benwri/GaryOut

import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
'model_name_or_path':'etmckinley/BERFALTER',
'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()

原始信息汇总

数据集概述

数据集名称

• 名称: etmckinley/BERFALTER

数据集配置

• 模型路径: etmckinley/BERFALTER
• 输出目录: /opt/ml/model

训练脚本

• 入口点: run_qa.py
• 源代码目录: ./examples/pytorch/question-answering
• Git仓库配置:
  • 仓库链接: https://github.com/huggingface/transformers.git
  • 分支: v4.17.0

训练环境

• 实例类型: ml.p3.2xlarge
• 实例数量: 1
• Python版本: py38
• PyTorch版本: 1.10.2
• Transformers版本: 4.17.0