|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: IDAT_red_aug_290_novel_Wav2Vec |
|
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. --> |
|
|
|
# IDAT_red_aug_290_novel_Wav2Vec |
|
|
|
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: 0.5251 |
|
- Accuracy: 0.7468 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 4 |
|
- 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: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.595 | 0.99 | 40 | 0.6560 | 0.6013 | |
|
| 0.4853 | 1.99 | 80 | 0.5651 | 0.7342 | |
|
| 0.46 | 2.98 | 120 | 0.5168 | 0.7468 | |
|
| 0.4401 | 4.0 | 161 | 0.5230 | 0.7848 | |
|
| 0.3787 | 4.99 | 201 | 0.6051 | 0.7468 | |
|
| 0.465 | 5.99 | 241 | 0.7267 | 0.6646 | |
|
| 0.4037 | 6.98 | 281 | 0.4611 | 0.7911 | |
|
| 0.4508 | 8.0 | 322 | 0.6489 | 0.7468 | |
|
| 0.4427 | 8.99 | 362 | 0.4774 | 0.7848 | |
|
| 0.399 | 9.94 | 400 | 0.5251 | 0.7468 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.1 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.13.3 |
|
|
