|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: ascend |
|
|
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. --> |
|
|
|
|
|
# ascend |
|
|
|
|
|
This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 1.3718 |
|
|
- Wer: 0.6412 |
|
|
- Cer: 0.2428 |
|
|
|
|
|
## 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: 16 |
|
|
- eval_batch_size: 16 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 20 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
|
|
| 0.5769 | 1.0 | 688 | 1.1864 | 0.7716 | 0.3159 | |
|
|
| 0.5215 | 2.0 | 1376 | 1.1613 | 0.7504 | 0.2965 | |
|
|
| 0.4188 | 3.0 | 2064 | 1.1644 | 0.7389 | 0.2950 | |
|
|
| 0.3695 | 4.0 | 2752 | 1.1937 | 0.7184 | 0.2815 | |
|
|
| 0.3404 | 5.0 | 3440 | 1.1947 | 0.7083 | 0.2719 | |
|
|
| 0.2885 | 6.0 | 4128 | 1.2314 | 0.7108 | 0.2685 | |
|
|
| 0.2727 | 7.0 | 4816 | 1.2243 | 0.6850 | 0.2616 | |
|
|
| 0.2417 | 8.0 | 5504 | 1.2506 | 0.6767 | 0.2608 | |
|
|
| 0.2207 | 9.0 | 6192 | 1.2804 | 0.6922 | 0.2595 | |
|
|
| 0.2195 | 10.0 | 6880 | 1.2582 | 0.6818 | 0.2575 | |
|
|
| 0.1896 | 11.0 | 7568 | 1.3101 | 0.6814 | 0.2545 | |
|
|
| 0.1961 | 12.0 | 8256 | 1.2793 | 0.6706 | 0.2526 | |
|
|
| 0.1752 | 13.0 | 8944 | 1.2643 | 0.6584 | 0.2509 | |
|
|
| 0.1638 | 14.0 | 9632 | 1.3152 | 0.6588 | 0.2482 | |
|
|
| 0.1522 | 15.0 | 10320 | 1.3098 | 0.6433 | 0.2439 | |
|
|
| 0.1351 | 16.0 | 11008 | 1.3253 | 0.6537 | 0.2447 | |
|
|
| 0.1266 | 17.0 | 11696 | 1.3394 | 0.6365 | 0.2418 | |
|
|
| 0.1289 | 18.0 | 12384 | 1.3718 | 0.6412 | 0.2443 | |
|
|
| 0.1204 | 19.0 | 13072 | 1.3708 | 0.6433 | 0.2433 | |
|
|
| 0.1189 | 20.0 | 13760 | 1.3718 | 0.6412 | 0.2428 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.17.0 |
|
|
- Pytorch 1.10.0+cu111 |
|
|
- Datasets 2.0.0 |
|
|
- Tokenizers 0.11.6 |
|
|
|
