|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: facebook/w2v-bert-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: w2v-bert-cv-grain-lg_both_v2 |
|
results: [] |
|
--- |
|
|
|
<!-- 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-cv-grain-lg_both_v2 |
|
|
|
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0892 |
|
- Wer: 0.0443 |
|
- Cer: 0.0123 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 4 |
|
- 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 |
|
- num_epochs: 80 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:| |
|
| 0.2889 | 1.0 | 10812 | 0.1708 | 0.1703 | 0.0386 | |
|
| 0.1849 | 2.0 | 21624 | 0.1342 | 0.1274 | 0.0285 | |
|
| 0.1512 | 3.0 | 32436 | 0.1144 | 0.1044 | 0.0244 | |
|
| 0.1313 | 4.0 | 43248 | 0.1033 | 0.0918 | 0.0217 | |
|
| 0.117 | 5.0 | 54060 | 0.1034 | 0.0738 | 0.0191 | |
|
| 0.1056 | 6.0 | 64872 | 0.0906 | 0.0738 | 0.0181 | |
|
| 0.0962 | 7.0 | 75684 | 0.0959 | 0.0655 | 0.0168 | |
|
| 0.0885 | 8.0 | 86496 | 0.0860 | 0.0592 | 0.0155 | |
|
| 0.0807 | 9.0 | 97308 | 0.0844 | 0.0603 | 0.0154 | |
|
| 0.0742 | 10.0 | 108120 | 0.0814 | 0.0573 | 0.0144 | |
|
| 0.0683 | 11.0 | 118932 | 0.0858 | 0.0588 | 0.0154 | |
|
| 0.0629 | 12.0 | 129744 | 0.0944 | 0.0538 | 0.0146 | |
|
| 0.0581 | 13.0 | 140556 | 0.0842 | 0.0558 | 0.0151 | |
|
| 0.0528 | 14.0 | 151368 | 0.0873 | 0.0503 | 0.0141 | |
|
| 0.0479 | 15.0 | 162180 | 0.0820 | 0.0503 | 0.0138 | |
|
| 0.0429 | 16.0 | 172992 | 0.0815 | 0.0427 | 0.0125 | |
|
| 0.0392 | 17.0 | 183804 | 0.0864 | 0.0466 | 0.0128 | |
|
| 0.035 | 18.0 | 194616 | 0.0899 | 0.0479 | 0.0128 | |
|
| 0.0316 | 19.0 | 205428 | 0.0872 | 0.0430 | 0.0120 | |
|
| 0.0286 | 20.0 | 216240 | 0.0821 | 0.0425 | 0.0114 | |
|
| 0.0254 | 21.0 | 227052 | 0.0898 | 0.0466 | 0.0122 | |
|
| 0.0229 | 22.0 | 237864 | 0.0864 | 0.0417 | 0.0120 | |
|
| 0.021 | 23.0 | 248676 | 0.0893 | 0.0408 | 0.0122 | |
|
| 0.0192 | 24.0 | 259488 | 0.0878 | 0.0430 | 0.0118 | |
|
| 0.0171 | 25.0 | 270300 | 0.0994 | 0.0473 | 0.0128 | |
|
| 0.0156 | 26.0 | 281112 | 0.0892 | 0.0443 | 0.0123 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.1 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.1 |
|
|
