---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-bengali-model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-bengali-model

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1759
- Wer: 0.1737

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.4772        | 0.4336 | 2000  | 0.3677          | 0.3733 |
| 0.3599        | 0.8671 | 4000  | 0.3126          | 0.3000 |
| 0.2908        | 1.3007 | 6000  | 0.2661          | 0.2659 |
| 0.2594        | 1.7342 | 8000  | 0.2433          | 0.2441 |
| 0.2041        | 2.1678 | 10000 | 0.2290          | 0.2279 |
| 0.1933        | 2.6013 | 12000 | 0.2107          | 0.2146 |
| 0.1676        | 3.0349 | 14000 | 0.2096          | 0.2117 |
| 0.1463        | 3.4685 | 16000 | 0.1956          | 0.1981 |
| 0.1332        | 3.9020 | 18000 | 0.1772          | 0.1848 |
| 0.106         | 4.3356 | 20000 | 0.1816          | 0.1788 |
| 0.1014        | 4.7691 | 22000 | 0.1759          | 0.1737 |


### Framework versions

- Transformers 4.53.0
- Pytorch 2.7.0+cu126
- Datasets 2.18.0
- Tokenizers 0.21.1