1. Zero-Shot Text Classification with Self-Training pdf:https://arxiv.org/abs/2210.17541 code:https://github.com/IBM/zero-shot-classification-boost-with-self-training 这篇文章出来的时候,ChatGPT 还没火出圈,所以它走的还是传统优化路线。 a. 基座分类模型 文本为 NLI(Natural Language Inference) 式...
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...
该模型基于NLI的零点文本分类模型,参看Yin et al (2019) Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. 另一个模型是distilbert-base-uncased-mnli,该模型是uncased DistilBERT model在Multi-Genre Natural Language Inference (MNLI)上微调产生的。MNLI语料库包含大约433k个...
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, or even a whole document. The...
1 引言 在所有的自然语言处理任务中,感觉《零点文本分类(Zero Shot Text Classification)》最有趣。然而,当把这种技术应用于实际项目时,会产生附加的问题。这个笔记简要讨论了一些实践中遇到的问题以及解决办法。 2 遇到的问题 最初想把段落划分成句子来分类,但在实践中
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...
How to deploy pre-trained zero-shot text classification models using the SageMaker JumpStart UI and run inference on the deployed model using short text data How to use the SageMaker Python SDK to access the pre-trained zero-shot text classification models in SageMaker JumpStart and use th...
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 ...
Example implementaton of zero-shot text classificationEdit description huggingface.co That is possible due to the well-trained BERT model as a general-purpose language model. It is capable of connecting texts that are subjects for classification, with the classes described with one or a few words...
Zero-shot Classification CLIP最恐怖的地方在于，基于400M数据上学得的先验，仅用数据集的标签文本，就...