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--- |
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license: apache-2.0 |
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language: |
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- zh |
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tags: |
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- NER |
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- TCM |
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- Traditional Chinese Medicine |
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- medical |
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widget: |
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- text: "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。" |
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example_title: "Example 1" |
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--- |
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# TCMNER |
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[About Author](https://github.com/huangxinping). |
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[Our Products](https://zhongyigen.com) |
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# Model description |
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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）. |
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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](https://www.haiweikexin.com/)'s Named Entity Recognition dataset. |
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**Currently, TCMRoBERTa is just a closed-source model for my own company and will be open-source in the future.** |
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# How to use |
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You can use this model with Transformers pipeline for NER. |
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``` |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Monor/TCMNER") |
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model = AutoModelForTokenClassification.from_pretrained("Monor/TCMNER") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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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。" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Training data |
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This model was fine-tuned on MY DATASET. |
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Abbreviation|Description |
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-|- |
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O|Outside of a named entity |
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B-方剂 |Beginning of a prescription entity right after another prescription entity |
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I-方剂 | Prescription entity |
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B-本草 |Beginning of a herb entity right after another herb entity |
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I-本草 |Herb entity |
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B-来源 |Beginning of a source of prescription right after another source of prescription |
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I-来源 |Source entity |
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B-病名 |Beginning of a disease's name right after another disease's name |
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I-病名 |Disease's name |
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B-症状 |Beginning of a symptom right after another symptom |
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I-症状 |Symptom |
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B-证型 |Beginning of a syndrome right after another syndrome |
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I-证型 |Syndrome |
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# Eval results |
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# Notices |
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1. The model is commercially available for free. |
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2. I am not going to write a paper about this model, if you use any details in your paper, please mention it, thanks. |
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--- |
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# Bonus |
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All of our TCM domain models will be open-sourced soon, including: |
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1. A series of pre-trained models |
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2. Named entity recognition for TCM |
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3. Text localization in ancient images |
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4. OCR for ancient images |
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And so on |
