README.md

metadata

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

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 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

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
)