|
|
--- |
|
|
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 |
|
|
--- |
|
|
|
|
|
<!-- 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-V3 |
|
|
|
|
|
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/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 |
|
|
|
