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        "author": "Ivan Sorokin",
        "author_email": "sorokin.ivan@inbox.ru",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# PyTorch bindings for CUDA-Warp Recurrent Neural Aligner\n\n\n```python\ndef rna_loss(\n        log_probs,  # type: torch.FloatTensor\n        labels,  # type: torch.IntTensor\n        frames_lengths,  # type: torch.IntTensor\n        labels_lengths,  # type: torch.IntTensor\n        average_frames=False,  # type: bool\n        reduction=None,  # type: Optional[AnyStr]\n        blank=0,  # type: int\n):\n    \"\"\"The CUDA-Warp Recurrent Neural Aligner loss.\n\n    Args:\n      log_probs (torch.Tensor): Input tensor (float) with shape\n        (T, N, U, V) where T is the maximum number of input frames, N is the\n        minibatch size, U is the maximum number of output labels and V is\n        the vocabulary of labels (including the blank).\n      labels (torch.IntTensor): Tensor with shape (N, U-1) representing the\n        reference labels for all samples in the minibatch.\n      frames_lengths (torch.IntTensor): Tensor with shape (N,) representing the\n        number of frames for each sample in the minibatch.\n      labels_lengths (torch.IntTensor): Tensor with shape (N,) representing the\n        length of the transcription for each sample in the minibatch.\n      average_frames (bool, optional): Specifies whether the loss of each\n        sample should be divided by its number of frames. Default: ``False''.\n      reduction (string, optional): Specifies the type of reduction.\n        Default: None.\n      blank (int, optional): label used to represent the blank symbol.\n        Default: 0.\n    \"\"\"\n    # type: (...) -> torch.Tensor\n```\n\n## Requirements\n\n- C++11 compiler (tested with GCC 5.4).\n- Python: 3.5, 3.6, 3.7 (tested with version 3.6).\n- [PyTorch](http://pytorch.org/) >= 1.0.0 (tested with version 1.1.0).\n- [CUDA Toolkit](https://developer.nvidia.com/cuda-zone) (tested with version 10.0).\n\n\n\n## Install\n\nCurrently, there is no compiled version of the package. The following setup instructions compile the package from the source code locally.\n\n### From Pypi\n\n```bash\npip install warp_rna\n```\n\n### From GitHub\n\n```bash\ngit clone https://github.com/1ytic/warp-rna\ncd warp-rna/pytorch_binding\npython setup.py install\n```",
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        "name": "warp-rna",
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        "summary": "PyTorch bindings for CUDA-Warp Recurrent Neural Aligner",
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