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.