PyTorch CRF with N-best decoding
PyTorch CRF with N-best Decoding
Implementation of Conditional Random Fields (CRF) in PyTorch 1.0. It supports top-N most probable paths decoding.
The package is based on pytorch-crf with only the following differences
_viterbi_decodethat decodes the most probable path get optimized. Running time gets reduced to 50% or less with batch size 15+ and sequence length 20+
- The class now supports decoding top-N most probable paths through the implementation of the method
- Python 3 (>= 3.6)
- PyTorch 1.0
pip install pytorchcrf
>>> import torch >>> from pytorchcrf import CRF >>> num_tags = 4 # number of tags is 4 >>> model = CRF(num_tags) >>> seq_length = 3 # maximum sequence length in a batch >>> batch_size = 2 # number of samples in the batch >>> emissions = torch.randn(seq_length, batch_size, num_tags) >>> model.decode(emissions) >>> model.decode(emissions, nbest=3)
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