "years": ["1901", "1901", "1901", "1902", "1902", "1902", "1902", "1902", "1903", "1903", "1905"], "game_score": ["4", "3", "4", "5", "5", "4", "5", "4", "5", "3", "7"], "game_score_delta": ["1", "-1", "1", "1", "1", "-1", "1", "-1", "2", "-2", "7"], "players": ["russia", "turkey"], "game_id": 10 } ``` ### Data Fields - speakers: the sender of the message (string format. Seven possible values: russia, turkey, england, austria, germany, france, italy) - receivers: the receiver of the message (string format. Seven possible values: russia, turkey, england, austria, germany, france, italy) - messages: the raw message string (string format. ranges in length from one word to paragraphs in length) - sender_labels: indicates if the sender of the message selected that the message is truthful, true, or deceptive, false. This is used for our ACTUAL_LIE calculation (true/false which can be bool or string format) - receiver_labels: indicates if the receiver of the message selected that the message is perceived as truthful, true, or deceptive, false. In <10% of the cases, no annotation was received. This is used for our SUSPECTED_LIE calculation (string format. true/false/"NOANNOTATION" ) - game_score: the current game score---supply centers---of the sender (string format that ranges can range from 0 to 18) - game_score_delta: the current game score---supply centers---of the sender minus the game score of the recipient (string format that ranges from -18 to 18) - absolute_message_index: the index the message is in the entire game, across all dialogs (int format) - relative_message_index: the index of the message in the current dialog (int format) - seasons: the season in Diplomacy, associated with the year (string format. Spring, Fall, Winter) - years: the year in Diplomacy, associated with the season (string format. 1901 through 1918) - game_id: which of the 12 games the dialog comes from (int format ranging from 1 to 12) ### Data Splits Train, Test and Validation splits ## 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 Unknown ### Citation Information @inproceedings{Peskov:Cheng:Elgohary:Barrow:Danescu-Niculescu-Mizil:Boyd-Graber-2020, Title = {It Takes Two to Lie: One to Lie and One to Listen}, Author = {Denis Peskov and Benny Cheng and Ahmed Elgohary and Joe Barrow and Cristian Danescu-Niculescu-Mizil and Jordan Boyd-Graber}, Booktitle = {Association for Computational Linguistics}, Year = {2020}, Location = {Seattle}, } ### Contributions Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789) for adding this dataset.
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finer
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2023-01-25T14:30:30Z
308
1
finer
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fi", "license:mit", "arxiv:1908.04212", "region:us" ]
2023-01-25T14:30:30Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - fi license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: finer pretty_name: Finnish News Corpus for Named Entity Recognition dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DATE '2': B-EVENT '3': B-LOC '4': B-ORG '5': B-PER '6': B-PRO '7': I-DATE '8': I-EVENT '9': I-LOC '10': I-ORG '11': I-PER '12': I-PRO - name: nested_ner_tags sequence: class_label: names: '0': O '1': B-DATE '2': B-EVENT '3': B-LOC '4': B-ORG '5': B-PER '6': B-PRO '7': I-DATE '8': I-EVENT '9': I-LOC '10': I-ORG '11': I-PER '12': I-PRO config_name: finer splits: - name: train num_bytes: 5159550 num_examples: 13497 - name: validation num_bytes: 387494 num_examples: 986 - name: test num_bytes: 1327354 num_examples: 3512 - name: test_wikipedia num_bytes: 1404397 num_examples: 3360 download_size: 3733127 dataset_size: 8278795 --- # 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:** [Github](https://github.com/mpsilfve/finer-data) - **Repository:** [Github](https://github.com/mpsilfve/finer-data) - **Paper:** [Arxiv](https://arxiv.org/abs/1908.04212) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each row consists of the following fields: * `id`: The sentence id * `tokens`: An ordered list of tokens from the full text * `ner_tags`: Named entity recognition tags for each token * `nested_ner_tags`: Nested named entity recognition tags for each token Note that by design, the length of `tokens`, `ner_tags`, and `nested_ner_tags` will always be identical. `ner_tags` and `nested_ner_tags` correspond to the list below: ``` [ "O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO" ] ``` IOB2 labeling scheme is used. ### 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 [@stefan-it](https://github.com/stefan-it) for adding this dataset.
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null
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giga_fren
null
2022-11-03T16:15:21Z
308
0
null
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "language:fr", "license:unknown", "region:us" ]
2022-11-03T16:15:21Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - en - fr license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: GigaFren dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr config_name: en-fr splits: - name: train num_bytes: 8690296821 num_examples: 22519904 download_size: 2701536198 dataset_size: 8690296821 --- # Dataset Card for GigaFren ## 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/giga-fren.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 Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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laroseda
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2022-11-18T20:18:11Z
308
0
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ro", "license:cc-by-4.0", "arxiv:2101.04197", "arxiv:1901.06543", "region:us" ]
2022-11-18T20:18:11Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ro license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: null pretty_name: LaRoSeDa dataset_info: features: - name: index dtype: string - name: title dtype: string - name: content dtype: string - name: starRating dtype: int64 config_name: laroseda splits: - name: train num_bytes: 2932819 num_examples: 12000 - name: test num_bytes: 700834 num_examples: 3000 download_size: 5257183 dataset_size: 3633653 --- # Dataset Card for LaRoSeDa ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/ancatache/LaRoSeDa) - **Repository:** [Github](https://github.com/ancatache/LaRoSeDa) - **Paper:** [Arxiv](https://arxiv.org/pdf/2101.04197.pdf) - **Leaderboard:** [Needs More Information] - **Point of Contact:** raducu.ionescu@gmail.com ### Dataset Summary LaRoSeDa - A **La**rge and **Ro**manian **Se**ntiment **Da**ta Set. LaRoSeDa contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative. The samples have one of four star ratings: 1 or 2 - for reviews that can be considered of negative polarity, and 4 or 5 for the positive ones. The 15,000 samples featured in the corpus and labelled with the star rating, are splitted in a train and test subsets, with 12,000 and 3,000 samples in each subset. ### Supported Tasks and Leaderboards [LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/) ### Languages The text dataset is in Romanian (`ro`). ## Dataset Structure ### Data Instances Below we have an example of sample from LaRoSeDa: ``` { "index": "9675", "title": "Nu recomand", "content": "probleme cu localizarea, mari...", "starRating": 1, } ``` where "9675" is the sample index, followed by the title of the review, review content and then the star rating given by the user. ### Data Fields - `index`: string, the unique indentifier of a sample. - `title`: string, the review title. - `content`: string, the content of the review. - `starRating`: integer, with values in the following set {1, 2, 4, 5}. ### Data Splits The train/test split contains 12,000/3,000 samples tagged with the star rating assigned to each sample in the dataset. ## Dataset Creation ### Curation Rationale The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543). ### Source Data #### Data Collection and Normalization For the data collection, one of the largest Romanian e-commerce platform was targetted. Along with the textual content of each review, the associated star ratings was also collected in order to automatically assign labels to the collected text samples. #### Who are the source language producers? The original text comes from one of the largest e-commerce platforms in Romania. ### Annotations #### Annotation process As mentioned above, LaRoSeDa is composed of product reviews from one of the largest e-commerce websites in Romania. The resulting samples are automatically tagged with the star rating assigned by the users. #### Who are the annotators? N/A ### Personal and Sensitive Information The textual data collected for LaRoSeDa consists in product reviews freely available on the Internet. To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures. In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language. ### Discussion of Biases *We note that most of the negative reviews (5,561) are rated with one star. Similarly, most of the positive reviews (6,238) are rated with five stars. Hence, the corpus is highly polarized.* ### Other Known Limitations *The star rating might not always reflect the polarity of the text. We thus acknowledge that the automatic labeling process is not optimal, i.e. some labels might be noisy.* ## Additional Information ### Dataset Curators Published and managed by Anca Tache, Mihaela Gaman and Radu Tudor Ionescu. ### Licensing Information CC BY-SA 4.0 License ### Citation Information ``` @article{ tache2101clustering, title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set}, author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu}, journal={ArXiv}, year = {2021} } ``` ### Contributions Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset.
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sepedi_ner
null
2023-01-25T14:44:06Z
308
1
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:nso", "license:other", "region:us" ]
2023-01-25T14:44:06Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - nso license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Sepedi NER Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: sepedi_ner splits: - name: train num_bytes: 3378134 num_examples: 7117 download_size: 22077376 dataset_size: 3378134 --- # Dataset Card for Sepedi NER Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Sepedi Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/328) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The Sepedi Ner Corpus is a Sepedi dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Sepedi language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Sesotho sa Leboa (Sepedi). ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ``` {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Maikemišetšo', 'a', 'websaete', 'ya', 'ditirelo'] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - sepedi. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{sepedi_ner_corpus, author = {D.J. Prinsloo and Roald Eiselen}, title = {NCHLT Sepedi Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/328}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
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telugu_books
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2022-11-03T16:07:57Z
308
2
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[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:te", "license:unknown", "region:us" ]
2022-11-03T16:07:57Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - te license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: TeluguBooks dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 315076011 num_examples: 25794 download_size: 0 dataset_size: 315076011 --- # Dataset Card for [telugu_books] ## 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:** [Telugu Books](https://www.kaggle.com/sudalairajkumar/telugu-nlp) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is created by scraping telugu novels from teluguone.com this dataset can be used for nlp tasks like topic modeling, word embeddings, transfer learning etc ### Supported Tasks and Leaderboards [More Information Needed] ### Languages TE - Telugu ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - Text: Sentence from a novel ### 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? Anusha Motamarri ### Annotations #### Annotation process Anusha Motamarri #### Who are the annotators? Anusha Motamarri ### 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 [@vinaykudari](https://github.com/vinaykudari) for adding this dataset.
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AlexMaclean/all-deletion-compressions
AlexMaclean
2021-12-07T00:29:41Z
308
1
null
[ "region:us" ]
2021-12-07T00:29:41Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Atsushi/fungi_indexed_mycological_papers_japanese
Atsushi
2023-10-08T21:33:33Z
308
0
null
[ "annotations_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ja", "license:cc-by-4.0", "region:us" ]
2023-10-08T21:33:33Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - other language: - ja license: - cc-by-4.0 multilinguality: - monolingual source_datasets: - original size_categories: - 1K<n<10K --- fungi_indexed_mycological_papers_japanese 大菌輪「論文3行まとめ」データセット 最終更新日:2023/10/9(R3-11041まで) ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。 本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ(索引)、掲載種一覧、比較種一覧をまとめたものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 また、本データセットを可視化したWebアプリを[Observableで公開](https://tinyurl.com/2tvryz8u)しています。 ## 関連データセット 「識別形質まとめ」 [Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese) 「Trait Circusデータセット」(統制形質) [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * ja_title_provisional_translate(仮訳和文題名) … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。 * original_title(原文題名) * published_year(出版年) * journal_title(雑誌名) * source(文献リンク) … 各情報の 出典(文献)のURLです。 * daikinrin_url … 大菌輪「論文3行まとめ」のURLです。 * tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。 * R3summary_1 … 3行抄録の「1行目」です。 * R3summary_2 … 3行抄録の「2行目」です。 * R3summary_3 … 3行抄録の「3行目」です。 * species_reported(報告種一覧) … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。 * ★=新種(新亜種・新品種・新変種) * ■= 新産種 * ▲=新組み合わせ * ◆=新学名 * ●=新階級 * (無印)=その他 * species_compared(比較種一覧) … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット([Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese))を参照してください。 * taxon_reported(分類群一覧) … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。
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Fraser/mnist-text-small
Fraser
2021-02-22T10:21:37Z
308
0
null
[ "region:us" ]
2021-02-22T10:21:37Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
MNIST dataset adapted to a text-based representation. Modified images to be ~1/4 the original area. Done by taking a max pool. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! % % C L a ^ ! ! 03 down ! ! ! - ` ` ` ` ` Y ` Q ! ! 04 down ! ! ! % ` ` ` R ^ ! ! ! ! ! 05 down ! ! ! ! $ G ` ! ! ! ! ! ! ! 06 down ! ! ! ! ! # ` Y < ! ! ! ! ! 07 down ! ! ! ! ! ! 5 ` ` F ! ! ! ! 08 down ! ! ! ! ! ! ! % ` ` 1 ! ! ! 09 down ! ! ! ! ! ! F ` ` ` ! ! ! ! 10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! ! 11 down ! ! L ` ` ` ` 5 ! ! ! ! ! ! 12 down ! ! ` ` V B ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label.
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Fraser/dream-coder
Fraser
2022-04-25T10:49:02Z
308
3
null
[ "language:en", "license:mit", "program-synthesis", "region:us" ]
2022-04-25T10:49:02Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - en thumbnail: "https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png" tags: - program-synthesis license: "mit" datasets: - program-synthesis --- # Program Synthesis Data Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec). Currently just supports text & list data. ![](https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png)
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Jack0508/vi-ko-TED-txt
Jack0508
2021-11-07T18:49:04Z
308
0
null
[ "region:us" ]
2021-11-07T18:49:04Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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null
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Jeska/vaccinchat
Jeska
2021-10-21T12:14:29Z
308
0
null
[ "region:us" ]
2021-10-21T12:14:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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JesseParvess/book_snippets_asr
JesseParvess
2021-12-09T10:36:16Z
308
0
null
[ "region:us" ]
2021-12-09T10:36:16Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Jiejie/asr_book_lm
Jiejie
2022-02-26T10:27:32Z
308
0
null
[ "region:us" ]
2022-02-26T10:27:32Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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LysandreJik/demo1
LysandreJik
2021-09-25T19:54:41Z
308
0
null
[ "region:us" ]
2021-09-25T19:54:41Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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null
allegro/klej-polemo2-in
allegro
2022-08-30T06:57:28Z
308
0
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]
2022-08-30T06:57:28Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'PolEmo2.0-IN' size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # klej-polemo2-in ## Description The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains. We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set. **In-Domain** is the first task, and we use accuracy to evaluate model performance within the in-domain context, i.e., on a test set of reviews from medicine and hotels domains. ## Tasks (input, output, and metrics) The task is to predict the correct label of the review. **Input** ('*text'* column): sentence **Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous) **Domain**: Online reviews **Measurements**: Accuracy **Example**: Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .` Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .` Output: `amb` (ambiguous) ## Data splits | Subset | Cardinality | |:-----------|--------------:| | train | 5783 | | test | 722 | | validation | 723 | ## Class distribution in train | Class | Sentiment | train | validation | test | |:------|:----------|------:|-----------:|------:| | minus | positive | 0.379 | 0.375 | 0.416 | | plus | negative | 0.271 | 0.289 | 0.273 | | amb | ambiguous | 0.182 | 0.160 | 0.150 | | zero | neutral | 0.168 | 0.176 | 0.162 | ## Citation ``` @inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991", abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).", } ``` ## License ``` Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ``` ## Links [HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-in) [Source](https://clarin-pl.eu/dspace/handle/11321/710) [Paper](https://aclanthology.org/K19-1092/) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("allegro/klej-polemo2-in") pprint(dataset['train'][0]) # {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie ' # 'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od ' # 'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże ' # 'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy ' # 'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . ' # 'Każda wizyta u specjalisty jest u niego konsultowana i uważam , ' # 'że jest lepszy od każdego z nich . Mamy do Niego prawie ' # 'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze ' # 'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest ' # 'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) ' # 'i dostęp do niego jest trudny , ale zawsze możliwy .', # 'target': '__label__meta_plus_m'} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("allegro/klej-polemo2-in") dataset = dataset.class_encode_column("target") references = dataset["test"]["target"] # generate random predictions predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] acc = load_metric("accuracy") f1 = load_metric("f1") acc_score = acc.compute(predictions=predictions, references=references) f1_score = f1.compute(predictions=predictions, references=references, average="macro") pprint(acc_score) pprint(f1_score) # {'accuracy': 0.25069252077562326} # {'f1': 0.23760962219870274} ```
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allegro/klej-polemo2-out
allegro
2022-08-30T06:57:07Z
308
0
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]
2022-08-30T06:57:07Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: 'PolEmo2.0-OUT' size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification --- # klej-polemo2-out ## Description The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains. We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set. **Out-of-Domain** is the second task, and we test the model on out-of-domain reviews, i.e., from product and university domains. Since the original test sets for those domains are scarce (50 reviews each), we decided to use the original out-of-domain training set of 900 reviews for testing purposes and create a new split of development and test sets. As a result, the task consists of 1000 reviews, comparable in size to the in-domain test dataset of 1400 reviews. ## Tasks (input, output, and metrics) The task is to predict the correct label of the review. **Input** ('*text'* column): sentence **Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous) **Domain**: Online reviews **Measurements**: Accuracy **Example**: Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .` Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .` Output: `amb` (ambiguous) ## Data splits | Subset | Cardinality | |:-----------|--------------:| | train | 5783 | | test | 722 | | validation | 723 | ## Class distribution | Class | Sentiment | train | validation | test | |:------|:----------|------:|-----------:|------:| | minus | positive | 0.379 | 0.334 | 0.368 | | plus | negative | 0.271 | 0.332 | 0.302 | | amb | ambiguous | 0.182 | 0.332 | 0.328 | | zero | neutral | 0.168 | 0.002 | 0.002 | ## Citation ``` @inproceedings{kocon-etal-2019-multi, title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", author = "Koco{\'n}, Jan and Mi{\l}kowski, Piotr and Za{\'s}ko-Zieli{\'n}ska, Monika", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/K19-1092", doi = "10.18653/v1/K19-1092", pages = "980--991", abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).", } ``` ## License ``` Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) ``` ## Links [HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-out) [Source](https://clarin-pl.eu/dspace/handle/11321/710) [Paper](https://aclanthology.org/K19-1092/) ## Examples ### Loading ```python from pprint import pprint from datasets import load_dataset dataset = load_dataset("allegro/klej-polemo2-out") pprint(dataset['train'][0]) # {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie ' # 'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od ' # 'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże ' # 'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy ' # 'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . ' # 'Każda wizyta u specjalisty jest u niego konsultowana i uważam , ' # 'że jest lepszy od każdego z nich . Mamy do Niego prawie ' # 'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze ' # 'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest ' # 'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) ' # 'i dostęp do niego jest trudny , ale zawsze możliwy .', # 'target': '__label__meta_plus_m'} ``` ### Evaluation ```python import random from pprint import pprint from datasets import load_dataset, load_metric dataset = load_dataset("allegro/klej-polemo2-out") dataset = dataset.class_encode_column("target") references = dataset["test"]["target"] # generate random predictions predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] acc = load_metric("accuracy") f1 = load_metric("f1") acc_score = acc.compute(predictions=predictions, references=references) f1_score = f1.compute(predictions=predictions, references=references, average="macro") pprint(acc_score) pprint(f1_score) # {'accuracy': 0.2894736842105263} # {'f1': 0.2484406098784191} ```
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huggingartists/viktor-tsoi
huggingartists
2022-10-25T09:49:55Z
308
0
null
[ "language:en", "huggingartists", "lyrics", "region:us" ]
2022-10-25T09:49:55Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/viktor-tsoi" ## 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 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) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **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 the generated dataset:** 0.189002 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/f9d03b2a6c45897724e74fab6a1aa86c.500x500x1.jpg&#39;)"> </div> </div> <a href="https://huggingface.co/huggingartists/viktor-tsoi"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Виктор Цой (Viktor Tsoi)</div> <a href="https://genius.com/artists/viktor-tsoi"> <div style="text-align: center; font-size: 14px;">@viktor-tsoi</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/viktor-tsoi). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/viktor-tsoi") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |118| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/viktor-tsoi") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## 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{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
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armanc/pubmed-rct20k
armanc
2022-11-11T08:23:24Z
308
0
null
[ "region:us" ]
2022-11-11T08:23:24Z
2022-11-11T04:20:56.000Z
2022-11-11T04:20:56
The small 20K version of the Pubmed-RCT dataset by Dernoncourt et al (2017). ``` @article{dernoncourt2017pubmed, title={Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts}, author={Dernoncourt, Franck and Lee, Ji Young}, journal={arXiv preprint arXiv:1710.06071}, year={2017} } ``` Note: This is the cleaned up version by Jin and Szolovits (2018). ``` @article{jin2018hierarchical, title={Hierarchical neural networks for sequential sentence classification in medical scientific abstracts}, author={Jin, Di and Szolovits, Peter}, journal={arXiv preprint arXiv:1808.06161}, year={2018} } ```
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cryptonite
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2023-06-01T14:59:47Z
307
2
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[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "arxiv:2103.01242", "region:us" ]
2023-06-01T14:59:47Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: null pretty_name: Cryptonite dataset_info: - config_name: default features: - name: agent_info sequence: - name: Bottomline dtype: string - name: Role dtype: string - name: Target dtype: float32 - name: agent_turn sequence: int32 - name: dialogue_acts sequence: - name: intent dtype: string - name: price dtype: float32 - name: utterance sequence: string - name: items sequence: - name: Category dtype: string - name: Images dtype: string - name: Price dtype: float32 - name: Description dtype: string - name: Title dtype: string splits: - name: train num_bytes: 8538836 num_examples: 5247 - name: test num_bytes: 1353933 num_examples: 838 - name: validation num_bytes: 966032 num_examples: 597 download_size: 25373618 dataset_size: 10858801 - config_name: cryptonite features: - name: clue dtype: string - name: answer dtype: string - name: enumeration dtype: string - name: publisher dtype: string - name: date dtype: int64 - name: quick dtype: bool - name: id dtype: string splits: - name: train num_bytes: 52228597 num_examples: 470804 - name: validation num_bytes: 2901768 num_examples: 26156 - name: test num_bytes: 2908275 num_examples: 26157 download_size: 21615952 dataset_size: 58038640 config_names: - cryptonite - default --- # Dataset Card for Cryptonite ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/aviaefrat/cryptonite) - **Repository:** [Github](https://github.com/aviaefrat/cryptonite) - **Paper:** [Arxiv](https://arxiv.org/pdf/2103.01242.pdf) - **Leaderboard:** - **Point of Contact:** [Twitter](https://twitter.com/AviaEfrat) ### Dataset Summary Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%). ### Languages English ## Dataset Structure ### Data Instances This is one example from the train set. ```python { 'clue': 'make progress socially in stated region (5)', 'answer': 'climb', 'date': 971654400000, 'enumeration': '(5)', 'id': 'Times-31523-6across', 'publisher': 'Times', 'quick': False } ``` ### Data Fields - `clue`: a string representing the clue provided for the crossword - `answer`: a string representing the answer to the clue - `enumeration`: a string representing the - `publisher`: a string representing the publisher of the crossword - `date`: a int64 representing the UNIX timestamp of the date of publication of the crossword - `quick`: a bool representing whether the crossword is quick (a crossword aimed at beginners, easier to solve) - `id`: a string to uniquely identify a given example in the dataset ### Data Splits Train (470,804 examples), validation (26,156 examples), test (26,157 examples). ## Dataset Creation ### Curation Rationale Crosswords from the Times and the Telegraph. ### 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 Avia Efrat, Uri Shaham, Dan Kilman, Omer Levy ### Licensing Information `cc-by-nc-4.0` ### Citation Information ``` @misc{efrat2021cryptonite, title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy}, year={2021}, eprint={2103.01242}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@theo-m](https://github.com/theo-m) for adding this dataset.
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null
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hausa_voa_ner
null
2023-01-25T14:31:51Z
307
2
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ha", "license:cc-by-4.0", "region:us" ]
2023-01-25T14:31:51Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ha license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Hausa VOA NER Corpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-DATE '8': I-DATE config_name: hausa_voa_ner splits: - name: train num_bytes: 483634 num_examples: 1015 - name: validation num_bytes: 69673 num_examples: 146 - name: test num_bytes: 139227 num_examples: 292 download_size: 324962 dataset_size: 692534 --- # Dataset Card for Hausa VOA NER Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.aclweb.org/anthology/2020.emnlp-main.204/ - **Repository:** [Hausa VOA NER](https://github.com/uds-lsv/transfer-distant-transformer-african/tree/master/data/hausa_ner) - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/ - **Leaderboard:** - **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de) ### Dataset Summary The Hausa VOA NER is a named entity recognition (NER) dataset for Hausa language based on the [VOA Hausa news](https://www.voahausa.com/) corpus. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Hausa. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-PER, 0, 0, B-LOC, 0], 'tokens': ['Trump', 'ya', 'ce', 'Rasha', 'ma'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity. ### Data Splits Training (1,014 sentences), validation (145 sentences) and test split (291 sentences) ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Hausa. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset is based on the news domain and was crawled from [VOA Hausa news](https://www.voahausa.com/). [More Information Needed] #### Who are the source language producers? The dataset was collected from VOA Hausa news. Most of the texts used in creating the Hausa VOA NER are news stories from Nigeria, Niger Republic, United States, and other parts of the world. [More Information Needed] ### Annotations Named entity recognition annotation #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated by Jesujoba Alabi and David Adelani for the paper: [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://www.aclweb.org/anthology/2020.emnlp-main.204/). [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the [Creative Commons Attribution 4.0 ](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{hedderich-etal-2020-transfer, title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages", author = "Hedderich, Michael A. and Adelani, David and Zhu, Dawei and Alabi, Jesujoba and Markus, Udia and Klakow, Dietrich", 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.204", doi = "10.18653/v1/2020.emnlp-main.204", pages = "2580--2591", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.
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oclar
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2022-11-03T16:15:26Z
307
1
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[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:unknown", "region:us" ]
2022-11-03T16:15:26Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-classification - sentiment-scoring paperswithcode_id: null pretty_name: OCLAR dataset_info: features: - name: pagename dtype: string - name: review dtype: string - name: rating dtype: int8 splits: - name: train num_bytes: 398204 num_examples: 3916 download_size: 382976 dataset_size: 398204 --- # Dataset Card for OCLAR ## 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:** [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#) - **Paper:** [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0) - **Point of Contact:** [Marwan Al Omari](marwanalomari@yahoo.com) ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`. ## Dataset Structure ### Data Instances A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is the review left by the user / client , and a `rating` which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative. An example from the OCLAR data set looks as follows: ``` "pagename": 'Ramlet Al Baida Beirut Lebanon', "review": 'مكان يطير العقل ويساعد على الاسترخاء', "rating": 5, ``` ### Data Fields - `pagename`: string name of the service / location being reviewed - `review`: string review left by the user / costumer - `rating`: number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total `3916` reviews : - `3465` are considered positive (a rating of 3 to 5) - `451` are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon) #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon). They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information [More Information Needed] ### Citation Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb ``` @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @InProceedings{AlOmari2019oclar, title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon}, authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.}, year={2019} } ``` ### Contributions Thanks to [@alaameloh](https://github.com/alaameloh) for adding this dataset.
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Atsushi/fungi_trait_circus_database
Atsushi
2022-12-26T10:38:17Z
307
0
null
[ "annotations_creators:other", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:ja", "license:cc-by-4.0", "region:us" ]
2022-12-26T10:38:17Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - other language: - en - ja multilinguality: - multilingual license: - cc-by-4.0 source_datasets: - original size_categories: - 100K<n<1M --- fungi_trait_circus_database 大菌輪「Trait Circus」データセット(統制形質) 最終更新日:2022/12/26 ==== ### Languages Japanese and English Please do not use this dataset for academic purposes for the time being. (casual use only) 当面の間仮公開とします。学術目的での使用はご遠慮ください。 # 概要 Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、菌類の記載文を自然言語処理の手法を利用して半自動的に処理し、菌類の形態、生態などに関する様々な「形質 (traits)」データを抽出して、集計や解析の便宜を図るために、あらかじめ設定された「統制語 (controlled term)」の形でまとめています。 抽出手法については「ニッチェ・ライフ」誌の[こちらの記事](https://media.niche-life.com/series/008/Niche008_06.pdf)(査読なし)で報告しています。 自動抽出という性質上、ある程度の誤りが含まれる可能性があることをご承知おきください。 統制語は「要素 (element)」「属性(attribute)」「値(value)」の3つ組からなります。 例えば「傘_色_黒」はそれぞれ「傘」「色」「黒」の要素/属性/値を持っています。一部の統制語では要素と属性が同一となっています(「生息環境」など) 参考までに、データ数上位3件は「要素」で「子実体」「傘」「胞子」、「属性」で「色」「形状」「表面性状」、「値」で「褐」「平滑」「黄」です。 また、菌類分類学の学習および同定支援の目的で、そのデータを基にしたインタラクティブな可視化Webアプリ「[Trait Circus](https://tinyurl.com/nrhcfksu)」を提供しています。 本データセットは、そのWebアプリの生データに相当し、容量の都合等でWebアプリに反映されていない情報も含まれています。 ## 関連データセット 「論文3行まとめ」 [Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese) 「識別形質まとめ」 [Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese) ## 各カラムの説明 * source … 各情報の出典のURLです。多くは学術文献またはMycoBankの記載文データベースを参照しています。 * hit_term … 抽出された形質の出典中における表現です。 * current_name … その形質を有する菌の現行学名です。MycoBankを参照していますが、最新の情報ではない可能性があります。 * element_j … 「要素」の日本語表記です。 * attribute_j … 「属性」の日本語表記です。 * value_j … 「値」の日本語表記です。 * element … 「要素」の英語表記です。 * attribute … 「属性」の英語表記です。 * value … 「値」の英語表記です。
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GEM/wiki_auto_asset_turk
GEM
2022-10-24T15:31:10Z
307
3
null
[ "task_categories:text2text-generation", "task_ids:text-simplification", "annotations_creators:crowd-sourced", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:other", "arxiv:1910.02677", "arxiv:2005.00352", "region:us" ]
2022-10-24T15:31:10Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - crowd-sourced language_creators: - unknown language: - en license: - other multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: - text-simplification pretty_name: wiki_auto_asset_turk --- # Dataset Card for GEM/wiki_auto_asset_turk ## Dataset Description - **Homepage:** n/a - **Repository:** https://github.com/chaojiang06/wiki-auto, [ASSET repository - **Paper:** https://aclanthology.org/2020.acl-main.709/, [ASSET - **Leaderboard:** N/A - **Point of Contact:** WikiAuto: Chao Jiang; ASSET: Fernando Alva-Manchego and Louis Martin; TURK: Wei Xu ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_auto_asset_turk). ### Dataset Summary WikiAuto is an English simplification dataset that we paired with ASSET and TURK, two very high-quality evaluation datasets, as test sets. The input is an English sentence taken from Wikipedia and the target a simplified sentence. ASSET and TURK contain the same test examples but have references that are simplified in different ways (splitting sentences vs. rewriting and splitting). You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/wiki_auto_asset_turk') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk). #### website n/a #### paper [WikiAuto](https://aclanthology.org/2020.acl-main.709/), [ASSET](https://aclanthology.org/2020.acl-main.424/), [TURK](https://aclanthology.org/Q16-1029/) #### authors WikiAuto: Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, Wei Xu; ASSET: Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, and Benoîıt Sagot, and Lucia Specia; TURK: Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch ## Dataset Overview ### Where to find the Data and its Documentation #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Wiki-Auto repository](https://github.com/chaojiang06/wiki-auto), [ASSET repository](https://github.com/facebookresearch/asset), [TURKCorpus](https://github.com/cocoxu/simplification) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [WikiAuto](https://aclanthology.org/2020.acl-main.709/), [ASSET](https://aclanthology.org/2020.acl-main.424/), [TURK](https://aclanthology.org/Q16-1029/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> WikiAuto: ``` @inproceedings{jiang-etal-2020-neural, title = "Neural {CRF} Model for Sentence Alignment in Text Simplification", author = "Jiang, Chao and Maddela, Mounica and Lan, Wuwei and Zhong, Yang and Xu, Wei", 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.709", doi = "10.18653/v1/2020.acl-main.709", pages = "7943--7960", } ``` ASSET: ``` @inproceedings{alva-manchego-etal-2020-asset, title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations", author = "Alva-Manchego, Fernando and Martin, Louis and Bordes, Antoine and Scarton, Carolina and Sagot, Beno{\^\i}t and Specia, Lucia", 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.424", pages = "4668--4679", } ``` TURK: ``` @article{Xu-EtAl:2016:TACL, author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch}, title = {Optimizing Statistical Machine Translation for Text Simplification}, journal = {Transactions of the Association for Computational Linguistics}, volume = {4}, year = {2016}, url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf}, pages = {401--415} } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> WikiAuto: Chao Jiang; ASSET: Fernando Alva-Manchego and Louis Martin; TURK: Wei Xu #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> jiang.1530@osu.edu, f.alva@sheffield.ac.uk, louismartincs@gmail.com, wei.xu@cc.gatech.edu #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### Whose Language? <!-- info: Whose language is in the dataset? --> <!-- scope: periscope --> Wiki-Auto contains English text only (BCP-47: `en`). It is presented as a translation task where Wikipedia Simple English is treated as its own idiom. For a statement of what is intended (but not always observed) to constitute Simple English on this platform, see [Simple English in Wikipedia](https://simple.wikipedia.org/wiki/Wikipedia:About#Simple_English). Both ASSET and TURK use crowdsourcing to change references, and their language is thus a combination of the WikiAuto data and the language of the demographic on mechanical Turk #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> other: Other license #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems. The authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config in this version of the dataset), then trained a neural CRF system to predict these alignments. The trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto` and `auto_acl` configs here). [ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus](https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations. TURKCorpus is a high quality simplification dataset where each source (not simple) sentence is associated with 8 human-written simplifications that focus on lexical paraphrasing. It is one of the two evaluation datasets for the text simplification task in GEM. It acts as the validation and test set for paraphrasing-based simplification that does not involve sentence splitting and deletion. #### Add. License Info <!-- info: What is the 'other' license of the dataset? --> <!-- scope: periscope --> WikiAuto: `CC BY-NC 3.0`, ASSET: `CC BY-NC 4.0`, TURK: `GNU General Public License v3.0` #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Simplification #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The goal is to communicate the main ideas of source sentence in a way that is easier to understand by non-native speakers of English. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic`, `industry` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Ohio State University, University of Sheffield, Inria, Facebook AI Research, Imperial College London, University of Pennsylvania, John Hopkins University #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> WikiAuto: Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, Wei Xu; ASSET: Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, and Benoîıt Sagot, and Lucia Specia; TURK: Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> WikiAuto: NSF, ODNI, IARPA, Figure Eight AI, and Criteo. ASSET: PRAIRIE Institute, ANR. TURK: NSF #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> GEM v1 had separate data cards for WikiAuto, ASSET, and TURK. They were contributed by Dhruv Kumar and Mounica Maddela. The initial data loader was written by Yacine Jernite. Sebastian Gehrmann merged and extended the data cards and migrated the loader to the v2 infrastructure. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `source`: A source sentence from one of the datasets - `target`: A single simplified sentence corresponding to `source` - `references`: In the case of ASSET/TURK, references is a list of strings corresponding to the different references. #### Reason for Structure <!-- info: How was the dataset structure determined? --> <!-- scope: microscope --> The underlying datasets have extensive secondary annotations that can be used in conjunction with the GEM version. We omit those annotations to simplify the format into one that can be used by seq2seq models. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { 'source': 'In early work, Rutherford discovered the concept of radioactive half-life , the radioactive element radon, and differentiated and named alpha and beta radiation .', 'target': 'Rutherford discovered the radioactive half-life, and the three parts of radiation which he named Alpha, Beta, and Gamma.' } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> In WikiAuto, which is used as training and validation set, the following splits are provided: | | Tain | Dev | Test | | ----- | ------ | ----- | ---- | | Total sentence pairs | 373801 | 73249 | 118074 | | Aligned sentence pairs | 1889 | 346 | 677 | ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training. For GEM, [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) will be used for training the model. Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below. | | Dev | Test | Total | | ----- | ------ | ---- | ----- | | Input Sentences | 2000 | 359 | 2359 | | Reference Simplifications | 20000 | 3590 | 23590 | The test and validation sets are the same as those of [TurkCorpus](https://github.com/cocoxu/simplification/). The split was random. There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting. TURKCorpus does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) or [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) (Jiang et. al 2020) for training. Each input sentence has 8 associated reference simplified sentences. 2,359 input sentences are randomly split into 2,000 validation and 359 test sentences. | | Dev | Test | Total | | ----- | ------ | ---- | ----- | | Input Sentences | 2000 | 359 | 2359 | | Reference Simplifications | 16000 | 2872 | 18872 | There are 21.29 tokens per reference on average. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> In our setup, we use WikiAuto as training/validation corpus and ASSET and TURK as test corpora. ASSET and TURK have the same inputs but differ in their reference style. Researchers can thus conduct targeted evaluations based on the strategies that a model should learn. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> WikiAuto is the largest open text simplification dataset currently available. ASSET and TURK are high quality test sets that are compatible with WikiAuto. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> It's unique setup with multiple test sets makes the task interesting since it allows for evaluation of multiple generations and systems that simplify in different ways. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> simplification ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `other` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> We removed secondary annotations and focus on the simple `input->output` format, but combine the different sub-datasets. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> we split the original test set according to syntactic complexity of the source sentences. To characterize sentence syntactic complexity, we use the 8-level developmental level (d-level) scale proposed by [Covington et al. (2006)](https://www.researchgate.net/publication/254033869_How_complex_is_that_sentence_A_proposed_revision_of_the_Rosenberg_and_Abbeduto_D-Level_Scale) and the implementation of [Lu, Xiaofei (2010)](https://www.jbe-platform.com/content/journals/10.1075/ijcl.15.4.02lu). We thus split the original test set into 8 subsets corresponding to the 8 d-levels assigned to source sentences. We obtain the following number of instances per level and average d-level of the dataset: | Total nb. sentences | L0 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | Mean Level | |-------------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ---------- | | 359 | 166 | 0 | 58 | 32 | 5 | 28 | 7 | 63 | 2.38 | #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> The goal was to assess performance when simplifying source sentences with different syntactic structure and complexity. ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> There are recent supervised ([Martin et al., 2019](https://arxiv.org/abs/1910.02677), [Kriz et al., 2019](https://www.aclweb.org/anthology/N19-1317/), [Dong et al., 2019](https://www.aclweb.org/anthology/P19-1331/), [Zhang and Lapata, 2017](https://www.aclweb.org/anthology/D17-1062/)) and unsupervised ([Martin et al., 2020](https://arxiv.org/abs/2005.00352v1), [Kumar et al., 2020](https://www.aclweb.org/anthology/2020.acl-main.707/), [Surya et al., 2019](https://www.aclweb.org/anthology/P19-1198/)) text simplification models that can be used as baselines. #### Technical Terms <!-- info: Technical terms used in this card and the dataset and their definitions --> <!-- scope: microscope --> The common metric used for automatic evaluation is SARI [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029/). ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Simplification #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics`, `BLEU` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> SARI: A simplification metric that considers both input and references to measure the "goodness" of words that are added, deleted, and kept. #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> The original authors of WikiAuto and ASSET used human evaluation to assess the fluency, adequacy, and simplicity (details provided in the paper). For TURK, the authors measured grammaticality, meaning-preservation, and simplicity gain (details in the paper). #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> no ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> Wiki-Auto provides a new version of the Wikipedia corpus that is larger, contains 75% less defective pairs and has more complex rewrites than the previous WIKILARGE dataset. ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus](https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy. The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on *lexical paraphrasing*, and so cannot be used to evaluate simplifications involving *compression* (deletion) or *sentence splitting*. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence. An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below: > **Original:** He settled in London, devoting himself chiefly to practical teaching. > > **TurkCorpus:** He rooted in London, devoting himself mainly to practical teaching. > > **HSplit:** He settled in London. He devoted himself chiefly to practical teaching. > > **ASSET:** He lived in London. He was a teacher. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The goal is to communicate the same information as the source sentence using simpler words and grammar. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> Wikipedia ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Single website` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The dataset uses language from Wikipedia: some demographic information is provided [here](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F). #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> The authors mention that they "extracted 138,095 article pairs from the 2019/09 Wikipedia dump using an improved version of the [WikiExtractor](https://github.com/attardi/wikiextractor) library". The [SpaCy](https://spacy.io/) library is used for sentence splitting. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> crowd-sourced #### Number of Raters <!-- info: What is the number of raters --> <!-- scope: telescope --> 11<n<50 #### Rater Qualifications <!-- info: Describe the qualifications required of an annotator. --> <!-- scope: periscope --> WikiAuto (Figure Eight): No information provided. ASSET (MTurk): - Having a HIT approval rate over 95%, and over 1000 HITs approved. No other demographic or compensation information is provided. - Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test. - Being a resident of the United States, United Kingdom or Canada. TURK (MTurk): - Reference sentences were written by workers with HIT approval rate over 95%. No other demographic or compensation information is provided. #### Raters per Training Example <!-- info: How many annotators saw each training example? --> <!-- scope: periscope --> 1 #### Raters per Test Example <!-- info: How many annotators saw each test example? --> <!-- scope: periscope --> >5 #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> yes #### Which Annotation Service <!-- info: Which annotation services were used? --> <!-- scope: periscope --> `Amazon Mechanical Turk`, `Appen` #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> WikiAuto: Sentence alignment labels were crowdsourced for 500 randomly sampled document pairs (10,123 sentence pairs total). The authors pre-selected several alignment candidates from English Wikipedia for each Simple Wikipedia sentence based on various similarity metrics, then asked the crowd-workers to annotate these pairs. Finally, they trained their alignment model on this manually annotated dataset to obtain automatically aligned sentences (138,095 document pairs, 488,332 sentence pairs). No demographic annotation is provided for the crowd workers. The [Figure Eight](https://www.figure-eight.com/) platform now part of Appen) was used for the annotation process. ASSET: The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf). TURK: The references are crowdsourced from Amazon Mechanical Turk. The annotators were asked to provide simplifications without losing any information or splitting the input sentence. No other demographic or compensation information is provided in the TURKCorpus paper. The instructions given to the annotators are available in the paper. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> none ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> Both Figure Eight and Amazon Mechanical Turk raters forfeit the right to their data as part of their agreements. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII #### Justification for no PII <!-- info: Provide a justification for selecting `no PII` above. --> <!-- scope: periscope --> Since the dataset is created from Wikipedia/Simple Wikipedia, all the information contained in the dataset is already in the public domain. ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946). ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> All the data is in the public domain. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946). #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> Since the test datasets contains only 2,359 sentences that are derived from Wikipedia, they are limited to a small subset of topics present on Wikipedia.
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GroNLP/ik-nlp-22_transqe
GroNLP
2022-10-21T08:06:50Z
307
0
null
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended|esnli", "language:en", "language:nl", "license:apache-2.0", "quality-estimation", "region:us" ]
2022-10-21T08:06:50Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated - machine-generated language: - en - nl license: - apache-2.0 multilinguality: - translation size_categories: - unknown source_datasets: - extended|esnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: iknlp22-transqe tags: - quality-estimation --- # Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference ## Table of Contents - [Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference](#dataset-card-for-ik-nlp-22-project-3-translation-quality-driven-data-selection-for-natural-language-inference) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Splits](#data-splits) - [Data Example](#data-example) - [Dataset Creation](#dataset-creation) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Source:** [Github](https://github.com/OanaMariaCamburu/e-SNLI) - **Point of Contact:** [Gabriele Sarti](mailto:ik-nlp-course@rug.nl) ### Dataset Summary This dataset contains the full [e-SNLI](https://huggingface.co/datasets/esnli) dataset, automatically translated to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model. The translation of each field has been anotated with two quality estimation scores using the referenceless version of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel. The intended usage of this corpus is restricted to the scope of final project for the 2022 edition of the Natural Language Processing course at the Information Science Master's Degree (IK) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) and [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti), with the assistance of [Anjali Nair](https://nl.linkedin.com/in/anjalinair012). *The e-SNLI corpus was made freely available by the authors on Github. The present dataset was created for educational purposes, and is based on the original e-SNLI dataset by Camburu et al..All rights of the present contents are attributed to the original authors.* ### Languages The language data of this corpus is in English (BCP-47 `en`) and Dutch (BCP-47 `nl`). ## Dataset Structure ### Data Instances The dataset contains a single condiguration by default, named `plain_text`, with the three original splits `train`, `validation` and `test`. Every split contains the following fields: | **Field** | **Description** | |------------|-----------------------------| |`premise_en`| The original English premise.| |`premise_nl`| The premise automatically translated to Dutch.| |`hypothesis_en`| The original English hypothesis.| |`hypothesis_nl`| The hypothesis automatically translated to Dutch.| |`label`| The label of the data instance (0 for entailment, 1 for neutral, 2 for contradiction).| |`explanation_1_en`| The first explanation for the assigned label in English.| |`explanation_1_nl`| The first explanation automatically translated to Dutch.| |`explanation_2_en`| The second explanation for the assigned label in English.| |`explanation_2_nl`| The second explanation automatically translated to Dutch.| |`explanation_3_en`| The third explanation for the assigned label in English.| |`explanation_3_nl`| The third explanation automatically translated to Dutch.| |`da_premise`| The quality estimation produced by the `wmt20-comet-qe-da` model for the premise translation.| |`da_hypothesis`| The quality estimation produced by the `wmt20-comet-qe-da` model for the hypothesis translation.| |`da_explanation_1`| The quality estimation produced by the `wmt20-comet-qe-da` model for the first explanation translation.| |`da_explanation_2`| The quality estimation produced by the `wmt20-comet-qe-da` model for the second explanation translation.| |`da_explanation_3`| The quality estimation produced by the `wmt20-comet-qe-da` model for the third explanation translation.| |`mqm_premise`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the premise translation.| |`mqm_hypothesis`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the hypothesis translation.| |`mqm_explanation_1`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the first explanation translation.| |`mqm_explanation_2`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the second explanation translation.| |`mqm_explanation_3`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the third explanation translation.| Explanation 2 and 3 and related quality estimation scores are only present in the `validation` and `test` splits. ### Data Splits | config| train | validation | test | |------------:|---------|------------|------| |`plain_text` | 549'367 | 9842 | 9824 | For your analyses, use the amount of data that is the most reasonable for your computational setup. The more, the better. ### Data Example The following is an example of entry 2000 taken from the `test` split: ```json { "premise_en": "A young woman wearing a yellow sweater and black pants is ice skating outdoors.", "premise_nl": "Een jonge vrouw met een gele trui en zwarte broek schaatst buiten.", "hypothesis_en": "a woman is practicing for the olympics", "hypothesis_nl": "een vrouw oefent voor de Olympische Spelen", "label": 1, "explanation_1_en": "You can not infer it's for the Olympics.", "explanation_1_nl": "Het is niet voor de Olympische Spelen.", "explanation_2_en": "Just because a girl is skating outdoors does not mean she is practicing for the Olympics.", "explanation_2_nl": "Alleen omdat een meisje buiten schaatst betekent niet dat ze oefent voor de Olympische Spelen.", "explanation_3_en": "Ice skating doesn't imply practicing for the olympics.", "explanation_3_nl": "Schaatsen betekent niet oefenen voor de Olympische Spelen.", "da_premise": "0.6099", "mqm_premise": "0.1298", "da_hypothesis": "0.8504", "mqm_hypothesis": "0.1521", "da_explanation_1": "0.0001", "mqm_explanation_1": "0.1237", "da_explanation_2": "0.4017", "mqm_explanation_2": "0.1467", "da_explanation_3": "0.6069", "mqm_explanation_3": "0.1389" } ``` ### Dataset Creation The dataset was created through the following steps: - Translating every field of the original e-SNLI corpus to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model. - Annotating the quality estimation of the translations with two referenceless versions of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel. ## Additional Information ### Dataset Curators For problems on this 🤗 Datasets version, please contact us at [ik-nlp-course@rug.nl](mailto:ik-nlp-course@rug.nl). ### Licensing Information The dataset is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html). ### Citation Information Please cite the authors if you use these corpora in your work: ```bibtex @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} } ```
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LysandreJik/demo2
LysandreJik
2021-09-25T19:57:03Z
307
0
null
[ "region:us" ]
2021-09-25T19:57:03Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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null
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Tevatron/scifact-corpus
Tevatron
2021-09-13T23:32:11Z
307
0
null
[ "region:us" ]
2021-09-13T23:32:11Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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null
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akumar33/manufacturing
akumar33
2021-10-14T04:51:48Z
307
1
null
[ "region:us" ]
2021-10-14T04:51:48Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
This dataset is associated with FabNER paper. https://link.springer.com/article/10.1007/s10845-021-01807-x Kindly cite if you use it.
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davanstrien/embellishments-sample
davanstrien
2022-02-24T15:44:29Z
307
0
null
[ "region:us" ]
2022-02-24T15:44:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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tarteel-ai/quranqa
tarteel-ai
2022-07-27T02:28:31Z
307
8
null
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:cc-by-nd-4.0", "quran", "qa", "region:us" ]
2022-07-27T02:28:31Z
2022-07-26T20:05:10.000Z
2022-07-26T20:05:10
--- annotations_creators: - expert-generated language: - ar language_creators: - expert-generated license: - cc-by-nd-4.0 multilinguality: - monolingual pretty_name: Qur'anic Reading Comprehension Dataset size_categories: - n<1K - 1K<n<10K source_datasets: - original tags: - quran - qa task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for the Qur'anic Reading Comprehension Dataset (QRCD) ## 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://sites.google.com/view/quran-qa-2022/home - **Repository:** https://gitlab.com/bigirqu/quranqa/-/tree/main/ - **Paper:** https://dl.acm.org/doi/10.1145/3400396 - **Leaderboard:** - **Point of Contact:** @piraka9011 ### Dataset Summary The QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are coupled with their extracted answers to constitute 1,337 question-passage-answer triplets. ### Supported Tasks and Leaderboards This task is evaluated as a ranking task. To give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully match one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure. It is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching. pRR is the official evaluation measure of this shared task. We will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer. The EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the gold answers. Whereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer. To get an overall evaluation score, each of the above measures is averaged over all questions. ### Languages Qur'anic Arabic ## Dataset Structure ### Data Instances To simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain one or more answers to that question, as shown below: ```json { "pq_id": "38:41-44_105", "passage": "واذكر عبدنا أيوب إذ نادى ربه أني مسني الشيطان بنصب وعذاب. اركض برجلك هذا مغتسل بارد وشراب. ووهبنا له أهله ومثلهم معهم رحمة منا وذكرى لأولي الألباب. وخذ بيدك ضغثا فاضرب به ولا تحنث إنا وجدناه صابرا نعم العبد إنه أواب.", "surah": 38, "verses": "41-44", "question": "من هو النبي المعروف بالصبر؟", "answers": [ { "text": "أيوب", "start_char": 12 } ] } ``` Each Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different question. Likewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a different Qur’anic passage. The source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the Holy Qur'an in several scripting styles. We have chosen the simple-clean text style of Tanzil version 1.0.2. ### Data Fields * `pq_id`: Sample ID * `passage`: Context text * `surah`: Surah number * `verses`: Verse range * `question`: Question text * `answers`: List of answers and their start character ### Data Splits | **Dataset** | **%** | **# Question-Passage Pairs** | **# Question-Passage-Answer Triplets** | |-------------|:-----:|:-----------------------------:|:---------------------------------------:| | Training | 65% | 710 | 861 | | Development | 10% | 109 | 128 | | Test | 25% | 274 | 348 | | All | 100% | 1,093 | 1,337 | ## 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 The QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License https://creativecommons.org/licenses/by-nd/4.0/legalcode For a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to https://creativecommons.org/licenses/by-nd/4.0/ ### Citation Information ``` @article{malhas2020ayatec, author = {Malhas, Rana and Elsayed, Tamer}, title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an}, year = {2020}, issue_date = {November 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {19}, number = {6}, issn = {2375-4699}, url = {https://doi.org/10.1145/3400396}, doi = {10.1145/3400396}, journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.}, month = {oct}, articleno = {78}, numpages = {21}, keywords = {evaluation, Classical Arabic} } ``` ### Contributions Thanks to [@piraka9011](https://github.com/piraka9011) for adding this dataset.
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humarin/chatgpt-paraphrases
humarin
2023-04-05T16:27:16Z
307
32
null
[ "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:en", "license:openrail", "region:us" ]
2023-04-05T16:27:16Z
2023-03-15T20:12:24.000Z
2023-03-15T20:12:24
--- license: openrail task_categories: - text2text-generation language: - en size_categories: - 100K<n<1M --- This is a dataset of paraphrases created by ChatGPT. Model based on this dataset is avaible: [model](https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base) ## We used this prompt to generate paraphrases Generate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}* This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN news dataset](https://huggingface.co/datasets/cnn_dailymail). We generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -&gt; 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs). ## We used - 247138 questions from the Quora dataset - 91983 texts from the Squad 2.0 dataset - 80076 texts from the CNN news dataset ## Structure of the dataset - text column - an original sentence or question from the datasets - paraphrases - a list of 5 paraphrases - category - question / sentence - source - quora / squad_2 / cnn_news ## Legal disclaimer Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI. ### BibTeX entry and citation info ```bibtex @inproceedings{chatgpt_paraphrases_dataset, author={Vladimir Vorobev, Maxim Kuznetsov}, title={ChatGPT paraphrases dataset}, year={2023} } ```
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Icaruas/input2
Icaruas
2023-11-19T15:36:27Z
307
0
null
[ "region:us" ]
2023-11-19T15:36:27Z
2023-11-18T03:34:03.000Z
2023-11-18T03:34:03
Entry not found
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reza-alipour/CelebA-HQ
reza-alipour
2023-11-26T15:42:38Z
307
0
null
[ "region:us" ]
2023-11-26T15:42:38Z
2023-11-19T19:30:37.000Z
2023-11-19T19:30:37
--- dataset_info: features: - name: id dtype: string - name: mm_id dtype: string - name: image dtype: image - name: mask dtype: image - name: landmark dtype: image - name: landmark_cropped dtype: image - name: captions sequence: string - name: captions_eng sequence: string - name: captions_pes sequence: string - name: captions_cmn sequence: string - name: captions_fra sequence: string - name: captions_deu sequence: string - name: captions_ita sequence: string - name: captions_spa sequence: string - name: captions_all sequence: string splits: - name: train num_bytes: 3740538169.5 num_examples: 28495 - name: test num_bytes: 223536502.0 num_examples: 1498 download_size: 3673908517 dataset_size: 3964074671.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
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menyo20k_mt
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2022-12-30T19:38:49Z
306
1
menyo-20k
[ "task_categories:translation", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:yo", "license:cc-by-nc-4.0", "arxiv:2103.08647", "region:us" ]
2022-12-30T19:38:49Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated - found language_creators: - found language: - en - yo license: - cc-by-nc-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: menyo-20k pretty_name: MENYO-20k dataset_info: features: - name: translation dtype: translation: languages: - en - yo config_name: menyo20k_mt splits: - name: train num_bytes: 2551345 num_examples: 10070 - name: validation num_bytes: 870011 num_examples: 3397 - name: test num_bytes: 1905432 num_examples: 6633 download_size: 5206234 dataset_size: 5326788 --- # Dataset Card for MENYO-20k ## 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/uds-lsv/menyo-20k_MT/ - **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Languages are English and Yoruba. ## Dataset Structure ### Data Instances An instance example: ``` {'translation': {'en': 'Unit 1: What is Creative Commons?', 'yo': 'Ìdá 1: Kín ni Creative Commons?' } } ``` ### Data Fields - `translation`: - `en`: English sentence. - `yo`: Yoruba sentence. ### Data Splits Training, validation and test splits are available. ## 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 The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use. The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE ### Citation Information If you use this dataset, please cite this paper: ``` @inproceedings{adelani-etal-2021-effect, title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation", author = "Adelani, David and Ruiter, Dana and Alabi, Jesujoba and Adebonojo, Damilola and Ayeni, Adesina and Adeyemi, Mofe and Awokoya, Ayodele Esther and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)", month = aug, year = "2021", address = "Virtual", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2021.mtsummit-research.6", pages = "61--75", abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.", } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
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ms_terms
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2022-11-03T16:08:00Z
306
3
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[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:af", "language:am", "language:ar", "language:as", "language:az", "language:be", "language:bg", "language:bn", "language:bs", "language:ca", "language:chr", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fil", "language:fr", "language:ga", "language:gd", "language:gl", "language:gu", "language:guc", "language:ha", "language:he", "language:hi", "language:hr", "language:hu", "language:hy", "language:id", "language:ig", "language:is", "language:it", "language:iu", "language:ja", "language:ka", "language:kk", "language:km", "language:kn", "language:knn", "language:ko", "language:ku", "language:ky", "language:lb", "language:lo", "language:lt", "language:lv", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:nb", "language:ne", "language:nl", "language:nn", "language:ory", "language:pa", "language:pl", "language:prs", "language:pst", "language:pt", "language:qu", "language:quc", "language:ro", "language:ru", "language:rw", "language:sd", "language:si", "language:sk", "language:sl", "language:sq", "language:sr", "language:st", "language:sv", "language:swh", "language:ta", "language:te", "language:tg", "language:th", "language:ti", "language:tk", "language:tn", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vi", "language:wo", "language:xh", "language:yo", "language:zh", "language:zu", "license:ms-pl", "region:us" ]
2022-11-03T16:08:00Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af - am - ar - as - az - be - bg - bn - bs - ca - chr - cs - cy - da - de - el - en - es - et - eu - fa - fi - fil - fr - ga - gd - gl - gu - guc - ha - he - hi - hr - hu - hy - id - ig - is - it - iu - ja - ka - kk - km - kn - knn - ko - ku - ky - lb - lo - lt - lv - mi - mk - ml - mn - mr - ms - mt - nb - ne - nl - nn - ory - pa - pl - prs - pst - pt - qu - quc - ro - ru - rw - sd - si - sk - sl - sq - sr - st - sv - swh - ta - te - tg - th - ti - tk - tn - tr - tt - ug - uk - ur - uz - vi - wo - xh - yo - zh - zu language_bcp47: - bn-IN - bs-Latn - es-MX - fr-CA - ms-BN - pt-BR - sr-BH - sr-Latn - zh-Hant-HK - zh-Hant-TW license: - ms-pl multilinguality: - multilingual - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: MsTerms dataset_info: features: - name: entry_id dtype: string - name: term_source dtype: string - name: pos dtype: string - name: definition dtype: string - name: term_target dtype: string splits: - name: train num_bytes: 6995497 num_examples: 33738 download_size: 0 dataset_size: 6995497 --- # Dataset Card for [ms_terms] ## 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:** [Microsoft Terminology Collection](https://www.microsoft.com/en-us/language/terminology) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Nearly 100 Languages. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@leoxzhao](https://github.com/leoxzhao), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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newsph
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2022-11-03T16:07:51Z
306
2
newsph-nli
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:fil", "language:tl", "license:gpl-3.0", "arxiv:2010.11574", "region:us" ]
2022-11-03T16:07:51Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - fil - tl license: - gpl-3.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: newsph-nli pretty_name: NewsPH-NLI dataset_info: features: - name: text dtype: string config_name: newsph splits: - name: train num_bytes: 298833914 num_examples: 2190465 download_size: 104086466 dataset_size: 298833914 --- # Dataset Card for NewsPH ## 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:** [Filipino Text Benchmarks](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks) - **Repository:** - **Paper:** [Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation](https://arxiv.org/abs/2010.11574) - **Leaderboard:** - **Point of Contact:** [Jan Christian Blaise Cruz](jan_christian_cruz@dlsu.edu.ph) ### Dataset Summary Raw collection of news articles in Filipino. Used to produce the NewsPH-NLI dataset in Cruz et al. (2020) ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Tagalog/Filipino ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `text` (`str`) The dataset is in plaintext and only has one field ("text"). It can be used for language modeling. ### 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 [@jcblaisecruz02](https://github.com/jcblaisecruz02) for adding this dataset.
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the_pile_stack_exchange
null
2023-11-27T15:00:44Z
306
10
null
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:2101.00027", "region:us" ]
2023-11-27T15:00:44Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Stack Exchange size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling dataset_info: features: - name: domain dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 11075434609 num_examples: 5096117 download_size: 36802959360 dataset_size: 11075434609 viewer: false --- # Dataset Card for Stack Exchange ## Table of Contents - [Dataset Card for Stack Exchange](#dataset-card-for-the_pile_stack_exchange) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [|split|num examples|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/EleutherAI/stackexchange-dataset) - **Repository:** [Needs More Information] - **Paper:** [arXiv](https://arxiv.org/abs/2101.00027) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### 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>Defunct:</b> Dataset "the_pile_stack_exchange" is defunct and no longer accessible due to unavailability of the source data.</p> </div> This dataset is part of EleutherAI/The Pile dataset and is a dataset for Language Models from processing stackexchange data dump, which is an anonymized dump of all user-contributed content on the Stack Exchange network. |download_size|34.28 Gib| |dataset_size|10.3 Gib| ### Supported Tasks and Leaderboards The dataset is used for Language Modeling. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ``` {'domain': 'chemistry', 'text':"\nQ: \n \nReviving old questions or asking a new one? \n \nI'm relatively new to the Chemistry SE community, and sometimes when I go to ask a question, I notice that the same (or similar) question has \nalready been asked. However, the previous question doesn't have a good answer (or is unanswered). In this case, is it better to ask the questi\non again in a new post (which might be marked as duplicate) or comment on the old post (which might be several years old)? In other words, wha\nt are the customs of this site in regards to reviving old questions/discussions?\n\nA:\n\nAs Martin commented, it really depends on the type of question. In any case, you always have the following possibilities:\n\nAsk a new question\nEdit the question to bump it to the first page\nAdd a bounty\nBring it to the attention of people in chat\n\nConsider the following cases:\n\nI have exactly the same question as asked and unanswered before!\n\nIf you ask a new question which turns out to be the same question, it may be closed as a dupe (depending on whether users remember the old que\nstion). Not the ideal option.\nIf you can find something substantial to edit and bump the question, do so. Maybe add a comment that you would really love an answer.\nIf you can spare some rep for a bounty (50 is usually enough), do so.\nYou can always bring it to the attention of people in chat.\n",} ``` ### Data Fields - `domain`: Stack Exchange domain of the sample - `text`: Text content containing both the question and the answer ### Data Splits |split|num examples| -------------------------------- |train|5096117| ## 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 ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions Thanks to [sdtblck](https://github.com/sdtblck) for creating the dataset. Thanks to [richarddwang](https://github.com/richarddwang) for adding the dataset.
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AHussain0418/demo_data
AHussain0418
2022-01-06T02:46:54Z
306
0
null
[ "region:us" ]
2022-01-06T02:46:54Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Baybars/parla_text_corpus
Baybars
2022-10-21T15:29:15Z
306
0
null
[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:various", "multilinguality:monolingual", "size_categories:100k<n<1M", "source_datasets:found", "language:ca", "license:cc-by-4.0", "robust-speech-event", "region:us" ]
2022-10-21T15:29:15Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - various language: - ca license: - cc-by-4.0 multilinguality: - monolingual pretty_name: ParlaTextCorpus size_categories: - 100k<n<1M source_datasets: - found task_categories: - sequence-modeling task_ids: - language-modeling tags: - robust-speech-event --- # ParlaTextCorpus Spoken text corpus for Catalan. Derived and cleaned from three sources. OpenSubtitles, Tv3Parla and Festcat.
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Nexdata/multi_language_conversation
Nexdata
2023-11-22T09:49:46Z
306
5
null
[ "task_categories:conversational", "region:us" ]
2023-11-22T09:49:46Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging task_categories: - conversational --- # Dataset Card for multi_language_conversation ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 12,000 hours of multi-language conversation speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. The speakers start the conversation around a familar topic, to ensure the smoothness and nature of the conversation. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions
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DiFronzo/Human_Activity_Recognition
DiFronzo
2022-02-08T11:18:07Z
306
1
null
[ "region:us" ]
2022-02-08T11:18:07Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Human Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories: - WALKING, - WALKING_UPSTAIRS, - WALKING_DOWNSTAIRS, - SITTING, - STANDING The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performed five activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING) wearing a smartphone (Samsung Galaxy S8) in the pucket. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. ```bash 'raw_data/labels.txt': include all the activity labels available for the dataset (1 per row). Column 1: experiment number ID, Column 2: user number ID, Column 3: activity number ID Column 4: Label start point (in number of signal log samples (recorded at 50Hz)) Column 5: Label end point (in number of signal log samples) activity_type: 1 WALKING 2 WALKING_UPSTAIRS 3 WALKING_DOWNSTAIRS 4 SITTING 5 STANDING ``` Repository: [DiFronzo/LSTM-for-Human-Activity-Recognition-classification](https://github.com/DiFronzo/LSTM-for-Human-Activity-Recognition-classification)
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Doohae/modern_music_re
Doohae
2021-12-06T05:58:20Z
306
0
null
[ "region:us" ]
2021-12-06T05:58:20Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Datasets for Relation Extraction Task Source from Wikipedia (CC-BY-2.0) Contributors : Doohae Jung, Hyesu Kim, Bosung Kim, Isaac Park, Miwon Jeon, Dagon Lee, Jihoo Kim
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DoyyingFace/github-embeddings-doy
DoyyingFace
2022-01-20T04:19:21Z
306
0
null
[ "region:us" ]
2022-01-20T04:19:21Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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KETI-AIR/aihub
KETI-AIR
2021-09-21T17:40:36Z
306
0
null
[ "region:us" ]
2021-09-21T17:40:36Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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SetFit/insincere-questions
SetFit
2022-01-19T18:15:51Z
306
1
null
[ "region:us" ]
2022-01-19T18:15:51Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
This is a version of the [Quora Insincere Questions Classification](https://www.kaggle.com/c/quora-insincere-questions-classification). An insincere question is defined as a question intended to make a statement rather than look for helpful answers. About 6% of questions are labeled as insincere.
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Tahsin-Mayeesha/Bengali-SQuAD
Tahsin-Mayeesha
2022-10-25T09:06:50Z
306
0
null
[ "task_categories:question-answering", "multilinguality:monolingual", "language:bn", "region:us" ]
2022-10-25T09:06:50Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - bn multilinguality: - monolingual task_categories: - question-answering --- # Overview This dataset contains the data for the paper [Deep learning based question answering system in Bengali](https://www.tandfonline.com/doi/full/10.1080/24751839.2020.1833136). It is a translated version of [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset to bengali language. Preprocessing details can be found in the paper.
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Tevatron/wikipedia-curated
Tevatron
2021-09-23T02:09:24Z
306
0
null
[ "region:us" ]
2021-09-23T02:09:24Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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Yeva/arm-summary
Yeva
2023-02-09T08:03:13Z
306
0
null
[ "language:hy", "region:us" ]
2023-02-09T08:03:13Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- language: - hy --- annotations_creators: - other language_creators: - other languages: - hy-AM licenses: - unknown multilinguality: - monolingual pretty_name: arm-sum size_categories: - unknown source_datasets: - original task_categories: - conditional-text-generation task_ids: - summarization
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adalbertojunior/punctuation-ptbr-light
adalbertojunior
2022-01-26T19:01:29Z
306
0
null
[ "region:us" ]
2022-01-26T19:01:29Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
Entry not found
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eugenesiow/PIRM
eugenesiow
2022-10-21T04:01:16Z
306
0
null
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "license:cc-by-nc-sa-4.0", "other-image-super-resolution", "arxiv:1809.07517", "region:us" ]
2022-10-21T04:01:16Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - machine-generated language_creators: - found language: [] license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: PIRM tags: - other-image-super-resolution --- # Dataset Card for PIRM ## 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://github.com/roimehrez/PIRM2018 - **Repository**: https://huggingface.co/datasets/eugenesiow/PIRM - **Paper**: https://arxiv.org/abs/1809.07517 - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 ### Dataset Summary The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution. This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018. 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/PIRM', '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 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 `validation` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_HR/1.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_LR_x2/1.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 |validation|test| |-------|---:|---:| |bicubic_x2|100|100| |bicubic_x3|100|100| |bicubic_x4|100|100| |unknown_x4|100|100| ## 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 No annotations. #### Who are the annotators? No annotators. ### 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 - **Original Authors**: [Blau et al. (2018)](https://arxiv.org/abs/1809.07517) ### Licensing Information This dataset is published under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information ```bibtex @misc{blau20192018, title={The 2018 PIRM Challenge on Perceptual Image Super-resolution}, author={Yochai Blau and Roey Mechrez and Radu Timofte and Tomer Michaeli and Lihi Zelnik-Manor}, year={2019}, eprint={1809.07517}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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camel-ai/physics
camel-ai
2023-05-23T21:12:11Z
306
23
null
[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "instruction-finetuning", "arxiv:2303.17760", "region:us" ]
2023-05-23T21:12:11Z
2023-04-11T22:49:01.000Z
2023-04-11T22:49:01
--- license: cc-by-nc-4.0 language: - en tags: - instruction-finetuning pretty_name: CAMEL Physics task_categories: - text-generation arxiv: 2303.17760 extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT." extra_gated_fields: Name: text Email: text I will adhere to the terms and conditions of this dataset: checkbox --- # **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society** - **Github:** https://github.com/lightaime/camel - **Website:** https://www.camel-ai.org/ - **Arxiv Paper:** https://arxiv.org/abs/2303.17760 ## Dataset Summary Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs. We provide the data in `physics.zip`. ## Data Fields **The data fields for files in `physics.zip` are as follows:** * `role_1`: assistant role * `topic`: physics topic * `sub_topic`: physics subtopic belonging to topic * `message_1`: refers to the problem the assistant is asked to solve. * `message_2`: refers to the solution provided by the assistant. **Download in python** ``` from huggingface_hub import hf_hub_download hf_hub_download(repo_id="camel-ai/physics", repo_type="dataset", filename="physics.zip", local_dir="datasets/", local_dir_use_symlinks=False) ``` ### Citation ``` @misc{li2023camel, title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society}, author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem}, year={2023}, eprint={2303.17760}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ## Disclaimer: This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes. --- license: cc-by-nc-4.0 ---
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chrishayuk/test
chrishayuk
2023-08-17T20:50:50Z
306
0
null
[ "license:bigcode-openrail-m", "region:us" ]
2023-08-17T20:50:50Z
2023-08-13T08:41:41.000Z
2023-08-13T08:41:41
--- license: bigcode-openrail-m ---
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rojagtap/tech-qa
rojagtap
2023-08-22T16:12:33Z
306
1
null
[ "license:mit", "region:us" ]
2023-08-22T16:12:33Z
2023-08-15T19:50:02.000Z
2023-08-15T19:50:02
--- license: mit ---
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kannada_news
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2023-01-25T14:33:33Z
305
1
null
[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:kn", "license:cc-by-sa-4.0", "region:us" ]
2023-01-25T14:33:33Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - other language_creators: - other language: - kn license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification pretty_name: KannadaNews Dataset dataset_info: features: - name: headline dtype: string - name: label dtype: class_label: names: '0': sports '1': tech '2': entertainment splits: - name: train num_bytes: 969216 num_examples: 5167 - name: validation num_bytes: 236817 num_examples: 1293 download_size: 0 dataset_size: 1206033 --- # Dataset Card for kannada_news dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Kaggle link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada) ### Dataset Summary The Kannada news dataset contains only the headlines of news article in three categories: Entertainment, Tech, and Sports. The data set contains around 6300 news article headlines which are collected from Kannada news websites. The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Kannada (kn) ## Dataset Structure ### Data Instances The data has two files. A train.csv and valid.csv. An example row of the dataset is as below: ``` { 'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ', 'label':'sports' } ``` NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed] ### Data Fields Data has two fields: - headline: text headline in kannada (string) - label : corresponding class label which the headlines pertains to in english (string) ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. | | train | validation | |-----------------|--------:|-----------:| | Input Sentences | 5167 | 1293 | ## 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 There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes. Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community. This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started. ### Licensing Information cc-by-sa-4.0 ### Citation Information https://www.kaggle.com/disisbig/kannada-news-dataset ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.
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ro_sent
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2023-01-25T14:43:14Z
305
0
null
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ro", "license:unknown", "arxiv:2009.08712", "region:us" ]
2023-01-25T14:43:14Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ro license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: RoSent dataset_info: features: - name: original_id dtype: string - name: id dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 8367687 num_examples: 17941 - name: test num_bytes: 6837430 num_examples: 11005 download_size: 14700057 dataset_size: 15205117 --- # Dataset Card for RoSent ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/dumitrescustefan/Romanian-Transformers/tree/examples/examples/sentiment_analysis) - **Repository:** [GitHub](https://github.com/dumitrescustefan/Romanian-Transformers/tree/examples/examples/sentiment_analysis) - **Paper:** [arXiv preprint](https://arxiv.org/pdf/2009.08712.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is a Romanian Sentiment Analysis dataset. It is present in a processed form, as used by the authors of [`Romanian Transformers`](https://github.com/dumitrescustefan/Romanian-Transformers) in their examples and based on the original data present in at [this GitHub repository](https://github.com/katakonst/sentiment-analysis-tensorflow). The original data contains product and movie reviews in Romanian. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset is present in Romanian language. ## Dataset Structure ### Data Instances An instance from the `train` split: ``` {'id': '0', 'label': 1, 'original_id': '0', 'sentence': 'acest document mi-a deschis cu adevarat ochii la ceea ce oamenii din afara statelor unite s-au gandit la atacurile din 11 septembrie. acest film a fost construit in mod expert si prezinta acest dezastru ca fiind mai mult decat un atac asupra pamantului american. urmarile acestui dezastru sunt previzionate din multe tari si perspective diferite. cred ca acest film ar trebui sa fie mai bine distribuit pentru acest punct. de asemenea, el ajuta in procesul de vindecare sa vada in cele din urma altceva decat stirile despre atacurile teroriste. si unele dintre piese sunt de fapt amuzante, dar nu abuziv asa. acest film a fost extrem de recomandat pentru mine, si am trecut pe acelasi sentiment.'} ``` ### Data Fields - `original_id`: a `string` feature containing the original id from the file. - `id`: a `string` feature . - `sentence`: a `string` feature. - `label`: a classification label, with possible values including `negative` (0), `positive` (1). ### Data Splits This dataset has two splits: `train` with 17941 examples, and `test` with 11005 examples. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The source dataset is present at the [this GitHub repository](https://github.com/katakonst/sentiment-analysis-tensorflow) and is based on product and movie reviews. The original source is unknown. #### 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 Stefan Daniel Dumitrescu, Andrei-Marious Avram, Sampo Pyysalo, [@katakonst](https://github.com/katakonst) ### Licensing Information [More Information Needed] ### Citation Information ``` @article{dumitrescu2020birth, title={The birth of Romanian BERT}, author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius and Pyysalo, Sampo}, journal={arXiv preprint arXiv:2009.08712}, year={2020} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@iliemihai](https://github.com/iliemihai) for adding this dataset.
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setswana_ner_corpus
null
2023-01-25T14:44:12Z
305
0
null
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:tn", "license:other", "region:us" ]
2023-01-25T14:44:12Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - tn license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Setswana NER Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC config_name: setswana_ner_corpus splits: - name: train num_bytes: 3874793 num_examples: 7944 download_size: 25905236 dataset_size: 3874793 --- # Dataset Card for Setswana NER Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Setswana Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/319) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The Setswana Ner Corpus is a Setswana dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Setswana language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Setswana. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ``` {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Ka', 'dinako', 'dingwe', ',', 'go'] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - setswana. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{sepedi_ner_corpus, author = {S.S.B.M. Phakedi and Roald Eiselen}, title = {NCHLT Setswana Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/341}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
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wrbsc
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2023-01-25T15:02:59Z
305
0
null
[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0", "region:us" ]
2023-01-25T15:02:59Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - pl license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - semantic-similarity-classification pretty_name: wrbsc dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: relationship dtype: class_label: names: '0': Krzyżowanie_się '1': Tło_historyczne '2': Źródło '3': Dalsze_informacje '4': Zawieranie '5': Opis '6': Uszczegółowienie '7': Parafraza '8': Spełnienie '9': Mowa_zależna '10': Zmiana_poglądu '11': Streszczenie '12': Tożsamość '13': Sprzeczność '14': Modalność '15': Cytowanie splits: - name: train num_bytes: 779881 num_examples: 2827 download_size: 1273815 dataset_size: 779881 --- # Dataset Card for wrbsc ## 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://clarin-pl.eu/dspace/handle/11321/305 - **Repository:** https://clarin-pl.eu/dspace/handle/11321/305 - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Polish ## Dataset Structure ### Data Instances An example contains two related sentences and a class representing the type of relationship between those sentences. ``` {'relationship': 0, 'sentence1': 'Znajdujące się w Biurze Bezpieczeństwa Narodowego akta Komisji Weryfikacyjnej WSI zostały przewiezione do siedziby Służby Kontrwywiadu Wojskowego.', 'sentence2': '2008-07-03: Wywiezienie akt dotyczących WSI – sprawa dla prokuratury?'} ``` ### Data Fields - `sentence1`: the first sentence being compared (`string`) - `sentence2`: the second sentence being compared (`string`) - `relationship`: the type of relationship between those sentences. Can be one of 16 classes listed below: - `Krzyżowanie_się`: crossing - `Tło_historyczne`: historical background - `Źródło`: source - `Dalsze_informacje`: additional information - `Zawieranie`: inclusion - `Opis`: description - `Uszczegółowienie`: further detail - `Parafraza`: paraphrase - `Spełnienie`: fulfillment - `Mowa_zależna`: passive voice - `Zmiana_poglądu`: change of opinion - `Streszczenie`: summarization - `Tożsamość`: identity - `Sprzeczność`: conflict - `Modalność`: modality - `Cytowanie`: quotation ### Data Splits Single train split ## 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 Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) ### Citation Information ``` @misc{11321/305, title = {{WUT} Relations Between Sentences Corpus}, author = {Oleksy, Marcin and Fikus, Dominika and Wolski, Micha{\l} and Podbielska, Ma{\l}gorzata and Turek, Agnieszka and Kędzia, Pawe{\l}}, url = {http://hdl.handle.net/11321/305}, note = {{CLARIN}-{PL} digital repository}, copyright = {Attribution-{ShareAlike} 3.0 Unported ({CC} {BY}-{SA} 3.0)}, year = {2016} } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.
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yoruba_bbc_topics
null
2023-01-25T15:03:35Z
305
0
null
[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:yo", "license:unknown", "region:us" ]
2023-01-25T15:03:35Z
2022-03-02T23:29:22.000Z
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - yo license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification pretty_name: Yoruba Bbc News Topic Classification Dataset (YorubaBbcTopics) dataset_info: features: - name: news_title dtype: string - name: label dtype: class_label: names: '0': africa '1': entertainment '2': health '3': nigeria '4': politics '5': sport '6': world - name: date dtype: string - name: bbc_url_id dtype: string splits: - name: train num_bytes: 197117 num_examples: 1340 - name: validation num_bytes: 27771 num_examples: 189 - name: test num_bytes: 55652 num_examples: 379 download_size: 265480 dataset_size: 280540 --- # Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics) ## 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/uds-lsv/transfer-distant-transformer-african - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/ - **Leaderboard:** - - **Point of Contact:** Michael A. Hedderich and David Adelani {mhedderich, didelani} (at) lsv.uni-saarland.de ### Dataset Summary A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id). ### Data Fields - `news_title`: A news title. - `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world. - `date`: The publication date (in Yorùbá). - `bbc_url_id`: The identifier of the article in the BBC URL. ### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.
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