sschet/scibert_scivocab_uncased-finetuned-ner

This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects.

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:

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