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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: mixed_model_finetuned_cremad |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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# mixed_model_finetuned_cremad |
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This model is a fine-tuned version of wav2vec2 on audio stream part and pretrained resnet3d_101 on video stream part ,[](https://huggingface.co/) |
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It was trained from scratch on [CremaD dataset](https://github.com/CheyneyComputerScience/CREMA-D). |
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This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are: |
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```python |
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emotions = ['angry', 'disgust', 'fearful', 'happy', 'neutral', 'sad'] |
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``` |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3098 |
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- Accuracy: 0.8972 |
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- F1: 0.8960 |
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- Recall: 0.8972 |
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- Precision: 0.8974 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 743 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.7914 | 1.0 | 186 | 1.0595 | 0.7171 | 0.7074 | 0.7171 | 0.7536 | |
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| 0.5971 | 2.0 | 372 | 0.4401 | 0.8414 | 0.8375 | 0.8414 | 0.8443 | |
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| 0.2891 | 3.0 | 558 | 0.3863 | 0.8548 | 0.8539 | 0.8548 | 0.8622 | |
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| 0.1833 | 3.9946 | 743 | 0.3098 | 0.8972 | 0.8960 | 0.8972 | 0.8974 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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