Resurrecting the Dead - Chinese

Text generation system based on a mixed corpus of 《毛澤東語錄》(Quotations From Chairman Mao Tse-Tung) and《論語》(Confucian Analects).

Framework	Model	Optimizer
PyTorch	RNN (LSTM)	Adam

穿越時空的偉人：用PyTorch重現偉人們的神經網絡

Usage

Mix corpus

$ cd src/corpus
$ python3 mix.py <first-corpus> <second-corpus> --output <output-corpus-text-file>

# Or directly run
$ ./run.sh

Train

$ cd src
$ python3 -m train.train <corpus-text-file> 

# For more options
$ python3 -m train.train -h

# Or directly run
$ ./train.sh

Outputs:

• model.bin: torch model
• corpus.bin: parsed corpus, mapping, & vocabulary

Text generation

$ cd src
$ python3 -m generate_text.gen <corpus-bin-file> <model-bin-file>

# For more options
$ python3 -m generate_text.gen -h

# Or directly run
$ ./gen.sh

Structure

├── corpus                                          # Raw & parsed corpus
│   ├── corpus.txt                                      # Main corpus file for training
│   ├── luen_yu_clean.txt                               # Raw corpus with irrelevant words removed
│   ├── luen_yu_raw.txt                                 # Raw corpus
│   ├── luen_yu_sent.txt                                # Clean corpus seperated into sentences
│   ├── mao_clean.txt                                   # Raw corpus with irrelevant words removed
│   ├── mao_raw.txt                                     # Raw corpus
│   └── mao_sent.txt                                    # Clean corpus seperated into sentences
├── output                                          # Results
│   ├── log                                             # Log files
│   └── model                                           # Pretrained models
│       └── slxx-bsxx-edxx-hdxx-lrxx-drxx-epxx              # seq_length, batch_size, embedding_dim, hidden_dim, 
│                                                           # learning_rate, dropout, epochs
└── src                                             # Source codes
    ├── corpus                                          # Corpus processing
    │   ├── mix.py                                          # Mix two corpora
    │   └── run.sh                                          # Running the script
    ├── generate_text                                   # Text generation
    │   └── gen.py                                          # Text generation
    ├── train                                           # Model training
    │   ├── data.py                                         # Parse data
    │   ├── model.py                                        # Main LSTM model
    │   └── train.py                                        # Training
    ├── gen.sh                                          # Running text generation script
    └── train.sh                                        # Running training script