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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Text Processing :: Linguistic"
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        "description": "# Innatis\n\nThis is a library of custom [Rasa NLU](https://github.com/RasaHQ/rasa_nlu/) components that we (CarLabs) are building.\n\n## What does the name mean?\n\n_Viribus Innatis_ means \"innate abilities\" in Latin. It's a joke...\n\n\"Rasa\" comes from \"tabula rasa\" - _blank slate_ in Latin. We (just Sam) thought it would be funny for this project to have the opposite name, since this is meant to be a suite of tools to fill in functionality for Rasa... that is, make it a not-blank-slate. So \"Innate Abilities\" -> Viribus Innatis.\n\n## Usage\n\n`$ pip install innatis`\n\nThen add to your pipeline in your `rasa_config.yml`. Example pipeline can be found in [`sample_rasa_innatis_config.yml`](sample_configs/sample_rasa_innatis_config.yml).\n\n## Components\n\n### Classifiers\n\n* `intent_classifier_bert` - Pulls the bert model from TF HUB and pretrains on given data.\n\n#### Example config\n\n```yaml\nlanguage: en\npipeline:\n  - name: \"tokenizer_whitespace\"\n  - name: \"ner_crf\"\n  - name: \"ner_synonyms\"\n  - name: \"innatis.classifiers.BertIntentClassifier\"\n    pretrained_model_dir: '/path/to/uncased_L-24_H-1024_A-16'\n    epochs: 10\n    batch_size: 64\n```\n\n### Extractors\n\n* `composite_entity_extractor` - Given entities extracted by another extractor (`ner_crf` seems to be the best for now), splits them into composite entities, similar to [DialogFlow](https://dialogflow.com/docs/entities/developer-entities#developer_composite).\n* EntitySynonymMapper (replaces `ner_synonyms`) - this is the `ner_synonyms` adapted for `composite_entity_extractor`. You most likely need it if you use `composite_entity_extractor`. It replaces the synonyms with the original entities inside composite entities. It can also do fuzzy matching when matching synonyms (enabled by default). See example config: [`config_composite_entities.yml`](sample_configs/config_composite_entities.yml)\n\n### Featurizers\n\n* `universal_sentence_encoder_featurizer` - Pulls the smaller USE model from TF HUB and embeds inputs as document vectors, and that vector gets sent downstream to be used as a feature.\n\n## Development\n\n```sh\ngit clone git@github.com:Revmaker/innatis.git\ncd innatis\npipenv install\n# pipenv install --skip-lock if locking takes too long\n\n# do some stuff\n# write some tests\n\npipenv run python test.py\n```\n\nDependencies suck, as is always the case in Python. I started out using `pipenv` because I thought that was _the_ package manager for Python. But I guess you need to put dependencies in the `install_requires` section of `setup.py`. So, the (currently manual, but automatable) process for moving dependencies from the `Pipfile` to `setup.py` is:\n\n```sh\n$ cd innatis\n# you are now in parent/innatis, not innatis/innatis\n$ python ir_from_pipfile.py | pbcopy\n['scikit-learn', 'scipy', 'sklearn-crfsuite', 'tensorflow', 'word2number', 'rasa_nlu==0.13.8', 'tensorflow-hub', 'spacy']\n\n# that array is copied; paste it into setup.py\n```\n\nAlso, please manually bump the version in a semver-ish way.",
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