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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- clinc_oos |
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metrics: |
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- accuracy |
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model-index: |
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- name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: clinc_oos |
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type: clinc_oos |
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args: plus |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.94 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Neuron conversation |
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# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc |
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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. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9389999 |
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## Deploy/use Model |
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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) |
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```python |
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from sagemaker.huggingface.model import HuggingFaceModel |
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# create Hugging Face Model Class |
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huggingface_model = HuggingFaceModel( |
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model_data=s3_model_uri, # path to your model and script |
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role=role, # iam role with permissions to create an Endpoint |
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transformers_version="4.12", # transformers version used |
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pytorch_version="1.9", # pytorch version used |
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py_version='py37', # python version used |
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) |
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# Let SageMaker know that we've already compiled the model via neuron-cc |
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huggingface_model._is_compiled_model = True |
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# deploy the endpoint endpoint |
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predictor = huggingface_model.deploy( |
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initial_instance_count=1, # number of instances |
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instance_type="ml.inf1.xlarge" # AWS Inferentia Instance |
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) |
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``` |
