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---
datasets:
- pubmed
language:
- en
tags:
- BERT
---
# Model Card for Model ID
base_model : [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased)
hidden_size : 1024
max_position_embeddings : 512
num_attention_heads : 16
num_hidden_layers : 24
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_large_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 |