README.md

---
language:
- en
tags:
- BERT
- medical
pipeline_tag: token-classification
widget:
- text: 63 year old woman with history of CAD presented to ER
  example_title: Example-1
- text: 63 year old woman diagnosed with CAD
  example_title: Example-2
---
# Model Card for Model ID

base_model : [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)

hidden_size : 768

max_position_embeddings : 512

num_attention_heads : 12

num_hidden_layers : 12

vocab_size : 30522

# Basic usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import numpy as np

# match tag
id2tag = {0:'O', 1:'B_MT', 2:'I_MT'}

# load model & tokenizer
MODEL_NAME = 'MDDDDR/bert_base_uncased_NER'

model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

# prepare input
text = 'mental disorder can also contribute to the development of diabetes through various mechanism including increased stress, poor self care behavior, and adverse effect on glucose metabolism.'
tokenized = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**tokenized)

# result
pred = np.argmax(output[0].cpu().detach().numpy(), axis=2)[0][1:-1]

# check pred
for txt, pred in zip(tokenizer.tokenize(text), pred):
    print("{}\t{}".format(id2tag[pred], txt))
    # B_MT mental 
    # B_MT disorder 
```

## Framework versions
- transformers : 4.39.1
- torch : 2.1.0+cu121
- datasets : 2.18.0
- tokenizers : 0.15.2
- numpy : 1.20.0