AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
English | 简体中文
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.
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.
You can try out our models via online demos built on ModelScope: [English NER] [Chinese NER] [CWS]
More tasks, more languages, more domains: All modelcards we released can be found in this page Modelcards.
We collected many datasets for sequence understanding tasks. All can be found in this page Datasets.
AdaSeq project is based on Python >= 3.7, PyTorch >= 1.8 and ModelScope >= 1.4. We assure that AdaSeq can run smoothly when ModelScope == 1.9.5.
pip install adaseq
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
To verify whether AdaSeq is installed properly, we provide a demo config for training a model (the demo config will be automatically downloaded).
adaseq train -c demo.yaml
You will see the training logs on your terminal. Once the training is done, the results on test set will be printed: test: {"precision": xxx, "recall": xxx, "f1": xxx}. A folder experiments/toy_msra/ will be generated to save all experimental results and model checkpoints.
All contributions are welcome to improve AdaSeq. Please refer to CONTRIBUTING.md for the contributing guideline.
This project is licensed under the Apache License (Version 2.0).
FAQs
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
We found that adaseq demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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