|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: wav2vec2-base_lr_4e-4 |
|
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. --> |
|
|
|
# wav2vec2-base_lr_4e-4 |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0997 |
|
- Accuracy: 0.9625 |
|
|
|
## 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: 0.0004 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:| |
|
| 1.6571 | 0.9851 | 33 | 1.3089 | 0.5679 | |
|
| 0.9453 | 2.0 | 67 | 0.6596 | 0.7769 | |
|
| 0.5682 | 2.9851 | 100 | 0.4865 | 0.8482 | |
|
| 0.5507 | 4.0 | 134 | 0.4255 | 0.8575 | |
|
| 0.4859 | 4.9851 | 167 | 0.2552 | 0.9044 | |
|
| 0.3461 | 6.0 | 201 | 0.3066 | 0.8969 | |
|
| 0.358 | 6.9851 | 234 | 0.1916 | 0.9269 | |
|
| 0.2854 | 8.0 | 268 | 0.1589 | 0.9447 | |
|
| 0.192 | 8.9851 | 301 | 0.1160 | 0.9550 | |
|
| 0.1969 | 9.8507 | 330 | 0.0997 | 0.9625 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
