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| import gradio as gr | |
| from spacy import displacy | |
| 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: 'DRUG', 2: 'DRUG', 3: 'ADVERSE EFFECT', 4: 'ADVERSE EFFECT'} # for grouping BIO tags back together | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| model.to("cpu") | |
| model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer, device=-1, grouped_entities=True) | |
| def extract_entities(sentence): | |
| """ Extract drug and reaction entities, and show using displaCy's NER visualizer. | |
| source: https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb | |
| """ | |
| tokens = model_pipeline(sentence) | |
| entities = [] | |
| for token in tokens: | |
| label = token["entity_group"] | |
| if label != "0": | |
| # label 0 corresponds to "Outside" any entity we care about | |
| token["label"] = label | |
| entities.append(token) | |
| params = [{"text": sentence, | |
| "ents": entities, | |
| "title": None}] | |
| return displacy.render(params, style="ent", manual=True, options={ | |
| "colors": { | |
| "DRUG": "#f08080", | |
| "ADVERSE EFFECT": "#9bddff", | |
| }, | |
| }) | |
| # the following examples of adverse effects are taken from Wikipedia: | |
| # https://en.wikipedia.org/wiki/Adverse_effect#Medications | |
| examples = [ | |
| "Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug.", | |
| "Addiction to many sedatives and analgesics, such as diazepam, morphine, etc.", | |
| "Birth defects associated with thalidomide", | |
| "Bleeding of the intestine associated with aspirin therapy", | |
| "Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx)", | |
| "Deafness and kidney failure associated with gentamicin (an antibiotic)", | |
| "Death, following sedation, in children using propofol (Diprivan)", | |
| "Depression or hepatic injury caused by interferon", | |
| "Diabetes caused by atypical antipsychotic medications (neuroleptic psychiatric drugs)" | |
| ] | |
| footer = """ | |
| <hr> | |
| This app automatically extracts drug names and adverse effects from the input text. An adverse effect occurs when a drug harms a patient in any way. | |
| The extraction is done by a <a target="_blank" rel="noopener noreferrer" href=https://huggingface.co/jsylee/scibert_scivocab_uncased-finetuned-ner>SciBERT model</a> fine-tuned on the <a target="_blank" rel="noopener noreferrer" href=https://huggingface.co/datasets/ade_corpus_v2>`ade_corpus_v2`</a> dataset. Fine-tuning code <a target="_blank" rel="noopener noreferrer" href=https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb>here</a>. | |
| This was made during the November 2021 Hugging Face Community Event. | |
| By <a target="_blank" rel="noopener noreferrer" href=http://www.columbia.edu/~jsl2239/>Justin S. Lee</a> | |
| """ | |
| iface = gr.Interface(fn=extract_entities, inputs=gr.inputs.Textbox(lines=5, placeholder="Abortion, miscarriage or uterine hemorrhage associated with misoprostol..."), | |
| outputs="html", examples=examples, | |
| title="NER for Drug Names and Adverse Effects", | |
| article=footer) | |
| iface.launch() |