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emotion-model
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: 0.2131
- Accuracy: 0.9335
- F1: 0.9336
Model description
Predicts the emotions of provided text
Intended uses & limitations
For sentiment analysis
Training and evaluation data
Data from "emotion" dataset
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 |
|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.3058 | 0.9125 | 0.9098 |
| 0.5417 | 2.0 | 500 | 0.2131 | 0.9335 | 0.9336 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
