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
---
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: 0.3251033282575593
---

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# 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: 0.5032
- Wer: 0.3251

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7516        | 0.79  | 100  | 2.4041          | 1.0089 |
| 1.0185        | 1.58  | 200  | 0.7642          | 0.6153 |
| 0.5366        | 2.37  | 300  | 0.6518          | 0.5328 |
| 0.4153        | 3.16  | 400  | 0.6116          | 0.4811 |
| 0.353         | 3.95  | 500  | 0.6357          | 0.4806 |
| 0.2876        | 4.74  | 600  | 0.6213          | 0.4434 |
| 0.2389        | 5.53  | 700  | 0.5103          | 0.4243 |
| 0.1735        | 6.32  | 800  | 0.5079          | 0.3753 |
| 0.1419        | 7.11  | 900  | 0.5264          | 0.3638 |
| 0.1031        | 7.91  | 1000 | 0.5454          | 0.3466 |
| 0.0743        | 8.7   | 1100 | 0.5286          | 0.3337 |
| 0.054         | 9.49  | 1200 | 0.5032          | 0.3251 |


### Framework versions

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