drug-term-classifier

[Colab]

A Named Entity Recognition (NER) classifier trained to detect novel terms referring to a drug, based on context. The classifier was trained on ~8,000 hand-labeled drug subreddit comments using a Transformer neural network in the SpaCy Python library.

Install

1. pip install spacy[transformers]
2. wget https://github.com/ptmorris03/drug-term-classifier/releases/download/spacy_weights/drug_detector_weights.zip
3. unzip drug_detector_weights.zip

Usage

import spacy

classifier = spacy.load("drug_detector_weights/")

text = """Nice, thanks for the trip report. 
    I'll probably be trying 4-FMA next week. 
    I'm curious to see how it compares to 6-APB.
    I think I might miss the psychedelic edge 6-APB offers, but we'll see."""

output = classifier(text)
drug_terms = output.ents

print(drug_terms)
for drug_term in drug_terms:
    print(str(drug_term), drug_term.start_char, drug_term.end_char)

(4-FMA, 6-APB, 6-APB)
4-FMA 58 63
6-APB 113 118
6-APB 162 167