This is a fine tuned roberta-base model for detecting whether paragraphs drawn from ethnographic source material are about 'Health, Illness, Medicine and Death'.
The easiest way to use this model at inference time is with the HF pipelines API.
1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="gptmurdock/classifier-main_subjects_health")
4classifier("Example text to classify")
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We use a 60-20-20 train-val-test split, and fine-tuned roberta-base for 5 epochs (lr = 2e-5, batch size = 40).
Evals on the test set are reported below.
