End of training

README.md

@@ -14,23 +14,17 @@ model-index:
 <!-- 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/z6qr57oj)
+[<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)
+[<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 was trained from scratch on [CremaD dataset](https://github.com/CheyneyComputerScience/CREMA-D) , To create this multimodal model, we employed wav2vec2, pretrained on audio data, and resnet3d, pretrained on video data
- 
-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']
-```
-
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5007
-- Accuracy: 0.8293
-- F1: 0.8284
-- Recall: 0.8293
-- Precision: 0.8304
+- Loss: 0.3098
+- Accuracy: 0.8972
+- F1: 0.8960
+- Recall: 0.8972
+- Precision: 0.8974
 
 ## Model description
 
@@ -50,24 +44,25 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
-- train_batch_size: 2
-- eval_batch_size: 2
+- train_batch_size: 4
+- eval_batch_size: 4
 - seed: 42
 - gradient_accumulation_steps: 8
-- total_train_batch_size: 16
+- 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
-- num_epochs: 3.0
+- training_steps: 743
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| 0.6929        | 0.9976 | 371  | 0.8748          | 0.6747   | 0.6698 | 0.6747 | 0.7234    |
-| 0.5794        | 1.9980 | 743  | 0.5378          | 0.8004   | 0.7999 | 0.8004 | 0.8047    |
-| 0.3597        | 2.9929 | 1113 | 0.5007          | 0.8293   | 0.8284 | 0.8293 | 0.8304    |
+| 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

model.safetensors

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training_args.bin

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