readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - f1
  model-index:
  - name: minilm-finetuned-emotion_nm
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        args: default
      metrics:
      - name: F1
        type: f1
        value: 0.9322805793931607
  ---
  
  <!-- 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. -->
  
  # minilm-finetuned-emotion_nm
  
  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: 0.1918
  - F1: 0.9323
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 1.3627        | 1.0   | 250  | 1.0048          | 0.5936 |
  | 0.8406        | 2.0   | 500  | 0.6477          | 0.8608 |
  | 0.5344        | 3.0   | 750  | 0.4025          | 0.9099 |
  | 0.3619        | 4.0   | 1000 | 0.3142          | 0.9188 |
  | 0.274         | 5.0   | 1250 | 0.2489          | 0.9277 |
  | 0.2225        | 6.0   | 1500 | 0.2320          | 0.9303 |
  | 0.191         | 7.0   | 1750 | 0.2083          | 0.9298 |
  | 0.1731        | 8.0   | 2000 | 0.1969          | 0.9334 |
  | 0.1606        | 9.0   | 2250 | 0.1928          | 0.9362 |
  | 0.1462        | 10.0  | 2500 | 0.1918          | 0.9323 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  