metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: w2v-V3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: ar
split: test
args: ar
metrics:
- name: Wer
type: wer
value: 0.16133249852681203
w2v-V3
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1847
- Wer: 0.1613
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
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.566 | 0.0428 | 300 | 0.6246 | 0.5686 |
| 0.462 | 0.0856 | 600 | 0.5791 | 0.3623 |
| 0.4407 | 0.1284 | 900 | 0.4428 | 0.3232 |
| 0.4036 | 0.1712 | 1200 | 0.4119 | 0.3066 |
| 0.328 | 0.2139 | 1500 | 0.3693 | 0.2684 |
| 0.3151 | 0.2567 | 1800 | 0.3102 | 0.2462 |
| 0.2907 | 0.2995 | 2100 | 0.3221 | 0.2411 |
| 0.2553 | 0.3423 | 2400 | 0.3061 | 0.2430 |
| 0.2156 | 0.3851 | 2700 | 0.2857 | 0.2104 |
| 0.2034 | 0.4279 | 3000 | 0.2516 | 0.2025 |
| 0.2038 | 0.4707 | 3300 | 0.2395 | 0.1995 |
| 0.1751 | 0.5135 | 3600 | 0.2372 | 0.1875 |
| 0.1697 | 0.5563 | 3900 | 0.2063 | 0.1809 |
| 0.1501 | 0.5991 | 4200 | 0.2005 | 0.1775 |
| 0.1428 | 0.6418 | 4500 | 0.2024 | 0.1701 |
| 0.1211 | 0.6846 | 4800 | 0.1883 | 0.1642 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0