在前面章节中已经知道如何从Hugging Face Hub上下载和缓存数据集(使用load_dataset直接指定Hub上已有的数据集名称)。但是我们经常会遇到需要加载本地和远程服务器上数据的情况,本节就是介绍如何使用Hugging Face的Datasets库来完成那些Hub没有的数据集加载方法。 处理本地和远程服务器上的数据集 Datasets库提...
We’ll use the AutoTokenizer class to tokenize the text, which requires a model ID from the Hugging Face Hub or a local path to the model to automatically load the appropriate tokenizer. Since we’ll be using the cased version of the BERT-base model, the code that loads the tokenizer is...
在本例中,我们使用 AWS 预置的 PyTorch 深度学习 AMI,其已安装了正确的 CUDA 驱动程序和 PyTorch。在此基础上,我们还需要安装一些 Hugging Face 库,包括 transformers 和 datasets。运行下面的代码就可安装所有需要的包。https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-pytorch.html # install ...
Hugging Face Hub 是一个拥有超过 120k 模型、20k 数据集和 50k 演示应用程序 (Spaces) 的平台 Repositories¶ Models, Spaces, and Datasets are hosted on the Hugging Face Hub as Git repositories, which means that version control and collaboration are core elements of the Hub. ...
I was following this huggingface tutorial on uploading my dataset (a json file) to the Hub. In the link they mention: or text data extensions like .csv, .json, .jsonl, and .txt, we recommend compressing them before uploading to the Hub (to .zip or .gz file extension for example) So...
Hugging Face 是一个开源库,用于构建、训练和部署最先进的 NLP 模型。Hugging Face 提供了两个主要的库,用于模型的transformers 和用于数据集的datasets 。可以直接使用 pip 安装它们。 代码语言:javascript 复制 pip install transformers datasets Pipeline
face portal for the first time, the connection mode was read only. however, we need a write credential to be able to push our model to the hugging face hub. after that, we need to log in again using the notebook_login function by using the newly created credentials. notebook_login()...
Upload dataset to the Hub Once you’ve created a dataset, you can share it to the Hub with the push_to_hub() method. Make sure you have the huggingface_hub library installed and you’re logged in to your Hugging Face account (see the Upload with Python tutorial for more details). ...
tutorial An Introduction to Using Transformers and Hugging Face Understand Transformers and harness their power to solve real-life problems. Zoumana Keita 15 min code-along Using Open Source AI Models with Hugging Face Deep dive into open source AI, explore the Hugging Face ecosystem, and build...
from transformers import BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") This model is a summarization model so I would recommend reading the summarization tutorial on Hugging Face's website. Share Improve this answer Follow answered Oct 15...