在huggingface上,我们将零样本分类(zero-shot-classification)模型按下载量从高到低排序,总计313个模型,文中facebook的bart排名第一。 三、总结 本文对transformers之pipeline的零样本文本分类(zero-shot-classification)从概述、技术原理、pipeline参数、pipeline实战、模型排名等方面进行介绍,读者可以基于pipeline使用文中的2...
Zero-shot-classification 接下来是对其中部分管道的介绍。 Zero-shot classification 使用场景:分类无标签文本 使用样例 fromtransformersimportpipelineclassifier=pipeline("zero-shot-classification")classifier("This is a course about the Transformers library",candidate_labels=["education","politics","business"],)...
"zero-shot-classification":将返回一个ZeroShotClassificationPipeline。 "zero-shot-image-classification":将返回一个ZeroShotImageClassificationPipeline。 "zero-shot-audio-classification":将返回一个ZeroShotAudioClassificationPipeline。 "zero-shot-object-detection":将返回一个ZeroShotObjectDetectionPipeline。 2.2.3 ...
该pipeline 可以通过 "zero-shot-image-classification" 任务标识符来使用 pipeline() 来加载。 参数:参考 transformers.Pipeline。 方法: __call__(images: typing.Union[str, typing.List[str], ForwardRef('Image.Image'), typing.List[ForwardRef('Image.Image')]], **kwargs):对 inputs 进行预测。 参数...
"zero-shot-classification": will return a ZeroShotClassificationPipeline. "conversational": will return a ConversationalPipeline. 下面可以可以来试试用pipeline直接来做一些任务: Have a try: Zero-shot-classification 零样本学习,就是训练一个可以预测任何标签的模型,这些标签可以不出现在训练集中。 一种零样本...
简介:【人工智能】Transformers之Pipeline(四):零样本音频分类(zero-shot-audio-classification) 一、引言 pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks)。
"zero-shot-audio-classification":将返回一个ZeroShotAudioClassificationPipeline。 "zero-shot-object-detection":将返回一个ZeroShotObjectDetectionPipeline。 2.2.3 task默认模型 针对每一个task,pipeline默认配置了模型,可以通过pipeline源代码查看: SUPPORTED_TASKS = {"audio-classification": {"impl": AudioClassifi...
1. 情感分析 from transformers import pipelineclassifier = pipeline("sentiment-analysis")classifier("I am happy.")输出：[{'label': 'POSITIVE', 'score': 0.9998760223388672}]也可以传列表作为参数。2. 零样本文本分类 from transformers import pipelineclassifier = pipeline("zero-shot-classification")...
# 导入pipeline from transformers import pipeline # 指定任务:零样本分类 classifier = pipeline("zero-shot-classification") classifier( "This is a course about the Transformers library", candidate_labels=["education", "politics", "business"], ) 生成结果每种label都有对应的概率: {'sequence': 'This...
classifier = pipeline("zero-shot-classification") classifier( ["This is a course about the Transformers library", "New policy mix to propel turnaround in China's economy"], candidate_labels=["education", "politics", "business"], )