CRF 的几点问题

[Zac Bi]

https://github.com/CLUEbenchmark/CLUENER2020/blob/da6631c21d050309117ea28640c757bb46a1255e/pytorch_version/models/crf.py#L41-L42

这里是不是应该写成这样?

self.transitions.detach()[:, :self.tag_dictionary[self.START_TAG]] = -10000

还有这里, 既然tags已经是包含[CLS]和[SEP]的标签序列了, 为什么还要分别在左边和右边cat上[CLS]和[SEP]? 有点不解.

https://github.com/CLUEbenchmark/CLUENER2020/blob/da6631c21d050309117ea28640c757bb46a1255e/pytorch_version/models/crf.py#L133-L150

    def _score_sentence(self, feats, tags, lens_):
        start = torch.LongTensor([self.tag_dictionary[self.START_TAG]]).to(self.device)
        start = start[None, :].repeat(tags.shape[0], 1)
        stop = torch.LongTensor([self.tag_dictionary[self.STOP_TAG]]).to(self.device)
        stop = stop[None, :].repeat(tags.shape[0], 1)
        pad_start_tags = torch.cat([start, tags], 1)
        pad_stop_tags = torch.cat([tags, stop], 1)
        for i in range(len(lens_)):
            pad_stop_tags[i, lens_[i] :] = self.tag_dictionary[self.STOP_TAG]
        score = torch.FloatTensor(feats.shape[0]).to(self.device)
        for i in range(feats.shape[0]):
            r = torch.LongTensor(range(lens_[i])).to(self.device)
            score[i] = torch.sum(
                self.transitions[
                    pad_stop_tags[i, : lens_[i] + 1], pad_start_tags[i, : lens_[i] + 1]
                ]
            ) + torch.sum(feats[i, r, tags[i, : lens_[i]]])
        return score

[Zac Bi]

不好意思走错了