|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition""" |
|
|
|
import os |
|
import json |
|
import datasets |
|
|
|
|
|
|
|
|
|
_CITATION = """\ |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
""" |
|
|
|
_URL = "https://huggingface.co/datasets/Red-8/NER_Gujarati_data/resolve/main/data/datas.zip" |
|
_TRAINING_FILE = "train_data.json" |
|
_DEV_FILE = "val_data.json" |
|
_TEST_FILE = "test_data.json" |
|
|
|
|
|
class RedConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for Red""" |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig forRed. |
|
|
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(RedConfig, self).__init__(**kwargs) |
|
|
|
|
|
class Red(datasets.GeneratorBasedBuilder): |
|
"""Red dataset.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
RedConfig(name="NER_Gujarati_data", version=datasets.Version("1.0.0"), description="Red dataset"), |
|
] |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"tokens": datasets.Sequence(datasets.Value("string")), |
|
"ner_tags": datasets.Sequence( |
|
datasets.features.ClassLabel( |
|
names=[ |
|
"O", |
|
"B-PERIOD", |
|
"I-PERIOD", |
|
"B-DURATION", |
|
"I-DURATION", |
|
"B-WEATHER", |
|
"I-WEATHER", |
|
"B-DIGIT", |
|
"I-DIGIT", |
|
"B-NUMINAL", |
|
"I-NUMINAL", |
|
] |
|
) |
|
), |
|
} |
|
), |
|
supervised_keys=None, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
downloaded_file = dl_manager.download_and_extract(_URL) |
|
data_files = { |
|
"train": os.path.join(downloaded_file, _TRAINING_FILE), |
|
"dev": os.path.join(downloaded_file, _DEV_FILE), |
|
"test": os.path.join(downloaded_file, _TEST_FILE), |
|
} |
|
|
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""Yields examples as (key, example) tuples.""" |
|
with open(filepath,encoding="utf-8") as f: |
|
for idx_, row in enumerate(f): |
|
data = json.loads(row) |
|
yield idx_, {"tokens": data["text"], "ner_tags": data["label"]} |