Health Claim Detector (DeBERTa-v3-base)
Binary sequence classifier that decides whether a social-media post contains a
checkable health claim (CLAIM) or not (NO_CLAIM). It is stage 1 of a
health-claim profiling pipeline from the Identifying Structure in Data project
seminar, used to filter raw Reddit posts before span localisation,
taxonomy classification, query reformulation, and evidence grounding.
- Base model:
microsoft/deberta-v3-base - Task: binary text classification (
NO_CLAIM= 0,CLAIM= 1) - Language: English
- Max sequence length: 512 tokens
Training data
~600 Reddit posts, manually annotated for claim presence, drawn from three health-related subreddits: r/AskDocs, r/nutrition, and r/supplements.
Note: the training texts themselves are not redistributed here. Only the fine-tuned weights are released.
Training procedure
- Fine-tuned from
microsoft/deberta-v3-basewith a 2-class classification head - Learning rate
2e-5, batch size16, max length512 - Standard Hugging Face
Trainer
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "<your-username>/claim-detector-deberta-v3-base" # replace with the pushed repo id
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = "Vitamin C prevents colds."
inputs = tok(text, truncation=True, max_length=512, return_tensors="pt")
with torch.no_grad():
probs = model(**inputs).logits.softmax(-1)[0]
label = model.config.id2label[int(probs.argmax())]
print(label, float(probs.max())) # e.g. CLAIM 0.98
Intended use & limitations
- Trained on English health-related Reddit posts; performance on other domains, platforms, or languages is not guaranteed.
- Detects whether a post carries a checkable claim — it does not assess the claim's truth. Verdict assignment is handled by later pipeline stages.
- Not a medical device; do not use for clinical decision-making.
Citation
Part of the Identifying Structure in Data project seminar (SS 2026), Computational Linguistics, Universität Heidelberg — Health Claim Profiling and Evidence Grounding in Social Media Discourse.