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--- |
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pretty_name: ExtraGLUE |
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language: |
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- pt |
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source_datasets: |
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- glue |
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- superglue |
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license: mit |
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viewer: false |
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task_categories: |
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- text-classification |
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- sentence-similarity |
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- question-answering |
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task_ids: |
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- language-modeling |
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- multi-class-classification |
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- natural-language-inference |
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- sentiment-classification |
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- semantic-similarity-scoring |
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- semantic-similarity-classification |
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--- |
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</br> |
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</br> |
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<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png"> |
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<p style="text-align: center;"> This is the dataset card for extraGLUE. |
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You may be interested in some of the other <a href="https://huggingface.co/PORTULAN">datasets for Portuguese</a> and in the models trained with them, |
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namely <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Gervásio (decoders) families</a>. |
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</p> |
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</br> |
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</br> |
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ExtraGLUE |
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=== |
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</br> |
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ExtraGLUE is a Portuguese dataset obtained by the automatic translation of some of the tasks in the GLUE and SuperGLUE benchmarks. |
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Two variants of Portuguese are considered, namely European Portuguese and American Portuguese. |
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The dataset is distributed for free under an open license. |
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The 14 tasks in extraGLUE cover different aspects of language understanding: |
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*Single sentence* |
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- **SST-2** is a task for predicting the sentiment polarity of movie reviews. |
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*Semantic similarity* |
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- **MRPC** is a task for determining whether a pair of sentences are mutual paraphrases. |
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- **STS-B** is a task for predicting a similarity score (from 1 to 5) for each sentence pair. |
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*Inference* |
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- **MNLI** is a task to determine if a given premise sentence entails, contradicts, or is neutral to a hypothesis sentence; this task includes **matched** (in-domain) and **mismatched** (cross-domain) validation and test sets. |
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- **QNLI** is a question-answering task converted to determine whether the context sentence contains the answer to the question. |
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- **RTE** is a task for determining whether a premise sentence entails a hypothesis sentence. |
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- **WNLI** is a pronoun resolution task formulated as sentence pair entailment classification where, in the second sentence, the pronoun is replaced by a possible referent. |
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- **CB** comprises short texts with embedded clauses; one such clause is extracted as a hypothesis and should be classified as neutral, entailment or contradiction. |
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- **AX_b** is designed to test models across a wide spectrum of linguistic, commonsense, and world knowledge; each instance contains a sentence pair labeled with entailment or not entailment. |
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- **AX_g** is designed to measure gender bias, where each premise sentence includes a male or female pronoun and a hypothesis includes a possible referent for the pronoun. |
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*Question answering* |
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- **BoolQ** is a question-answering task where yes/no questions are given for short text passages. |
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- **MultiRC** is a task where, given a context paragraph, a question, and an answer, the goal is to determine whether the answer is true; for the same context and question, more than one answer may be correct. |
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*Reasoning* |
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- **CoPA** is a casual reasoning task: given a premise, two choices, and a cause/effect prompt, the system must choose one of the choices. |
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If you use this dataset please cite: |
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@misc{osório2024portulan, |
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title={PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese}, |
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author={Tomás Osório and Bernardo Leite and Henrique Lopes Cardoso and Luís Gomes and João Rodrigues and Rodrigo Santos and António Branco}, |
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year={2024}, |
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eprint={2404.05333}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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# Acknowledgments |
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The research reported here was partially supported by: |
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PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the |
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grant PINFRA/22117/2016; |
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research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the |
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grant CPCA-IAC/AV/478395/2022; |
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innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação |
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under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização; |
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and Base Funding (UIDB/00027/2020) and Programmatic Funding (UIDP/00027/2020) of the Artificial Intelligence and Computer Science Laboratory (LIACC) funded by national funds through FCT/MCTES (PIDDAC). |
