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from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Monor/TCMNER")
model = AutoModelForTokenClassification.from_pretrained("Monor/TCMNER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "化滞汤,出处：《证治汇补》卷八。。组成：青皮20g，陈皮20g，厚朴20g，枳实20g，黄芩20g，黄连20g，当归20g，芍药20g，木香5g，槟榔8g，滑石3g，甘草4g。。主治：下痢因于食积气滞者。"
ner_results = nlp(example)
print(ner_results)| Abbreviation | Description |
|---|---|
| O | Outside of a named entity |
| B-方剂 | Beginning of a prescription entity right after another prescription entity |
| I-方剂 | Prescription entity |
| B-本草 | Beginning of a herb entity right after another herb entity |
| I-本草 | Herb entity |
| B-来源 | Beginning of a source of prescription right after another source of prescription |
| I-来源 | Source entity |
| B-病名 | Beginning of a disease's name right after another disease's name |
| I-病名 | Disease's name |
| B-症状 | Beginning of a symptom right after another symptom |
| I-症状 | Symptom |
| B-证型 | Beginning of a syndrome right after another syndrome |
| I-证型 | Syndrome |
