readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: MiniLMv2-L12-H384-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9208715596330275
  ---
  
  <!-- 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-sst2
  
  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 glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2195
  - Accuracy: 0.9209
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - distributed_type: sagemaker_data_parallel
  - num_devices: 8
  - total_train_batch_size: 256
  - total_eval_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5576        | 1.0   | 264  | 0.2690          | 0.8979   |
  | 0.2854        | 2.0   | 528  | 0.2077          | 0.9117   |
  | 0.2158        | 3.0   | 792  | 0.2195          | 0.9209   |
  | 0.1789        | 4.0   | 1056 | 0.2260          | 0.9163   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  