readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        args: plus
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9174193548387096
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # distilbert-base-uncased-finetuned-clinc
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb).
  
  It achieves the following results on the evaluation set:
  - Loss: 0.7773
  - Accuracy: 0.9174
  
  ## 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: 2e-05
  - train_batch_size: 48
  - eval_batch_size: 48
  - 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 | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 4.2923        | 1.0   | 318  | 3.2893          | 0.7423   |
  | 2.6307        | 2.0   | 636  | 1.8837          | 0.8281   |
  | 1.5483        | 3.0   | 954  | 1.1583          | 0.8968   |
  | 1.0153        | 4.0   | 1272 | 0.8618          | 0.9094   |
  | 0.7958        | 5.0   | 1590 | 0.7773          | 0.9174   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu102
  - Datasets 1.13.0
  - Tokenizers 0.10.3
  