pyproject.toml

[tool.poetry]
authors = ["Johannes Filter <hi@jfilter.de>"]
classifiers = [
  "Programming Language :: Python :: 3.6",
  "Programming Language :: Python :: 3.7",
  "Programming Language :: Python :: 3.8",
  "License :: OSI Approved :: BSD License",
  "Topic :: Scientific/Engineering :: Information Analysis",
  "Topic :: Scientific/Engineering :: Artificial Intelligence",
]
description = "Python package to construct word embeddings for small data"
keywords = ["natural-language-processing", "word-embeddings", "ppmi", "pmi", "pmi-svd"]
license = "BSD-2-Clause"
name = "hyperhyper"
readme = "README.md"
repository = "https://github.com/jfilter/hyperhyper"
version = "0.1.1"

[tool.poetry.dependencies]
dataset = "1.*"
gensim = "3.*"
importlib_resources = {version = "*", python = "<= 3.6"}
python = ">= 3.6"
tqdm = "*"

scikit-learn = {version = "*", optional = true}
spacy = {version = "2.*", optional = true}

[tool.poetry.dev-dependencies]
black = "*"
pylint = "*"
pytest = "^5.2"
scikit-learn = "*"
spacy = "*"

[tool.poetry.extras]
full = ["scikit-learn", "spacy"]

[build-system]
build-backend = "poetry.core.masonry.api"
requires = ["poetry-core>=1.0.0"]