emotions-gssi
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: 0.2205
- Accuracy: 0.9265
- F1: 0.9264
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.8297 | 1.0 | 250 | 0.3175 | 0.9085 | 0.9065 |
| 0.2503 | 2.0 | 500 | 0.2205 | 0.9265 | 0.9264 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
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Dataset used to train agil/emotions-gssi
Evaluation results
- Accuracy on emotionvalidation set self-reported0.926
- F1 on emotionvalidation set self-reported0.926