---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2_6_datasets
  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_6_datasets

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: 0.3804
- Wer: 0.2629

## 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    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 1.1149        | 0.3795 | 600   | 0.5531          | 0.4947 |
| 0.2052        | 0.7590 | 1200  | 0.4347          | 0.4689 |
| 0.1576        | 1.1385 | 1800  | 0.3204          | 0.3717 |
| 0.1263        | 1.5180 | 2400  | 0.3928          | 0.4128 |
| 0.1205        | 1.8975 | 3000  | 0.3214          | 0.3607 |
| 0.0993        | 2.2770 | 3600  | 0.3063          | 0.3514 |
| 0.091         | 2.6565 | 4200  | 0.3078          | 0.3390 |
| 0.0877        | 3.0361 | 4800  | 0.2673          | 0.3165 |
| 0.0716        | 3.4156 | 5400  | 0.2798          | 0.3039 |
| 0.0681        | 3.7951 | 6000  | 0.2710          | 0.2948 |
| 0.0592        | 4.1746 | 6600  | 0.2728          | 0.3072 |
| 0.0525        | 4.5541 | 7200  | 0.2828          | 0.3133 |
| 0.0497        | 4.9336 | 7800  | 0.3039          | 0.3132 |
| 0.0402        | 5.3131 | 8400  | 0.2741          | 0.2832 |
| 0.0389        | 5.6926 | 9000  | 0.2837          | 0.3018 |
| 0.0371        | 6.0721 | 9600  | 0.2732          | 0.2830 |
| 0.0286        | 6.4516 | 10200 | 0.2998          | 0.2794 |
| 0.028         | 6.8311 | 10800 | 0.2904          | 0.2769 |
| 0.0232        | 7.2106 | 11400 | 0.3183          | 0.2752 |
| 0.0201        | 7.5901 | 12000 | 0.3045          | 0.2665 |
| 0.0197        | 7.9696 | 12600 | 0.3137          | 0.2733 |
| 0.0139        | 8.3491 | 13200 | 0.3438          | 0.2670 |
| 0.0128        | 8.7287 | 13800 | 0.3385          | 0.2651 |
| 0.0115        | 9.1082 | 14400 | 0.3669          | 0.2671 |
| 0.0079        | 9.4877 | 15000 | 0.3695          | 0.2613 |
| 0.008         | 9.8672 | 15600 | 0.3804          | 0.2629 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1