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    "info": {
        "author": "Hironsan",
        "author_email": "hiroki.nakayama.py@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "# anaGo\n\n**anaGo** is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras.\n\nanaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo don't need to define any language dependent features. Thus, we can easily use anaGo for any languages.\n\nAs an example of anaGo, the following image shows named entity recognition in English:\n\n[anaGo Demo](https://anago.herokuapp.com/)\n\n![English NER](./docs/images/anago.gif)\n\n<!--\n![English NER](https://github.com/Hironsan/anago/blob/docs/docs/images/example.en2.png?raw=true)\n\n![Japanese NER](https://github.com/Hironsan/anago/blob/docs/docs/images/example.ja2.png?raw=true)\n-->\n\n## Get Started\n\nIn anaGo, the simplest type of model is the `Sequence` model. Sequence model includes essential methods like `fit`, `score`, `analyze` and `save`/`load`. For more complex features, you should use the anaGo modules such as `models`, `preprocessing` and so on.\n\nHere is the data loader:\n\n```python\n>>> from anago.utils import load_data_and_labels\n\n>>> x_train, y_train = load_data_and_labels('train.txt')\n>>> x_test, y_test = load_data_and_labels('test.txt')\n>>> x_train[0]\n['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']\n>>> y_train[0]\n['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']\n```\n\nYou can now iterate on your training data in batches:\n\n```python\n>>> import anago\n\n>>> model = anago.Sequence()\n>>> model.fit(x_train, y_train, epochs=15)\nEpoch 1/15\n541/541 [==============================] - 166s 307ms/step - loss: 12.9774\n...\n```\n\nEvaluate your performance in one line:\n\n```python\n>>> model.score(x_test, y_test)\n80.20  # f1-micro score\n# For more performance, you have to use pre-trained word embeddings.\n# For now, anaGo's best score is 90.90 f1-micro score.\n```\n\nOr tagging text on new data:\n\n```python\n>>> text = 'President Obama is speaking at the White House.'\n>>> model.analyze(text)\n{\n    \"words\": [\n        \"President\",\n        \"Obama\",\n        \"is\",\n        \"speaking\",\n        \"at\",\n        \"the\",\n        \"White\",\n        \"House.\"\n    ],\n    \"entities\": [\n        {\n            \"beginOffset\": 1,\n            \"endOffset\": 2,\n            \"score\": 1,\n            \"text\": \"Obama\",\n            \"type\": \"PER\"\n        },\n        {\n            \"beginOffset\": 6,\n            \"endOffset\": 8,\n            \"score\": 1,\n            \"text\": \"White House.\",\n            \"type\": \"LOC\"\n        }\n    ]\n}\n```\n\nTo download a pre-trained model, call `download` function:\n\n```python\n>>> from anago.utils import download\n\n>>> url = 'https://storage.googleapis.com/chakki/datasets/public/ner/conll2003_en.zip'\n>>> weights, params, preprocessor = download(url)\n>>> model = anago.Sequence.load(weights, params, preprocessor)\n>>> model.score(x_test, y_test)\n0.9090262970859986\n```\n\n## Feature Support\n\nanaGo supports following features:\n\n* Model Training\n* Model Evaluation\n* Tagging Text\n* Custom Model Support\n* Downloading pre-trained model\n* GPU Support\n* Character feature\n* CRF Support\n* Custom Callback Support\n\nanaGo officially supports Python 3.4\u20133.6.\n\n## Installation\n\nTo install anaGo, simply use `pip`:\n\n```bash\n$ pip install anago\n```\n\nor install from the repository:\n\n```bash\n$ git clone https://github.com/Hironsan/anago.git\n$ cd anago\n$ python setup.py install\n```\n\n## Documentation\n\n(coming soon)\n\nFantastic documentation is available at [http://example.com/](http://example.com/).\n\n<!--\n## Data and Word Vectors\n\nTraining data takes a tsv format.\nThe following text is an example of training data:\n\n```\nEU\tB-ORG\nrejects\tO\nGerman\tB-MISC\ncall\tO\nto\tO\nboycott\tO\nBritish\tB-MISC\nlamb\tO\n.\tO\n\nPeter\tB-PER\nBlackburn\tI-PER\n```\n\nanaGo supports pre-trained word embeddings like [GloVe vectors](https://nlp.stanford.edu/projects/glove/).\n-->\n\n## Reference\n\nThis library uses bidirectional LSTM + CRF model based on\n[Neural Architectures for Named Entity Recognition](https://arxiv.org/abs/1603.01360)\nby Lample, Guillaume, et al., NAACL 2016.\n\n",
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