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.

⚠️Notice: This project is under quick development. This means some interfaces could be changed in the future.

• 2022-07: [SemEval 2023] Our U-RaNER paper won Best Paper Award!
• 2022-03: [SemEval 2023] Our U-RaNER won 1st place in 9 tracks at SemEval 2023 Task2: Multilingual Complex Named Entity Recognition! Model introduction and source code can be found here.
• 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)

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.

• 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

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.

• 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

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).