|
--- |
|
license: mit |
|
base_model: sagorsarker/bangla-bert-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bangla-bert-base-MLTC-1 |
|
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. --> |
|
|
|
# bangla-bert-base-MLTC-1 |
|
|
|
This model is a fine-tuned version of [sagorsarker/bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3627 |
|
- F1: 0.8553 |
|
- Roc Auc: 0.8521 |
|
- Accuracy: 0.5707 |
|
- Hamming Loss: 0.1478 |
|
- Jaccard Score: 0.7473 |
|
- Zero One Loss: 0.4293 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:| |
|
| 0.3717 | 1.0 | 146 | 0.3740 | 0.8447 | 0.8438 | 0.5398 | 0.1562 | 0.7312 | 0.4602 | |
|
| 0.3812 | 2.0 | 292 | 0.3627 | 0.8373 | 0.8420 | 0.5476 | 0.1581 | 0.7201 | 0.4524 | |
|
| 0.2373 | 3.0 | 438 | 0.3830 | 0.8450 | 0.8386 | 0.5476 | 0.1613 | 0.7316 | 0.4524 | |
|
| 0.1688 | 4.0 | 584 | 0.3610 | 0.8555 | 0.8534 | 0.5758 | 0.1465 | 0.7475 | 0.4242 | |
|
| 0.153 | 5.0 | 730 | 0.3627 | 0.8553 | 0.8521 | 0.5707 | 0.1478 | 0.7473 | 0.4293 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
