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        "author": "Dalton Fury",
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        "description": "# TrueCase\n\nA language independent, statistical, language modeling\nbased tool in Python that restores case information for text.\n\nThe model was inspired by the paper of [Lucian Vlad Lita  et al., tRuEcasIng](https://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf) but with some simplifications.\n\n\nA model trained on NLTK English corpus comes with the package by default, \nand for other languages, a script is provided to create the model. This model is \nnot perfect, train the system on a large and recent dataset to achieve \nthe best results (e.g. on a recent dump of Wikipedia).\n\n### Prerequisites\n\nPython 3\n\nThe project uses NLTK. Find install instructions [here](https://www.nltk.org/install.html).\n\n### Installing\n\n\n```\npip install truecase\n```\n\n## Usage\n\nSimple usecase:\n```\n>>> import truecase\n>>> truecase.get_true_case('hey, what is the weather in new york?')\n'Hey, what is the weather in New York?''\n```\n\n## Training your own model\n\nTODO. For now refer to Trainer.py\n\n## Contributing\n\nI see a lot of space for improvement. Feel free to fork and improve. Do sent a pull request.\n\n## Authors\n\n* **Dalton Fury** - *Initial work* - [daltonfury42](https://github.com/daltonfury42)\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Acknowledgments\n\n* [Lucian Vlad Lita  et al., tRuEcasIng](https://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf)\n* Borrowed a lot of code, and the idea from [truecaser](https://github.com/nreimers/truecaser/blob/master/README.md) by [nreimers](https://github.com/nreimers)\n\n\n",
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