This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l
This is the official repository for "CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors" (ACL 2023).
This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper: On-the-fly Definition Augmentation of LLMs for Biomedical NER
Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".
Principalele funcționalități ale milvlg/prophet sunt: In Context Learning.
Alternativele open-source pentru milvlg/prophet includ: alirezadir/machine-learning-interviews — This project is a comprehensive machine learning interview guide and technical study resource designed for individuals… allenai/visprog — Official code for VisProg (CVPR 2023 Best Paper!). artpli/codeie — This is the official repository for "CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors"… baaivision/emu — Emu Series: Generative Multimodal Models from BAAI. chen700564/metaner-icl — An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition. allenai/beacon — This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper:…