Metadata-Version: 2.1
Name: genn
Version: 0.7.4
Summary: High level interface for text applications using PyTroch RNN's.
Home-page: https://github.com/FahedSabellioglu/genn
Author: Abdelrahman Mahmoud, Fahed Sabellioglu
Author-email: magedmahmoud@std.sehir.edu.tr, fahedshaabani@std.sehir.edu.tr
License: MIT
Description: # GeNN
        [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt)
        
        GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.
        
        
        ## Features
        
        1.  Preprocessing: 
        	- Parsing txt, json, and csv files.
        	- NLTK, regex and spacy tokenization support.
        	- GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.
        2. Architectures and customization:
        	- GPT-2 with small, medium, and large variants.
        	- LSTM and GRU, with variable size.
        	- Variable number of layers and batches.
        	- Dropout.
        3. Text generation:
        	- Random seed sampling from the n first tokens in all instances, or the most frequent token.
        	- Top-K sampling for next token prediction with variable K.
        	- Nucleus sampling for next token prediction with variable probability threshold.
        
        ## Getting started
        
        ### How to install
        ```bash
        pip install genn
        ```
        ### Prerequisites
        * PyTorch 1.4.0
        ```bash
        pip install torch==1.4.0
        ```
        * Pytorch Transformers
        ```bash
        pip install pytorch_transformers
        ```
        * NumPy
        ```bash
        pip install numpy
        ```
        * fastText
        ```bash
        pip install fasttext
        ```
        Use the package manager [pip](https://pypi.org/project/genn) to install genn.
        
        ## Usage
        
        ```python
        from genn import Preprocessing, LSTMGenerator, GPT2
        #LSTM example
        ds = Preprocessing("data.txt")
        gen = LSTMGenerator(ds, nLayers = 2,
                                batchSize = 16,
                                embSize = 64,
                                lstmSize = 16,
                                epochs = 20)
        			
        #Train the model
        gen.run()
        
        # Generate 5 new documents
        print(gen.generate_document(5))
        
        #GPT-2 example
        gen = GPT2("data.txt",
         	    taskToken = "Movie:",
        	    epochs = 7,
        	    variant = "medium")
        #Train the model
        gen.run()
        
        #Generate 10 new documents
        print(gen.generate_document(10))
        	
        
        ```
        #### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md).
        #### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md).
        #### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md)
        ## Contributing
         Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        ## License
        Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information.
        
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