---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-V2
  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-V2

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1706
- Wer: 0.1496

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.3589        | 0.1049 | 300  | 0.2921          | 0.2762 |
| 0.3512        | 0.2099 | 600  | 0.2855          | 0.2767 |
| 0.2998        | 0.3148 | 900  | 0.2872          | 0.2550 |
| 0.3419        | 0.4197 | 1200 | 0.2641          | 0.2620 |
| 0.2757        | 0.5247 | 1500 | 0.2633          | 0.2332 |
| 0.2827        | 0.6296 | 1800 | 0.2473          | 0.2090 |
| 0.265         | 0.7345 | 2100 | 0.2304          | 0.2226 |
| 0.2985        | 0.8395 | 2400 | 0.2266          | 0.2109 |
| 0.2555        | 0.9444 | 2700 | 0.2279          | 0.1891 |
| 0.255         | 1.0493 | 3000 | 0.2129          | 0.1927 |
| 0.2194        | 1.1542 | 3300 | 0.1991          | 0.1821 |
| 0.172         | 1.2592 | 3600 | 0.1963          | 0.1710 |
| 0.2018        | 1.3641 | 3900 | 0.1860          | 0.1724 |
| 0.2098        | 1.4690 | 4200 | 0.1783          | 0.1717 |
| 0.1996        | 1.5740 | 4500 | 0.1709          | 0.1563 |
| 0.1926        | 1.6789 | 4800 | 0.1706          | 0.1496 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0