distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of distilbert-base-uncased on a truncated IMDB dataset. It achieves the following results on the evaluation set:
- Loss: 1.7208
- Accuracy: {'accuracy': 0.876}
Model description
The purpose of this model is to turn distilbert into a sentiment classification model.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 2.0890 | {'accuracy': 0.862} |
| 0.2005 | 2.0 | 500 | 1.8919 | {'accuracy': 0.874} |
| 0.2005 | 3.0 | 750 | 1.7205 | {'accuracy': 0.871} |
| 0.0963 | 4.0 | 1000 | 1.7208 | {'accuracy': 0.876} |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
Model tree for smend0/distilbert-base-uncased-lora-text-classification
Base model
distilbert/distilbert-base-uncased