transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:common_voice_16_0 base_model:ylacombe/w2v-bert-2.0 model-index endpoints_compatible region:us
---
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
base_model: ylacombe/w2v-bert-2.0
model-index:
- name: w2v-bert-2.0-ur
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: ur
      split: test
      args: ur
    metrics:
    - type: wer
      value: 0.2984838198687486
      name: Wer
---

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# w2v-bert-2.0-ur

This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.2985

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2789        | 2.4   | 300  | inf             | 0.3200 |
| 0.2724        | 4.8   | 600  | inf             | 0.3320 |
| 0.1912        | 7.2   | 900  | inf             | 0.2935 |
| 0.0931        | 9.6   | 1200 | inf             | 0.2985 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
