mokarami/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:

Split	Count
Train	20,000
Validation	2,000
Test	2,000

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

Metrics

Metric	Value
Test loss	0.5157
Test accuracy	0.8105
Test macro F1	0.8057
Test weighted F1	0.8094

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.