bangla-bert-base-MLTC-BB2
This model is a fine-tuned version of sagorsarker/bangla-bert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3996
- F1: 0.8514
- F1 Weighted: 0.8510
- Roc Auc: 0.8483
- Accuracy: 0.5373
- Hamming Loss: 0.1517
- Jaccard Score: 0.7412
- Zero One Loss: 0.4627
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | F1 | F1 Weighted | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
---|---|---|---|---|---|---|---|---|---|---|
0.3931 | 1.0 | 73 | 0.3859 | 0.8319 | 0.8312 | 0.8310 | 0.5244 | 0.1690 | 0.7123 | 0.4756 |
0.3635 | 2.0 | 146 | 0.3638 | 0.8540 | 0.8546 | 0.8528 | 0.5707 | 0.1472 | 0.7453 | 0.4293 |
0.2688 | 3.0 | 219 | 0.3676 | 0.8483 | 0.8465 | 0.8425 | 0.5553 | 0.1575 | 0.7366 | 0.4447 |
0.1841 | 4.0 | 292 | 0.3600 | 0.8562 | 0.8558 | 0.8534 | 0.5758 | 0.1465 | 0.7486 | 0.4242 |
0.1701 | 5.0 | 365 | 0.3677 | 0.8568 | 0.8565 | 0.8528 | 0.5681 | 0.1472 | 0.7495 | 0.4319 |
0.1499 | 6.0 | 438 | 0.3701 | 0.8551 | 0.8541 | 0.8534 | 0.5630 | 0.1465 | 0.7469 | 0.4370 |
0.1034 | 7.0 | 511 | 0.3825 | 0.8582 | 0.8576 | 0.8540 | 0.5527 | 0.1459 | 0.7516 | 0.4473 |
0.0793 | 8.0 | 584 | 0.4103 | 0.8443 | 0.8428 | 0.8393 | 0.5167 | 0.1607 | 0.7306 | 0.4833 |
0.0801 | 9.0 | 657 | 0.3960 | 0.8484 | 0.8484 | 0.8470 | 0.5373 | 0.1530 | 0.7367 | 0.4627 |
0.0812 | 10.0 | 730 | 0.3996 | 0.8514 | 0.8510 | 0.8483 | 0.5373 | 0.1517 | 0.7412 | 0.4627 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Base model
sagorsarker/bangla-bert-base