This repository provides a comprehensive collection of simplified TensorFlow 1.x implementations for various Natural Language Processing (NLP) tasks, targeting researchers and developers seeking to understand and experiment with deep learning models. It aims to make complex, state-of-the-art NLP architectures more accessible through Jupyter Notebooks.

How It Works

The project offers implementations of numerous NLP models, including sequence-to-sequence architectures with various attention mechanisms (Luong, Bahdanau), Transformers, BERT, and more. It covers a wide spectrum of tasks such as text classification, machine translation, summarization, chatbots, and speech-to-text, often providing multiple variations and configurations for each task. The code is designed to be beginner-friendly, simplifying original research implementations and including links to external repositories for models not implemented from scratch.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Requires TensorFlow versions 1.13 to 1.99.

Highlighted Details

• Extensive coverage of NLP tasks: abstractive/extractive summarization, chatbots, dependency parsing, entity tagging, NMT, speech-to-text, text classification, and more.
• Includes implementations of advanced architectures like BERT, Transformer-XL, and Tacotron.
• Provides detailed accuracy metrics for many models, often based on specific datasets and training epochs.
• Offers simplified versions of complex research papers and links to original codebases.

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The README does not explicitly state a license.

Limitations & Caveats

The project is strictly limited to TensorFlow versions between 1.13 and 2.0, making it incompatible with modern TensorFlow 2.x and later. Accuracy metrics are often based on limited training (e.g., 10 epochs) and specific datasets, which may not generalize.