few-shot-learning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

FSL-Mate: A collection of resources for few-shot learning (FSL).

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

[TPAMI 2023] LibFewShot: A Comprehensive Library for Few-shot Learning.

LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.

CIKM2023 Best Demo Paper Award. HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊

[CVPR 2024] | LAMP: Learn a Motion Pattern for Few-Shot Based Video Generation

HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊 HugNLP will released to @HugAILab

Reproducible scaling laws for contrastive language-image learning (https://arxiv.org/abs/2212.07143)

Plug-and-play document AI with zero-shot models.

State-of-the-art prompting techniques implementation with DSpy - Manager-style prompts, role personas, meta-prompting, and more

Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Language-interfaced fine-tuning for chemistry

[EMNLP 2022] Continual Training of Language Models for Few-Shot Learning

A pipeline for Prompt-tuning

Generate README.md with GPT-3 few-shot learning