---
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
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# 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