---
base_model: csebuetnlp/banglabert
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: banglabert-MLTC-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# banglabert-MLTC-1

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: 0.3669
- F1: 0.8607
- Roc Auc: 0.8579
- Accuracy: 0.5835
- Hamming Loss: 0.1420
- Jaccard Score: 0.7555
- Zero One Loss: 0.4165

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.4242        | 1.0   | 146  | 0.4373          | 0.8248 | 0.8161  | 0.5013   | 0.1838       | 0.7018        | 0.4987        |
| 0.39          | 2.0   | 292  | 0.3895          | 0.8407 | 0.8400  | 0.5784   | 0.1600       | 0.7252        | 0.4216        |
| 0.2973        | 3.0   | 438  | 0.3736          | 0.8595 | 0.8547  | 0.5758   | 0.1452       | 0.7535        | 0.4242        |
| 0.2465        | 4.0   | 584  | 0.3644          | 0.8638 | 0.8605  | 0.5913   | 0.1395       | 0.7602        | 0.4087        |
| 0.2918        | 5.0   | 730  | 0.3669          | 0.8607 | 0.8579  | 0.5835   | 0.1420       | 0.7555        | 0.4165        |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1