---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-Tamil-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-Tamil-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: 0.1815
- Wer: 0.2176
- Cer: 0.0328

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.0779        | 0.75  | 300  | 0.4934          | 0.6338 | 0.1189 |
| 0.3653        | 1.5   | 600  | 0.4045          | 0.5424 | 0.0975 |
| 0.2632        | 2.25  | 900  | 0.3148          | 0.4421 | 0.0723 |
| 0.2084        | 3.0   | 1200 | 0.2297          | 0.3499 | 0.0576 |
| 0.1359        | 3.75  | 1500 | 0.2042          | 0.3060 | 0.0464 |
| 0.1049        | 4.5   | 1800 | 0.1939          | 0.2836 | 0.0446 |
| 0.0823        | 5.25  | 2100 | 0.1827          | 0.2504 | 0.0382 |
| 0.0561        | 6.0   | 2400 | 0.1731          | 0.2419 | 0.0368 |
| 0.0352        | 6.75  | 2700 | 0.1802          | 0.2275 | 0.0335 |
| 0.0224        | 7.5   | 3000 | 0.1815          | 0.2176 | 0.0328 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1