pyproject.toml

[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "hausa-sentiment-xlmr"
version = "1.0.0"
description = "Cross-lingual fine-tuning of XLM-R for Hausa-English code-switched sentiment analysis."
readme = "README.md"
requires-python = ">=3.9"
license = { text = "MIT" }
authors = [
    { name = "Osei Wusu Brempong Jnr" },
    { name = "Junaid Hussain Muzamal" },
    { name = "Liu Zhaobin" },
]
keywords = [
    "sentiment-analysis", "code-switching", "hausa", "xlm-roberta",
    "low-resource-nlp", "multilingual", "ghanaian-pidgin",
]
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: MIT License",
    "Intended Audience :: Science/Research",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]
dependencies = [
    "torch>=2.0",
    "transformers>=4.40",
    "datasets>=2.18",
    "scikit-learn>=1.3",
    "numpy>=1.24",
    "pandas>=2.0",
    "pyyaml>=6.0",
    "tqdm>=4.66",
]

[project.optional-dependencies]
viz = ["matplotlib>=3.7", "seaborn>=0.13"]
onnx = ["onnx>=1.15", "onnxruntime>=1.17"]
marian = ["sentencepiece>=0.1.99", "sacremoses>=0.1.1"]
dev = ["pytest>=7.4", "pytest-cov>=4.1", "ruff>=0.4", "black>=24.0"]
all = [
    "matplotlib>=3.7", "seaborn>=0.13",
    "onnx>=1.15", "onnxruntime>=1.17",
    "sentencepiece>=0.1.99", "sacremoses>=0.1.1",
    "pytest>=7.4", "pytest-cov>=4.1", "ruff>=0.4", "black>=24.0",
]

[project.scripts]
hausa-sentiment-train = "hausa_sentiment.train:main"
hausa-sentiment-eval = "hausa_sentiment.evaluate:main"

[project.urls]
Homepage = "https://github.com/your-org/hausa-sentiment-xlmr"
Paper = "https://doi.org/10.1145/3830015"

[tool.setuptools.packages.find]
where = ["src"]

[tool.setuptools.package-data]
hausa_sentiment = ["py.typed"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q"
markers = [
    "slow: tests that download a (tiny) model or run training",
]

[tool.ruff]
line-length = 100
target-version = "py39"

[tool.black]
line-length = 100