Kyubyong/transformer
Transformer
This project is a TensorFlow implementation of a transformer model, providing a text-to-text deep learning framework designed to recognize and generate sequence patterns. It functions as an attention-based sequence model and a neural machine translation framework for converting text from one language to another.
The system implements the transformer network architecture, utilizing multi-head attention and positional encoding to process sequential data. It provides the necessary tools for transformer model training and machine translation inference, allowing for the execution of trained models to translate text and evaluate output quality.
The project covers the full natural language processing pipeline, including text preprocessing for vocabulary generation and sequence-to-sequence modeling. It incorporates structural components such as encoder-decoder architectures, layer-based normalization, and softmax-based probability distributions for token selection.
Features
- Neural Machine Translation - Provides a system for translating text between natural languages using sequence-to-sequence architectures.
- Encoder-Decoder Architectures - Implements a structural pattern that maps input sequences to output sequences via intermediate representations.
- Multi-Head Attention Mechanisms - Uses parallel attention heads to capture diverse dependencies within input sequences.
- Natural Language Processing - Implements techniques for analyzing and processing human language data through a full NLP pipeline.
- Neural Machine Translation Frameworks - Ships a toolkit for building and training multilingual models to translate text between languages.
- Sequence-to-Sequence Models - Utilizes encoder-decoder neural network architectures to transform input sequences into target sequences.
- Text-to-Text Frameworks - Implements a framework that unifies NLP tasks by formatting all inputs and outputs as raw text sequences.
- Transformer Architecture Implementation - Implements a full sequence-to-sequence transformer architecture using self-attention mechanisms.
- Positional Encodings - Injects sequence order information into the attention-based model using positional encodings.
- Text Sequence Model Training - Provides a training process to optimize neural network weights for sequence-pattern recognition tasks.
- Text Preprocessing Pipelines - Provides automated workflows for cleaning and preparing raw text data for model training.
- Text Translation Inference - Enables the generation of translated text from a source input using a trained model.
- Translation Model Training - Implements specific processes for training sequence-to-sequence models to map source languages to target languages.
- Natural Language Processing - Transformer implementation in TensorFlow.
