|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: facebook/w2v-bert-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: w2v-bert-2.0-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1 |
|
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-2.0-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1 |
|
|
|
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: 0.9564 |
|
- Wer: 0.2347 |
|
- Cer: 0.0832 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 16 |
|
- 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: 100 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:| |
|
| 3.3297 | 0.9972 | 175 | 0.7637 | 0.3590 | 0.1248 | |
|
| 1.348 | 2.0 | 351 | 0.6631 | 0.3172 | 0.1132 | |
|
| 1.0874 | 2.9972 | 526 | 0.6409 | 0.2720 | 0.0965 | |
|
| 0.9039 | 4.0 | 702 | 0.5691 | 0.2759 | 0.1002 | |
|
| 0.7405 | 4.9972 | 877 | 0.5492 | 0.2552 | 0.0905 | |
|
| 0.6896 | 6.0 | 1053 | 0.6369 | 0.2470 | 0.0855 | |
|
| 0.5831 | 6.9972 | 1228 | 0.5966 | 0.2508 | 0.0893 | |
|
| 0.5089 | 8.0 | 1404 | 0.6115 | 0.2403 | 0.0857 | |
|
| 0.4478 | 8.9972 | 1579 | 0.6523 | 0.2300 | 0.0810 | |
|
| 0.4046 | 10.0 | 1755 | 0.6435 | 0.2459 | 0.0842 | |
|
| 0.3745 | 10.9972 | 1930 | 0.6615 | 0.2336 | 0.0821 | |
|
| 0.3461 | 12.0 | 2106 | 0.6885 | 0.2466 | 0.0850 | |
|
| 0.3225 | 12.9972 | 2281 | 0.6068 | 0.2524 | 0.0871 | |
|
| 0.278 | 14.0 | 2457 | 0.6808 | 0.2483 | 0.0850 | |
|
| 0.2494 | 14.9972 | 2632 | 0.7234 | 0.2469 | 0.0846 | |
|
| 0.2273 | 16.0 | 2808 | 0.7661 | 0.2414 | 0.0850 | |
|
| 0.2022 | 16.9972 | 2983 | 0.8284 | 0.2451 | 0.0864 | |
|
| 0.1811 | 18.0 | 3159 | 0.7355 | 0.2431 | 0.0855 | |
|
| 0.1541 | 18.9972 | 3334 | 0.7872 | 0.2426 | 0.0860 | |
|
| 0.1505 | 20.0 | 3510 | 0.7831 | 0.2523 | 0.0875 | |
|
| 0.1373 | 20.9972 | 3685 | 0.8248 | 0.2366 | 0.0845 | |
|
| 0.1213 | 22.0 | 3861 | 0.8190 | 0.2364 | 0.0826 | |
|
| 0.1161 | 22.9972 | 4036 | 0.8505 | 0.2422 | 0.0849 | |
|
| 0.1031 | 24.0 | 4212 | 0.9564 | 0.2347 | 0.0832 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.1 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.1 |
|
|
