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        "description": "# pytorch-partial-crf\n\nPartial/Fuzzy conditional random field in PyTorch.\n\n## How to use\n\n### Install\n\n```\npip install pytorch-partial-crf\n```\n\n## Use CRF\n\n```\nimport torch\nfrom pytorch_partial_crf import CRF\n\n# Create \nnum_tags = 6\nmodel = CRF(num_tags)\n\nbatch_size, sequence_length = 3, 5\nemissions = torch.randn(batch_size, sequence_length, num_tags)\n\ntags = torch.LongTensor([\n    [1, 2, 3, 3, 5],\n    [1, 3, 4, 2, 1],\n    [1, 0, 2, 4, 4],\n])\n\n# Computing log likelihood\nmodel(emissions, tags)\n```\n\n### Use partial CRF\n\n```\nimport torch\nfrom pytorch_partial_crf import PartialCRF\n\n# Create \nnum_tags = 6\nmodel = PartialCRF(num_tags)\n\nbatch_size, sequence_length = 3, 5\nemissions = torch.randn(batch_size, sequence_length, num_tags)\n\n# Set unknown tag to -1\ntags = torch.LongTensor([\n    [1, 2, 3, 3, 5],\n    [-1, 3, 3, 2, -1],\n    [-1, 0, -1, -1, 4],\n])\n\n# Computing log likelihood\nmodel(emissions, tags)\n```\n\n### Use viterbi decode\n\n```\nimport torch\nfrom pytorch_partial_crf import CRF\nfrom pytorch_partial_crf import PartialCRF\n\n\nnum_tags = 6\nmodel = CRF(num_tags) # or FuzzyCRF\n\nbatch_size, sequence_length = 3, 5\nemissions = torch.randn(batch_size, sequence_length, num_tags)\n\nmodel.viterbi_decode(emissions)\n\n# restricted viterbi decode\npossible_tags = torch.randn(batch_size, sequence_length, num_tags)\npossible_tags[possible_tags<=0] = 0\npossible_tags[possible_tags>0] = 1\npossible_tags = possible_tags.byte()  \n\nmodel.restricted_viterbi_decode(emissions, possible_tags)\n```\n\n## License\n\nMIT\n\n### References\n\nThe implementation is based on AllenNLP CRF module and pytorch-crf.\n\n\n",
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