30 open-source projects similar to oezyurty/replm, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best REPLM alternative.
Data and code for ACL 2023 Findings: Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors.
This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!. EMNLP'2023 (Findings).
CHisIEC: An Information Extraction Corpus for Ancient Chinese History
This is the official repository for the paper "GPT-RE: In-context Learning for Relation Extraction using Large Language Models" (EMNLP 2023).
DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured knowledge graphs. It provides a pipeline for identifying and classifying named entities, semantic relations, and events, converting raw datasets into structured triples. The project utilizes large language models as tool callers through a standardized context protocol to drive automated data extraction processes. It supports schema-driven extraction across multiple domains and bilingual text, employing joint entity and relation extraction to identify components in a single structured
BioGPT is a biomedical large language model and domain-specific transformer designed for processing and creating specialized medical text. It functions as a generative tool and knowledge extraction engine trained on large-scale scientific literature to produce human-like scientific prose and factual responses to queries. The project provides specialized capabilities for biomedical named entity recognition and the extraction of complex relations from unstructured medical corpora. It is designed to identify and classify biological entities through data mining and relation extraction to support
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
An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition
Universal Information Extraction, codes for the NeurIPS-2022 paper: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model.
Code and prompt templates for evaluation-filtering Some data were not uploaded due to size restrictions, but you can find all the datasets covered in this paper by the references in the paper.
Updates | Datasets | Models | Environment | Running | Results | Website | Paper
This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models.
MMICL, a state-of-the-art VLM with the in context learning ability from ICL, PKU
Zhao Zhang 3   Ziwei Liu ✉,1 1 S-Lab, Nanyang Technological University  2 Shanghai Jiao Tong University  3 SenseTime Research  4 Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China   Equal Contribution  † Project Lead …
Please save your dataset in data folder. Note that CoNLL2003 and WNUT2017 are open-source datasets, ACE2004 and ACE2005 are not free. We keep our CoNLL2003 and WNUT2017 train and test JSON files in data folder.
This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.
If you are just looking to download the LoRA weights directly, use this url: https://figshare.com/ndownloader/files/43044994 and view the data entry on Figshare.
Code, data, and results described in the paper "Mining experimental data from materials science literature with large language models: an evaluation study", https://www.tandfonline.com/doi/full/10.1080/27660400.2024.2356506
🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.
Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
Code for ICCV 2023 Paper : “ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction”
JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA, AAAI 2022 (Oral)
Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".
This is the codebase of the paper: ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis.
Sequence Generation with Label Augmentation for Relation Extraction. Bo Li, Dingyao Yu, Wei Ye, Jinglei Zhang, Shikun Zhang. AAAI2023 Oral Paper
Source code for the EMNLP' 21 paper Document-level Entity-based Extraction as Template Generation.