|
--- |
|
license: mit |
|
base_model: arslanarjumand/wav2vec-reptiles |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: wav2vec-reptiles |
|
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. --> |
|
|
|
# wav2vec-reptiles |
|
|
|
This model is a fine-tuned version of [arslanarjumand/wav2vec-reptiles](https://huggingface.co/arslanarjumand/wav2vec-reptiles) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 182.3516 |
|
- Pcc Accuracy: 0.6684 |
|
- Pcc Fluency: 0.6499 |
|
- Pcc Total Score: 0.7110 |
|
- Pcc Content: 0.6788 |
|
|
|
## 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: 5.5e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 6 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 16 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.4 |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:| |
|
| 2719.4074 | 0.97 | 500 | 2790.7349 | 0.1171 | 0.1116 | 0.1218 | 0.1245 | |
|
| 386.8535 | 1.93 | 1000 | 361.3293 | 0.1481 | 0.1332 | 0.1511 | 0.1445 | |
|
| 273.8093 | 2.9 | 1500 | 304.4040 | 0.2869 | 0.2915 | 0.3062 | 0.2849 | |
|
| 280.8214 | 3.87 | 2000 | 277.9273 | 0.4065 | 0.4344 | 0.4465 | 0.4131 | |
|
| 264.1531 | 4.84 | 2500 | 265.5385 | 0.5012 | 0.5234 | 0.5490 | 0.5117 | |
|
| 211.6362 | 5.8 | 3000 | 226.9335 | 0.5675 | 0.5768 | 0.6171 | 0.5817 | |
|
| 217.8737 | 6.77 | 3500 | 218.1019 | 0.6089 | 0.5984 | 0.6525 | 0.6194 | |
|
| 180.3319 | 7.74 | 4000 | 201.4108 | 0.6296 | 0.6142 | 0.6721 | 0.6395 | |
|
| 174.7695 | 8.7 | 4500 | 201.3474 | 0.6427 | 0.6297 | 0.6872 | 0.6542 | |
|
| 182.4466 | 9.67 | 5000 | 189.6567 | 0.6566 | 0.6333 | 0.6957 | 0.6619 | |
|
| 184.7177 | 10.64 | 5500 | 182.7654 | 0.6628 | 0.6405 | 0.7033 | 0.6713 | |
|
| 174.6915 | 11.61 | 6000 | 181.2284 | 0.6635 | 0.6479 | 0.7077 | 0.6755 | |
|
| 187.671 | 12.57 | 6500 | 180.5753 | 0.6676 | 0.6486 | 0.7099 | 0.6773 | |
|
| 166.4409 | 13.54 | 7000 | 181.2506 | 0.6682 | 0.6493 | 0.7105 | 0.6781 | |
|
| 176.7043 | 14.51 | 7500 | 182.3516 | 0.6684 | 0.6499 | 0.7110 | 0.6788 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.0 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.1 |
|
|