This repository provides a collection of Jupyter notebooks demonstrating how to apply PyTorch for common Natural Language Processing (NLP) tasks. It targets developers and researchers looking to learn and implement deep learning models for text classification, text generation, and machine translation. The notebooks offer practical examples for understanding and utilizing PyTorch's capabilities in NLP.

How It Works

The notebooks cover a range of deep learning architectures and techniques, including Bag-of-Words models, recurrent neural networks (RNNs) with and without attention mechanisms, and Transformer models. They illustrate practical applications such as sentiment analysis on IMDB reviews, character-level text generation, and English-to-French translation, providing hands-on experience with these NLP tasks.

Quick Start & Requirements

• Primary install / run command: Access notebooks via Google Colab (File -> Open Notebook -> Upload).
• Non-default prerequisites and dependencies: Google account for Colab, GPU recommended (available for free via Colab Runtime).
• Setup time: Minimal, as it's designed for cloud execution.
• Links: nbviewer

Highlighted Details

• Covers sentiment analysis, text generation, and machine translation.
• Demonstrates both RNN-based and Transformer-based translation models.
• Includes fine-tuning of a pre-trained GPT-2 model.
• Notebooks were presented at PyData events in 2019.

Maintenance & Community

• Last updated around 2019 based on event dates.
• Contributions via Pull Requests are welcomed for cleanups and error fixes.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The content appears to be from 2019, and dependencies or best practices may have evolved significantly since then. There is no explicit mention of licensing, which could impact commercial use or integration into proprietary projects.