Datasets:
File size: 1,870 Bytes
9c271ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import csv
from datasets import DatasetInfo, Features, Value, SplitGenerator, Split, GeneratorBasedBuilder
_CITATION = ""
_DESCRIPTION = "Medical translation pairs with semantic glosses and UMLS CUIs."
class MedicalTSV(GeneratorBasedBuilder):
VERSION = "1.0.0"
def _info(self):
return DatasetInfo(
description=_DESCRIPTION,
features=Features({
"sentence_id": Value("string"),
"src_lang": Value("string"),
"tgt_lang": Value("string"),
"gender_variant": Value("string"),
"source_text": Value("string"),
"target_text": Value("string"),
"semantic_gloss": Value("string"),
"CUI_semantic_gloss": Value("string"),
}),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# If you later add dev/test, replicate with different files or config.
train_path = dl_manager.manual_dir / "train.tsv"
return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": train_path})]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for i, row in enumerate(reader):
yield i, {
"sentence_id": row.get("sentence_id", ""),
"src_lang": row.get("src_lang", ""),
"tgt_lang": row.get("tgt_lang", ""),
"gender_variant": row.get("gender_variant", ""),
"source_text": row.get("source_text", ""),
"target_text": row.get("target_text", ""),
"semantic_gloss": row.get("semantic_gloss", ""),
"CUI_semantic_gloss": row.get("CUI_semantic_gloss", ""),
}
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