In the paper, we report the results for the NLPCC2021 shared-task of Few-shot Learning for Chinese NLP. This shared task is proposed in the context of pre-trained language models, where models only have access to limited human-labeled data. The goal of the task is to compare different ...
nlp、大模型31 人赞同了该文章 目录 收起 一、背景 二、技术方案 1.few-shot 介绍 2.meta-learning介绍 三、实验 zero-shot 和 few-shot (Random labels 、gold labels) 对比 meta-learning random labels correct 提示few-shot数量 manual templates out-of-distribution (OOD) text 总结 论文解读——带...
通过metric learningsiamese networkSiamese Neural Networks for One-shot Image RecognitionKNNMatching Networks for One Shot LearningBregman散度(平方欧氏距离和Mahalanobis距离)Prototypical Networks fo…
nlp few-shot-learning sentence-transformers Updated Sep 19, 2024 Jupyter Notebook tristandeleu / pytorch-meta Star 2k Code Issues Pull requests A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch pytorch meta-learning few-shot-learning Updated Jul ...
Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key...
Few-shot learning-the ability to train models with access to limited data-has become increasingly popular in the natural language processing (NLP) domain, as large language models such as GPT and T0 have been empirically shown to achieve high performance in numerous tasks with access to just a...
Among other things, MLM is a deep learning technique widely used in natural language processing (NLP) tasks. In MLM, a portion of the input text is “masked” or randomly replaced with special tokens (usually [MASK]), and the model is trained to predict the original tokens based on their...
PromptCBLUE: a large-scale instruction-tuning dataset for multi-task and few-shot learning in the medical domain in Chinese - boom-R123/PromptCBLUE
Semantic Prompt for Few-Shot Image Recognition 原论文于2023.11.6撤稿,原因:缺乏合法的授权,详见此处 Abstract 在小样本学习中(Few-shot Learning, FSL)中,有通过利用额外的语义信息,如类名的文本Embedding,通过将语义原型与视觉原型相结合来解决样本稀少的问题。但这种方法可能会遇到稀有样本中学到噪声特征导致收益...
few-shot learning,这里shot 有计量的意思,指少样本学习,机器学习模型在学习了一定类别的大量数据后,对于新的类别,只需要少量的样本就能快速学习,对应的有one-shot learning, 一样本学习,也算样本少到为一的情况下的一种few-shot learning, 这里的少样本学习的研究领域与迁移学习有一大部分交集部分,即在源域有足够...