FinnHillengass/claim-detector-deberta-v3-base

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-base with a 2-class classification head
• Learning rate 2e-5, batch size 16, max length 512
• 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.