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A novel tokenization architecture that proves a data-efficient, semantic-aware approach can surpass industry standards in robustness, efficiency, and linguistic intelligence.

Modern Large Language Models (LLMs) are incredibly powerful, but they all rely on a foundational weakness: a "semantically blind" tokenizer. Standard methods like Byte-Pair Encoding (BPE) build vocabularies by simply merging the most frequent pairs of characters. This is efficient but leads to major problems:

• Poor Morphological Understanding: They split words in ways that ignore their linguistic structure (e.g., token + ##izer instead of token-iz-er).
• Brittleness to Modern Text: They fail spectacularly on text common today, replacing emojis, code symbols, or non-Latin characters with [UNK] tokens, resulting in massive information loss.
• Data Inefficiency: They require massive, terabyte-scale datasets to learn a robust vocabulary.

Attention-Guided BPE (AG-BPE) is a new take on this classic algorithm. Instead of just counting, AG-BPE uses a lightweight Transformer model (the ContextAnalyzer) to "understand" the context and guide the merge process.

The core innovation is a hybrid scoring mechanism:

MergeScore(pair) = Frequency(pair) + λ * AttentionScore(pair)

This process favors the creation of tokens that are not just statistically frequent but also semantically coherent. The result is a tokenizer that learns the fundamental, compositional building blocks of a language.

Trained on a modest 302 MB dataset, our 16k-vocabulary AG-BPE tokenizer was benchmarked against industry standards, including OpenAI's Tiktoken series. The results are conclusive.

AG-BPE achieves:

• A compression ratio competitive with GPT-4 using a vocabulary 6x smaller.
• A decoding speed up to 30x faster than traditional tokenizers.
• Perfect robustness on complex, multilingual text where all other tested tokenizers fail.

The true power of AG-BPE is revealed in its segmentation.

Test Sentence: L'intelligence artificielle est fascinante.

• AG-BPE: L' | int | ell | ig | ence | ar | tif | ic | i | elle | ...
• BERT: l' | intelligence | art | ##ific | ##iel | ##le | ...

AG-BPE is the only tokenizer that correctly identifies the fundamental morphological units, providing a more interpretable and compositional representation for downstream models.

This repository provides the pre-trained AG-BPE tokenizer, ready to use in your projects.

No special libraries are needed beyond regex. The tokenizer is self-contained.

pip install regex

Download the ag_bpe_tokenizer.json file from this repository. It contains the vocabulary and the learned merge rules.

The following script shows how to load and use the tokenizer.

# how_to_use.py
import json
import regex as re
from pathlib import Path

# The self-contained tokenizer class (can be copied from this repo)
class AGBPETokenizer:
    # ... (copier-coller la classe AGBPETokenizer de how_to_use.py ici) ...

# --- Main usage ---
try:
    tokenizer = AGBPETokenizer.from_file("ag_bpe_tokenizer.json")
    print(f"✅ Tokenizer loaded successfully. Vocab size: {len(tokenizer.vocab)}")

    text = "L'IA utilise des tokenizers intelligents 🚀"
    
    encoded = tokenizer.encode(text)
    decoded = tokenizer.decode(encoded)

    print(f"\nOriginal: '{text}'")
    print(f"Encoded IDs: {encoded}")
    print(f"Decoded Text: '{decoded}'")
    print("-> ✅ Perfect reconstruction!")

except FileNotFoundError:
    print("Error: 'ag_bpe_tokenizer.json' not found. Please download it from the repository.")

For a detailed explanation of the methodology, architecture, and a full analysis of the results, please refer to our paper:

AG-BPE: Attention-Guided Byte-Pair Encoding for Semantic-Aware Tokenization

This project proves the superiority of the AG-BPE approach. Future work will focus on:

• Training larger-scale AG-BPE models.
• Evaluating the impact on downstream NLP tasks.
• Optimizing the training loop for even faster performance.

Théo M. B. CHARLET

• GitHub: @RDTvlokip