---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mixed_model_finetuned_cremad
  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/gt6e5ppa)
# mixed_model_finetuned_cremad

This model is a fine-tuned version of wav2vec2 on audio stream part and pretrained resnet3d_101 on video stream part ,[](https://huggingface.co/) 
It was trained from scratch on [CremaD dataset](https://github.com/CheyneyComputerScience/CREMA-D). 
This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are:

```python
emotions = ['angry', 'disgust', 'fearful', 'happy', 'neutral', 'sad']
``` 
It achieves the following results on the evaluation set:
- Loss: 0.3098
- Accuracy: 0.8972
- F1: 0.8960
- Recall: 0.8972
- Precision: 0.8974

## 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: 743
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7914        | 1.0    | 186  | 1.0595          | 0.7171   | 0.7074 | 0.7171 | 0.7536    |
| 0.5971        | 2.0    | 372  | 0.4401          | 0.8414   | 0.8375 | 0.8414 | 0.8443    |
| 0.2891        | 3.0    | 558  | 0.3863          | 0.8548   | 0.8539 | 0.8548 | 0.8622    |
| 0.1833        | 3.9946 | 743  | 0.3098          | 0.8972   | 0.8960 | 0.8972 | 0.8974    |


### Framework versions

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