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finetuned-Sentiment-classfication-DistilBert-model
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: 0.2934
- Rmse: 0.3657
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|---|---|---|---|---|
| 0.7234 | 2.72 | 500 | 0.4388 | 0.5622 |
| 0.1723 | 5.43 | 1000 | 0.2934 | 0.3657 |
| 0.0454 | 8.15 | 1500 | 0.3104 | 0.3099 |
| 0.021 | 10.86 | 2000 | 0.3128 | 0.2884 |
| 0.013 | 13.58 | 2500 | 0.3428 | 0.2907 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
