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        "description": "# Keras Transformer-XL\n\n[![Travis](https://travis-ci.org/CyberZHG/keras-transformer-xl.svg)](https://travis-ci.org/CyberZHG/keras-transformer-xl)\n[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-transformer-xl/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-transformer-xl)\n[![Version](https://img.shields.io/pypi/v/keras-transformer-xl.svg)](https://pypi.org/project/keras-transformer-xl/)\n![Downloads](https://img.shields.io/pypi/dm/keras-transformer-xl.svg)\n![License](https://img.shields.io/pypi/l/keras-transformer-xl.svg)\n\n![](https://img.shields.io/badge/keras-tensorflow-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/eager-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/2.0_beta-blue.svg)\n\n\\[[\u4e2d\u6587](https://github.com/CyberZHG/keras-transformer-xl/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-transformer-xl/blob/master/README.md)\\]\n\nUnofficial implementation of [Transformer-XL](https://arxiv.org/pdf/1901.02860.pdf).\n\n## Install\n\n```bash\npip install keras-transformer-xl\n```\n\n## Usage\n\n### Load Pretrained Weights\n\nSeveral configuration files can be found at [the info directory](./keras_transformer_xl/info).\n\n```python\nimport os\nfrom keras_transformer_xl import load_trained_model_from_checkpoint\n\ncheckpoint_path = 'foo/bar/sota/enwiki8'\nmodel = load_trained_model_from_checkpoint(\n    config_path=os.path.join(checkpoint_path, 'config.json'),\n    checkpoint_path=os.path.join(checkpoint_path, 'model.ckpt')\n)\nmodel.summary()\n```\n\n### About IO\n\nThe generated model has two inputs, and the second input is the lengths of memories.\n\nYou can use `MemorySequence` wrapper for training and prediction:\n\n```python\nimport keras\nimport numpy as np\nfrom keras_transformer_xl import MemorySequence, build_transformer_xl\n\n\nclass DummySequence(keras.utils.Sequence):\n\n    def __init__(self):\n        pass\n\n    def __len__(self):\n        return 10\n\n    def __getitem__(self, index):\n        return np.ones((3, 5 * (index + 1))), np.ones((3, 5 * (index + 1), 3))\n\n\nmodel = build_transformer_xl(\n    units=4,\n    embed_dim=4,\n    hidden_dim=4,\n    num_token=3,\n    num_block=3,\n    num_head=2,\n    batch_size=3,\n    memory_len=20,\n    target_len=10,\n)\nseq = MemorySequence(\n    model=model,\n    sequence=DummySequence(),\n    target_len=10,\n)\n\nmodel.predict(model, seq, verbose=True)\n```\n\n### Use `tf.keras`\n\nAdd `TF_KERAS=1` to environment variables to use `tensorflow.python.keras`.",
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