

This is a model for named entity recognition of Japanese medical documents.

### How to use

Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- text.txt
- NER_medNLP.py
- predict.py

You can use this model by running predict.py.

```
python3 predict.py
```

### Input Example

```
肥大型心筋症、心房細動に対してＷＦ投与が開始となった。
治療経過中に非持続性心室頻拍が認められたためアミオダロンが併用となった。
```

### Output Example

```
 <d certainty="positive">肥大型心筋症、心房細動</d>に対して<m-key state="executed">ＷＦ</m-key>投与が開始となった。
<timex3 type="med">治療経過中</timex3>に<d certainty="positive">非持続性心室頻拍</d>が認められたため<m-key state="executed">アミオダロン</m-key>が併用となった。
```
{"language": ["ja"], "license": ["cc-by-4.0"], "tags": ["NER", "medical documents"], "datasets": ["MedTxt-CR-JA-training-v2.xml"], "metrics": ["NTCIR-16 Real-MedNLP subtask 1"]}
transformers pytorch bert token-classification NER medical documents ja dataset:MedTxt-CR-JA-training-v2.xml license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 



This is a model for named entity recognition of Japanese medical documents.

### How to use

Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- URL
- NER_medNLP.py
- URL

You can use this model by running URL.



### Input Example



### Output Example


### How to use

Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- URL
- NER_medNLP.py
- URL

You can use this model by running URL. ### Input Example ### Output Example
TAGS
#transformers #pytorch #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use

Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- URL
- NER_medNLP.py
- URL

You can use this model by running URL. ### Input Example ### Output Example
passage: TAGS
#transformers #pytorch #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### How to use

Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- URL
- NER_medNLP.py
- URL

You can use this model by running URL.### Input Example### Output Example