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Update README.md
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            - art
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            size_categories:
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            - n>1T
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            ---
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            import sagemaker
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            import boto3
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            from sagemaker.huggingface import HuggingFace
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            # gets role for executing training job
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            iam_client = boto3.client('iam')
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            role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']
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            hyperparameters = {
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            	'model_name_or_path':'databricks/dolly-v2-12b',
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            	'output_dir':'/opt/ml/model'
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            	# add your remaining hyperparameters
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            	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering
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            }
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            # git configuration to download our fine-tuning script
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            git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}
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            # creates Hugging Face estimator
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            huggingface_estimator = HuggingFace(
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            	entry_point='run_qa.py',
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            	source_dir='./examples/pytorch/question-answering',
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            	instance_type='ml.p3.2xlarge',
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            	instance_count=1,
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            	role=role,
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            	git_config=git_config,
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            	transformers_version='4.17.0',
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            	pytorch_version='1.10.2',
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            	py_version='py38',
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            	hyperparameters = hyperparameters
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            )
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            # starting the train job
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            huggingface_estimator.fit()
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            - art
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            size_categories:
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            - n>1T
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            ---
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