gawa 0.1.0

Gaussian-Weighted Abstraction for Word Architecture (GAWA)

GAWA — Gaussian-Weighted Abstraction for Word Architecture

Overview

GAWA is a word-level morphological autoencoder that encodes any word — including unseen or morphologically complex words — into a dense embedding vector (eword) using character-level representations weighted by a Gaussian positional prior.

Unlike subword tokenizers (BPE, WordPiece, SentencePiece), GAWA treats each word as a sequence of characters and compresses it into a single fixed-size vector. This makes it:

• Language-agnostic: Works on any character-based language without a pretrained vocabulary
• Morphology-aware: Positional weighting captures prefix/suffix importance naturally
• Compact: The output sequence length equals the number of words, not subword tokens

GAWA is designed to plug in as the front-end morphological module of a Global Transformer, replacing the tokenizer entirely.

OOV Similarity (Indonesian)

GAWA can distinguish words by morphological footprint and still place misspellings or variants near their correct forms. Below are examples of OOV (out-of-vocabulary) queries from Indonesian words.

OOV: makann
  makanan       sim=0.9846
  makan         sim=0.9779
  mkan          sim=0.8618

OOV: mkan
  mknn          sim=0.8653
  makan         sim=0.8627
  makann        sim=0.8618

OOV: berlarr
  berlari       sim=0.9857
  permaenan     sim=0.5540
  permainan     sim=0.4860

OOV: permaenan
  permainan     sim=0.9255
  memakan       sim=0.5783
  berlarr       sim=0.5540

The pretrained model was trained on Indonesian language data (~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)

Architecture

Input Word (characters)
        │
        ├──► Char Embedding  (trainable)
        │
        ├──► Gaussian Positional Encoding  (fixed, non-trainable)
        │         μ_j = j,   σ_j = √j
        │
        └──► Concat → Fusion MLP
                          │
                    Weighted Pooling
                    (Gaussian Prior + Learnable Δ)
                          │
                    Output Projection
                          │
                       EWORD Vector  ──────────────────────────┐
                                                               │
                                                    ┌──────────▼──────────┐
                                                    │    GAWA Decoder     │
                                                    │  Init GRU Hidden    │
                                                    │  Char Emb + Concat  │
                                                    │  GRU Cell           │
                                                    │  Cross-Attention    │
                                                    │  Residual + Logits  │
                                                    └─────────────────────┘

Installation

1. Install via GitHub (pip)

pip install git+https://github.com/AiRukua/gawa.git

2. Local Development Install

git clone https://github.com/AiRukua/gawa.git
cd gawa
pip install -e .

3. Optional Dev Dependencies

pip install -e ".[dev]"

Quick Start (CLI)

1. Prepare Data

GAWA expects a word list (one word per line). You can build it from raw text:

gawa-prepare --input data/raw.txt --output data/processed/train.txt --lower

2. Train

Use the YAML configs in configs/:

gawa-train --config configs/gawa_small.yaml

Checkpoints are saved to the directory defined in the config (default: checkpoints/).

3. Encode Word Embeddings

gawa-encode \
  --checkpoint checkpoints/gawa_small/best.pt \
  --words "makan,memakan,makanan"

Default output is JSONL. Use --output to write to a file.

4. Evaluate / Reconstructions

gawa-evaluate --config configs/gawa_small.yaml --checkpoint checkpoints/gawa_small/best.pt

Quick Start (Python - Training)

from gawa import load_config, train_from_config

# 1) Load YAML config
cfg = load_config("configs/gawa_small.yaml")

# 2) Train from config (checkpoints saved to the directory in the YAML)
train_from_config(cfg)

Pretrained Model (Hugging Face)

If you want to use the pretrained GAWA model, you can load it directly from Hugging Face:

from gawa import GAWAModel

model = GAWAModel.from_pretrained("AiRukua/gawa")

kept_words, embs = model.encode_words(["makan", "memakan", "makanan"])
kept_words, recs = model.decode_words(["makan", "memakan", "makanan"])

Config Guide (YAML)

GAWA uses a YAML config file for training (see configs/). The key sections are:

data

• train_path: Path to a text file with one word per line.
• max_word_len: Maximum word length (characters). Words longer than this are filtered.

model

• char_emb_dim: Character embedding dimension.
• pos_enc_dim: Gaussian positional encoding dimension.
• hidden_dim: Fusion MLP & decoder GRU hidden size.
• eword_dim: Output word embedding dimension.
• max_word_len: Must match data.max_word_len.
• encoder_lambda_adjust: Weight for learnable position delta.
• decoder_num_layers: Number of GRU layers in the decoder.
• decoder_num_heads: Number of cross-attention heads.

training

• batch_size: Training batch size.
• epochs: Number of training epochs.
• lr: Learning rate.
• sample_every: How often to log reconstructions.

Example snippet:

data:
  train_path: data/processed/train.txt
  max_word_len: 32

model:
  char_emb_dim: 64
  pos_enc_dim: 64
  hidden_dim: 256
  eword_dim: 768
  max_word_len: 32
  encoder_lambda_adjust: 0.3
  decoder_num_layers: 1
  decoder_num_heads: 2

training:
  batch_size: 256
  epochs: 20
  lr: 3.0e-4
  sample_every: 1

To train with your config:

gawa-train --config configs/gawa_small.yaml

Model Dimensions

Parameter	Default	Description
char_emb_dim	64	Character embedding size
pos_enc_dim	64	Gaussian PE dimension
hidden_dim	256	Fusion MLP & GRU hidden size
eword_dim	768	Output word embedding dimension
max_word_len	32	Maximum word length in characters
lambda_adjust	0.3	Weight of learnable position delta

Why GAWA?

Feature	BPE / WordPiece	GAWA
Handles unseen words	✗ (UNK/fallback)	✓ (char-based)
Morphology-aware	Partial	✓ Explicit
Sequence length	Longer (subwords)	Shorter (words)
Language-specific vocab needed	✓	✗
Trainable end-to-end	✓	✓
Positional character weighting	✗	✓ Gaussian

Project Structure

• model/: Encoder, decoder, and core model.
• training/: Training loop, scheduler, and checkpointing.
• data/: Data prep utilities.
• eval/: Evaluation and encoding helpers.
• scripts/: CLI entrypoints.
• configs/: YAML configuration examples.

License

MIT License. See LICENSE for details.