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https://github.com/modelscope/AdaSeq

AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
https://github.com/modelscope/AdaSeq

bert chinese-nlp crf entity-typing information-extraction multi-modal-ner named-entity-recognition natural-language-processing natural-language-understanding ner nlp pytorch relation-extraction sequence-labeling token-classification word-segmentation

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AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models

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# AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models






[![license](https://img.shields.io/github/license/modelscope/adaseq.svg)](./LICENSE)

[![modelscope](https://img.shields.io/badge/modelscope->=1.4.0-624aff.svg)](https://modelscope.cn/)

![version](https://img.shields.io/github/tag/modelscope/adaseq.svg)

[![issues](https://img.shields.io/github/issues/modelscope/adaseq.svg)](https://github.com/modelscope/AdaSeq/issues)

[![stars](https://img.shields.io/github/stars/modelscope/adaseq.svg)](https://github.com/modelscope/AdaSeq/stargazers)

[![downloads](https://static.pepy.tech/personalized-badge/adaseq?period=total&left_color=grey&right_color=yellowgreen&left_text=downloads)](https://pypi.org/project/adaseq)

[![contribution](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](./CONTRIBUTING.md)









English | [简体中文](./README_zh.md)






## Introduction

***AdaSeq*** (**A**libaba **D**amo **A**cademy **Seq**uence Understanding Toolkit) is an easy-to-use all-in-one library, built on [ModelScope](https://modelscope.cn/home), 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.



![](./docs/imgs/task_examples_en.png)



🌟 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

- 2022-07: [SemEval 2023] Our U-RaNER paper won [Best Paper Award](https://semeval.github.io/SemEval2023/awards)!

- 2022-03: [SemEval 2023] Our U-RaNER won ***1st place in 9 tracks*** at [SemEval 2023 Task2](https://multiconer.github.io/results): Multilingual Complex Named Entity Recognition! [Model introduction and source code can be found here](./examples/U-RaNER).

- 2022-12: [[EMNLP 2022] Retrieval-augmented Multimodal Entity Understanding Model (MoRe)](./examples/MoRe)

- 2022-11: [[EMNLP 2022] Ultra-Fine Entity Typing Model (NPCRF)](./examples/NPCRF)

- 2022-11: [[EMNLP 2022] Unsupervised Boundary-Aware Language Model (BABERT)](./examples/babert)



## ⚡ Quick Experience

You can try out our models via online demos built on ModelScope:

[[English NER]](https://modelscope.cn/models/damo/nlp_raner_named-entity-recognition_english-large-news/summary)

[[Chinese NER]](https://modelscope.cn/models/damo/nlp_raner_named-entity-recognition_chinese-base-news/summary)

[[CWS]](https://modelscope.cn/models/damo/nlp_structbert_word-segmentation_chinese-base/summary)



More tasks, more languages, more domains: All modelcards we released can be found in this page [Modelcards](./docs/modelcards.md).



## 🛠️ Model Zoo



Supported models:



- [Transformer-based CRF](./examples/bert_crf)

- [Partial CRF](./examples/partial_bert_crf)

- [Retrieval Augmented NER](./examples/RaNER)

- [Biaffine NER](./examples/biaffine_ner)

- [Global-Pointer](./examples/global_pointer)

- [Multi-label Entity Typing](./examples/entity_typing)

- ...



## 💾 Dataset Zoo

We collected many datasets for sequence understanding tasks. All can be found in this page [Datasets](./docs/datasets.md).



## 📦 Installation

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

```



### Verify the Installation

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.



## 📖 Tutorials

- [Quick Start](./docs/tutorials/quick_start.md)

- Basics

  - [Learning about Configs](./docs/tutorials/learning_about_configs.md)

  - [Customizing Dataset](./docs/tutorials/customizing_dataset.md)

  - [TODO] Common Architectures

  - [TODO] Useful Hooks

  - [Hyperparameter Optimization](./docs/tutorials/hyperparameter_optimization.md)

  - [Training with Multiple GPUs](./docs/tutorials/training_with_multiple_gpus.md)

- Best Practice

  - [Training a Model with Custom Dataset](./docs/tutorials/training_a_model.md)

  - [Reproducing Results in Published Papers](./docs/tutorials/reproducing_papers.md)

  - [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](./CONTRIBUTING.md) for the contributing guideline.



## 📄 License

This project is licensed under the Apache License (Version 2.0).