Skip to main content

Character Level Language Model implemented following "Bengio.et.al 2003"

Project description

PYPI Package Link

Character-Level Language Models Repo 🕺🏽

This repository contains multiple character-level language models (charLLM). Each language model is designed to generate text at the character level, providing a granular level of control and flexibility.

🌟 Available Language Models

  • Character-Level MLP LLM (First MLP LLM)
  • GPT-2 (under process)

Character-Level MLP

The Character-Level MLP language model is implemented based on the approach described in the paper "A Neural Probabilistic Language Model" by Bential et al. (2002). It utilizes a multilayer perceptron architecture to generate text at the character level.

Installation

With PIP

This repository is tested on Python 3.8+, and PyTorch 2.0.0+.

First, create a virtual environment with the version of Python you're going to use and activate it.

Then, you will need to install PyTorch.

When backends has been installed, CharLLMs can be installed using pip as follows:

pip install charLLM

With GIT

CharLLMs can be installed using conda as follows:

git clone https://github.com/RAravindDS/Neural-Probabilistic-Language-Model.git

Quick Tour

To use the Character-Level MLP language model, follow these steps:

  1. Install the package dependencies.
  2. Import the CharMLP class from the charLLM module.
  3. Create an instance of the CharMLP class.
  4. Train the model on a suitable dataset.
  5. Generate text using the trained model.

Demo for NPLM

# Import the class 
>>> from charLLM import NPLM # Neurl Probablistic Language Model 
>>> text_path = "path-to-text-file.txt" 
>>> model_parameters = {
    "block_size" :3, 
    "train_size" :0.8, 
    'epochs' :10000, 
    'batch_size' :32, 
    'hidden_layer' :100, 
    'embedding_dimension' :50,
    'learning_rate' :0.1 
    }
>>> obj = NPLM(text_path, model_parameters)  # Initialize the class 
>>> obj.train_model() 
## It outputs the val_loss and image 
>>> obj.sampling(words_needed=10) #It samples 10 tokens. 
Model Output Graph

Feel free to explore the repository and experiment with the different language models provided.

Contributions

Contributions to this repository are welcome. If you have implemented a novel character-level language model or would like to enhance the existing models, please consider contributing to the project.

License

This repository is licensed under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

charLLM-0.0.4.tar.gz (8.5 kB view details)

Uploaded Source

Built Distribution

charLLM-0.0.4-py3-none-any.whl (8.9 kB view details)

Uploaded Python 3

File details

Details for the file charLLM-0.0.4.tar.gz.

File metadata

  • Download URL: charLLM-0.0.4.tar.gz
  • Upload date:
  • Size: 8.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for charLLM-0.0.4.tar.gz
Algorithm Hash digest
SHA256 d4829a5a9d356eee6c0e2fdf49f0d8ef67677986b11c43f53d561803bf075cd4
MD5 426e8d83d2c539760e1cd1c2bc127bd0
BLAKE2b-256 3295b5609d675e51999f7690e338b3b0a40411fbe149b59be7fa4278d84bad7f

See more details on using hashes here.

File details

Details for the file charLLM-0.0.4-py3-none-any.whl.

File metadata

  • Download URL: charLLM-0.0.4-py3-none-any.whl
  • Upload date:
  • Size: 8.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for charLLM-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 722cd739e7b0549bb7eb046a630441f0021e8c5e38da085138696a16b61a213a
MD5 71d216158db34731a301e0600cff6396
BLAKE2b-256 bb22738cf70c80533e1d329472bce80ada2d42c49bb8f5023f336be4dfd195bf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page