dataset: gqnli added #2989
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dataset: gqnli added #2989
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Samoed
reviewed
Aug 6, 2025
| license="cc-by-4.0", | ||
| annotations_creators="human-annotated", | ||
| dialect=[], | ||
| sample_creation="found", |
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The dataset seems to be translated - not found. Can you also make sure that this is reflected in the description?
| eval_splits=["test"], | ||
| eval_langs=["fra-Latn"], | ||
| main_score="max_accuracy", | ||
| date=("2025-08-05", "2025-08-05"), |
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This date appears to be incorrect - it should be the date range during which the texts were written.
Co-authored-by: Kenneth Enevoldsen <[email protected]>
MedAmineYoussef
commented
Aug 7, 2025
| publisher = "ELRA and ICCL", | ||
| url = "https://aclanthology.org/2024.lrec-main.1065", | ||
| pages = "12173--12186", | ||
| abstract = "This paper introduces DACCORD, an original dataset in French for automatic detection of contradictions between sentences. It also presents new, manually translated versions of two datasets, namely the well known dataset RTE3 and the recent dataset GQNLI, from English to French, for the task of natural language inference / recognising textual entailment, which is a sentence-pair classification task. These datasets help increase the admittedly limited number of datasets in French available for these tasks. DACCORD consists of 1034 pairs of sentences and is the first dataset exclusively dedicated to this task and covering among others the topic of the Russian invasion in Ukraine. RTE3-FR contains 800 examples for each of its validation and test subsets, while GQNLI-FR is composed of 300 pairs of sentences and focuses specifically on the use of generalised quantifiers. Our experiments on these datasets show that they are more challenging than the two already existing datasets for the mainstream NLI task in French (XNLI, FraCaS). For languages other than English, most deep learning models for NLI tasks currently have only XNLI available as a training set. Additional datasets, such as ours for French, could permit different training and evaluation strategies, producing more robust results and reducing the inevitable biases present in any single dataset.", |
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@MedAmineYoussef can you also add and complete the dataset checklist (see lik in your own PR comment) |
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checkllist done |
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You can't push these - simply copy and paste the checklist into the main PR message
| from mteb.abstasks.TaskMetadata import TaskMetadata | ||
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| class GqnliTask(AbsTaskPairClassification): |
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Suggested change
| class GqnliTask(AbsTaskPairClassification): | |
| class GqnliPairClassification(AbsTaskPairClassification): |
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| class GqnliTask(AbsTaskPairClassification): | ||
| metadata = TaskMetadata( | ||
| name="gqnli", |
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Suggested change
| name="gqnli", | |
| name="GqnliPairClassification", |
and rename file to match
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This pull request has been automatically marked as stale due to inactivity. |
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@MedAmineYoussef are you still working on this PR? |
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If you add a model or a dataset, please add the corresponding checklist: