readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: MiniLMv2-L12-H384-distilled-finetuned-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        config: plus
        split: validation
        revision: b189f1fa78f41282a748b673231c21dfb07182b5
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9529032258064516
        verified: false
  ---
  
  <!-- 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-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: 0.3058
  - Accuracy: 0.9529
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 33
  - 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 | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.9908        | 1.0   | 239  | 1.6816          | 0.3910   |
  | 1.5212        | 2.0   | 478  | 1.2365          | 0.7697   |
  | 1.129         | 3.0   | 717  | 0.9209          | 0.8706   |
  | 0.8462        | 4.0   | 956  | 0.6978          | 0.9152   |
  | 0.6497        | 5.0   | 1195 | 0.5499          | 0.9342   |
  | 0.5124        | 6.0   | 1434 | 0.4447          | 0.9445   |
  | 0.4196        | 7.0   | 1673 | 0.3797          | 0.9455   |
  | 0.3587        | 8.0   | 1912 | 0.3358          | 0.95     |
  | 0.3228        | 9.0   | 2151 | 0.3133          | 0.9513   |
  | 0.3052        | 10.0  | 2390 | 0.3058          | 0.9529   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  