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BanglaClickbaitBERT: Domain-adaptive pretrained BanglaBERT for Bengali clickbait detection
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metadata
library_name: transformers
tags:
  - generated_from_trainer
model-index:
  - name: bangla-clickbait-multitask
    results: []

bangla-clickbait-multitask

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2984
  • Binary Accuracy: 0.8662
  • Binary F1 Macro: 0.8652
  • Binary F1 Weighted: 0.8662
  • Multi Accuracy: 0.5400
  • Multi F1 Macro: 0.4968
  • Multi F1 Weighted: 0.5394

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • 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: linear
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Binary Accuracy Binary F1 Macro Binary F1 Weighted Multi Accuracy Multi F1 Macro Multi F1 Weighted
1.4507 1.0 605 1.4136 0.8233 0.8214 0.8230 0.3881 0.2760 0.3054
1.2076 2.0 1210 1.1768 0.8704 0.8701 0.8706 0.5095 0.4319 0.4811
1.0117 3.0 1815 1.0525 0.8728 0.8722 0.8730 0.5640 0.4899 0.5411
0.9297 4.0 2420 1.0100 0.8786 0.8775 0.8785 0.5739 0.4984 0.5505
0.7527 5.0 3025 1.0259 0.8671 0.8664 0.8672 0.5640 0.4943 0.5456
0.7040 6.0 3630 1.0172 0.8720 0.8713 0.8721 0.5673 0.5028 0.5580
0.6455 7.0 4235 1.0516 0.8737 0.8724 0.8735 0.5467 0.5036 0.5487
0.5461 8.0 4840 1.0951 0.8695 0.8688 0.8696 0.5533 0.5049 0.5524
0.4756 9.0 5445 1.1419 0.8646 0.8641 0.8648 0.5698 0.5117 0.5616
0.5018 10.0 6050 1.2189 0.8613 0.8609 0.8615 0.5607 0.5015 0.5540
0.4339 11.0 6655 1.2076 0.8662 0.8650 0.8661 0.5533 0.5075 0.5537
0.4350 12.0 7260 1.2696 0.8679 0.8672 0.8680 0.5186 0.4927 0.5225
0.4042 13.0 7865 1.2740 0.8654 0.8644 0.8654 0.5376 0.4912 0.5358
0.3195 14.0 8470 1.2879 0.8687 0.8677 0.8687 0.5425 0.4991 0.5421
0.3332 15.0 9075 1.2984 0.8662 0.8652 0.8662 0.5400 0.4968 0.5394

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2