transformers pytorch bert token-classification Named Entity Recognition SciBERT Adverse Effect Drug Medical en dataset:ade_corpus_v2 dataset:tner/bc5cdr dataset:commanderstrife/jnlpba dataset:bc2gm_corpus dataset:drAbreu/bc4chemd_ner dataset:linnaeus dataset:chintagunta85/ncbi_disease autotrain_compatible endpoints_compatible region:us
---
datasets:
- ade_corpus_v2
- tner/bc5cdr
- commanderstrife/jnlpba
- bc2gm_corpus
- drAbreu/bc4chemd_ner
- linnaeus
- chintagunta85/ncbi_disease
language:
- en
tags:
- Named Entity Recognition
- SciBERT
- Adverse Effect
- Drug
- Medical
widget:
- text: Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec),
    a labor-inducing drug.
  example_title: Abortion, miscarriage, ...
- text: Addiction to many sedatives and analgesics, such as diazepam, morphine, etc.
  example_title: Addiction to many...
- text: Birth defects associated with thalidomide
  example_title: Birth defects associated...
- text: Bleeding of the intestine associated with aspirin therapy
  example_title: Bleeding of the intestine...
- text: Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx)
  example_title: Cardiovascular disease...
---

This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. 
![model image](https://raw.githubusercontent.com/jsylee/personal-projects/master/Hugging%20Face%20ADR%20Fine-Tuning/hf_adr.png)

This model classifies input tokens into one of five classes:

- `B-DRUG`: beginning of a drug entity
- `I-DRUG`: within a drug entity
- `B-EFFECT`: beginning of an AE entity
- `I-EFFECT`: within an AE entity
- `O`: outside either of the above entities 

To get started using this model for inference, simply set up an NER `pipeline` like below: 

```python
from transformers import (AutoModelForTokenClassification, 
                          AutoTokenizer, 
                          pipeline,
                          )

model_checkpoint = "jsylee/scibert_scivocab_uncased-finetuned-ner"
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=5,
                                                        id2label={0: 'O', 1: 'B-DRUG', 2: 'I-DRUG', 3: 'B-EFFECT', 4: 'I-EFFECT'} 
                                                        )                                                        
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer)

print( model_pipeline ("Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug."))
``` 

SciBERT: https://huggingface.co/allenai/scibert_scivocab_uncased

Dataset: https://huggingface.co/datasets/ade_corpus_v2
