Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
File size: 2,253 Bytes
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""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[mit_restaurant NER dataset](https://groups.csail.mit.edu/sls/downloads/)"""
_NAME = "mit_restaurant"
_VERSION = "1.0.0"
_HOME_PAGE = "https://github.com/asahi417/tner"
_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
_URLS = {
str(datasets.Split.TEST): [f'{_URL}/test.json'],
str(datasets.Split.TRAIN): [f'{_URL}/train.json'],
str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'],
}
class MITRestaurantConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MITRestaurantConfig, self).__init__(**kwargs)
class MITRestaurant(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
MITRestaurantConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
def _generate_examples(self, filepaths):
_key = 0
for filepath in filepaths:
logger.info(f"generating examples from = {filepath}")
with open(filepath, encoding="utf-8") as f:
_list = [i for i in f.read().split('\n') if len(i) > 0]
for i in _list:
data = json.loads(i)
yield _key, data
_key += 1
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Sequence(datasets.Value("int32")),
}
),
supervised_keys=None,
homepage=_HOME_PAGE
)
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