distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.3663
- Accuracy: 0.8885
- F1: 0.8819
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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.5574 | 0.822 | 0.7956 |
| 0.7483 | 2.0 | 250 | 0.3663 | 0.8885 | 0.8819 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
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Dataset used to train Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionself-reported0.888
- F1 on emotionself-reported0.882
- Accuracy on emotiontest set self-reported0.892
- Precision Macro on emotiontest set self-reported0.892
- Precision Micro on emotiontest set self-reported0.892
- Precision Weighted on emotiontest set self-reported0.894
- Recall Macro on emotiontest set self-reported0.768
- Recall Micro on emotiontest set self-reported0.892
- Recall Weighted on emotiontest set self-reported0.892
- F1 Macro on emotiontest set self-reported0.790