其中最火的两个概念就是contrastive Learning(对比学习,simcse是对比学习框架下较为出名的算法)和prompt-based learning(模板学习)。我们都知道AI领域除了算力贵,其实有价值的标注数据也非常昂贵。而无论是对比学习还是模板学习都开始解决少量标注样本,甚至没有标注样本时,让模型也能有不错的效果。这里我通过一个简单的...
其中最火的两个概念就是contrastive Learning(对比学习,simcse是对比学习框架下较为出名的算法)和prompt-based learning(模板学习)。我们都知道AI领域除了算力贵,其实有价值的标注数据也非常昂贵。而无论是对比学习还是模板学习都开始解决少量标注样本,甚至没有标注样本时,让模型也能有不错的效果。这里我通过一个简单的...
This study presents a prompt-based contrastive learning approach that can be employed to address this issue. This method was designed to overcome challenges such as data scarcity and class imbalance commonly found in social media. Fighting the infodemic is modeled as a series of text classification...
We employ a prompt-based contrastive learning framework to meet the challenges. A prompt is a passage of text or query fed into a pretrained language model (PLM) to elicit a response. Prompts provide clear guidance and precise context for language models, which has been shown to make them ...
PromCSE: Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning Our code is modified based on SimCSE and P-tuning v2. Here we would like to sincerely thank them for their excellent works. *** Updates *** 2023/04/05: We released our sentence ...
2、Revisiting Prompt Learning 对比语言-图像预训练(Contrastive Language-Image Pre-Training,CLIP)模型由一个图像编码器和一个文本编码器组成,分别对图像和相应的自然语言描述进行编码。 零样本推理(Zero-shot inference)。预训练的CLIP模型通过手工制作的提示(prompts)适应下游任务,而不是微调模型本身。文本始终被手动...
python tools/generate_k_shot_data.py for seed in 13 21 42 87 100 ### random seeds for different train-test splits do for bs in 40 ### batch size do for lr in 1e-5 ### learning rate for MLM loss do for supcon_lr in 1e-5 ### learning rate for SupCon loss do TAG=exp TY...
SCL-CVD: Supervised contrastive learning for code vulnerability detection via GraphCodeBERT Comput. Secur. (2024) YangS. et al. Automatic bi-modal question title generation for stack overflow with prompt learning Empir. Softw. Eng. (2024) YangG. et al. CCGIR: Information retrieval-based code ...
Methods AddRemove Contrastive Learning
Continual Few-Shot Relation Extraction withPrompt-Based Contrastive Learningdoi:10.1007/978-981-97-2421-5_21Continual relation extraction (CRE) aims to continually learn new relations while maintaining knowledge of previous relations in the data streams. Recently, continual few-shot relation extraction (...