Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
Update indian_names.py
Browse files- indian_names.py +0 -40
indian_names.py
CHANGED
@@ -58,46 +58,6 @@ class indian_names(datasets.GeneratorBasedBuilder):
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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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-
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# def _generate_examples(self, filepath):
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# logger.info("⏳ Generating examples from = %s", 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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# # Check if the delimiter ("\t") is present in the row
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# if "\t" in row:
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# token, label = row.split("\t")
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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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# # New sentence
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# if not current_tokens:
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# # Consecutive empty lines will cause empty sentences
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# continue
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# assert len(current_tokens) == len(current_labels), "💔 between len of tokens & 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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# # Don't forget the last sentence in the dataset 🧐
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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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def _generate_examples(self, filepath):
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logger.info("⏳ Generating examples from = %s", filepath)
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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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logger.info("⏳ Generating examples from = %s", filepath)
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