#zero-shot learning

Explore an extensive collection of open-source datasets, models, and tools dedicated to instruction tuning in various NLP tasks. This project offers modified datasets from traditional NLP tasks and those generated by large language models (LLMs), providing multilingual resources for global accessibility. It includes translation tools and processes to improve translation quality in low-resource languages. Additionally, it provides a curated list of academic papers and repositories for exploring instruction-based learning, in-context learning, reasoning, and frameworks, making it a valuable resource for researchers and developers in AI enhancement.

The project efficiently demonstrates zero- and few-shot learning using OpenAI models and Prodigy for creating high-quality datasets with minimal annotation. It details the setup of Prodigy for named-entity recognition and text categorization, utilizing OpenAI predictions to build a gold-standard dataset. Task-specific prompt configuration enables precise classification and model training, while addressing imbalanced data and exporting annotations to spaCy or HuggingFace transformers.

Discover how the OpenAI Vision API powers imaginative experiments in visual AI for novices and veterans alike. Explore limitations and integrations, such as combining GPT-4V with GroundingDINO, to surpass current capabilities.

Explore 1.84M CoT rationales across 1,060 tasks for zero-shot and few-shot learning improvements. Access datasets and CoT-T5 models via Huggingface, and utilize rationale augmentation for enhanced training. Available under OpenAI's non-commercial terms. Reference the research paper for citations.