readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - f1
  model-index:
  - name: emotion_model
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        config: default
        split: train
        args: default
      metrics:
      - name: F1
        type: f1
        value: 0.14545454545454545
  ---
  
  <!-- 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. -->
  
  # emotion_model
  
  This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7815
  - F1: 0.1455
  
  ## 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: 10
  - eval_batch_size: 10
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 1.7968        | 1.0   | 2    | 1.7804          | 0.2286 |
  | 1.7918        | 2.0   | 4    | 1.7812          | 0.2286 |
  | 1.7867        | 3.0   | 6    | 1.7822          | 0.08   |
  | 1.7884        | 4.0   | 8    | 1.7816          | 0.08   |
  | 1.7833        | 5.0   | 10   | 1.7815          | 0.1455 |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1
  - Datasets 2.5.2
  - Tokenizers 0.11.0
  