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. -->
  # Neuron conversation
  
  # 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:
  - Accuracy: 0.9389999
  
  ## Deploy/use Model
  
  If you want to use this model checkout the following notenbook: [sagemaker/18_inferentia_inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb)
  
  ```python
  from sagemaker.huggingface.model import HuggingFaceModel
  
  
  # create Hugging Face Model Class
  huggingface_model = HuggingFaceModel(
     model_data=s3_model_uri,       # path to your model and script
     role=role,                    # iam role with permissions to create an Endpoint
     transformers_version="4.12",  # transformers version used
     pytorch_version="1.9",        # pytorch version used
     py_version='py37',            # python version used
  )
  
  # Let SageMaker know that we've already compiled the model via neuron-cc
  huggingface_model._is_compiled_model = True
  
  # deploy the endpoint endpoint
  predictor = huggingface_model.deploy(
      initial_instance_count=1,      # number of instances
      instance_type="ml.inf1.xlarge" # AWS Inferentia Instance
  )
  ```