fromtransformersimportpipeline# 创建一个用于情感分析的管道classifier=pipeline("sentiment-analysis")# 输入文本text="I love using the Transformers library for my projects."# 获取预测结果result=classifier(text)print(result) 原理解释 Pi
三、基础使用:Pipeline 快速推理 pipeline()是 Transformers 库的核心接口,支持一键调用预训练模型完成常见任务。 情感分析示例 from transformers import pipeline classifier = pipeline("sentiment-analysis") result = classifier("I love using transformers library!") print(result) # 输出:[{'label': 'POSITIVE'...
fromtransformersimportGPT2Tokenizer,GPT2LMHeadModel# 加载模型和分词器tokenizer=GPT2Tokenizer.from_pretrained('gpt2')model=GPT2LMHeadModel.from_pretrained('gpt2')# 输入文本并生成文本input_text="Once upon a time"inputs=tokenizer.encode(input_text,return_tensors='pt')# 生成文本outputs=model.generat...
from transformers import pipeline classifier = pipeline("sentiment-analysis") print(classifier("I lo...
26. Hugging Face Transformers Hugging Face Transformers is an open-source library by Hugging Face. Transformers allow APIs to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you time from having to...
Transformers Interpret— explain models in the🤗 transformerspackage with 2 lines of code. Shapley— various methods to compute (approximate) the Shapley value of players (models) in weighted voting games (ensemble games). UpliftML—uplift modeling, great for studying causality in personalization/mar...
5. Hugging Face Transformers 核心价值 预训练模型生态 包含超过 30 万个模型，支持：Text Generation（GPT-2 微调）、Image Classification（ViT 模型）、Multimodal（CLIP 模型）。部署流程 加载模型：model = AutoModel.from_pretrained("bert-base-uncased")微调训练：使用 Hugging Face Training Library 6. ONNX ...
In a previous tutorial, we built a CNN-based image classifier from scratch using the Keras API. In this tutorial, you will learn how to finetune the state-of-the-art vision transformer (ViT) on your custom image classification dataset using the Huggingface Transformers library in Python....
解释: 这段代码展示了如何使用Hugging Face Transformers进行情感分析。pipeline函数可以快速加载预训练模型并进行预测。BertTokenizer和BertForSequenceClassification类用于加载BERT模型并进行文本分类。 实战案例:文本情感分析 假设你有一个电商网站的用户评论数据集,需要对其进行情感分析,以了解用户对产品的整体满意度。我们可...
Transformers介绍:https://www.analyticsvidhya.com/blog/2019/07/pytorch-transformers-nlp-python/?utm_source=blog&utm_medium=openai-gpt2-text-generator-pythonStanfordNLP介绍:https://www.analyticsvidhya.com/blog/2019/02/stanfordnlp-nlp-library-python/?utm_source=blog&utm_medium=openai-gpt2-text-...