readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - massive
  metrics:
  - accuracy
  model-index:
  - name: BERT-tiny-Massive-intent
    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: 0.8475159862272503
  ---
  
  <!-- 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-tiny-Massive-intent
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6740
  - Accuracy: 0.8475
  
  ## 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: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 3.6104        | 1.0   | 720   | 3.0911          | 0.3601   |
  | 2.8025        | 2.0   | 1440  | 2.3800          | 0.5165   |
  | 2.2292        | 3.0   | 2160  | 1.9134          | 0.5991   |
  | 1.818         | 4.0   | 2880  | 1.5810          | 0.6744   |
  | 1.5171        | 5.0   | 3600  | 1.3522          | 0.7108   |
  | 1.2876        | 6.0   | 4320  | 1.1686          | 0.7442   |
  | 1.1049        | 7.0   | 5040  | 1.0355          | 0.7683   |
  | 0.9623        | 8.0   | 5760  | 0.9466          | 0.7885   |
  | 0.8424        | 9.0   | 6480  | 0.8718          | 0.7875   |
  | 0.7473        | 10.0  | 7200  | 0.8107          | 0.8028   |
  | 0.6735        | 11.0  | 7920  | 0.7710          | 0.8180   |
  | 0.6085        | 12.0  | 8640  | 0.7404          | 0.8210   |
  | 0.5536        | 13.0  | 9360  | 0.7180          | 0.8229   |
  | 0.5026        | 14.0  | 10080 | 0.6980          | 0.8318   |
  | 0.4652        | 15.0  | 10800 | 0.6970          | 0.8337   |
  | 0.4234        | 16.0  | 11520 | 0.6822          | 0.8372   |
  | 0.3987        | 17.0  | 12240 | 0.6691          | 0.8436   |
  | 0.3707        | 18.0  | 12960 | 0.6679          | 0.8455   |
  | 0.3433        | 19.0  | 13680 | 0.6740          | 0.8475   |
  | 0.3206        | 20.0  | 14400 | 0.6760          | 0.8451   |
  | 0.308         | 21.0  | 15120 | 0.6704          | 0.8436   |
  | 0.2813        | 22.0  | 15840 | 0.6701          | 0.8416   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  