---
base_model: ylacombe/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-ukrainian-colab-CV16.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_1
      type: mozilla-foundation/common_voice_16_1
      config: uk
      split: test
      args: uk
    metrics:
    - name: Wer
      type: wer
      value: 0.0987
license: mit
datasets:
- mozilla-foundation/common_voice_16_1
language:
- uk
pipeline_tag: automatic-speech-recognition
library_name: transformers
---

<!-- 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-ukrainian-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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1438
- Wer: 0.0987

Note: the model was finetuned on Ukrainian alphabet in lowercase plus "'" sign. Therefore this model can't add punctuation or capitalization.

## 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: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0371        | 1.98  | 525  | 0.1509          | 0.1498 |
| 0.0728        | 3.96  | 1050 | 0.1256          | 0.1279 |
| 0.0382        | 5.94  | 1575 | 0.1260          | 0.1041 |
| 0.0213        | 7.92  | 2100 | 0.1333          | 0.0997 |
| 0.0118        | 9.91  | 2625 | 0.1438          | 0.0987 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1