readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - accuracy
  model-index:
  - name: BERT-tiny-emotion-intent
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.91
  ---
  
  <!-- 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-emotion-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 emotion dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3620
  - Accuracy: 0.91
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.2603        | 1.0   | 1000  | 0.7766          | 0.7815   |
  | 0.5919        | 2.0   | 2000  | 0.4117          | 0.884    |
  | 0.367         | 3.0   | 3000  | 0.3188          | 0.8995   |
  | 0.2848        | 4.0   | 4000  | 0.2928          | 0.8985   |
  | 0.2395        | 5.0   | 5000  | 0.2906          | 0.898    |
  | 0.2094        | 6.0   | 6000  | 0.2887          | 0.907    |
  | 0.1884        | 7.0   | 7000  | 0.2831          | 0.9065   |
  | 0.1603        | 8.0   | 8000  | 0.3044          | 0.9065   |
  | 0.1519        | 9.0   | 9000  | 0.3124          | 0.9095   |
  | 0.1291        | 10.0  | 10000 | 0.3256          | 0.9065   |
  | 0.1179        | 11.0  | 11000 | 0.3651          | 0.9035   |
  | 0.1091        | 12.0  | 12000 | 0.3620          | 0.91     |
  | 0.0977        | 13.0  | 13000 | 0.3992          | 0.907    |
  | 0.0914        | 14.0  | 14000 | 0.4285          | 0.908    |
  | 0.0876        | 15.0  | 15000 | 0.4268          | 0.9055   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  