Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
Create kriyans.py
Browse files- kriyans.py +83 -0
kriyans.py
ADDED
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import datasets
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_URL = "https://colab.research.google.com/drive/1aSWUrQFWU_RJOt9NmVDRnn9ci-11dC9x#scrollTo=LOncaJUnrl8O"
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# Define the path to your CSV file
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csv_file_path = '/indian-name-org.csv'
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class YourCustomConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super(YourCustomConfig, self).__init__(**kwargs)
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class YourCustomDataset(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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YourCustomConfig(
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name="your_custom_dataset",
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version=datasets.Version("1.0.0"),
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description="Your Custom Dataset",
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description="Your custom dataset 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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"B-PER",
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"I-ORG",
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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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)
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def _split_generators(self, dl_manager):
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urls_to_download = {
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"train": f"{_URL}{csv_file_path}",
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}
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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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]
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def _generate_examples(self, filepath):
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with open(filepath, encoding="utf-8") as f:
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current_tokens = []
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current_labels = []
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sentence_counter = 0
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for row in f:
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row = row.rstrip()
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if row:
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token, label = row.split(",")
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current_tokens.append(token)
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current_labels.append(label)
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else:
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if not current_tokens:
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continue
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assert len(current_tokens) == len(current_labels), "Mismatch between tokens and labels"
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sentence = (
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sentence_counter,
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{
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"id": str(sentence_counter),
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"tokens": current_tokens,
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"ner_tags": current_labels,
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},
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)
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sentence_counter += 1
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current_tokens = []
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current_labels = []
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yield sentence
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if current_tokens:
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yield sentence_counter, {
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"id": str(sentence_counter),
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"tokens": current_tokens,
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"ner_tags": current_labels,
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}
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