| from __future__ import annotations |
|
|
| from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification |
| from mteb.abstasks.TaskMetadata import TaskMetadata |
|
|
| N_SAMPLES = 1000 |
|
|
|
|
| class SickBrPC(AbsTaskPairClassification): |
| metadata = TaskMetadata( |
| name="SICK-BR-PC", |
| dataset={ |
| "path": "eduagarcia/sick-br", |
| "revision": "0cdfb1d51ef339011c067688a3b75b82f927c097", |
| }, |
| description="SICK-BR is a Portuguese inference corpus, human translated from SICK", |
| reference="https://linux.ime.usp.br/~thalen/SICK_PT.pdf", |
| type="PairClassification", |
| category="s2s", |
| eval_splits=["test"], |
| eval_langs=["por-Latn"], |
| main_score="ap", |
| date=("2018-01-01", "2018-09-01"), |
| form=["written"], |
| domains=["Web"], |
| task_subtypes=["Textual Entailment"], |
| license="unknown", |
| socioeconomic_status="mixed", |
| annotations_creators="human-annotated", |
| dialect=[], |
| text_creation="human-translated and localized", |
| bibtex_citation=""" |
| @inproceedings{real18, |
| author="Real, Livy |
| and Rodrigues, Ana |
| and Vieira e Silva, Andressa |
| and Albiero, Beatriz |
| and Thalenberg, Bruna |
| and Guide, Bruno |
| and Silva, Cindy |
| and de Oliveira Lima, Guilherme |
| and C{\^a}mara, Igor C. S. |
| and Stanojevi{\'{c}}, Milo{\v{s}} |
| and Souza, Rodrigo |
| and de Paiva, Valeria" |
| year ="2018", |
| title="SICK-BR: A Portuguese Corpus for Inference", |
| booktitle="Computational Processing of the Portuguese Language. PROPOR 2018.", |
| doi ="10.1007/978-3-319-99722-3_31", |
| isbn="978-3-319-99722-3" |
| } |
| """, |
| n_samples={"test": N_SAMPLES}, |
| avg_character_length={"test": 54.89}, |
| ) |
|
|
| def dataset_transform(self): |
| _dataset = {} |
|
|
| |
| self.dataset.pop("train") |
| self.dataset.pop("validation") |
|
|
| self.dataset = self.stratified_subsampling( |
| self.dataset, |
| seed=self.seed, |
| splits=self.metadata.eval_splits, |
| label="entailment_label", |
| n_samples=N_SAMPLES, |
| ) |
|
|
| for split in self.metadata.eval_splits: |
| print(self.dataset[split]["entailment_label"]) |
| |
| hf_dataset = self.dataset[split].filter( |
| lambda x: x["entailment_label"] in [0, 2] |
| ) |
| hf_dataset = hf_dataset.map( |
| lambda example: {"label": 0 if example["entailment_label"] == 2 else 1} |
| ) |
| _dataset[split] = [ |
| { |
| "sent1": hf_dataset["sentence_A"], |
| "sent2": hf_dataset["sentence_B"], |
| "labels": hf_dataset["label"], |
| } |
| ] |
| self.dataset = _dataset |
|
|
