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https://github.com/nicolay-r/AREnets
Tensorflow-based framework which lists attentive implementation of the conventional neural network models (CNN, RNN-based), applicable for Relation Extraction classification tasks as well as API for custom model implementation
https://github.com/nicolay-r/AREnets
attention bilstm classification cnn cnn-attention cnn-model lstm ml neural-network nlp nlp-machine-learning pcnn rcnn-model relation-extraction rnn self-attention self-attentive-rnn tensorflow
Last synced: 3 days ago
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Tensorflow-based framework which lists attentive implementation of the conventional neural network models (CNN, RNN-based), applicable for Relation Extraction classification tasks as well as API for custom model implementation
- Host: GitHub
- URL: https://github.com/nicolay-r/AREnets
- Owner: nicolay-r
- License: mit
- Created: 2022-12-12T16:58:12.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-11-08T13:39:58.000Z (about 1 year ago)
- Last Synced: 2024-12-15T19:48:57.672Z (6 days ago)
- Topics: attention, bilstm, classification, cnn, cnn-attention, cnn-model, lstm, ml, neural-network, nlp, nlp-machine-learning, pcnn, rcnn-model, relation-extraction, rnn, self-attention, self-attentive-rnn, tensorflow
- Language: Python
- Homepage:
- Size: 1.34 MB
- Stars: 7
- Watchers: 3
- Forks: 0
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-sentiment-attitude-extraction - [github
- awesome-sentiment-attitude-extraction - [github
README
# AREnets
[](https://colab.research.google.com/github/nicolay-r/AREnets/blob/master/arenets_colab_tutorial.ipynb)
**AREnets** -- is an [OpenNRE](https://github.com/thunlp/OpenNRE) like project, but the kernel based on [tensorflow](https://www.tensorflow.org/)
library, with implementation of neural networks on top of it, designed for **A**ttitude and **R**elation **E**xtraction tasks.
AREnets is a result of advances in [Sentiment Attitude Extraction task](http://nlpprogress.com/russian/sentiment-analysis.html)
but introduced in generalized form and applicable for other relation-extraction related classification tasks.
It provides ready to use [neural networks](#models-list) and API for `subject`→`object` pairs classification in a given samples.
This project is powered by
[AREkit](https://github.com/nicolay-r/AREkit)
core API, squeezed into a tiny
[kernel](https://github.com/nicolay-r/AREnets/tree/dev/arenets/arekit).
## Contents
* [Installation](#installation)
* [Quick Start](#quick-start)
* [Models List](#models-list)
* [FAQ](#faq)
* [Test Details](#test-details)
* [How to cite](#how-to-cite)
## Installation
```bash
pip install git+https://github.com/nicolay-r/AREnets@master
```
## Quick Start
[](https://colab.research.google.com/github/nicolay-r/AREnets/blob/master/arenets_colab_tutorial.ipynb)
Simply just open and follow the [google-colab](https://colab.research.google.com/github/nicolay-r/AREnets/blob/master/arenets_colab_tutorial.ipynb)
version like IDE to modify the train and inference code of provided tutorial:
The complete examples are in [tutorials](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](docs/input_data.md).
* **Embeddings** could be obtained from [NLPL repository](http://vectors.nlpl.eu/repository/),
with `model.txt` file placed at `_data` folder;
* See [downloading script](tutorials/_data/download_embedding.sh);
More on input features could be [found here](docs/input_features.md).
### Train
```python
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
```python
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]](arenets/attention/architectures/mlp.py) /
[[github:nicolay-r]](https://github.com/nicolay-r/mlp-attention);
* **Self-based Attentive encoders**:
- P. Zhou et. al.
[[code]](arenets/attention/architectures/self_p_zhou.py) /
[[github:SeoSangwoo]](https://github.com/SeoSangwoo/Attention-Based-BiLSTM-relation-extraction);
- Z. Yang et. al.
[[code]](arenets/attention/architectures/self_z_yang.py) /
[[github:ilivans]](https://github.com/ilivans/tf-rnn-attention);
* **Single Sentence Based Architectures**:
- CNN
[[code]](arenets/context/architectures/cnn.py) /
[[github:roomylee]](https://github.com/roomylee/cnn-relation-extraction);
- CNN + Aspect-based MLP Attention
[[code]](arenets/context/architectures/base/att_cnn_base.py);
- PCNN
[[code]](arenets/context/architectures/pcnn.py) /
[[github:nicolay-r]](https://github.com/nicolay-r/sentiment-pcnn);
- PCNN + Aspect-based MLP Attention
[[code]](arenets/context/architectures/base/att_pcnn_base.py);
- RNN (LSTM/GRU/RNN)
[[code]](arenets/context/architectures/rnn.py) /
[[github:roomylee]](https://github.com/roomylee/rnn-text-classification-tf);
- IAN (frames based)
[[code]](arenets/context/architectures/ian_frames.py) /
[[github:lpq29743]](https://github.com/lpq29743/IAN);
- RCNN (BiLSTM + CNN)
[[code]](arenets/context/architectures/rcnn.py) /
[[github:roomylee]](https://github.com/roomylee/rcnn-text-classification);
- RCNN + Self Attention
[[code]](arenets/context/architectures/rcnn_self.py);
- BiLSTM
[[code]](arenets/context/architectures/bilstm.py) /
[[github:roomylee]](https://github.com/roomylee/rnn-text-classification-tf);
- Bi-LSTM + Aspect-based MLP Attention
[[code]](arenets/context/architectures/base/att_bilstm_base.py)
- Bi-LSTM + Self Attention
[[code]](arenets/context/architectures/self_att_bilstm.py) /
[[github:roomylee]](https://github.com/roomylee/self-attentive-emb-tf);
- RCNN + Self Attention
[[code]](arenets/context/architectures/att_self_rcnn.py);
* **Multi Sentence Based Encoders Architectures**:
- Self Attentive
[[code]](arenets/multi/architectures/att_self.py);
- Max Pooling
[[code]](arenets/multi/architectures/max_pooling.py) /
[[paper]](https://pdfs.semanticscholar.org/8731/369a707046f3f8dd463d1fd107de31d40a24.pdf);
- Single MLP
[[code]](arenets/multi/architectures/base/base_single_mlp.py);
## FAQ
#### [How to prepare input data?](docs/input_data.md)
#### [How to setup `jsonl` or `csv` data reader?](docs/input_readers.md)
#### [How to implement a custom model with attention?](docs/tutorial_attention.md)
#### [How to customize the prediction output?](docs/tutorial_predict_output.md)
## 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](docs/pip-freeze-list.txt)
## 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:
```bibtex
@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},
}
```