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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: bert-finetuned-claim-detection |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-finetuned-claim-detection |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2241 |
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- Accuracy: 0.9135 |
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- F1: 0.9138 |
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## Model description |
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This model is a fine-tuned version of [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) |
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on the **Claim Detection** dataset ([Nithiwat/claim-detection](https://huggingface.co/datasets/Nithiwat/claim-detection)). |
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The goal of this model is to classify whether a given sentence is a **check-worthy claim** or not. |
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It is trained as a **binary text classification** task using the Hugging Face `Trainer` API. |
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## Intended uses & limitations |
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This model is designed for text-level claim detection, with potential applications in: |
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* ✅ Insurance claim screening |
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* ✅ Fraud detection and compliance document filtering |
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* ✅ Fact-checking or misinformation detection |
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* ✅ News or policy statement classification |
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### Limitations |
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* Trained only on English-language data |
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* Detects checkworthiness, not truthfulness — the model identifies statements that can be fact-checked, not whether they are true |
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* May require fine-tuning for domain-specific text (e.g., legal, financial) |
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## Training and evaluation data |
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Training and evaluation were conducted using the Hugging Face `Trainer` class with |
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custom metric computation (Accuracy and F1-score). |
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The model was fine-tuned on 11,000 training samples and evaluated on the full test split. |
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## Example Usage |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="EllenLiu/bert-finetuned-claim-detection") |
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text = "The new policy will save the government $20 billion annually." |
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print(pipe(text)) |
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# Output: [{'label': 'LABEL_1', 'score': 0.987}] |
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``` |
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Interpretation: |
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* LABEL_1: Claim (check-worthy statement) |
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* LABEL_0: Non-claim (non-factual or subjective statement) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 400 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| 0.7193 | 0.0727 | 50 | 0.6618 | 0.5366 | 0.6830 | |
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| 0.5819 | 0.1453 | 100 | 0.4477 | 0.8360 | 0.8298 | |
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| 0.4376 | 0.2180 | 150 | 0.3658 | 0.8695 | 0.8642 | |
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| 0.3847 | 0.2907 | 200 | 0.3159 | 0.8790 | 0.8826 | |
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| 0.2957 | 0.3634 | 250 | 0.3077 | 0.8800 | 0.8719 | |
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| 0.2932 | 0.4360 | 300 | 0.2654 | 0.8947 | 0.8948 | |
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| 0.2577 | 0.5087 | 350 | 0.2917 | 0.8936 | 0.8879 | |
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| 0.3273 | 0.5814 | 400 | 0.2663 | 0.8936 | 0.8925 | |
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| 0.2356 | 0.6541 | 450 | 0.2463 | 0.9012 | 0.9038 | |
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| 0.2833 | 0.7267 | 500 | 0.2619 | 0.8896 | 0.8951 | |
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| 0.2853 | 0.7994 | 550 | 0.2292 | 0.9067 | 0.9074 | |
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| 0.2386 | 0.8721 | 600 | 0.2370 | 0.9069 | 0.9063 | |
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| 0.2411 | 0.9448 | 650 | 0.2403 | 0.9094 | 0.9087 | |
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| 0.2377 | 1.0174 | 700 | 0.2264 | 0.9075 | 0.9076 | |
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| 0.1773 | 1.0901 | 750 | 0.2280 | 0.9073 | 0.9057 | |
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| 0.1827 | 1.1628 | 800 | 0.2233 | 0.9057 | 0.9084 | |
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| 0.205 | 1.2355 | 850 | 0.2153 | 0.9087 | 0.9109 | |
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| 0.1642 | 1.3081 | 900 | 0.2355 | 0.9109 | 0.9099 | |
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| 0.1446 | 1.3808 | 950 | 0.2308 | 0.9075 | 0.9084 | |
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| 0.1588 | 1.4535 | 1000 | 0.2153 | 0.9115 | 0.9113 | |
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| 0.1413 | 1.5262 | 1050 | 0.2243 | 0.9127 | 0.9129 | |
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| 0.172 | 1.5988 | 1100 | 0.2274 | 0.9082 | 0.9102 | |
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| 0.1419 | 1.6715 | 1150 | 0.2227 | 0.9112 | 0.9123 | |
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| 0.1669 | 1.7442 | 1200 | 0.2244 | 0.9141 | 0.9140 | |
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| 0.138 | 1.8169 | 1250 | 0.2242 | 0.9145 | 0.9143 | |
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| 0.1518 | 1.8895 | 1300 | 0.2241 | 0.9134 | 0.9139 | |
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| 0.1246 | 1.9622 | 1350 | 0.2241 | 0.9135 | 0.9138 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
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## Author |
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* 💼 Focus: AI model fine-tuning, deployment, and financial systems integration |
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* 🔗 [LinkedIn](https://www.linkedin.com/in/xiaojing-ellen-liu/) | [GitHub](https://github.com/EllenLiu2019) |
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## Citation |
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```bibtex |
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@misc{ellenliu2025claimdetection, |
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title={BERT-base Uncased Fine-tuned for Claim Detection}, |
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author={Xiaojing-Ellen-Liu}, |
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year={2025}, |
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howpublished={\url{https://huggingface.co/XiaojingEllen/bert-finetuned-claim-detection}}, |
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} |
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``` |
