| 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): |
| |
| 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", ""), |
| } |
|
|
