transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer base_model:facebook/w2v-bert-2.0 license:mit endpoints_compatible region:us
---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2_bert_ru
  results: []
---

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# w2v2_bert_ru

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: inf
- Wer: 0.0538

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.711         | 0.73  | 300  | inf             | 0.1267 |
| 0.1026        | 1.46  | 600  | inf             | 0.0925 |
| 0.0748        | 2.18  | 900  | inf             | 0.0732 |
| 0.0591        | 2.91  | 1200 | inf             | 0.0710 |
| 0.0437        | 3.64  | 1500 | inf             | 0.0675 |
| 0.0382        | 4.37  | 1800 | inf             | 0.0675 |
| 0.0302        | 5.1   | 2100 | inf             | 0.0620 |
| 0.0243        | 5.83  | 2400 | inf             | 0.0590 |
| 0.0219        | 6.55  | 2700 | inf             | 0.0584 |
| 0.0173        | 7.28  | 3000 | inf             | 0.0577 |
| 0.015         | 8.01  | 3300 | inf             | 0.0560 |
| 0.0115        | 8.74  | 3600 | inf             | 0.0551 |
| 0.0099        | 9.47  | 3900 | inf             | 0.0538 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
