load_dataset()函数的data_files参数非常灵活。它可以是单个文件路径,文件路径列表或者是字典(键为split名称,值为数据文件路径),也可以使用glob库来匹配满足指定格式的数据文件(例如使用data_files="*.json",可以一次性加载本地路径上的所有json后缀名文件),具体可以参考链接documentation。 Datasets库的加载脚...
単一ノード GPU で Hugging Face Transformers を使用して、自然言語処理モデルを微調整する方法について学習します。
限制Restrictions:地理限制、时间限制、行为限制、转授权能力、版税条款等。 以上两小节从知识产权的角度解构了License的内涵,下面让我们从佶屈聱牙的法律条文中逃出来吧,简明地看看Hugging Face提供的Licenses究竟有哪些。 Hugging Face Licenses Hugging Face 提供了大量的Licenses供Space应用开发者选择,具体列表可以在这里找...
Hugging Face is most notable for its Transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets.This connector is available in the following products and regions:Développer le tableau ServiceClassRegions Logic ...
Hugging Face You can use some features of theHugging Faceplatform while working on machine learning tasks in PyCharm.
Optimum-AMD is the interface between Hugging Face libraries and the ROCm software stack. For a deeper dive into using Hugging Face libraries on AMD accelerators and GPUs, refer to the Optimum-AMD page on Hugging Face for guidance on using Flash Attention 2, GPTQ quantization and the ONNX Run...
Documentation Feedback Email Us! » 1. PyTorch and Hugging Face Accelerate with DeepSpeed on DGX Cloud 1. PyTorch and Hugging Face Accelerate with DeepSpeed on DGX Cloud1.1. Overview This guide introduces how to finetune a multi-lingual NMT model, namely ALMA (Advanced Language Model-...
Python API documentation Common API Framework-specific API Examples and Tutorials Using FP8 with Transformer Engine Performance Optimizations Accelerating a Hugging Face Llama 2 and Llama 3 models with Transformer Engine Dependencies for this tutorial Table of contents From “Transformer” to ...
build_main_documentation.yml: responsible for building the docs for themainbranch, releases etc. build_pr_documentation.yml: responsible for building the docs on each PR. upload_pr_documentation.yml: responsible for uploading the PR artifacts to the Hugging Face Hub. ...
Once you're happy with your changes, you can preview how they'll look by first installing thedoc-buildertool that we use for building all documentation at Hugging Face: pip install hf-doc-builder doc-builder preview course ../course/chapters/LANG-ID --not_python_module ...