readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - massive
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-massive-intent-detection-english
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: massive
        type: massive
        args: en-US
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.886684599865501
  ---
  
  <!-- 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-massive-intent-detection-english
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the massive dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4873
  - Accuracy: 0.8867
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 2.5849        | 1.0   | 360  | 1.3826          | 0.7359   |
  | 1.0662        | 2.0   | 720  | 0.7454          | 0.8357   |
  | 0.5947        | 3.0   | 1080 | 0.5668          | 0.8642   |
  | 0.3824        | 4.0   | 1440 | 0.5007          | 0.8770   |
  | 0.2649        | 5.0   | 1800 | 0.4829          | 0.8824   |
  | 0.1877        | 6.0   | 2160 | 0.4843          | 0.8824   |
  | 0.1377        | 7.0   | 2520 | 0.4858          | 0.8834   |
  | 0.1067        | 8.0   | 2880 | 0.4924          | 0.8864   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  