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        "description": "dictionary-model\n----------------\n\nTo use, first create CharPredictor object::\n\n    >>> predictor = CharPredictor()\n\nThis may take a while as model is being downloaded and loaded.\n\nThen, to track utterance context, you can add letter index to context::\n\n    >>> letter_index = 1    # 1 -> a,   letters should be indexed in order: ' abcdefghijklmnopqrstuvwxyz' (0 -> space)\n    >>> predictor.add_to_context(letter_index)\n\nor you can add letter as string of length 1 (make sure it is one of AsciiEncoder.AVAILABLE_CHARS)::\n\n    >>> letter = 'a'\n    >>> predictor.add_to_context(letter)\n\nor you can add probability distribution for all AsciiEncoder.AVAILABLE_CHARS letters::\n\n    >>> import numpy as np\n    >>> import AsciiEncoder as AE\n    >>>\n    >>> num_chars = len(AE.AVAILABLE_CHARS)\n    >>> letter_distr = np.random.random((1, num_chars)) # random proba distribution\n    >>> predictor.add_to_context(letter_distr)\n\nAnd finally - you can predict probabilities of each letter coming next after text stored in context. (Letters are indexed in order shown below)::\n\n    >>> predictor.transform()\n\nLetters order::\n\n     ' abcdefghijklmnopqrstuvwxyz' # space character comes at index 0, then alphabetical order for indices from 1 to 26\n\n",
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