metadata
base_model: ylacombe/w2v-bert-2.0-600m-turkish-colab
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: mactest2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.3088954056695992
mactest2
This model is a fine-tuned version of ylacombe/w2v-bert-2.0-600m-turkish-colab on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5663
- Wer: 0.3089
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.305 | 1.6 | 100 | 0.4562 | 0.2952 |
| 0.0505 | 3.2 | 200 | 0.4923 | 0.3284 |
| 0.0298 | 4.8 | 300 | 0.4925 | 0.3157 |
| 0.0156 | 6.4 | 400 | 0.5194 | 0.3069 |
| 0.0058 | 8.0 | 500 | 0.5420 | 0.3050 |
| 0.004 | 9.6 | 600 | 0.5663 | 0.3089 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0