|
--- |
|
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 |
|
|
