二、Hugging Face如何使用Hugging Face主要有以下3大核心板块：1.Models模型中心这是Hugging Face核心板块！你可以按任务类型（如自然语言处理、计算机视觉、音频处理、多模态等）、技术框架、语言等维度筛选查找模型，多达150万个，应有尽有。点进某个模型，不仅能看到介绍，很多还能直接在线测试，甚至查看源代码、部署使用
from transformers import AutoModelWithLMHead, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased") model = AutoModelWithLMHead.from_pretrained("distilbert-base-cased") sequence = f"Distilled models are smaller than the models they mimic. Using them instead o...
Natural Language Processing(自然语言处理):包括 Translation(机器翻译)、Fill-Mask(填充掩码,预测句子中被遮掩的词)、Token Classification(词分类)、Sentence Similarity(句子相似度)、Question Answering(问答系统),Summarization(总结,缩句)、Zero-Shot Classification (零样本分类)、Text Classification(文本分类)、Text2T...
join(sent_tokenize(text)[:3]) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 summaries["baseline"] = three_sentence_summary(sample_text) GPT-2 “Language Models are Unsupervised Multitask Learners”. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_...
3.Huggingface 的 Models 点开Models。可以看到下图的任务 ,再点开+23 Tasks。 可以看到下图所有的任务。 其中,主要包括计算机视觉、 自然语言处理、语音处理、多模态、表格处理、强化学习。 展开介绍: Computer Vision(计算机视觉任务):包括 lmage Classification(图像分类) ,lmage Segmentation(图像分割)、zero-Shot ...
encoder-decoder models (eg,BART,T5)会根据传入的labels 自行创建它们的 decoder_input_ids。在这样的模型中,传递 the labels 的进行训练首选的方式。 Feed Forward Chunking 介绍 大概是这样的,因为在下图的encoder的部分,我们可以看到 self-attention layer后面往往跟着前馈网络,而存储前馈网络的权重有比较占内存,所...
["How many pretrained models are available in 🤗 Transformers?","What does 🤗 Transformers provide?","🤗 Transformers provides interoperability between which frameworks?",]for question in questions:inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")input_ids = ...
seq2seq or sequence-to-sequence: 序列到序列(模型)。从输入中生成新序列的模型,如翻译模型或总结模型(如Bart或T5)。models that generate a new sequence from an input, like translation models, or summarization models (such as Bart or T5).
MODELS=`[ //... { "name": "summarization-chain", //model-name "endpoints" : [{ "type": "langserve", "url" : "http://127.0.0.1:8100", }] }, ]` Model Context Protocol (MCP) Support (Upcoming) The project is planning to introduce support for the Model Context Protocol (MCP)...
🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone. ...