AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
Project description
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
English | 简体中文
Introduction
AdaSeq (Alibaba Damo Academy Sequence Understanding Toolkit) is an easy-to-use all-in-one library, built on ModelScope, that allows researchers and developers to train custom models for sequence understanding tasks, including part-of-speech tagging (POS Tagging), chunking, named entity recognition (NER), entity typing, relation extraction (RE), etc.
🌟 Features:
-
Plentiful Models:
AdaSeq provide plenty of cutting-edge models, training methods and useful toolkits for sequence understanding tasks.
-
State-of-the-Art:
Our aim to develop the best implementation, which can beat many off-the-shelf frameworks on performance.
-
Easy-to-Use:
One line of command is all you need to obtain the best model.
-
Extensible:
It's easy to register a module, or build a customized sequence understanding model by assembling the predefined modules.
⚠️Notice: This project is under quick development. This means some interfaces could be changed in the future.
📢 What's New
- 2022-12: [EMNLP 2022] Retrieval-augmented Multimodal Entity Understanding Model (MoRe)
- 2022-11: [EMNLP 2022] Ultra-Fine Entity Typing Model (NPCRF)
- 2022-11: [EMNLP 2022] Unsupervised Boundary-Aware Language Model (BABERT)
⚡ Quick Experience
You can try out our models via online demos built on ModelScope: [English NER] [Chinese NER] [CWS]
All modelcards we released can be found in this page Modelcards.
🛠️ Model Zoo
Supported models:
💾 Dataset Zoo
We collected many datasets for sequence understanding tasks. All can be found in this page Datasets.
📦 Installation
AdaSeq project is based on Python version >= 3.7
and PyTorch version >= 1.8
.
- installation via pip:
pip install adaseq
- installation from source:
git clone https://github.com/modelscope/adaseq.git
cd adaseq
pip install -r requirements.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
📖 Tutorials
- Quick Start
- Basics
- Learning about Configs
- Customizing Dataset
- [TODO] Common Architectures
- [TODO] Useful Hooks
- Hyperparameter Optimization
- Training with Multiple GPUs
- Best Practice
- Training a Model with Custom Dataset
- Reproducing Results in Published Papers
- [TODO] Uploading Saved Model to ModelScope
- [TODO] Customizing your Model
- [TODO] Serving with AdaLA
📝 Contributing
All contributions are welcome to improve AdaSeq. Please refer to CONTRIBUTING.md for the contributing guideline.
📄 License
This project is licensed under the Apache License (Version 2.0).
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