---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mixed_model_finetuned_ravdess
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yassmenyoussef55-arete-global/huggingface/runs/fbii58qd)
# mixed_model_finetuned_ravdess

This model is a fine-tuned version of [](https://huggingface.co/) on RAVDESS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2728
- Accuracy: 0.9271
- F1: 0.9267
- Recall: 0.9271
- Precision: 0.9292

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 144
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.8272        | 1.0   | 36   | 1.3361          | 0.5312   | 0.4687 | 0.5312 | 0.5125    |
| 1.0357        | 2.0   | 72   | 0.7544          | 0.7674   | 0.7490 | 0.7674 | 0.8045    |
| 0.5699        | 3.0   | 108  | 0.3596          | 0.9097   | 0.9094 | 0.9097 | 0.9149    |
| 0.3445        | 4.0   | 144  | 0.2728          | 0.9271   | 0.9267 | 0.9271 | 0.9292    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1