readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        args: plus
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9319354838709677
  ---
  
  <!-- 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-finetuned-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: 1.5252
  - Accuracy: 0.9319
  
  ## 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   | 4.6555          | 0.1887   |
  | No log        | 2.0   | 120  | 3.8771          | 0.4784   |
  | No log        | 3.0   | 180  | 3.2507          | 0.7352   |
  | 3.9668        | 4.0   | 240  | 2.7445          | 0.8365   |
  | 3.9668        | 5.0   | 300  | 2.3475          | 0.8865   |
  | 3.9668        | 6.0   | 360  | 2.0370          | 0.8926   |
  | 3.9668        | 7.0   | 420  | 1.8099          | 0.9145   |
  | 2.0924        | 8.0   | 480  | 1.6433          | 0.9190   |
  | 2.0924        | 9.0   | 540  | 1.5563          | 0.9281   |
  | 2.0924        | 10.0  | 600  | 1.5252          | 0.9319   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.11.0
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  