Open-source alternatives to GNER

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

This repository provides an overview of all components used for the creation of BLOOMZ & mT0 and xP3 introduced in the paper Crosslingual Generalization through Multitask Finetuning. Link to 25min video on the paper by Samuel Albanie; Link to 4min video on the paper by Niklas Muennighoff.

Toolkit for creating, sharing and using natural language prompts.

This repo releases our implementation for the InstructUIE model. - It is built based on the pretrained Flan T5 model, and finetuned on our data (IE INSTRUCTIONS). - If you are looking for NER datasets or models, you may also refer to our recent work: B2NER. It provides a standardized and…

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.

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.

unikg

Metaseq is a transformer sequence modeling toolkit designed for training, fine-tuning, and deploying sequence-to-sequence models using open pre-trained weights. It provides a comprehensive framework for large language model training, including dedicated tools for sequence dataset processing and a standalone inference server for generating text via API requests. The project features specialized utilities for model quantization to reduce parameter precision to eight bits, which lowers memory usage and increases inference speed. It also includes a checkpoint conversion pipeline to transform mode

The code for EMNLP24 paper "Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition"

Dependencies are managed using Poetry. Use poetry install to install dependencies. You can then use poetry shell to obtain a shell with the created virtual environment activated.

This repository provides an overview of all components from the paper OctoPack: Instruction Tuning Code Large Language Models. Link to 5-min video on the paper presented by Niklas Muennighoff.

G uideline f o llowing L arge L anguage Model for I nformation E xtraction

This is a machine learning framework for treating diverse natural language processing tasks as a unified text-to-text problem. It provides a toolkit for pre-training and fine-tuning large-scale transformer models, utilizing a system where both inputs and outputs are formatted as raw text sequences. The framework is distinguished by its distributed training system, which uses mesh-based strategies to scale model weights and training batches across multiple TPU cores. It supports multi-task learning by combining diverse datasets into a single training stream using configurable mixture rates, al

🤗 HF Repo 📄 Paper 📚 6K Data 📚 10K Data

OpenChat is a framework for the training, fine-tuning, and deployment of large language models optimized for conversational and mathematical reasoning tasks. It provides a comprehensive lifecycle for these models, ranging from training pipelines and deployment stacks to a web-based chat interface. The project focuses on enabling high-performance model execution on consumer-grade hardware without the need for enterprise-grade accelerators. It includes a production-ready inference server that implements the OpenAI chat completion protocol and utilizes dynamic request batching to optimize hardwa

🎩 Models | 📚 Dataset | 🚀 Quick Start | 👀 Demo | 📝 Citation | 🙏 Acknowledgements

An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition

Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition

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

Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License