Package: adaseq
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding ModelsEnglish |简体中文IntroductionAdaSeq(AlibabaDamoAcademySequence Understanding Toolkit) is an easy-to-use all-in-one library, built onModelScope, 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 New2022-07: [SemEval 2023] Our U-RaNER paper wonBest Paper Award!2022-03: [SemEval 2023] Our U-RaNER won1st place in 9 tracksatSemEval 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)⚡ Quick ExperienceYou 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 pageModelcards.🛠️ Model ZooSupported models:Transformer-based CRFPartial CRFRetrieval Augmented NERBiaffine NERGlobal-PointerMulti-label Entity Typing...💾 Dataset ZooWe collected many datasets for sequence understanding tasks. All can be found in this pageDatasets.📦 InstallationAdaSeq project is based onPython >= 3.7,PyTorch >= 1.8andModelScope >= 1.4. We assure that AdaSeq can run smoothly whenModelScope == 1.9.5.installation via pip：pip install adaseqinstallation 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.htmlVerify the InstallationTo 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.yamlYou 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 folderexperiments/toy_msra/will be generated to save all experimental results and model checkpoints.📖 TutorialsQuick StartBasicsLearning about ConfigsCustomizing Dataset[TODO] Common Architectures[TODO] Useful HooksHyperparameter OptimizationTraining with Multiple GPUsBest PracticeTraining a Model with Custom DatasetReproducing Results in Published Papers[TODO] Uploading Saved Model to ModelScope[TODO] Customizing your Model[TODO] Serving with AdaLA📝 ContributingAll contributions are welcome to improve AdaSeq. Please refer toCONTRIBUTING.mdfor the contributing guideline.📄 LicenseThis project is licensed under the Apache License (Version 2.0).