---
base_model: ylacombe/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-mongolian-colab-CV16.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: mn
      split: test
      args: mn
    metrics:
    - name: Wer
      type: wer
      value: 0.32330867957363496
---

<!-- 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-2.0-mongolian-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 common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5065
- Wer: 0.3233

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8092        | 0.79  | 100  | 2.1220          | 1.0404 |
| 0.9265        | 1.58  | 200  | 0.7650          | 0.6125 |
| 0.5241        | 2.37  | 300  | 0.6422          | 0.5244 |
| 0.4165        | 3.16  | 400  | 0.6275          | 0.4711 |
| 0.3393        | 3.95  | 500  | 0.6290          | 0.4884 |
| 0.2664        | 4.74  | 600  | 0.5784          | 0.4712 |
| 0.2315        | 5.53  | 700  | 0.5370          | 0.4160 |
| 0.1819        | 6.32  | 800  | 0.5268          | 0.3813 |
| 0.1339        | 7.11  | 900  | 0.5100          | 0.3643 |
| 0.0993        | 7.91  | 1000 | 0.5368          | 0.3549 |
| 0.0739        | 8.7   | 1100 | 0.5405          | 0.3378 |
| 0.055         | 9.49  | 1200 | 0.5065          | 0.3233 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0