Where2Spreddit

A collection of self-trained Neural Networks for classifying Reddit posts and Comments.

Description

Where2Spreddit classifies any sentence into a relevant category on the popular discussion site Reddit. The included trainer allows automated training (with customizable options) on four different neural network architectures.

• Convolutional Neural Networks (CNNs)
• Simple Recurrent Neural Networks (RNNs)
• Long short-term Memory Networks (LSTMs)
• Gated Recurrent Units (GRUs)

The following diagram illustrates the flow of the Where2Spreddit neural networks:

Installation Instructions

1. Clone this repository to your local workspace

2. Install required Python packages specified in requirements.txt

From root directory of repo, run pip3 install -r requirements.txt. It is highly recommended to use a clean virtual environment to prevent conflicts with other packages.

3. Install redditscore package

Run pip install git+https://github.com/crazyfrogspb/RedditScore.git

4. Install packages for spaCy tokenizer

Run

python -m spacy download en_core_web_lg
python -m spacy download en_core_web_sm

5. (Optional) Install CUDA for GPU training acceleration

If you are planning to train the models on a GPU, install the following version of PyTorch instead:

Run

pip3 install torch==1.9.0+cu102 torchvision==0.10.0+cu102 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Usage

Inference

For inference, run python predictor.py in the virtual environment. The predictor script reads directly from user input to classify and recommend a appropriate subreddit. Due to the Convolution layer dimensions for the CNN, the input sentence must include at least 4 words.

Example:

Enter a sentence: How much mass does a black hole have?                       

----- Baseline -----

     askscience:    100.0%
     askreddit:    0.0%
     history:    0.0%

----- CNN -----

     askscience:    91.22%
     jokes:    8.055%
     askreddit:    0.725%

----- RNN -----

     askscience:    99.874%
     science:    0.126%
     history:    0.0%

----- GRU -----

     askscience:    100.0%
     askreddit:    0.0%
     science:    0.0%

----- LSTM -----

     askscience:    93.295%
     jokes:    6.203%
     history:    0.249%

Training

For training, run python main.py in the virtual environment. The main script accepts the following options:

• model -- type of model to train ['baseline', 'rnn', 'cnn', 'gru', 'lstm']
• batch_size -- size of mini batch to use for training
• epochs -- number of iterations to train through
• lr -- adjust the learning rate of the current model
• emd-dim -- how many embedded dimension are included in each word
• rnn-hidden-dim -- the number of hidden dimensions in the RNN model
• save -- save the trained model as a .pt file
• tokenzer -- specify which word tokenizer to use ['spacy', 'crazy', 'nltk']