finetuning-sentiment-model-300-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9609
- Accuracy: 0.765
- F1: 0.7648
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: 5e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2642 | 1.0 | 55 | 0.6175 | 0.783 | 0.7826 |
| 0.2391 | 2.0 | 110 | 0.6746 | 0.761 | 0.7607 |
| 0.1272 | 3.0 | 165 | 0.8233 | 0.764 | 0.7636 |
| 0.0772 | 4.0 | 220 | 0.9219 | 0.76 | 0.7594 |
| 0.0647 | 5.0 | 275 | 0.9609 | 0.765 | 0.7648 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for DaisyQue/finetuning-sentiment-model-300-samples
Base model
distilbert/distilbert-base-uncased