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---
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: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice uk
type: common_voice
args: uk
metrics:
- name: Test WER
type: wer
value: 9.81
license: mit
datasets:
- common_voice
language:
- uk
pipeline_tag: automatic-speech-recognition
---
<!-- 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: 0.1386
- Wer: 0.0981
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8074 | 1.98 | 520 | 0.1498 | 0.1461 |
| 0.0694 | 3.96 | 1040 | 0.1243 | 0.1213 |
| 0.0369 | 5.94 | 1560 | 0.1221 | 0.1059 |
| 0.0214 | 7.92 | 2080 | 0.1257 | 0.0987 |
| 0.0115 | 9.9 | 2600 | 0.1386 | 0.0981 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.1 |