hatim00101/bert-scam-classifier

bert-scam-call-classifier

A fine-tuned bert-base-uncased binary text classifier that flags phone-call transcripts as scam or non_scam, based on conversational/behavioral patterns rather than topic keywords alone.

Model description

• Base model: bert-base-uncased
• Task: Binary sequence classification (0 = non_scam, 1 = scam)
• Fine-tuning framework: Hugging Face transformers.Trainer
• Max sequence length: 256 tokens

Training data

Fine-tuned on the Scam and Non-Scam Call Conversation Dataset (Kaggle), built for the paper "Classifying Scam Calls through Content Analysis with Dynamic Sparsity Top-k Attention Regularization" (IEEE Access).

| Split | Count | |---| | Total conversations | 800 | | Scam | 400 | | Non-scam | 400 | | Train | 640 | | Validation | 80 | | Test | 80 |

(Exact split counts will vary slightly depending on your run — the notebook fills these in automatically from train_df/val_df/test_df.)

The dataset is released under CC BY-NC-ND 4.0 — non-commercial use only, and this model inherits that restriction.

Training procedure

• Optimizer: AdamW (via Trainer defaults)
• Learning rate: 2e-5
• Epochs: 4 (with early stopping on validation F1)
• Batch size: 16 (train) / 32 (eval)
• Precision: fp16 (if a GPU was available), else fp32

Evaluation results (held-out test set)

| Metric | Score | |---| | Accuracy | 1.0000 | | Precision | 1.0000 | | Recall | 1.0000 | | F1 | 1.0000 |

How to use

from transformers import pipeline

clf = pipeline("text-classification", model="your-hf-username/bert-scam-call-classifier")
clf("Hello, this is your bank calling. We need your card number and PIN to verify your account immediately.")

Citation

If you use this dataset, please cite the original paper:

Classifying Scam Calls through Content Analysis with Dynamic Sparsity Top-k Attention Regularization, IEEE Access.

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*Model card generated for this fine-tuning run.