hyunwoongko/transformer
Transformer
This project is a PyTorch implementation of an attention-based neural network designed for sequence-to-sequence deep learning tasks. It serves as a library for constructing deep learning sequence models that utilize encoder and decoder structures to process natural language and sequential data.
The implementation centers on a multi-head attention mechanism to capture diverse relationships between tokens without using recurrence. It includes sinusoidal positional encoding to maintain sequence order and point-wise feed-forward networks to transform token positions independently.
The architecture incorporates layer-based normalization to stabilize training and accelerate convergence. It provides the necessary components for neural network architecture design within the domains of natural language processing and sequence-to-sequence learning.
Features
- Multi-Head Attention Mechanisms - Implements a multi-head attention mechanism that processes sequences through parallel heads to capture diverse dependencies.
- Attention Mechanisms - Implements the mathematical and computational logic for attention layers using PyTorch.
- Encoder-Decoder Architectures - Implements a structural pattern for mapping input sequences to output sequences via encoder and decoder layers.
- Experimental Transformer Libraries - Serves as a modular PyTorch library providing components for research-oriented transformer architectures.
- Neural Network Design Frameworks - Provides composable modules for the structural design and implementation of transformer-based neural networks.
- Attention-Based Networks - Implements a neural network design that uses multi-head attention to process sequential data without recurrence.
- PyTorch Implementations - Provides a research-oriented implementation of a transformer model using the PyTorch framework.
- Sequence Learning Models - Provides architectures and training methods for mapping input sequences to output sequences.
- Sequence-to-Sequence Transformer Architectures - Constructs integrated transformer architectures combining encoder and decoder components for sequence mapping tasks.
- Transformer Architecture Implementation - Provides a full implementation of sequence-to-sequence models using self-attention mechanisms, encoders, and decoders.
- Feed-Forward Network Layers - Ships feed-forward network layers that perform non-linear transformations at each token position independently.
- Normalization Layers - Provides normalization layers that standardize input activations to stabilize training and accelerate convergence.
- Natural Language Processing - Designed for natural language processing tasks by analyzing word context within sequences.
- Sinusoidal Encodings - Implements sinusoidal encodings to inject absolute position information into token embeddings.
