readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - massive
  metrics:
  - accuracy
  model-index:
  - name: bert-uncased-massive-intent-classification
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: massive
        type: massive
        config: en-US
        split: train
        args: en-US
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8853910477127398
  ---
  
  <!-- 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. -->
  
  # bert-uncased-massive-intent-classification
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8396
  - Accuracy: 0.8854
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.4984        | 1.0   | 720   | 0.6402          | 0.8495   |
  | 0.4376        | 2.0   | 1440  | 0.5394          | 0.8731   |
  | 0.2318        | 3.0   | 2160  | 0.5903          | 0.8760   |
  | 0.1414        | 4.0   | 2880  | 0.6221          | 0.8805   |
  | 0.087         | 5.0   | 3600  | 0.7072          | 0.8819   |
  | 0.0622        | 6.0   | 4320  | 0.7121          | 0.8819   |
  | 0.036         | 7.0   | 5040  | 0.7750          | 0.8805   |
  | 0.0234        | 8.0   | 5760  | 0.7767          | 0.8834   |
  | 0.0157        | 9.0   | 6480  | 0.8243          | 0.8805   |
  | 0.0122        | 10.0  | 7200  | 0.8198          | 0.8839   |
  | 0.0092        | 11.0  | 7920  | 0.8105          | 0.8849   |
  | 0.0047        | 12.0  | 8640  | 0.8561          | 0.8844   |
  | 0.0038        | 13.0  | 9360  | 0.8367          | 0.8815   |
  | 0.0029        | 14.0  | 10080 | 0.8396          | 0.8854   |
  | 0.0014        | 15.0  | 10800 | 0.8410          | 0.8849   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  