Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hindi
Size:
100K<n<1M
ArXiv:
License:
dipteshkanojia
commited on
Commit
•
f0480f1
1
Parent(s):
41675a9
changes
Browse files- .gitignore +1 -1
- HiNER-collapsed.py +91 -0
.gitignore
CHANGED
@@ -128,4 +128,4 @@ dmypy.json
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# Pyre type checker
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.pyre/
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-
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# Pyre type checker
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.pyre/
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*.ipynb
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HiNER-collapsed.py
ADDED
@@ -0,0 +1,91 @@
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import os
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import datasets
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from typing import List
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import json
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """
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"""
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_DESCRIPTION = """
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This is the repository for HiNER - a large Hindi Named Entity Recognition dataset.
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"""
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class HiNERCollapsedConfig(datasets.BuilderConfig):
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"""BuilderConfig for Conll2003"""
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def __init__(self, **kwargs):
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"""BuilderConfig forConll2003.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(SDUtestConfig, self).__init__(**kwargs)
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class HiNERCollapsedConfig(datasets.GeneratorBasedBuilder):
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"""SDU Filtered dataset."""
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BUILDER_CONFIGS = [
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HiNERCollapsedConfig(name="HiNER-Collapsed", version=datasets.Version("0.0.2"), description="Hindi Named Entity Recognition Dataset"),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"O",
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"B-PERSON",
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"I-PERSON",
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"B-LOCATION",
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"I-LOCATION",
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"B-ORGANIZATION",
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"I-ORGANIZATION"
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]
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)
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),
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}
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),
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supervised_keys=None,
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homepage="",
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citation=_CITATION,
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)
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_URL = "https://huggingface.co/datasets/cfiltnlp/HiNER-collapsed/raw/main/"
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_URLS = {
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"train": _URL + "train.json",
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"validation": _URL + "validation.json",
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"test": _URL + "test.json"
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}
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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urls_to_download = self._URLS
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downloaded_files = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logger.info("generating examples from = %s", filepath)
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with open(filepath) as f:
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data = json.load(f)
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for object in data:
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id_ = int(object['id'])
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yield id_, {
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"id": str(id_),
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"tokens": object['tokens'],
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#"pos_tags": object['pos_tags'],
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"ner_tags": object['ner_tags'],
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}
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