---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-CV_Fleurs-lg-5hrs-v5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-2.0-CV_Fleurs-lg-5hrs-v5

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: 0.4806
- Wer: 0.4843
- Cer: 0.1060

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.5225        | 1.0   | 515  | 0.4996          | 0.5506 | 0.1117 |
| 0.4435        | 2.0   | 1030 | 0.4619          | 0.4623 | 0.0969 |
| 0.3765        | 3.0   | 1545 | 0.4473          | 0.5063 | 0.1061 |
| 0.3573        | 4.0   | 2060 | 0.4596          | 0.4672 | 0.0962 |
| 0.3421        | 5.0   | 2575 | 0.4621          | 0.5073 | 0.1093 |
| 0.3235        | 6.0   | 3090 | 0.4548          | 0.5074 | 0.1057 |
| 0.3263        | 7.0   | 3605 | 0.4454          | 0.4664 | 0.1011 |
| 0.3125        | 8.0   | 4120 | 0.5261          | 0.5385 | 0.1251 |
| 0.2963        | 9.0   | 4635 | 0.4753          | 0.4890 | 0.1108 |
| 0.2527        | 10.0  | 5150 | 0.4803          | 0.4869 | 0.1085 |
| 0.2328        | 11.0  | 5665 | 0.4830          | 0.4710 | 0.1008 |
| 0.2077        | 12.0  | 6180 | 0.4806          | 0.4843 | 0.1060 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1