transformers safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:common_voice_16_0 base_model:facebook/w2v-bert-2.0 license:mit model-index endpoints_compatible region:us
---
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-test
  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: 0.872688853671421
---

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# w2v-bert-2.0-mongolian-colab-CV16.0-test

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: 0.5486
- Wer: 0.8727

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 100
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7431        | 0.79  | 200  | 0.7963          | 0.9926 |
| 0.4379        | 1.58  | 400  | 0.6480          | 0.9805 |
| 0.3109        | 2.37  | 600  | 0.5584          | 0.9546 |
| 0.2444        | 3.17  | 800  | 0.5261          | 0.9429 |
| 0.2048        | 3.96  | 1000 | 0.5208          | 0.9329 |
| 0.1512        | 4.75  | 1200 | 0.5084          | 0.9229 |
| 0.1161        | 5.54  | 1400 | 0.5248          | 0.9197 |
| 0.0882        | 6.33  | 1600 | 0.5248          | 0.9017 |
| 0.0728        | 7.12  | 1800 | 0.5295          | 0.8885 |
| 0.0608        | 7.91  | 2000 | 0.5178          | 0.8833 |
| 0.0386        | 8.7   | 2200 | 0.5317          | 0.8732 |
| 0.0234        | 9.5   | 2400 | 0.5486          | 0.8727 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2
