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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# w2v-bert-Telugu-large

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: 0.2203
- Wer: 0.2210
- Cer: 0.0392

## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 2.9356        | 0.6579 | 300  | 0.5106          | 0.5659 | 0.1265 |
| 0.4276        | 1.3158 | 600  | 0.4152          | 0.4787 | 0.0953 |
| 0.3481        | 1.9737 | 900  | 0.3907          | 0.4076 | 0.0824 |
| 0.239         | 2.6316 | 1200 | 0.3014          | 0.3680 | 0.0660 |
| 0.1957        | 3.2895 | 1500 | 0.3159          | 0.3361 | 0.0629 |
| 0.1454        | 3.9474 | 1800 | 0.2517          | 0.2744 | 0.0489 |
| 0.1           | 4.6053 | 2100 | 0.2371          | 0.2621 | 0.0469 |
| 0.0748        | 5.2632 | 2400 | 0.2243          | 0.2469 | 0.0432 |
| 0.0453        | 5.9211 | 2700 | 0.2188          | 0.2381 | 0.0409 |
| 0.029         | 6.5789 | 3000 | 0.2203          | 0.2210 | 0.0392 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1