| # TCMNER | |
| # Model description | |
| TCMNER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Traditional Chinese medicine and achieves state-of-the-art performance for the NER task. It has been trained to recognize six types of entities: prescription (方剂), herb (本草), source (来源), disease (病名), symptom (症状) and syndrome（证型）. | |
| Specifically, this model is a TCMRoBERTa model, a fine-tuned model of RoBERTa for Traditional Chinese medicine, that was fine-tuned on the Chinese version of the Haiwei AI Lab's Named Entity Recognition dataset. | |
| **Currently, TCMRoBERTa is only a closed-source model for my own company, and I will open source it in the future.** | |
| # How to use | |
| You can use this model with Transformers pipeline for NER. | |
| ``` | |
| 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) | |
| ``` | |
| ## Training data | |
| This model was fine-tuned on My own dataset. | |
| 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 soure of prescription right after another soure 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 | |
| # Eval results | |
|  | |
| # Notices | |
| 1. The moodel is commercially available for free. | |
| 2. I am not going to write a paper about this model, if you use any details of this model in your paper, please mention it, thanks. |