arenets 0.23.0

Tensorflow-based framework which lists implementation of conventional neural network models (CNN, RNN-based) for Relation Extraction classification tasks as well as API for custom model implementation

AREnets

AREnets -- is an OpenNRE like project, but the kernel based on tensorflow library, with implementation of neural networks on top of it, designed for Attitude and Relation Extraction tasks. AREnets is a result of advances in Sentiment Attitude Extraction task but introduced in generalized form and applicable for other relation-extraction related classification tasks. It provides ready to use neural networks and API for subject→object pairs classification in a given samples. This project is powered by AREkit core API, squeezed into a tiny kernel.

Contents

• Installation
• Quick Start
• Models List
• Test Details
• How to cite

Installation

pip install git+https://github.com/nicolay-r/AREnets@master

Quick Start

Simply just open and follow the google-colab version like IDE to modify the train and inference code of provided tutorial:

The complete examples are in tutorials folder.

First of all, prepare your _data folder with data required for training model and performing inference.

• Input samples: check out input data formatting guide.
• Embeddings could be obtained from NLPL repository, with model.txt file placed at _data folder;
  • See downloading script;

More on input features could be found here.

Train

from arenets.quickstart.train import train
from arenets.enum_name_types import ModelNames

train(input_data_dir="_data", labels_count=3, model_name=ModelNames.CNN, epochs_count=10)

Runs cnn model with 10 epochs for 3-class classification problem; all the model-related details will be stored at _data model by default.

Predict

from arenets.quickstart.predict import predict
from arenets.arekit.common.data_type import DataType
from arenets.enum_name_types import ModelNames

predict(input_data_dir="_data", output_dir="_out", labels_count=3, model_name=ModelNames.CNN, data_type=DataType.Test)

Predict test results for pre-trained cnn model and saves them into _out folder

Models List

• Aspect-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];

Test Details

This project has been tested under the following setup:

• NVidia GTX-1060/1080 TI
• Python 3.6.9
• Pip freeze package list
• Cuda compilation tools, release 10.0, V10.0.130

How to cite

Our 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 neural-network applicable 
         models for attitude and relation extraction tasks},
  year={2023},
  url={https://github.com/nicolay-r/AREnets},
}