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        "author": "Junjie Wu",
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        "description": "# Agreementr\n\n## Intro\nAgreementr is a package used to predict the value of agreement of texts.\n\nIt is based on a fine tuned BERT model.\n## Install \n\n### Use pip\nIf `pip` is installed, agreementr could be installed directly from it:\n\n    pip install agreementr\n\n### Dependencies\n\tpython>=3.6.0\n\ttorch>=0.4.1\n\tnumpy\n\tpandas\n\tunidecode\n\tpytorch-pretrained-bert\n\tpytorch-transformers\n\n\n\n## Usage and Example\n\n### Notes: During your first usage, the package will download a model file automatically, which is about 400MB.\n\n### `predict`\n`predict` is the core method of this package, \nwhich takes a single text of a list of texts, and returns a list of raw values in `[1,5]` (higher means more agreement, while lower means less).\n\n### Simplest usage\n\nYou may directly import `agreementr` and use the default predict method, e.g.:\n\n    >>> import agreementr\n    >>> agreementr.predict([\"I am totally agree with you\"])\n    [4.3568916]\n\n### Construct from class\nAlternatively, you may also construct the object from class, where you could customize the model path and device:\n\n\t>>> from agreementr import Agreementr\n\t>>> ar = Agreementr()\n\n\t# Predict a single text\n\t>>> ar.predict([\"I am totally agree with you\"])\n\t[4.3568916]\n\n\t# Predict a list of texts\n\t>>> preds = ar.predict(['I am totally agree with you','I hate you'])\n    >>> f\"Raw values are {preds}\"\n    [4.3568916 2.42935]\n\n\n\nMore detail on how to construct the object is available in docstrings.\n\n### Model using multiprocessing when preprocessing a large dataset into BERT input features \nIf you want to use several cpu cores via multiprocessing while preprocessing a large dataset, you may construct the object via\n\n    >>> ar = Agreementr(CPU_COUNT=cpu_cpunt, CHUNKSIZE=chunksize)\n\nIf you want to faster the code through multi gpus, you may construct the object via\n\n    >>> ar = Agreementr(is_paralleled=False, BATCH_SIZE = batch_size)\n\n\n## Contact\nJunjie Wu (wujj38@mail2.sysu.edu.cn)\n\n\n",
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