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Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra- ...
May 16, 2023 · Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter- ...
Dec 16, 2023 · We propose a Soft Contrastive learning-based Prompt (\texttt{SCP}) model for few-shot sentiment analysis.
To address this problem, we propose a Soft Contrastive learning-based Prompt (SCP) model for few-shot sentiment analysis. First, we design a sentiment-aware ...
The Ensemble learning of data resampling can improve the ELM classification accuracy of a few classes. We propose a class resampling technique and advance an ...
The typical setup aims to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an ...
We present a contrastive learning framework that clusters inputs from the same class for better generality of models trained with only limited examples.
Missing: Sentiment Classification.
Nov 16, 2024 · Explore few-shot classification techniques using contrastive learning. Discover code examples and implementations on GitHub. | Restackio.
Missing: Sentiment | Show results with:Sentiment
Prompt-based fine tuning with demonstrations to help model to know what is “great” / “terrible”.
Secondly, contrastive learning is applied to the implicit word vectors obtained twice during the training stage to alleviate over-fitting in few-shot learning ...