tnqeet 0.1.2

Arabic text diacritization (dotting) library: restoring dots to undotted Arabic Rasm.

tnqeet

tnqeet is an Arabic dots restoration (dotting) library. It restores dots to dotless Arabic text (Rasm), where several letters collapse to the same basic shape once their dots are removed — for example ب ت ث all share one dotless form. Restoring the dots requires resolving this ambiguity from context.

For instance, the dotted sentence:

لسان الفتى شطر وشطر فؤاده

has the dotless (Rasm) form:

لساں الڡٮى سطر وسطر ڡواده

and the dotting task is to recover the former from the latter.

The library implements and evaluates several approaches to dotting: sequence-labeling (BiLSTM), Transformer, CANINE, n-gram language models (KenLM), and LLM-based models.

Installation

pip install tnqeet

This installs everything needed for the neural dotters (BiLSTM, Transformer, CANINE). The n-gram method additionally requires KenLM, which is not installed automatically because it compiles from source — see N-gram method (KenLM).

Usage

Remove dots

Convert dotted Arabic text to its dotless Rasm form:

from tnqeet import remove_dots

remove_dots("لسان الفتى شطر وشطر فؤاده")
# -> "لساں الڡٮى سطر وسطر ڡواده"

Restore dots

Load a pretrained model and restore the dots. Weights are downloaded from the Hugging Face Hub on first use and cached locally:

from tnqeet.dotting_models.transformer.models import TransformerDottingModel

model = TransformerDottingModel.from_pretrained()  # default size (6L)

model.restore_dots("لساں الڡٮى سطر وسطر ڡواده")
# -> "لسان الفتى شطر وشطر فؤاده"

restore_dots accepts a single string (returns a string) or a list of strings (returns a list).

Available models

Each method exposes from_pretrained(size) with friendly size keys (the default is used when no size is given):

Method	Class	Sizes (default in bold)
BiLSTM	LSTMDottingModel	1L–6L (4L)
Transformer	TransformerDottingModel	3L, 6L, 9L, 12L
CANINE	CanineDottingModel	c, s
n-gram	NgramDotter	order 2–8 (6)

from tnqeet.dotting_models.canine.models import CanineDottingModel

model = CanineDottingModel.from_pretrained("c")   # pick a specific size

The three neural models are PyTorch modules; move them to a GPU with the usual .to("cuda") and switch to eval mode with .eval() for inference.

N-gram method (KenLM)

The n-gram dotter is backed by KenLM, which compiles from source and is therefore not a dependency of tnqeet. Install it yourself, setting MAX_ORDER to at least the highest n-gram order you intend to load. tnqeet publishes orders up to 8, so build with MAX_ORDER=8 (KenLM's own default is 6, which cannot load orders 7–8):

MAX_ORDER=8 pip install "git+https://github.com/kpu/kenlm.git"

Then:

from tnqeet.dotting_models.ngrams.models import NgramDotter

dotter = NgramDotter.from_pretrained(order=6, beam_size=10)

dotter.restore_dots("لساں الڡٮى سطر وسطر ڡواده")
# -> "لسان الفتى شطر وشطر فؤاده"

Calling NgramDotter.from_pretrained(...) without KenLM installed raises an ImportError containing the install command above.

See CLAUDE.md for the full inference APIs and project layout.

Development

The project uses uv for dependency management and packaging. Clone the repository and set up the environment:

git clone https://github.com/MagedSaeed/tnqeet
cd tnqeet
uv sync   # installs the project plus the dev dependency group

uv sync installs everything needed to train, evaluate, and run the notebooks — including KenLM (built from source via MAX_ORDER, double check its installation) and the training/visualization tools (wandb, matplotlib, seaborn, ipython) that the published package omits. To build KenLM for a higher order, set MAX_ORDER before syncing:

MAX_ORDER=8 uv sync

Run the quality checks and tests:

uv run isort --check .
uv run black --check .
uv run ruff check .
uv run mypy .
uv run pytest

Building and publishing is handled by CI on a version tag (see .github/workflows/publish.yml); to build locally:

uv build

License

Apache-2.0. See LICENSE.