---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-wol-v1
  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-2.0-wol-v1

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: 0.1008
- Wer: 0.0792

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.6351        | 0.6857 | 300  | 0.2974          | 0.3040 |
| 0.4591        | 1.3714 | 600  | 0.2215          | 0.2307 |
| 0.3833        | 2.0571 | 900  | 0.1950          | 0.1900 |
| 0.329         | 2.7429 | 1200 | 0.1637          | 0.1614 |
| 0.2797        | 3.4286 | 1500 | 0.1515          | 0.1479 |
| 0.2558        | 4.1143 | 1800 | 0.1435          | 0.1337 |
| 0.2166        | 4.8    | 2100 | 0.1296          | 0.1295 |
| 0.1876        | 5.4857 | 2400 | 0.1178          | 0.1129 |
| 0.1695        | 6.1714 | 2700 | 0.1107          | 0.1005 |
| 0.137         | 6.8571 | 3000 | 0.1064          | 0.0933 |
| 0.1078        | 7.5429 | 3300 | 0.1049          | 0.0929 |
| 0.0904        | 8.2286 | 3600 | 0.1002          | 0.0871 |
| 0.0685        | 8.9143 | 3900 | 0.0973          | 0.0810 |
| 0.049         | 9.6    | 4200 | 0.1008          | 0.0792 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1