1. embeddings(嵌入): 将数据从高维空间映射到低维空间。对于我们要进行的文本分类工作来说,embeddings是文本数据的向量/矩阵形式,这种格式便于计算机的处理。 2. zero-shot classification(零样本分类): 模型对没有经过训练的类别进行分类。也就是说 ,模型在进行分类时,要从一个全新的类别中做出预测,而不是仅限于...
广义零样本(Zero-shot)文本分类旨在对可见类(seen classes)和增量出现的未见类(unseen classes)的文本实例进行分类。由于参数在学习过程中仅对可见类进行优化,而未考虑未见类,且参数在预测过程中保持稳定,因此大多数现有方法的泛化能力较差。为了解决上述挑战,本文提出了一个新的学习适应(Learn to Adapt,LTA)网络,该...
另一种非常有趣的主题模拟技术称为零点文本分类(Zero Shot Text Classification),这种技术是根据用户指定的分类标签来判断一段文本是否属于这个类别。例如:“one day I will see the world"这个句子,我们给定三个分类标签['travel', 'cooking', 'dancing'],尽管句子中没有出现"travel", 通过学习我们可以判别出这个...
Zero-Shot Text Classification Text classification is one of the most common applications of natural language processing (NLP). It is the task of assigning a set of predefined categories to a text snippet. Depending on the type of problem, the text snippet could be a sentence, a paragraph, ...
How to use the SageMaker Python SDK to access the pre-trained zero-shot text classification models in SageMaker JumpStart and use the inference script to deploy the model to a SageMaker endpoint for a real-time text classification use case How to use the SageMaker Python SDK to access ...
我们在UCI News Aggregator和Tweet Classification数据集上测试了我们的模型。这些数据集中使用的文本类与源数据集的SEO标记之间存在细微差别,与UCI类相比,SEO标记是更原子的概念。例如,句子“Bitcoin futures could open the floodgates for institutional investors”的SEO标签是:Bitcoin, Commodity,Futures, Cryptoc...
Zero-shot text classification aims to predict classes which never been seen in training stage. The lack of annotated data and huge semantic gap between seen and unseen classes make this task extremely hard. Most of existing methods employ binary classifier-based framework, and regard it as a ...
Zero-Shot Learning in Modern NLP Of course, some research has in fact been done in this area. In this post, I will present a few techniques, both from… joeddav.github.io http://huggingface.com/zero-shot/ Example implementaton of zero-shot text classificationEdit description ...
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data with billion-scale natural language generation (NLG) models, we propose a retrieval-enhanced framework to create training...
Cross-lingual text classification is a challenging task that aims to train classifiers with data in one language, known as the source language, and apply the acquired knowledge to data in another language, referred to as the target language. Recent advancements in multilingual pre-trained language ...