{
  "channels": [
    "defaults",
    "local",
    "https://repo.anaconda.com/pkgs/main"
  ],
  "conda_build_version": "3.9.1+193.gd10dbde.dirty",
  "conda_private": false,
  "conda_version": "4.5.4",
  "description": "thinc is a Cython library for learning models with millions of parameters\nand dozens of classes. It drives https://spacy.io , a pipeline of very\nefficient NLP components. I\u2019ve only used thinc from Cython; no real Python\nAPI is currently available.\n",
  "dev_url": "https://github.com/explosion/thinc",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "home": "https://github.com/explosion/thinc/",
  "license": "MIT",
  "license_file": "LICENSE",
  "root_pkgs": [
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    "asn1crypto 0.24.0 py36_0",
    "atomicwrites 1.1.5 py36_0",
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    "beautifulsoup4 4.6.0 py36h72d3c9f_1",
    "bzip2 1.0.6 h1de35cc_5",
    "ca-certificates 2018.03.07 0",
    "certifi 2018.4.16 py36_0",
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    "chardet 3.0.4 py36h96c241c_1",
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    "pytest 3.6.2 py36_0",
    "pytest-cov 2.5.1 py36hf4510af_0",
    "python 3.6.6 hc167b69_0",
    "python-dateutil 2.7.3 py36_0",
    "python-libarchive-c 2.8 py36_3",
    "python.app 2 py36_8",
    "pytz 2018.4 py36_0",
    "pyyaml 3.12 py36h2ba1e63_1",
    "readline 7.0 hc1231fa_4",
    "requests 2.19.1 py36_0",
    "ruamel_yaml 0.15.40 py36h1de35cc_2",
    "setuptools 39.2.0 py36_0",
    "six 1.11.0 py36h0e22d5e_1",
    "sqlite 3.24.0 ha441bb4_0",
    "tk 8.6.7 h35a86e2_3",
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    "xz 5.2.4 h1de35cc_4",
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  "summary": "thinc: Learn super-sparse multi-class models"
}