distilbert-base-uncased-finetuned-3d-sentiment
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6641
- Accuracy: 0.7366
- Precision: 0.7377
- Recall: 0.7366
- F1: 0.7364
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 12762
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.8078 | 1.0 | 3190 | 0.8133 | 0.6628 | 0.6885 | 0.6628 | 0.6607 |
| 0.6227 | 2.0 | 6380 | 0.7637 | 0.6855 | 0.7103 | 0.6855 | 0.6849 |
| 0.5431 | 3.0 | 9570 | 0.6889 | 0.7047 | 0.7201 | 0.7047 | 0.7017 |
| 0.4585 | 4.0 | 12760 | 0.6641 | 0.7366 | 0.7377 | 0.7366 | 0.7364 |
| 0.3455 | 5.0 | 15950 | 0.8322 | 0.7203 | 0.7323 | 0.7203 | 0.7187 |
| 0.223 | 6.0 | 19140 | 0.9541 | 0.7205 | 0.7316 | 0.7205 | 0.7204 |
| 0.145 | 7.0 | 22330 | 1.1726 | 0.7196 | 0.7305 | 0.7196 | 0.7200 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
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