Overview

The RNN Language Model Boilerplate is a fantastic toolkit designed for those looking to explore language modeling in a streamlined manner. Built on the DyNet framework, this boilerplate provides an intuitive setup that allows users to quickly start experiments. With easy-to-follow commands and configurations, it caters not only to beginners in machine learning but also to seasoned practitioners who wish to delve deeper into recurrent neural networks (RNNs).

This platform is ideal for those who want to experiment with different architectures and datasets. Whether you're training on the Penn Treebank or your own custom dataset, this boilerplate empowers you to conduct experiments efficiently without having to wade through complex setups.

Features

Easy Setup: Quickly get started with minimal boilerplate code and clear instructions for training RNN models.
Custom Corpus Reader: Easily implement data sources by defining a new CorpusReader in util.py, allowing flexible data usage.
Model Saving & Loading: Save your trained models using the --save flag and effortlessly load them later with the --load flag, preserving all parameter settings.
Training Options: Start training a baseline RNN model with straightforward commands, choosing between character-level and word-level models.
Configurable Parameters: Adjust model size, dropout rates, and various architectural features using user-friendly flags during training.
Visualization Tools: Generate logs for your model's performance and visualize the training process over time with visualize_log.py.
Scalable Model Sizes: Choose from four preset model sizes or customize parameters to fit specific needs, from small to enormous architectures.

Jbuckman

Boilerplate Dynet Rnn Lm