GAWA - Gaussian-Weighted Abstraction for Word Architecture

Model Overview

GAWA is a word-level morphological autoencoder. It maps a word (sequence of characters) into a single dense vector (eword) using a Gaussian positional prior, then reconstructs the word with an autoregressive decoder.

Why this matters:

• No subword vocabulary: works with any character-based language.
• Handles unseen words: char-based encoding avoids UNK issues.
• Compact sequence length: one word -> one vector.

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What This Repo Provides

• A pretrained checkpoint (see checkpoints/ on the model page)
• Python utilities to encode and decode words
• CLI utilities for training, evaluation, and encoding
• Source code: https://github.com/AiRukua/gawa

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Installation

pip install gawa

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How To Use

from gawa import GAWAModel

# Load from Hugging Face Hub
model = GAWAModel.from_pretrained("AiRukua/gawa")

# Encode / decode directly from model
kept_words, embs = model.encode_words(["makan", "memakan", "makanan"])
kept_words, recs = model.decode_words(["makan", "memakan", "makanan"])

Intended Use

• Produce word embeddings for downstream models
• Reconstruct words for qualitative evaluation
• Explore morphology-aware word representations
• Exchange your BPE with GAWA

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Limitations

• Reconstruction quality depends on training data and config.
• Very long or rare character patterns may be filtered by max_word_len.
• This model focuses on word-level encoding; it does not model full sentences by itself.

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Training Data

This model was trained on ~8.2 million unique words extracted from Indo4B: https://huggingface.co/datasets/taufiqdp/Indo4B).

Training Details

• Decoder training: 2 epochs
• Accuracy: 94%
• Dataset: ~8.2 million words extracted from Indo4B
• Training time: ~12 hours
• Hardware: NVIDIA T4 (Kaggle)

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License

MIT License. See LICENSE in the repository.