simple-transformer 0.0.1

A simple and modular implementation of the Transformer model in PyTorch

Simple Transformer

A simple and modular implementation of the Transformer model in PyTorch. This package provides an intuitive interface to build Transformer models for a variety of NLP tasks like translation, summarization, and more.

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🌟 Features

• Encoder, Decoder, and Self-Attention layers implemented.
• Highly customizable architecture (e.g., number of layers, attention heads, embedding size).
• Supports Masked and Unmasked attention mechanisms.
• Easy to integrate and extend for different use cases.

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🚀 Installation

Via pip

You can install the package directly from PyPI:

pip install simple-transformer

From Source

Alternatively, clone the repository and install it locally:

git clone https://github.com/nullHawk/simple-transformer.git
cd simple-transformer
pip install .

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📝 Usage

Basic Transformer Model

Here's how you can use the basic Transformer model in your project:

import torch
from simple_transformer import Transformer

# Define input parameters
src_vocab_size = 10
trg_vocab_size = 10
src_pad_idx = 0
trg_pad_idx = 0

# Initialize the Transformer model
model = Transformer(
    src_vocab_size=src_vocab_size,
    trg_vocab_size=trg_vocab_size,
    src_pad_idx=src_pad_idx,
    trg_pad_idx=trg_pad_idx,
)

# Example input (batch_size=2, sequence_length=9)
src = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0], [1, 8, 7, 3, 4, 5, 6, 7, 2]])
trg = torch.tensor([[1, 7, 4, 3, 5, 9, 2, 0], [1, 5, 6, 2, 4, 7, 6, 2]])

# Forward pass through the Transformer model
out = model(src, trg[:, :-1])
print(out.shape)

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🔧 Components

• Encoder: Processes the source sequence, extracting context information.
• Decoder: Generates the target sequence based on the encoded input.
• Self-Attention: Enables each word in a sequence to focus on other words in the same sequence.
• Transformer: The central model combining the encoder and decoder for sequence-to-sequence tasks.

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📦 Requirements

• Python >= 3.7
• PyTorch >= 1.9.0

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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🙏 Acknowledgements

• This package is inspired by the original Transformer model.

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