calamanCy: NLP pipelines for Tagalog [WIP]

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.

🔧 Installation

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

pip install calamancy

👩‍💻 Usage

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

import calamancy
from 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")

This will download the model to the .calamancy directory of your home directory. You can also download a model to a specific directory:

calamancy.download_model("tl_calamancy_md-0.1.0", save_directory)
nlp = calamancy.load_model("tl_calamancy_md-0.1.0", save_directory)

The nlp object is an instance of spaCy's Language class, and you can use it as any other spaCy pipeline. Head over to the documentation for more tutorials.

📦 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.

Datasets

You can find structured evaluation results for each dataset in the datasets/ directory.

Pipelines

The training procedure for each pipeline can be found in the training/ directory. They are further subdivided into versions. Each folder is an instance of a spaCy project.