InstructUIE offers a unified approach to information extraction by leveraging instruction learning, making it suitable for researchers and practitioners in NLP. It aims to simplify multi-task information extraction by framing diverse tasks as instruction-following problems, built upon the Flan T5 model.

How It Works

The model utilizes instruction tuning, a method that fine-tunes a pre-trained language model (Flan T5) on a dataset of instructions and corresponding outputs. This allows the model to generalize across various information extraction tasks, such as Named Entity Recognition (NER) and relation extraction, by understanding natural language instructions that specify the desired extraction.

Quick Start & Requirements

• Install: bash setup.sh
• Prerequisites: CUDA (11.3), cuDNN (8.2.0.53), PyTorch (1.10.0), Transformers (4.26.1), DeepSpeed (0.7.7).
• Data: Download from Baidu NetDisk or Google Drive.
• Training/Evaluation: Scripts provided at scripts/train_flan-t5.sh and scripts/eval_flan-t5.sh.
• Checkpoint: 11B UIE model available for download.

Highlighted Details

• Built on Flan T5 for instruction-based information extraction.
• Supports multi-task learning for unified information extraction.
• Released 11B UIE model checkpoint.
• Related work B2NER offers bilingual NER datasets for LLM training.

Maintenance & Community

The project is associated with the paper "InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction" (arXiv:2304.08085). No specific community channels or active maintenance signals are mentioned in the README.

Licensing & Compatibility

The README does not explicitly state a license. The code is provided for research purposes. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project's experimental environment specifies older versions of CUDA, cuDNN, PyTorch, and Transformers, which may pose compatibility challenges with newer hardware and software stacks. The README does not detail specific limitations of the model's performance or scope.