Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-input-text-classification
Languages:
Catalan
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
CLiC-UB
License:
| # Loading script for the ReviewsFinder dataset. | |
| import json | |
| import csv | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """ """ | |
| _DESCRIPTION = """ Parafraseja is a dataset of 16,584 pairs of sentences with a label that indicates if they are paraphrases or not. The original sentences were collected from TE-ca and STS-ca. For each sentence, an annotator wrote a sentence that was a paraphrase and another that was not. The guidelines of this annotation are available. """ | |
| _HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """ | |
| _URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/" | |
| _TRAINING_FILE = "train.jsonl" | |
| _DEV_FILE = "dev.jsonl" | |
| _TEST_FILE = "test.jsonl" | |
| class ParafrasejaConfig(datasets.BuilderConfig): | |
| """ Builder config for the Parafraseja dataset """ | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for parafrasis. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(ParafrasejaConfig, self).__init__(**kwargs) | |
| class Parafraseja(datasets.GeneratorBasedBuilder): | |
| """ Parafrasis Dataset """ | |
| BUILDER_CONFIGS = [ | |
| ParafrasejaConfig( | |
| name="Parafraseja", | |
| version=datasets.Version("1.0.0"), | |
| description="Parafraseja dataset", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "sentence1": datasets.Value("string"), | |
| "sentence2": datasets.Value("string"), | |
| "label": datasets.features.ClassLabel | |
| (names= | |
| [ | |
| "No Parafrasis", | |
| "Parafrasis", | |
| ] | |
| ), | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| urls_to_download = { | |
| "train": f"{_URL}{_TRAINING_FILE}", | |
| "dev": f"{_URL}{_DEV_FILE}", | |
| "test": f"{_URL}{_TEST_FILE}", | |
| } | |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| with open(filepath, encoding="utf-8") as f: | |
| data = [json.loads(line) for line in f] | |
| for id_, article in enumerate(data): | |
| yield id_, { | |
| "sentence1": article['original'], | |
| "sentence2": article['new'], | |
| "label": article['label'], | |
| } | |