Datasets:
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
expert-generated
Annotations Creators:
expert-generated
ArXiv:
License:
| """NLUCat dataset.""" | |
| import json | |
| import datasets | |
| _HOMEPAGE = "" | |
| _CITATION = """\ | |
| """ | |
| _DESCRIPTION = """\ | |
| NLUCat - Natural Language Understanding in Catalan | |
| """ | |
| _TRAIN_FILE = "train.json" | |
| _DEV_FILE = "dev.json" | |
| _TEST_FILE = "test.json" | |
| class Nlucat(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "example": datasets.Value("string"), | |
| "intent": datasets.Value("string"), | |
| "slot_text": datasets.Sequence(datasets.Value("string")), | |
| "slot_tag": datasets.Sequence(datasets.Value("string")), | |
| "start_char": datasets.Sequence(datasets.Value("string")), | |
| "end_char": datasets.Sequence(datasets.Value("string")) | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| urls_to_download = { | |
| "train": f"{_TRAIN_FILE}", | |
| "dev": f"{_DEV_FILE}", | |
| "test": f"{_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"], "split": "train"}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"], "split": "test"}), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Yields examples.""" | |
| with open(filepath, encoding="utf-8") as f: | |
| dataset = json.load(f) | |
| for row in dataset["data"]: | |
| example = row["example"] | |
| intent = row["annotation"]["intent"] | |
| slot_text = [slot["Text"] for slot in row["annotation"]["slots"]] | |
| slot_tag = [slot["Tag"] for slot in row["annotation"]["slots"]] | |
| start_char = [answer["Start_char"] for answer in row["annotation"]["slots"]] | |
| end_char = [answer["End_char"] for answer in row["annotation"]["slots"]] | |
| yield row["id"], { | |
| "example": example, | |
| "intent": intent, | |
| "slot_text": slot_text, | |
| "slot_tag": slot_tag, | |
| "start_char": start_char, | |
| "end_char": end_char | |
| } | |