readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        args: plus
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.94
  ---
  
  <!-- 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. -->
  
  # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
  
  This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3479
  - Accuracy: 0.94
  
  ## 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: 0.0001
  - train_batch_size: 256
  - eval_batch_size: 256
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 60   | 0.8171          | 0.2490   |
  | No log        | 2.0   | 120  | 0.7039          | 0.6568   |
  | No log        | 3.0   | 180  | 0.6067          | 0.7932   |
  | 0.7269        | 4.0   | 240  | 0.5270          | 0.8674   |
  | 0.7269        | 5.0   | 300  | 0.4659          | 0.9010   |
  | 0.7269        | 6.0   | 360  | 0.4201          | 0.9194   |
  | 0.7269        | 7.0   | 420  | 0.3867          | 0.9352   |
  | 0.4426        | 8.0   | 480  | 0.3649          | 0.9352   |
  | 0.4426        | 9.0   | 540  | 0.3520          | 0.9403   |
  | 0.4426        | 10.0  | 600  | 0.3479          | 0.94     |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.11.0
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  