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    "info": {
        "author": "Nicholas Tietz-Sokolsky",
        "author_email": "me@ntietz.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n\n# modmod\n\nmodmod is a library for making *Mod*-ular *Mod*-els. The primary problem that\nmodmod solves is how to load models at runtime without instantiating them\nmultiple times; in that respect, it is essentially a dependency injection\nsystem for models.\n\n# Installation\n\nTo use modmod, just install it with your package manager in the usual way. If\nyou use [Pipenv](https://docs.pipenv.org/), you can copy/paste this:\n\n```\npipenv install modmod\n```\n\n# Usage\n\nThere are two main pieces of modmod: Models and Pools.\n\nA `Pool` is a container for models. A `Model` can be treated like an augmented\nfunction which is a `Model` factory.\n\nHere's an example of defining the simplest possible model:\n\n```\nfrom modmod.model import Model\n\nclass AddThings(Model):\n    def call(self, x: int, y: int) -> int:\n        return x + y\n```\n\nAnd here is how you would use it:\n\n```\nimport modmod.pool\n\npool = modmod.pool.get()\n\nadder = pool.get(AddThings)\n\nz = adder(1, 2)\nprint(z) # prints 3\n```\n\nYou can also take a shortcut to get the model:\n\n```\nadder = AddThings.get()\n```\n\nHowever, this should never be done inside a model, bceause it will use the\ndefault pool and will have strange side effects if anyone tries to use your\nmodel in a non-default pool.\n\n## Models with initialization\n\nSometimes a model needs to be initialized to load in data or do other one-time\nstartup tasks. To do this, you just override the constructor and the `create`\nmethod. Here's an example for stripping stopwords:\n\n```\nimport nltk\nfrom modmod.model import Model\n\nclass RemoveStopwords(Model):\n  def __init__(self, pool: Pool, config: Dict[str, Any], stopwords: List[str]) -> None:\n    super().__init__(pool, config)\n    self.stopwords = stopwords\n\n  @classmethod\n  def create(cls, pool: Pool, config: Dict[str, Any]) -> 'RemoveStopwords':\n    nltk.download('stopwords')\n    stopwords = nltk.corpus.stopwords.words('english')\n    stopwords.append('')\n    stopwords.remove('not')\n    stopwords.remove('no')\n    return RemoveStopwords(pool, config, stopwords)\n\n  def call(self, words: List[str]) -> List[str]:\n    return list(filter(lambda w: w not in self.stopwords, words))\n```\n\nThe `create` method is invoked when you call `RemoveStopwords.get()`. It is\nonly called the _first_ time you get a model; after that, the created model\nlives in the pool, and it will not be re-initialized.\n\n*Why are *`__init__`* and *`create`* both required?* This is a good question.\nThe reason comes down to configurability and use in testing environments.\nIn the example above, if you wanted to experiment with a new list of\nstopwords, you could use the constructor to create a model with that list and\nthen add it into the pool:\n```\npool = modmod.pool.get('stopwords-experiment')\nconfig = {}\n\nremove_new_stopwords = RemoveStopwords(pool, config, ['stop', 'word', 'list'])\npool.add_model(remove_new_stopwords, RemoveStopwords)\n```\nOnce it's added to the pool, any calls to\n`RemoveStopwords.get('stopwords-experiment')` will find and retrieve the\nmanually created model.\n\nNote: `create` is generally overridden if you have to do a heavy operation,\nlike downloading a file or reading in some data. If you are just using the pool\nand the config object, it's perfectly acceptable to override `__init__` and\nleave the default behavior for `create`.\n\n\n## Configuring the pool\n\nEvery model gets configuration passed into them, and this comes from the pool.\nSo, if you need configuration, you need to configure the pool.\n\n**Note:** the pool must be configured *before* you get any models, since\nconfiguring it overwrites the existing pool.\n\nTo configure the default pool:\n\n```\nimport modmod.pool\n\nconfig = {'opt1': 2}\n\nmodmod.pool.configure(config)\n```\n\n## Non-default Pools\n\nSometimes you will want separate pools for separate tasks. One example of this\nis for unit testing: you may want to test with multiple configurations of the\nmodel. To do this, you can use separate pools.\n\nThe first step is to configure the pool:\n\n```\nimport modmod.pool\n\npoolname = 'my-pool'\nconfig = {'opt1': 2}\n\nmodmod.pool.configure(config, poolname)\n```\n\nThe second step is just to use the pool!\n\n```\nimport modmod.pool\n\npool = modmod.pool.get('my-pool')\n\nadder = pool.get(AddThings)\n# Equivalent:\nadder = AddThings.get('my-pool')\n```\n\n# Roadmap\n\nWe have a few initiatives on the roadmap. Each of these will be a version bump:\n\n* [ ] Add support for data and model versioning, add support for model training\n* [ ] Add hooks for profiling, debugging, caching\n\n\n\n",
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