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        "author": "Thibault Cl\u00e9rice",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Text Processing :: Linguistic"
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        "description": "\nTarte\n=======================\n\nA secondary layer for pie for disambiguation\n\n# What it aims to do\n\n- This tagger is supposed to come as a secondary layer for lemma that should be disambiguated.\n- Its core object (`Tarte`) should filter things that need to be disambiguated\n- Its training capacities should reorganize a training set so that it dispatch training samples across all sets and\nit should not care about sample not containing unambiguous tokens.\n- It takes POS, lemma context and form characters into the network to predict the disambiguated form.\n\n# Notes\n\n- Given that not all sentences will have things to disambiguate, pretraining vector might be an important task. It\n is [possible with PyTorch](https://stackoverflow.com/questions/49710537/pytorch-gensim-how-to-load-pre-trained-word-embeddings)\n to load Gensim data easily. This would **require** to generate temps file where lemma AND pos are fed to \n fake sentences.\n\n",
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        "keywords": "",
        "license": "MIT",
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        "maintainer_email": "",
        "name": "nlp-tarte",
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        "summary": "A framework for disambiguation",
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