Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Portuguese
Size:
10K - 100K
License:
| license: mit | |
| task_categories: | |
| - text-classification | |
| language: | |
| - pt | |
| tags: | |
| - NLI | |
| - datasets | |
| pretty_name: InferBR | |
| size_categories: | |
| - 10K<n<100K | |
| dataset_info: | |
| features: | |
| - name: sentence_pair_id | |
| dtype: int64 | |
| - name: premise | |
| dtype: string | |
| - name: hypothesis | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': CONTRADICTION | |
| '1': ENTAILMENT | |
| '2': NEUTRAL | |
| # InferBR | |
| This is the InferBR dataset for Natural Language Inference in Portuguese. This version removes the flagged low-quality samples from the original dataset, | |
| keeping 10.528 samples. The Github repo with the raw data can be found at: https://github.com/lbencke/InferBR. | |
| ## Columns | |
| **sentence_pair_id**: Identifier for premise-hypothesis sentence pairs. | |
| **premise**: The premise sentence. | |
| **hypothesis**: The hypothesis sentence. | |
| **label**: The generated label for the hypothesis considering the premise. | |
| 0 – Contradiction | |
| 1 – Entailment | |
| 2 – Neutral | |
| # Citation | |
| @inproceedings{bencke-etal-2024-inferbr-natural, | |
| title = "{I}nfer{BR}: A Natural Language Inference Dataset in {P}ortuguese", | |
| author = "Bencke, Luciana and | |
| Pereira, Francielle Vasconcellos and | |
| Santos, Moniele Kunrath and | |
| Moreira, Viviane", | |
| editor = "Calzolari, Nicoletta and | |
| Kan, Min-Yen and | |
| Hoste, Veronique and | |
| Lenci, Alessandro and | |
| Sakti, Sakriani and | |
| Xue, Nianwen", | |
| booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", | |
| month = may, | |
| year = "2024", | |
| address = "Torino, Italy", | |
| publisher = "ELRA and ICCL", | |
| url = "https://aclanthology.org/2024.lrec-main.793", | |
| pages = "9050--9060", | |
| } |