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            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "## lmproof - Language Model Proof Reader\n\nLibrary to do proof-reading corrections for Grammatical Errors, spelling errors, confused word errors and other errors using pre-trained Language Models.\n\n## Usage\nInstall spacy model by `python -m spacy download en`.\nThen try out this snippet.\n\n``` python\nimport lmproof\nproof_reader = lmproof.load(\"en\")\nsource = \"The foxes living on the Shire is brown.'\"\ncorrected = proof_reader.proofread(source) # \"The foxes living in the Shire are brown.\"\n```\n\n## How it works?\n\nWe use the language model based scoring approach mentioned in [Christopher Bryant and Ted Briscoe. 2018](http://aclweb.org/anthology/W18-0529) with few changes.\n\nUnlike many approaches to GEC, this approach does NOT require annotated training data and mainly depends on a monolingual language model. The program works by iteratively comparing certain words in a text against alternative candidates and applying a correction if one of these candidates is more probable than the original word. These correction candidates are variously generated by a word inflection library or are otherwise defined manually. Currently, this system only corrects:\n\n    Non-words (e.g. freind and informations)\n    Morphology (e.g. eat, ate, eaten, eating, etc.)\n    Common Determiners and Prepositions (e.g. the, a, in, at, to, etc.)\n    Commonly Confused Words (e.g. bear/bare, lose/loose, etc.)\n\nThis work builds upon https://github.com/chrisjbryant/lmgec-lite/\n\n## Components\n\n### Language Models\n* [transformers](https://github.com/huggingface/transformers)\n### Inflection generators\n* [LemmInflect](https://github.com/bjascob/LemmInflect) is used to lemmatize and generate inflections for candidate proposals to the language model.\n### Spell Checker\n* [symspellpy](https://github.com/mammothb/symspellpy) is used for obtaining spell check candidates.\n\nThe components are highly modularised to facilitate experimentation with newer scorers and support more languages.\nPre-trained language models for other languages, inflectors, common error patterns can be easily added to support more languages.\n\n## TODOs\n\n* Use edits in existing GEC corpus to generate candidates.\n* Tests\n* Publish benchmarks of the model.\n* Think of simple ways to generate insertion candidates.\n* Add more languages.\n",
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