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: 0.1624
- Accuracy: 0.927
- F1: 0.9270
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7758 | 1.0 | 250 | 0.2698 | 0.915 | 0.9136 |
| 0.2169 | 2.0 | 500 | 0.1722 | 0.9265 | 0.9263 |
| 0.1473 | 3.0 | 750 | 0.1624 | 0.927 | 0.9270 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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Dataset used to train Mozzipa/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.927
- F1 on emotionvalidation set self-reported0.927