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Top 22 few-shot-learning Open-Source Projects
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transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Awesome-Prompt-Engineering
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
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Hands-On-Meta-Learning-With-Python
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
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SaaSHub
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self-refine
LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
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HugNLP
CIKM2023 Best Demo Paper Award. HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊 (by HugAILab)
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HugNLP
HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊 HugNLP will released to @HugAILab
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LAMP
Official implement code of LAMP: Learn a Motion Pattern by Few-Shot Tuning a Text-to-Image Diffusion Model (Few-shot-based text-to-video diffusion)
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deep-kernel-transfer
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
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Awesome-Weak-Shot-Learning
A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
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Hands-On-Deep-Learning-Algorithms-with-Python
Hands-On Deep Learning Algorithms with Python, By Packt
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ORBIT-Dataset
The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.
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SaaSHub
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Yes, there are a lot of different resources online, especially for generative AI. The Awesome Prompt Engineering github is probably a good place to start https://github.com/promptslab/Awesome-Prompt-Engineering. If you're focusing directly on OpenAI's models then the OpenAI Prompt Engineering Guide would be my recommendation https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api.
Project mention: Self-Refine: Iterative Refinement with Self-Feedback | news.ycombinator.com | 2023-11-23
Project mention: A transformer-based method for zero and few-shot biomedical NER | news.ycombinator.com | 2023-05-12
Project mention: List of Stable Diffusion research softwares that I don't think gotten widespread adoption. | /r/StableDiffusion | 2023-12-10
few-shot-learning related posts
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Measuring Multitask Language Understanding
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Mixtral 7B MoE beats LLaMA2 70B in MMLU
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BREAKING: Google just released its ChatGPT Killer
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Self-Refine: Iterative Refinement with Self-Feedback
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Smarter Summaries with Finetuning GPT-3.5 and Chain of Density
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Remote Work 2.0: The Tools, Trends, and Challenges of the Post-Pandemic Work Era
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[Colab Notebook] Launch quantized MPT-30B-Chat on Vast.ai using text-generation-inference, integrated with ConversationChain
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A note from our sponsor - SaaSHub
www.saashub.com | 6 May 2024
Index
What are some of the best open-source few-shot-learning projects? This list will help you:
Project | Stars | |
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1 | transferlearning | 12,880 |
2 | awesome-domain-adaptation | 4,898 |
3 | Awesome-Prompt-Engineering | 3,212 |
4 | setfit | 1,990 |
5 | FSL-Mate | 1,645 |
6 | finetuner | 1,427 |
7 | Hands-On-Meta-Learning-With-Python | 1,123 |
8 | test | 946 |
9 | mmfewshot | 660 |
10 | self-refine | 488 |
11 | pal | 436 |
12 | HugNLP | 370 |
13 | memprompt | 320 |
14 | zshot | 320 |
15 | HugNLP | 247 |
16 | LAMP | 223 |
17 | deep-kernel-transfer | 190 |
18 | Awesome-Weak-Shot-Learning | 183 |
19 | Hands-On-Deep-Learning-Algorithms-with-Python | 107 |
20 | prompt-lib | 98 |
21 | ORBIT-Dataset | 86 |
22 | TaiChi | 78 |
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