---
license: mit
base_model: sagorsarker/bangla-bert-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bangla-bert-base-MLTC-BB2
  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-BB2

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: 0.3996
- 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