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---
base_model: csebuetnlp/banglabert
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: banglabert-MLTC-BB1
results: []
---
<!-- 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. -->
# banglabert-MLTC-BB1
This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co./csebuetnlp/banglabert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3620
- F1: 0.8590
- F1 Weighted: 0.8576
- Roc Auc: 0.8573
- Accuracy: 0.5810
- Hamming Loss: 0.1427
- Jaccard Score: 0.7528
- Zero One Loss: 0.4190
## 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: 24
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.5572 | 1.0 | 49 | 0.5051 | 0.8005 | 0.7870 | 0.7871 | 0.4216 | 0.2127 | 0.6673 | 0.5784 |
| 0.3995 | 2.0 | 98 | 0.4366 | 0.8273 | 0.8258 | 0.8160 | 0.5167 | 0.1838 | 0.7055 | 0.4833 |
| 0.3671 | 3.0 | 147 | 0.3832 | 0.8493 | 0.8493 | 0.8451 | 0.5630 | 0.1549 | 0.7380 | 0.4370 |
| 0.3239 | 4.0 | 196 | 0.3671 | 0.8600 | 0.8595 | 0.8573 | 0.5810 | 0.1427 | 0.7544 | 0.4190 |
| 0.2786 | 5.0 | 245 | 0.3593 | 0.8573 | 0.8557 | 0.8541 | 0.5784 | 0.1459 | 0.7503 | 0.4216 |
| 0.2783 | 6.0 | 294 | 0.3608 | 0.8530 | 0.8509 | 0.8502 | 0.5733 | 0.1497 | 0.7437 | 0.4267 |
| 0.2247 | 7.0 | 343 | 0.3576 | 0.8579 | 0.8564 | 0.8573 | 0.5758 | 0.1427 | 0.7511 | 0.4242 |
| 0.2354 | 8.0 | 392 | 0.3631 | 0.8591 | 0.8579 | 0.8560 | 0.5861 | 0.1440 | 0.7530 | 0.4139 |
| 0.2517 | 9.0 | 441 | 0.3630 | 0.8553 | 0.8541 | 0.8534 | 0.5758 | 0.1465 | 0.7472 | 0.4242 |
| 0.223 | 10.0 | 490 | 0.3620 | 0.8590 | 0.8576 | 0.8573 | 0.5810 | 0.1427 | 0.7528 | 0.4190 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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