| --- |
| language: |
| - en |
| base_model: |
| - distilbert/distilroberta-base |
| pipeline_tag: text-classification |
| library_name: transformers |
| --- |
| |
| DistilRoBERTa finetuned for Emotion Recognition Task. |
|
|
| 🗨️ Base Model: [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) |
|
|
| 🎯 Accuracy: 0.9006 \ |
| ✔️ F1 Score: 0.8991 \ |
| 📉 Loss: 0.3183 |
|
|
|
|
| ### Training Hyperparameters |
| train_epochs: 20 \ |
| Batch train_batch_size: 32 \ |
| warmup_steps: 50 \ |
| weight_decay: 0.02 |
| |
| |
| ### Datasets: |
| 1️⃣ [Emotion Dataset](https://www.kaggle.com/datasets/abdallahwagih/emotion-dataset), \ |
| 2️⃣ [Emotion Dataset](https://www.kaggle.com/datasets/parulpandey/emotion-dataset), \ |
| 3️⃣ [Emotion Dataset](https://www.kaggle.com/datasets/chanakyar/emotion-dataset-link), |
| |
| |
| |
| ### Emotions |
| (0) anger |
| (1) disgust |
| (2) fear |
| (3) joy |
| (4) love |
| (5) neutral |
| (6) sadness |
| (7) surprise |
| |
| |
| ### Classification Report |
| ``` |
| precision recall f1-score support |
| |
| anger 0.8970 0.8714 0.8840 3679 |
| disgust 0.9777 1.0000 0.9887 3680 |
| fear 0.9035 0.8647 0.8836 3680 |
| joy 0.8348 0.7399 0.7845 3680 |
| love 0.9756 1.0000 0.9877 3680 |
| neutral 0.9351 0.9984 0.9657 3680 |
| sadness 0.8649 0.7916 0.8266 3680 |
| surprise 0.8133 0.9389 0.8716 3680 |
| |
| accuracy 0.9006 29439 |
| macro avg 0.9002 0.9006 0.8991 29439 |
| weighted avg 0.9002 0.9006 0.8991 29439 |
| ``` |
| |
| *Sneak Peak*: To be used as a part of a larger multimodal emotion recognition framework. (Late Fusion, Early Fusion and RL based approach 😱) |
