Rumour Detection with PyTorch

Welcome to the Rumour Detection repository! This project focuses on implementing and exploring various configurations for rumour detection using PyTorch. The repository not only includes the PyTorch implementation of the classification models but also features an intuitive interface for easily tuning, testing different training configurations, and adapting preprocessing steps.

Here are some results of training models using this interface.

Key Features

• PyTorch Implementation: The core of this project is robust rumour detection models built with PyTorch, offering a powerful and flexible framework for training and evaluation.

• Configurable Interface: An easy-to-use interface allows users to tune different aspects of the training process using a config.ini file. This empowers users to experiment with various hyperparameters and configurations effortlessly.

• Google Sheet Reporting: Some of the results of model training experiments that are accompanied by a clear overview of model performance under different settings, are reported to this Google Sheet. Needless to say, all of the reported models were trained just by editing config.ini file.

• Downloadable Models: Some of the Pre-trained models, resulting from experiments with various hyperparameters, are available for download in the provided google sheet. These models can be directly applied or serve as a starting point for further fine-tuning.

Getting Started

Installation

Clone the repository to your local machine or Google colab:

!git https://github.com/E-Ghafour/RumourDetection.git
!cd RumourDetection
!pip install -r requirements.txt

Usage

Configuring Training:

• Download rumour detection data from provided google sheet and set the train and test path in config.ini file.
• Open the config.ini file to adjust hyperparameters and training configurations.
• Description about tuning the hyperparameters

Parameter	Value	Description	Possible Values
type	LSTM	Type of the model (Recurrent Neural Network)	LSTM, GRU, RNN.
input_size	768	Size of the output layer of the embedding
hidden_size	128	Size of the hidden layer	Any positive integer
output_size	1	for binary classification like Rumour Detection should be 1	Any positive integer
learning_rate	0.0005	Learning rate for optimization	Any positive float
batch_size	128	Number of input samples used in each iteration	Any positive integer
n_layer	3	Number of recurrent layers	Any positive integer
bidirectional	True	Whether the RNN is bidirectional or not	True, False
inner_dropout	0.5	Dropout rate within the RNN layers	Any float between 0 and 1
dropout	0.4	Dropout rate applied to the RNN output	Any float between 0 and 1
epochs	50	Number of training epochs	Any positive integer
pad_len	250	Padding length for input sequences	Any positive integer
trainable_embedding	False	Whether the embedding layer is trainable	True, False
embedding_type	bert	Type of embedding used (BERT in this case)	bert, glove, fasttext.
validation_size	0.15	Size of the validation set as a percentage of the data	Any float between 0 and 1
trainable_last_encoder	False	Whether the last layer of the encoder is trainable	True, False
bert_type	bert-base-uncased	Type of BERT model used(if the embedding_type is bert)	bert-base-cased, bert-large-uncased, etc.

Training a Model:

!python train.py

Contribution

Contributions are welcome! Feel free to open issues or submit pull requests for improvements, bug fixes, or new features. Help to broden the results of the provided google sheet by inserting the result of your trained model in this google sheet.