deeplatent-nlp 0.3.5

DeepLatent - Morphology-aware tokenizer for Arabic/English bilingual text with native Rust core

DeepLatent

DeepLatent - SARF Tokenizer for Arabic/English bilingual text with native Rust core.

This package provides the SARF (Sarf-Aware Representation Framework) tokenizer that achieves excellent Arabic/English parity (1.09) by applying morpheme-level preprocessing before BPE tokenization.

Installation

pip install deeplatent-nlp

Optional Extras

pip install deeplatent-nlp[hf]    # HuggingFace transformers integration
pip install deeplatent-nlp[all]   # Everything

Building from Source

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh  # Install Rust
pip install .

Quick Start

Native Mode (Recommended)

Native mode uses the compiled Rust core. It requires encrypted tokenizer data files (generated by scripts/prepare_tokenizer_data.py or bundled during build):

from deeplatent import SARFTokenizer

# Auto-detect bundled data
tokenizer = SARFTokenizer.from_native()

# Or load from explicit paths
tokenizer = SARFTokenizer.from_native(
    morpheme_map_path="path/to/morpheme_map.bin.enc",
    bpe_data_path="path/to/bpe.bin.enc",
)

# Encode
ids = tokenizer.encode("مرحبا بكم في العالم")
print(f"Token IDs: {ids}")
print(f"Token count: {len(ids)}")

# Decode
text = tokenizer.decode(ids)
print(f"Decoded: {text}")

# Batch operations
texts = ["مرحبا", "Hello world", "كتب الطالب الدرس"]
batch_ids = tokenizer.encode_batch(texts)
decoded = tokenizer.decode_batch(batch_ids)

HuggingFace Mode

from deeplatent import SARFTokenizer

# Requires: pip install deeplatent-nlp[hf]
tokenizer = SARFTokenizer.from_pretrained("almaghrabima/deeplatent-tokenizer")

# Full HF-compatible API
result = tokenizer.encode(
    "مرحبا بكم",
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="pt",
)

Roundtrip Guarantee

The SARF tokenizer provides an exact roundtrip guarantee:

decode(encode(text)) == normalize(text)

The encoder applies Arabic text normalization (the same normalization used during BPE training) before tokenization. Character variants are unified, diacritics are stripped, and Indic digits are converted to ASCII. The roundtrip returns the normalized form of the input.

from deeplatent import SARFTokenizer

tokenizer = SARFTokenizer.from_native()

# English roundtrips exactly
text = "Hello world"
assert tokenizer.decode(tokenizer.encode(text)) == text

# Arabic roundtrips to normalized form
assert tokenizer.decode(tokenizer.encode("أحمد")) == "احمد"

# Character variants produce identical token IDs
assert tokenizer.encode("أحمد") == tokenizer.encode("احمد")

# Diacritics are stripped
assert tokenizer.encode("كَتَبَ") == tokenizer.encode("كتب")

# Indic digits map to ASCII
assert tokenizer.encode("١٢٣") == tokenizer.encode("123")

What Gets Normalized

Input	Output	Rule
أ إ آ ٱ	ا	Alef unification
ى	ي	Ya normalization
ؤ	و	Hamza-on-waw
ئ	ي	Hamza-on-ya
كَتَبَ	كتب	Diacritic removal
ـعربيـ	عربي	Tatweel removal
١٢٣	123	Indic digit conversion
Zero-width chars	(removed)	ZWJ/ZWNJ/BOM cleanup

This matches standard Arabic NLP practice and is the same as GPT-family tokenizers that normalize Unicode on input.

Validated on eval_1b

Roundtrip fidelity verified on 10,000 samples from the eval_1b dataset:

Samples tested:  10,000
Passed:          10,000 (100.00%)
Failed:          0
Avg tokens/char: 0.3649

Performance

Metric	With SARF	Without
Arabic Fertility	2.29	5.65
English Fertility	2.10	2.91
Parity (Ar/En)	1.09	1.94

Fertility = average tokens per word. Lower is better. Parity closer to 1.0 means more equal treatment between languages.

Supported Platforms

Pre-built wheels are published for every release:

Platform	Architectures	Python
Linux (manylinux)	x86_64, aarch64	3.8 - 3.13
macOS	x86_64, arm64	3.10 - 3.13
Windows	x86_64	3.10 - 3.13

Source distribution is also available for other platforms (requires Rust toolchain).

API Reference

Loading

from deeplatent import SARFTokenizer

# Native mode — fast, no network, no Python dependencies
tokenizer = SARFTokenizer.from_native()
tokenizer = SARFTokenizer.from_native("morpheme_map.bin.enc", "bpe.bin.enc")

# HuggingFace mode — full transformers compatibility
tokenizer = SARFTokenizer.from_pretrained("almaghrabima/deeplatent-tokenizer")

# Local directory (HF format)
tokenizer = SARFTokenizer.from_directory("./my_tokenizer")

Encoding

# Single text
ids = tokenizer.encode("مرحبا بكم")

# Batch
batch_ids = tokenizer.encode_batch(["مرحبا", "Hello", "كتب الدرس"])

# HF mode options
result = tokenizer.encode("text", padding=True, truncation=True,
                           max_length=512, return_tensors="pt")

Decoding

# Single sequence
text = tokenizer.decode(ids)

# Batch
texts = tokenizer.decode_batch(batch_ids)

Token Inspection

# Tokenize to strings
tokens = tokenizer.tokenize("مرحبا بكم")

# Convert between tokens and IDs
token_id = tokenizer.token_to_id("hello")
token_str = tokenizer.id_to_token(42)

# Vocabulary info
print(tokenizer.vocab_size)          # 65792
print(tokenizer.using_native)        # True
print(tokenizer.preprocessing_enabled)  # True

Normalization (Rust Core)

from deeplatent._core import normalize_arabic_text

normalized = normalize_arabic_text("أحمد")  # "احمد"

What is SARF?

SARF (صَرْف) is the Arabic term for morphology. In Arabic linguistics, sarf refers to the system that governs word formation, roots and patterns (جذر / وزن), prefixes, suffixes, infixes, tense, gender, number, and derivation.

Most tokenizers treat Arabic as bytes or characters. SARF treats Arabic as a language.

License

CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0 International).

For commercial licensing: [e-mail]

Links

• PyPI Package
• HuggingFace Model
• Evaluation Dataset