readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-distilled-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        config: plus
        split: train
        args: plus
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9503225806451613
  ---
  
  <!-- 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-distilled-clinc
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2656
  - Accuracy: 0.9503
  
  ## 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: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 9
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 3.1212        | 1.0   | 1271  | 1.2698          | 0.8558   |
  | 0.6441        | 2.0   | 2542  | 0.3528          | 0.9326   |
  | 0.149         | 3.0   | 3813  | 0.2512          | 0.9494   |
  | 0.0647        | 4.0   | 5084  | 0.2510          | 0.95     |
  | 0.0406        | 5.0   | 6355  | 0.2575          | 0.9510   |
  | 0.0318        | 6.0   | 7626  | 0.2592          | 0.9494   |
  | 0.026         | 7.0   | 8897  | 0.2629          | 0.9503   |
  | 0.023         | 8.0   | 10168 | 0.2682          | 0.95     |
  | 0.0207        | 9.0   | 11439 | 0.2656          | 0.9503   |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  