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distilbert-base-uncased-Regression-Edmunds_Car_Reviews-Non_European_Imports
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: 0.2240
- Mae: 0.3140
- Mse: 0.2240
- Rmse: 0.4733
Model description
This project works to predict the rating of a car based on the review (only vehicles from non-America and non-European-headquartered automanufacturers).
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/NLP%20Regression/HF-Edmunds_Consumer_car-Regression-Non_European_Imports.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Mse | Rmse |
|---|---|---|---|---|---|---|
| 0.6594 | 1.0 | 715 | 0.2436 | 0.3319 | 0.2436 | 0.4935 |
| 0.2324 | 2.0 | 1430 | 0.2274 | 0.3210 | 0.2274 | 0.4769 |
| 0.1975 | 3.0 | 2145 | 0.2303 | 0.3198 | 0.2303 | 0.4799 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1
