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        "author": "Ryuya Ikeda",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
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            "Topic :: Text Processing"
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        "description": "# Torch CRF\n[![CircleCI](https://circleci.com/gh/s14t284/TorchCRF.svg?style=svg)](https://circleci.com/gh/s14t284/TorchCRF) [![Coverage Status](https://coveralls.io/repos/github/s14t284/TorchCRF/badge.svg)](https://coveralls.io/github/s14t284/TorchCRF)\n\nImplementation of CRF (Conditional Random Fields) in PyTorch 1.0\n\n## Requirements\n\n- python3 (>=3.6)\n- PyTorch 1.0\n\n## Installation\n\n    $ pip install TorchCRF\n\n## Usage\n\n```python\n>>> import torch\n>>> from TorchCRF import CRF\n>>> batch_size = 2\n>>> sequence_size = 3\n>>> num_labels = 5\n>>> mask = torch.FloatTensor([[1, 1, 1], [1, 1, 0]]) # (batch_size. sequence_size)\n>>> labels = torch.LongTensor([[0, 2, 3], [1, 4, 1]])  # (batch_size, sequence_size)\n>>> hidden = torch.randn((batch_size, sequence_size, num_labels), requires_grad=True)\n>>> crf = CRF(num_labels)\n```\n\n### Computing log-likelihood (used where forward)\n\n```python\n>>> crf.forward(hidden, labels, mask)\ntensor([-7.6204, -3.6124], grad_fn=<ThSubBackward>)\n```\n\n### Decoding (predict labels of sequences)\n\n```python\n>>> crf.viterbi_decode(hidden, mask)\n[[0, 2, 2], [4, 0]]\n```\n\n## License\n\nMIT\n\n## References\n\n- [threelittlemonkeys/lstm-crf-pytorch](https://github.com/threelittlemonkeys/lstm-crf-pytorch)\n- [kmkurn/pytorch-crf](https://github.com/kmkurn/pytorch-crf)\n\n\n",
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