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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 -- 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.
pip install git+https://github.com/nicolay-r/AREnets@master
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.
model.txt
file placed at _data
folder;
More on input features could be found here.
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.
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
jsonl
or csv
data reader?This project has been tested under the following setup:
CSV
reading)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},
}
FAQs
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
We found that arenets demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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