readme: ---
  language: 
    - en
  tags:
  - Named Entity Recognition
  - SciBERT
  - Adverse Effect
  - Drug
  - Medical
  datasets:
  - ade_corpus_v2
  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
  