bert-finetuned-claim-detection

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

bert-finetuned-claim-detection

This model is a fine-tuned version of bert-base-uncased on the Claim Detection dataset (Nithiwat/claim-detection).

The goal of this model is to classify whether a given sentence is a check-worthy claim or not.
It is trained as a binary text classification task using the Hugging Face Trainer API.

It achieves the following results on the evaluation set:

• Loss: 0.2241
• Accuracy: 0.9135
• F1: 0.9138

Intended uses & limitations

This model is designed for text-level claim detection, with potential applications in:

• ✅ Insurance claim screening
• ✅ Fraud detection and compliance document filtering
• ✅ Fact-checking or misinformation detection
• ✅ News or policy statement classification

Limitations

• Trained only on English-language data
• Detects checkworthiness, not truthfulness — the model identifies statements that can be fact-checked, not whether they are true
• May require fine-tuning for domain-specific text (e.g., legal, financial)

Training and evaluation data

Training and evaluation were conducted using the Hugging Face Trainer class with custom metric computation (Accuracy and F1-score).
The model was fine-tuned on 11,000 training samples and evaluated on the full test split.

Example Usage

from transformers import pipeline

pipe = pipeline("text-classification", model="EllenLiu/bert-finetuned-claim-detection")

text = "The new policy will save the government $20 billion annually."
print(pipe(text))
# Output: [{'label': 'LABEL_1', 'score': 0.987}]

Interpretation:

• LABEL_1: Claim (check-worthy statement)
• LABEL_0: Non-claim (non-factual or subjective statement)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 400
• num_epochs: 2

Training results

Framework versions

• Transformers 4.57.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1

Author

• 💼 Focus: AI model fine-tuning, deployment, and financial systems integration
• 🔗 LinkedIn | GitHub

Citation

@misc{liu2025_claim_detection,
  title={BERT-base Uncased Fine-tuned for Claim Detection},
  author={Xiaojing Liu},
  year={2025},
  howpublished={\url{https://huggingface.co/XiaojingEllen/bert-finetuned-claim-detection}},
  note={Version 1.0}
}