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calamanCy: NLP pipelines for Tagalog

calamanCy is a Tagalog natural language preprocessing framework made with spaCy. Its goal is to provide pipelines and datasets for downstream NLP tasks. This repository contains material for using calamanCy, reproduction of results, and guides on usage.

calamanCy takes inspiration from other language-specific spaCy Universe frameworks such as DaCy, huSpaCy, and graCy. The name is based from calamansi, a citrus fruit native to the Philippines and used in traditional Filipino cuisine.

🌐 Website: https://ljvmiranda921.github.io/calamanCy

📰 News

• [2025-05-15] UD-NewsCrawl, a work that currently powers calamanCy v2 and where I'm one of the lead authors, has been accepted at ACL 2025! I will be presenting this work in Vienna on July 29! See you :)
• [2025-01-19] Released v0.2.0 models with significantly improved performance on syntactic parsing and NER! All thanks to the newly-released UD-NewsCrawl treebank! See full changes in this blogpost.
• [2024-08-01] Released new NER-only models based on GLiNER! You can find the models in this HuggingFace collection. Span-Marker and calamanCy models are still superior, but GLiNER offers a lot of extensibility on unseen entity labels. You can find the training pipeline here.
• [2024-07-02] I talked about calamanCy during my guest lecture, "Artisanal Filipino NLP Resources in the time of Large Language Models," @ DLSU Manila. You can find the slides (and an accompanying blog post) here.
• [2023-12-05] We released the paper calamanCy: A Tagalog Natural Language Processing Toolkit and will be presented in the NLP-OSS workshop at EMNLP 2023! Feel free to check out the Tagalog NLP collection in HuggingFace.
• [2023-11-01] The named entity recognition (NER) dataset used to train the NER component of calamanCy has now a corresponding paper: Developing a Named Entity Recognition Dataset for Tagalog! It will be presented in the SEALP workshop at IJCNLP-AACL 2023! The dataset is also available in HuggingFace. I've also talked about my thoughts on the annotation process in my blog.
• [2023-08-01] First release of calamanCy! Please check out this blog post to learn more and read some of my preliminary work back in February here.

🔧 Installation

To get started with calamanCy, simply install it using pip by running the following line in your terminal:

pip install calamanCy

Development

If you are developing calamanCy, first clone the repository:

git clone git@github.com:ljvmiranda921/calamanCy.git

Then, create a virtual environment and install the dependencies:

python -m venv .venv
.venv/bin/pip install -e .  # requires pip>=23.0
.venv/bin/pip install .[dev]
# Activate the virtual environment
source venv/bin/activate

[Experimental] If you want to use uv, then install via the following commands:

uv sync --dev
# Activate the virtual environment
source .venv/bin/activate

We also require using pre-commit hooks to standardize formatting. The pre-commit dependency should be in your virtual environment if the installation steps above were successful:

pre-commit install

Running the tests

We use pytest as our test runner:

python -m pytest --pyargs calamancy

👩‍💻 Usage

To use calamanCy you first have to download either the medium, large, or transformer model. To see a list of all available models, run:

import calamancy
for model in calamancy.models():
    print(model)

# ..
# tl_calamancy_md-0.1.0
# tl_calamancy_lg-0.1.0
# tl_calamancy_trf-0.1.0

To download and load a model, run:

nlp = calamancy.load("tl_calamancy_md-0.1.0")
doc = nlp("Ako si Juan de la Cruz")

The nlp object is an instance of spaCy's Language class and you can use it as any other spaCy pipeline. You can also access these models on Hugging Face 🤗.

📦 Models and Datasets

calamanCy provides Tagalog models and datasets that you can use in your spaCy pipelines. You can download them directly or use the calamancy Python library to access them. The training procedure for each pipeline can be found in the models/ directory. They are further subdivided into versions. Each folder is an instance of a spaCy project.

Here are the models for the latest release:

| Model | Pipelines | Description | | --------------------------- | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | | tl_calamancy_md (73.7 MB) | tok2vec, tagger, morphologizer, parser, ner | CPU-optimized Tagalog NLP model. Pretrained using the TLUnified dataset. Using floret vectors (50k keys) | | tl_calamancy_lg (431.9 MB) | tok2vec, tagger, morphologizer, parser, ner | CPU-optimized large Tagalog NLP model. Pretrained using the TLUnified dataset. Using fastText vectors (714k) | | tl_calamancy_trf (775.6 MB) | transformer, tagger, parser, ner | GPU-optimized transformer Tagalog NLP model. Uses roberta-tagalog-base as context vectors. |

📓 API

The calamanCy library contains utility functions that help you load its models and infer on your text. You can think of these functions as "syntactic sugar" to the spaCy API. We highly recommend checking out the spaCy Doc object, as it provides the most flexibility.

Loaders

The loader functions provide an easier interface to download calamanCy models. These models are hosted on HuggingFace so you can try them out first before downloading.

<kbd>function</kbd> get_latest_version

Return the latest version of a calamanCy model.

| Argument | Type | Description | | ----------- | ----- | -------------------------------- | | model | str | The string indicating the model. | | RETURNS | str | The latest version of the model. |

<kbd>function</kbd> models

Get a list of valid calamanCy models.

| Argument | Type | Description | | ----------- | ----------- | ------------------------------ | | RETURNS | List[str] | List of valid calamanCy models |

<kbd>function</kbd> load

Load a calamanCy model as a spaCy language pipeline.

| Argument | Type | Description | | ----------- | ------------------------------------------- | -------------------------------------------------------------------------------------------- | | model | str | The model to download. See the available models at calamancy.models(). | | force | bool | Force download the model. Defaults to False. | | **kwargs | dict | Additional arguments to spacy.load(). | | RETURNS | Language | A spaCy language pipeline. |

Inference

Below are lightweight utility classes for users who are not familiar with spaCy's primitives. They are only useful for inference and not for training. If you wish to train on top of these calamanCy models (e.g., text categorization, task-specific NER, etc.), we advise you to follow the standard spaCy training workflow.

General usage: first, you need to instantiate a class with the name of a model. Then, you can use the __call__ method to perform the prediction. The out