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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
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        "description": "=================\npytorch-fast-elmo\n=================\n\n\n.. image:: https://img.shields.io/pypi/v/pytorch_fast_elmo.svg\n        :target: https://pypi.python.org/pypi/pytorch_fast_elmo\n\n.. image:: https://img.shields.io/travis/cnt-dev/pytorch-fast-elmo.svg\n        :target: https://travis-ci.org/cnt-dev/pytorch-fast-elmo\n\n.. image:: https://img.shields.io/badge/License-MIT-yellow.svg\n        :target: https://travis-ci.org/cnt-dev/pytorch-fast-elmo\n\n\nIntroduction\n------------\n\nA fast ELMo implementation with features:\n\n- **Lower execution overhead.** The core components are reimplemented in Libtorch in order to reduce the Python execution overhead (**45%** speedup).\n- **A more flexible design.** By redesigning the workflow, the user could extend or change the ELMo behavior easily.\n\nBenchmark\n---------\n\nHardware:\n\n- CPU: i7-7800X\n- GPU: 1080Ti\n\nOptions:\n\n- Batch size: 32\n- Warm up iterations: 20\n- Test iterations: 1000\n- Word length: [1, 20]\n- Sentence length: [1, 30]\n- Random seed: 10000\n\n+--------------------------------------+------------------------+------------------------+\n| Item                                 | Mean Of Durations (ms) | cumtime(synchronize)%  |\n+======================================+========================+========================+\n| Fast ELMo (CUDA, no synchronize)     | **31**                 | N/A                    |\n+--------------------------------------+------------------------+------------------------+\n| AllenNLP ELMo (CUDA, no synchronize) | 56                     | N/A                    |\n+--------------------------------------+------------------------+------------------------+\n| Fast ELMo (CUDA, synchronize)        | 47                     | **26.13%**             |\n+--------------------------------------+------------------------+------------------------+\n| AllenNLP ELMo (CUDA, synchronize)    | 57                     | 0.02%                  |\n+--------------------------------------+------------------------+------------------------+\n| Fast ELMo (CPU)                      | 1277                   | N/A                    |\n+--------------------------------------+------------------------+------------------------+\n| AllenNLP ELMo (CPU)                  | 1453                   | N/A                    |\n+--------------------------------------+------------------------+------------------------+\n\nUsage\n-----\n\nPlease install **torch==1.0.0** first. Then, simply run this command to install.\n\n.. code-block:: bash\n\n    pip install pytorch-fast-elmo\n\n\n``FastElmo`` should have the same behavior as AllenNLP's ``ELMo``.\n\n.. code-block:: python\n\n    from pytorch_fast_elmo import FastElmo, batch_to_char_ids\n\n    options_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_options.json'\n    weight_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5'\n\n    elmo = FastElmo(options_file, weight_file)\n\n    sentences = [['First', 'sentence', '.'], ['Another', '.']]\n    character_ids = batch_to_ids(sentences)\n\n    embeddings = elmo(character_ids)\n\n\nUse ``FastElmoWordEmbedding`` if you have disabled ``char_cnn`` in ``bilm-tf``, or have exported the Char CNN representation to a weight file.\n\n.. code-block:: python\n\n    from pytorch_fast_elmo import FastElmoWordEmbedding, load_and_build_vocab2id, batch_to_word_ids\n\n    options_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_options.json'\n    weight_file = '/path/to/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5'\n\n    vocab_file = '/path/to/vocab.txt'\n    embedding_file = '/path/to/cached_elmo_embedding.hdf5'\n\n    elmo = FastElmoWordEmbedding(\n            options_file,\n            weight_file,\n            # Could be omitted if the embedding weight is in `weight_file`.\n            word_embedding_weight_file=embedding_file,\n    )\n    vocab2id = load_and_build_vocab2id(vocab_file)\n\n    sentences = [['First', 'sentence', '.'], ['Another', '.']]\n    word_ids = batch_to_word_ids(sentences, vocab2id)\n\n    embeddings = elmo(word_ids)\n\n\nCLI commands:\n\n.. code-block:: bash\n\n    # Cache the Char CNN representation.\n    fast-elmo cache-char-cnn ./vocab.txt ./options.json ./lm_weights.hdf5 ./lm_embd.hdf5\n\n    # Export word embedding.\n    fast-elmo export-word-embd ./vocab.txt ./no-char-cnn.hdf5 ./embd.txt\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2019-01-02)\n------------------\n\n* First release on PyPI.",
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