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        "description": "# UTTUT\n\n[![travis][travis-image]][travis-url]\n[![codecov][codecov-image]][codecov-url]\n[![pypi][pypi-image]][pypi-url]\n![release][release-image]\n\n[travis-image]: https://img.shields.io/travis/Yoctol/uttut.svg?style=flat\n[travis-url]: https://travis-ci.org/Yoctol/uttut\n[pypi-image]: https://img.shields.io/pypi/v/uttut.svg?style=flat\n[pypi-url]: https://pypi.python.org/pypi/uttut\n[codecov-image]: https://codecov.io/gh/Yoctol/uttut/branch/master/graph/badge.svg\n[codecov-url]: https://codecov.io/gh/Yoctol/uttut\n[release-image]: https://img.shields.io/github/release/Yoctol/uttut.svg\n\n\nUTTerance UTilities for dialogue system. This package provides some general utils when processing chatbot utterance data.\n\n# BERT Pipe\n\nTo create a pipe for BERT preprocessing, please take a look at [BERT](https://github.com/Yoctol/uttut/tree/master/uttut/pipeline/bert).\n\n# Installation\n\n```\n$ pip install uttut\n```\n\n# Usage\n\nLet's create a Pipe to preprocess a Datum with English utterance.\n\n## Build a Pipe\n\n```python\n>>> from uttut.pipeline.pipe import Pipe\n\n>>> p = Pipe()\n>>> p.add('IntTokenWithSpace')\n>>> p.add('FloatTokenWithSpace')\n>>> p.add('MergeWhiteSpaceCharacters')\n>>> p.add('StripWhiteSpaceCharacters')\n>>> p.add('EngTokenizer')  # word-level (ref: BERT)\n>>> p.add('AddSosEos', checkpoint='result_of_add_sos_eos')\n>>> p.add('Pad', {'maxlen': 5})\n>>> p.add(\n    'Token2Index',\n    {\n       'token2index': {\n            '<sos>': 0, '<eos>': 1,  # for  AddSosEos\n            '<unk>': 2, '<pad>': 3,  # for Pad\n            '_int_': 4,  # for IntTokenWithSpace\n            '_float_': 5,  # for FloatTokenWithSpace\n            'I': 6,\n            'apples': 7,\n        },\n    },\n)\n```\n\n## transform\n\n```python\n>>> from uttut.elements import Datum, Entity, Intent\n>>> datum = Datum(\n    utterance='I like apples.',\n    intents=[Intent(label=1), Intent(label=2)],\n    entities=[Entity(start=7, end=13, value='apples', label=7)],\n)\n>>> output_indices, intent_labels, entity_labels, label_aligner, intermediate = p.transform(datum)\n>>> output_indices\n[0, 6, 2, 7, 1, 3, 3]\n>>> intent_labels\n[1, 2]\n>>> entity_labels\n[0, 0, 0, 7, 0, 0, 0]\n\n# intermediate\n>>> intermediate.get_from_checkpoint('result_of_add_sos_eos')\n[\"<sos>\", \"I\", \"like\", \"apples\", \"<eos>\"] \n\n# label_aligner\n>>> label_aligner.inverse_transform(entity_labels)\n[0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0]\n```\n\n## transform sequence\n\n```python\n>>> output_sequence, label_aligner, intermediate = p.transform_sequence('I like apples.')\n>>> output_sequence\n[0, 6, 2, 7, 1, 3, 3]\n\n# label_aligner\n>>> label_aligner.transform([0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0])\n[0, 0, 0, 7, 0, 0, 0]\n>>> label_aligner.inverse_transform([0, 0, 0, 7, 0, 0, 0])\n[0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0]\n\n# intermediate\n>>> intermediate.get_from_checkpoint('result_of_add_sos_eos')\n[\"<sos>\", \"I\", \"like\", \"apples\", \"<eos>\"]\n```\n\n# Serialization\n\n## Serialize\n\n```python\n>>> serialized_str = p.serialize()\n```\n\n##  Deserialize \n\n```python\n>>> from uttut.pipeline.pipe import Pipe\n>>> p = Pipe.deserialize(serialized_str )\n```",
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