readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: distilbert-base-uncased-Regression-Edmunds_Car_Reviews-European_Made
    results: []
  ---
  # distilbert-base-uncased-Regression-Edmunds_Car_Reviews
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1999
  - Mse: 0.1999
  - Rmse: 0.4471
  - Mae: 0.2824
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  I used this to improve my skillset. I thank all of authors of the different technologies and dataset(s) for 
  their contributions that have this possible. I am not too worried about getting credit for my part, but make 
  sure to properly cite the authors of the different technologies and dataset(s) as they 
  absolutely deserve credit for their contributions.
  
  ## Training and evaluation data
  
  Dataset Source: https://www.kaggle.com/datasets/ankkur13/edmundsconsumer-car-ratings-and-reviews
  
  I only used car manufacturers headquartered in Europe that are not luxury brands. 
  Additionally, I removed manufacturers with limited reviews.
  
  ## Training procedure
  
  The script for this project will be uploaded to my GitHub profile soon. Once it is, I will make sure to add the link here.
  
  ### 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mse    | Rmse   | Mae    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
  | 1.4892        | 1.0   | 236  | 0.2587          | 0.2587 | 0.5086 | 0.3120 |
  | 0.2384        | 2.0   | 472  | 0.2359          | 0.2359 | 0.4857 | 0.2994 |
  | 0.188         | 3.0   | 708  | 0.2304          | 0.2304 | 0.4800 | 0.2948 |
  | 0.1558        | 4.0   | 944  | 0.2443          | 0.2443 | 0.4942 | 0.2981 |
  | 0.133         | 5.0   | 1180 | 0.2410          | 0.2410 | 0.4909 | 0.2983 |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  