Mykes
/

med-bert-ner

@@ -1,199 +1,77 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+tags:
+- medical
+language:
+- ru
+base_model:
+- Babelscape/wikineural-multilingual-ner
 ---
+# Model Card
+The model for NER recognition of medical requests
 ### Model Description
+This model is finetuned on 4756 russian patient requests
+**The NER entities are**:
+- **B-SIM, I-SIM**: simptoms;
+- **B-SUBW, I-SUBW**: subway;
+- **GEN**: gender;
+- **CHILD**: child mention;
+- **B-SPEC, I-SPEC**: physician speciality;
+It's based on the [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner) 177M mBERT model.
+## Training info
+Training parameters:
+```
+MAX_LEN = 256
+TRAIN_BATCH_SIZE = 4
+VALID_BATCH_SIZE = 2
+EPOCHS = 5
+LEARNING_RATE = 1e-05
+MAX_GRAD_NORM = 10
+```
+The loss and accurancy on 5 EPOCH:
+```
+Training loss epoch: 0.004890048759878736
+Training accuracy epoch: 0.9896078955134066
+```
+The validations results:
+```
+Validation Loss: 0.008194072216433625
+Validation Accuracy: 0.9859073599112612
+```
+Detailed metrics (mostly f1-score):
+```
+              precision    recall  f1-score   support
+          EN       1.00      0.98      0.99        84
+        HILD       1.00      0.99      0.99       436
+         SIM       0.96      0.96      0.96      5355
+        SPEC       0.99      1.00      0.99       751
+        SUBW       0.99      1.00      0.99       327
+   micro avg       0.96      0.97      0.97      6953
+   macro avg       0.99      0.98      0.99      6953
+weighted avg       0.96      0.97      0.97      6953
+```
+## Results:
+The model does not always identify words completely, but at the same time it detects individual pieces of words correctly even if the words are misspelled
+For example, the query "У меня треога и норушения сна. Подскажи хорошего психотервта в районе метро Октбрьской." returns the result:
+```
+B-SIM I-SIM I-SIM  B-SIM  I-SIM   I-SIM     B-SPEC I-SPEC I-SPEC I-SPEC  I-SPEC     B-SUBW  I-SUBW I-SUBW  I-SUBW
+т      ре    ога    но     ру    шения сна    пс     их   о      тер     вта          ок       т     брь    ской
+```
+As you can see it correctly detects event misspelled word: треога, норушения, психотервта
+## The simplest way to use the model with 🤗 transformers pipeline:
+```
+pipe = pipeline(task="ner", model='Mykes/med_bert_ner', tokenizer='Mykes/med_bert_ner', aggregation_strategy="simple")
+query = "У меня треога и норушения сна. Подскажи хорошего психотервта в районе метро Октбрьской."
+results = pipe(query.lower())
+```