claimlens-m-verifier

ClaimLens-M Verifier

ClaimLens-M Verifier is a fine-tuned XLM-RoBERTa sequence-classification model for evidence-grounded claim verification.

The model classifies an evidence-claim pair into:

• entailment
• contradiction
• neutral

In the full ClaimLens-M pipeline, these NLI labels are mapped to:

• SUPPORTED
• REFUTED
• NOT_ENOUGH_INFO

Training Data

The verifier was fine-tuned on a FEVER-NLI subset:

The project also includes curated Italian demo pairs for multilingual product testing.

Metrics

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

repo_id = "mokarami/claimlens-m-verifier"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)

premise = "QLoRA enables fine-tuning large language models with 4-bit quantization, reducing GPU memory requirements."
hypothesis = "QLoRA reduces GPU memory requirements during fine-tuning."

inputs = tokenizer(premise, hypothesis, truncation=True, max_length=384, return_tensors="pt")
with torch.no_grad():
    probs = torch.softmax(model(**inputs).logits[0], dim=-1)

label_id = int(torch.argmax(probs))
print(model.config.id2label[label_id], float(probs[label_id]))

Full Project

The full ClaimLens-M application combines:

• BM25 retrieval
• lexical dense retrieval fallback
• hybrid evidence ranking
• XLM-RoBERTa NLI verification
• calibrated verdict aggregation
• structured JSON outputs
• FastAPI and Gradio interfaces

The Hugging Face Gradio Space package is prepared separately, but creating Gradio Spaces on this account currently requires a Hugging Face PRO subscription.