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# w2v-bert-2.0-dutch-colab-CV16.0

This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0920
- Wer: 0.0573

## 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: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3106        | 1.0   | 358  | 0.1833          | 0.1350 |
| 0.0924        | 2.0   | 716  | 0.1365          | 0.0932 |
| 0.0521        | 3.0   | 1074 | 0.1121          | 0.0732 |
| 0.033         | 3.99  | 1432 | 0.0957          | 0.0619 |
| 0.0221        | 4.99  | 1790 | 0.0920          | 0.0573 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.2

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "ylacombe/w2v-bert-2.0", "model-index": [{"name": "w2v-bert-2.0-dutch-colab-CV16.0", "results": []}]}
transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer base_model:ylacombe/w2v-bert-2.0 endpoints_compatible has_space region:us