ez6047/CIREE-fact-detector

CIREE: Claim Identification for Research in Environmental Evidence

CIREE is a RoBERTa-based text classification model fine-tuned to detect claims in environmental documentary / media content. It predicts whether a text segment is a Claim or Non-claim, which is useful as a first step before deeper verification/fact-checking.

Intended Use

• Primary use: Environmental media analysis (claim detection).
• Typical input: Segmented transcripts
• Output: label + score distribution over classes.

Model Details

• Architecture: RoBERTa base (Hugging Face Transformers)
• Training objective: sequence classification (claim vs non-claim)
• License: MIT

How to Use

Python

pip install transformers torch pandas scikit-learn

from transformers import pipeline

classifier = pipeline("text-classification", model="ez6047/CIREE-fact-detector")

text = "Enter your sentence here."
result = classifier(text, top_k=None)

prob = next(r["score"] for r in result if r["label"] == "LABEL_1")
print(f"Fact probability: {prob:.3f}")

For batch inference with CSV input, thresholding, and evaluation metrics, see run_inference.py in this repo.