Package: arenets
AREnetsAREnets-- is anOpenNRElike project, but the kernel based ontensorflowlibrary, with implementation of neural networks on top of it, designed forAttitude andRelationExtraction tasks.
AREnets is a result of advances inSentiment Attitude Extraction taskbut introduced in generalized form and applicable for other relation-extraction related classification tasks.
It provides ready to useneural networksand API forsubject→objectpairs classification in a given samples.
This project is powered byAREkitcore API, squeezed into a tinykernel.ContentsInstallationQuick StartModels ListFAQTest DetailsHow to citeInstallationpipinstallgit+https://github.com/nicolay-r/AREnets@masterQuick StartSimply just open and follow thegoogle-colabversion like IDE to modify the train and inference code of provided tutorial:The complete examples are intutorialsfolder.First of all, prepare your_datafolder with data required for training model and performing inference.Input samples: check outinput data formatting guide.Embeddingscould be obtained fromNLPL repository,
withmodel.txtfile placed at_datafolder;Seedownloading script;More on input features could befound here.Trainfromarenets.quickstart.trainimporttrainfromarenets.enum_name_typesimportModelNamestrain(input_data_dir="_data",labels_count=3,model_name=ModelNames.CNN,epochs_count=10)Runscnnmodel with10epochs for3-classclassification problem;
all the model-related details will be stored at_datamodel by default.Predictfromarenets.quickstart.predictimportpredictfromarenets.arekit.common.data_typeimportDataTypefromarenets.enum_name_typesimportModelNamespredict(input_data_dir="_data",output_dir="_out",labels_count=3,model_name=ModelNames.CNN,data_type=DataType.Test)Predicttestresults for pre-trainedcnnmodel and saves them into_outfolderModels ListAspect-based Attentive encoders:Multilayer Perceptron (MLP)[code]/[github:nicolay-r];Self-based Attentive encoders:P. Zhou et. al.[code]/[github:SeoSangwoo];Z. Yang et. al.[code]/[github:ilivans];Single Sentence Based Architectures:CNN[code]/[github:roomylee];CNN + Aspect-based MLP Attention[code];PCNN[code]/[github:nicolay-r];PCNN + Aspect-based MLP Attention[code];RNN (LSTM/GRU/RNN)[code]/[github:roomylee];IAN (frames based)[code]/[github:lpq29743];RCNN (BiLSTM + CNN)[code]/[github:roomylee];RCNN + Self Attention[code];BiLSTM[code]/[github:roomylee];Bi-LSTM + Aspect-based MLP Attention[code]Bi-LSTM + Self Attention[code]/[github:roomylee];RCNN + Self Attention[code];Multi Sentence Based Encoders Architectures:Self Attentive[code];Max Pooling[code]/[paper];Single MLP[code];FAQHow to prepare input data?How to setupjsonlorcsvdata reader?How to implement a custom model with attention?How to customize the prediction output?Test DetailsThis project has been tested under the following setup:NVidia GTX-1060/1080 TICUDA compilation tools, release 10.0, V10.0.130Python 3.6.9Pandas 0.25.3 (Optional, only forCSVreading)Pip freeze package listHow to citeOur one and my personal interest is to help you better explore and analyze attitude and relation extraction related tasks with AREnets.
A great research is also accompanied with the faithful reference.
if you use or extend our work, please cite as follows:@misc{arenets2023,
  author={Nicolay Rusnachenko},
  title={{AREnets}: Tensorflow-based framework of attentive neural-network 
         models for text classfication and relation extraction tasks},
  year={2023},
  url={https://github.com/nicolay-r/AREnets},
}