distilbert-base-uncased-Regression-Edmunds_Car_Reviews-all_car_brands

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: 0.2232
• Mse: 0.2232
• Rmse: 0.4724
• Mae: 0.3150

Model description

This project works to predict the rating of a car based on the review for all automanufacturers.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/NLP%20Regression/Edmunds%20Car%20Reviews%20(All%20Brands)/Edmunds_Consumer_car-Regression-All%20Manufacturers.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/ankkur13/edmundsconsumer-car-ratings-and-reviews

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1.5e-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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse	Mae
0.3936	1.0	2592	0.2282	0.2282	0.4777	0.3158
0.2163	2.0	5184	0.2160	0.2160	0.4647	0.3106

Framework versions

• Transformers 4.21.3
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1