---
library_name: transformers
license: mit
base_model: facebook/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:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: mn
      split: test
      args: mn
    metrics:
    - type: wer
      value: 0.965662968832541
      name: Wer
---

<!-- 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 [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6984
- Wer: 0.9657

## 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: Use OptimizerNames.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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.4128        | 1.9763 | 500  | 3.2537          | 1.0    |
| 1.7038        | 3.9526 | 1000 | 1.5989          | 1.0    |
| 0.722         | 5.9289 | 1500 | 0.9174          | 0.9878 |
| 0.4558        | 7.9051 | 2000 | 0.7443          | 0.9746 |
| 0.3257        | 9.8814 | 2500 | 0.6984          | 0.9657 |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.21.1