arenets 0.23.1

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
• FAQ
• 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];

FAQ

How to prepare input data?

How to setup jsonl or csv data reader?

How to implement a custom model with attention?

How to customize the prediction output?

Test Details

This project has been tested under the following setup:

• NVidia GTX-1060/1080 TI
• CUDA compilation tools, release 10.0, V10.0.130
• Python 3.6.9
• Pandas 0.25.3 (Optional, only for CSV reading)
• Pip freeze package list

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