from transformers import pipeline vision_classifier = pipeline(model="google/vit-base-patch16-224") preds = vision_classifier( images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ) preds = [{"score": round(pred["score"], 4), "label":...
也有github的中文教程(非官方,但是写的很好):https://github.com/lansinuote/Huggingface_Toturials 抱抱脸确实是一个很好用的包,基本上大量的NLP模型都在抱抱脸中有实现。感觉就像CV中的timm库。作为新人的我,肯定要学习起来的(非常适合我这种代码能力,工程能力都不行的人)。 我的tensorflow不行,所以我写的都是py...
To check which version of Hugging Face is included in your configuredDatabricks RuntimeML version, see the Python libraries section on the relevantrelease notes. Why use Hugging Face Transformers? For many applications, such as sentiment analysis and text summarization, pre-trained models work we...
HuggingFace Transformers is a library that is meant for somewhat much wider acceptability for Natural Language Processing. It covers almost anything on NLP with a wide variety of applications for different NLP tasks. The ones presented among the most popular tasks because of their application with Hu...
我们建议将与文档相关的图像放在以下存储库中:huggingface/documentation-images。您可以在此数据集存储库上打开一个 PR,并请求 Hugging Face 成员合并它。 有关在拉取请求上运行的检查的更多信息,请查看我们的 拉取请求上的检查 指南。 测试 包含了一个广泛的测试套件,用于测试库的行为和几个示例。库测试可以在 ...
format(question) >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16) >...
pip install "git+https://github.com/huggingface/transformers.git#egg=transformers[agents]"智能体 2.0 将在 v4.41.0 版本中发布，预计将于五月中旬上线。自我修正的检索增强生成 快速定义: 检索增强生成 (RAG) 是“使用 LLM 回答用户查询，但基于从知识库检索到的信息来回答”。与使用普通或微调的 LLM ...
The platform allows developers to leverage these powerful models for tasks such as text classification, translation, summarization, and question answering, without the need for extensive training and computational resources. With its user-friendly interface and comprehensive documentation, HuggingFace makes it...
https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tts_example.wav 您的浏览器不支持音频元素。 | agent.run("In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",document=document,) ...
from transformers import pipeline pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base") pipeline( image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg", question="What is in the image?", ) [...