Datasets:
Tasks:
Text Classification
Size:
100K - 1M
File size: 2,191 Bytes
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import json
import datasets
_DESCRIPTION = "MTOP: Multilingual Task-Oriented Semantic Parsing"
_LANGUAGES = ["en", "de", "es", "fr", "hi", "th"]
URL = "" # https://huggingface.co./datasets/mteb/mtop/resolve/main/"
_URLs = {
split: {
"train": URL + f"{split}/train.jsonl",
"test": URL + f"{split}/test.jsonl",
"validation": URL + f"{split}/validation.jsonl",
}
for split in _LANGUAGES
}
class MTOP_DOMAIN(datasets.GeneratorBasedBuilder):
"""MTOP Dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=name, description=f"This part of my dataset covers {name} part of MTOP Dataset.",)
for name in _LANGUAGES
]
DEFAULT_CONFIG_NAME = "en"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int64"),
"text": datasets.Value("string"),
"label": datasets.Value("int32"),
"label_text": datasets.Value("string"),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"text_path": data_dir["train"]},
),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"text_path": data_dir["validation"]},),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"text_path": data_dir["test"]},
),
]
def _generate_examples(self, text_path):
"""Yields examples."""
with open(text_path, encoding="utf-8") as f:
texts = f.readlines()
for i, text in enumerate(texts):
yield i, json.loads(text) |