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

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

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: 0.3626
- F1: 0.8666
- Roc Auc: 0.8624
- Accuracy: 0.5861
- Hamming Loss: 0.1375
- Jaccard Score: 0.7646
- Zero One Loss: 0.4139

## 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     | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.4962        | 1.0   | 73   | 0.4868          | 0.8020 | 0.7962  | 0.4730   | 0.2037       | 0.6694        | 0.5270        |
| 0.3992        | 2.0   | 146  | 0.3993          | 0.8420 | 0.8386  | 0.5656   | 0.1613       | 0.7272        | 0.4344        |
| 0.3163        | 3.0   | 219  | 0.3647          | 0.8616 | 0.8586  | 0.5810   | 0.1414       | 0.7569        | 0.4190        |
| 0.2545        | 4.0   | 292  | 0.3626          | 0.8666 | 0.8624  | 0.5861   | 0.1375       | 0.7646        | 0.4139        |
| 0.2464        | 5.0   | 365  | 0.3537          | 0.8626 | 0.8612  | 0.5835   | 0.1388       | 0.7584        | 0.4165        |
| 0.2534        | 6.0   | 438  | 0.3591          | 0.8600 | 0.8566  | 0.5707   | 0.1433       | 0.7544        | 0.4293        |
| 0.194         | 7.0   | 511  | 0.3525          | 0.8644 | 0.8624  | 0.5938   | 0.1375       | 0.7612        | 0.4062        |


### Framework versions

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