---
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: 0.3600
- F1: 0.8593
- Roc Auc: 0.8580
- Accuracy: 0.5733
- Hamming Loss: 0.1420
- Jaccard Score: 0.7533
- Zero One Loss: 0.4267

## 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     | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.3266        | 1.0   | 49   | 0.3574          | 0.8629 | 0.8592  | 0.5810   | 0.1407       | 0.7588        | 0.4190        |
| 0.2635        | 2.0   | 98   | 0.3531          | 0.8645 | 0.8618  | 0.5938   | 0.1382       | 0.7614        | 0.4062        |
| 0.27          | 3.0   | 147  | 0.3520          | 0.8553 | 0.8541  | 0.5835   | 0.1459       | 0.7472        | 0.4165        |
| 0.2254        | 4.0   | 196  | 0.3627          | 0.8622 | 0.8592  | 0.5913   | 0.1407       | 0.7577        | 0.4087        |
| 0.1966        | 5.0   | 245  | 0.3600          | 0.8593 | 0.8580  | 0.5733   | 0.1420       | 0.7533        | 0.4267        |


### Framework versions

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