Comments (2)
Hi, you can use the nbest decoding, the probability of each output sequence will be given. Notice the sequence probability of nbest decoding is normalized within the n decided sequence (I.e. the sum of the probabilities on n decoded sequence is 1.)
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Hi @jiesutd (and 👏 for the amazing library). In the docstring of both _viterbi_decode_nbest
and _viterbi_decode
it says not implemented
for path_score
. However, it looks to me that it is implemented indeed (and even more according to your comment above). Is that an oudated docstring?
def _viterbi_decode_nbest(self, feats, mask, nbest): """ input: feats: (batch, seq_len, self.tag_size+2) mask: (batch, seq_len) output: decode_idx: (batch, nbest, seq_len) decoded sequence path_score: (batch, nbest) corresponding score for each sequence (to be implementated) nbest decode for sentence with one token is not well supported, to be optimized
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Related Issues (20)
- Conll 使用内存大 HOT 2
- nbest score HOT 1
- Provision for Custom Features? HOT 1
- Bug in forward method when calling _viterbi_decode, mask is not provided
- about requirement HOT 1
- Can I just use the CRF layer? HOT 1
- 大神你好,请教一些关于报错的问题 HOT 1
- About "tcmalloc: large alloc" message and training aborted HOT 3
- Difference between main_parse.py and main.py HOT 1
- Different results on CPU versus GPU HOT 1
- Test Data Format for Chunking
- Experimenting with Transformer models? HOT 1
- Decode config parameter when not using CRF HOT 1
- Reading fasttext word embeddings HOT 2
- About MAX_SENTENCE_LENGTH parameter HOT 2
- Decode error with nbest=0 when not using CRF
- Resume model training from a checkpoint
- 训练一段时间后,f1为-1 HOT 2
- Unknown labels in decoding
- Support trained model serialization to ONNX format HOT 1
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