#Be sure to have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/lengyue233/content-vec-best #To clone the repo without large files – just their pointers #prepend git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
Lengyue233/content-vec-best model: Difference between revisions
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==Deployment== | ==Deployment== | ||
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==Model Card== | ==Model Card== | ||
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Latest revision as of 03:27, 23 May 2023
Hugging Face
Name
content-vec-best
User / Organization
Library
License
Related to
Lengyue233/content-vec-best model is a Natural Language Processing model used for .
Model Description
Clone Model Repository
#Be sure to have git-lfs installed (https://git-lfs.com) git lfs install git clone [email protected]:lengyue233/content-vec-best #To clone the repo without large files – just their pointers #prepend git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
Hugging Face Transformers Library
from transformers import AutoProcessor, HubertModelWithFinalProj processor = AutoProcessor.from_pretrained("lengyue233/content-vec-best") model = HubertModelWithFinalProj.from_pretrained("lengyue233/content-vec-best")
Deployment
Inference API
Amazon SageMaker
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'automatic-speech-recognition' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'automatic-speech-recognition' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'conversational' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'conversational' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'feature-extraction' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'feature-extraction' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'fill-mask' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'fill-mask' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'image-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'image-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'question-answering' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'question-answering' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'summarization' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'summarization' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'table-question-answering' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'table-question-answering' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text-generation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text-generation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text2text-generation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'text2text-generation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'token-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'token-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'translation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'translation' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import sagemaker role = sagemaker.get_execution_role() # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'zero-shot-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
from sagemaker.huggingface import HuggingFaceModel import boto3 iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] # Hub Model configuration. https://huggingface.co/models hub = { 'HF_MODEL_ID':'lengyue233/content-vec-best', 'HF_TASK':'zero-shot-classification' } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type ) predictor.predict({ 'inputs': No input example has been defined for this model task. })
Spaces
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Training
Amazon SageMaker
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_clm.py', source_dir='./examples/pytorch/language-modeling', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_clm.py', source_dir='./examples/pytorch/language-modeling', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_mlm.py', source_dir='./examples/pytorch/language-modeling', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_mlm.py', source_dir='./examples/pytorch/language-modeling', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_qa.py', source_dir='./examples/pytorch/question-answering', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_qa.py', source_dir='./examples/pytorch/question-answering', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_summarization.py', source_dir='./examples/pytorch/seq2seq', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_summarization.py', source_dir='./examples/pytorch/seq2seq', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_glue.py', source_dir='./examples/pytorch/text-classification', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_glue.py', source_dir='./examples/pytorch/text-classification', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/token-classification } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_ner.py', source_dir='./examples/pytorch/token-classification', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/token-classification } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_ner.py', source_dir='./examples/pytorch/token-classification', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker from sagemaker.huggingface import HuggingFace # gets role for executing training job role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_translation.py', source_dir='./examples/pytorch/seq2seq', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
import sagemaker import boto3 from sagemaker.huggingface import HuggingFace # gets role for executing training job iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn'] hyperparameters = { 'model_name_or_path':'lengyue233/content-vec-best', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/seq2seq } # git configuration to download our fine-tuning script git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'} # creates Hugging Face estimator huggingface_estimator = HuggingFace( entry_point='run_translation.py', source_dir='./examples/pytorch/seq2seq', instance_type='ml.p3.2xlarge', instance_count=1, role=role, git_config=git_config, transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters ) # starting the train job huggingface_estimator.fit()
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