metadata
language:
- en
tags:
- Named Entity Recognition
- SciBERT
- Adverse Effect
- Drug
- Medical
datasets:
- ade_corpus_v2
- tner/bc5cdr
- commanderstrife/jnlpba
- bc2gm_corpus
- drAbreu/bc4chemd_ner
- linnaeus
- chintagunta85/ncbi_disease
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.
This model classifies input tokens into one of five classes:
B-DRUG: beginning of a drug entityI-DRUG: within a drug entityB-EFFECT: beginning of an AE entityI-EFFECT: within an AE entityO: 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