birnn-lstm-crf模型代码

以下是一个简单的基于BiLSTM-CRF模型的代码示例：

import torch
import torch.nn as nn

class BiLSTM_CRF(nn.Module):
    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2, num_layers=1, bidirectional=True)

        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))
        self.transitions.data[tag_to_ix[START_TAG], :] = -10000
        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
        self.transitions.data[tag_to_ix[PAD_TAG], :] = -10000
        self.transitions.data[:, tag_to_ix[PAD_TAG]] = -10000

        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (torch.randn(2, 1, self.hidden_dim // 2),
                torch.randn(2, 1, self.hidden_dim // 2))

    def forward(self, sentence):
        embeds = self.word_embeds(sentence)
        lstm_out, self.hidden = self.lstm(embeds.view(len(sentence), 1, -1), self.hidden)
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        tag_space = self.hidden2tag(lstm_out)
        return tag_space

    def _score_sentence(self, feats, tags):
        score = torch.zeros(1)
        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
        for i, feat in enumerate(feats):
            score = score + self.transitions[tags[i], tags[i+1]] + feat[tags[i+1]]
        score = score + self.transitions[tags[-1], self.tag_to_ix[STOP_TAG]]
        return score

    def _viterbi_decode(self, feats):
        backpointers = []

        init_vvars = torch.full((1, self.tagset_size), -10000.)
        init_vvars[0][self.tag_to_ix[START_TAG]] = 0

        forward_var = init_vvars
        for feat in feats:
            bptrs_t = []
            viterbivars_t = []

            for next_tag in range(self.tagset_size):
                next_tag_var = forward_var + self.transitions[:, next_tag]
                best_tag_id = argmax(next_tag_var)
                bptrs_t.append(best_tag_id)
                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        best_tag_id = argmax(terminal_var)
        path_score = terminal_var[0][best_tag_id]

        best_path = [best_tag_id]
        for bptrs_t in reversed(backpointers):
            best_tag_id = bptrs_t[best_tag_id]
            best_path.append(best_tag_id)
        start = best_path.pop()
        assert start == self.tag_to_ix[START_TAG]
        best_path.reverse()
        return path_score, best_path

    def neg_log_likelihood(self, sentence, tags):
        feats = self.forward(sentence)
        forward_score = self._forward_alg(feats)
        gold_score = self._score_sentence(feats, tags)
        return forward_score - gold_score

    def forward_decode(self, sentence):
        feats = self.forward(sentence)
        score, tag_seq = self._viterbi_decode(feats)
        return score, tag_seq

在这个简单的代码实现中，我们定义了一个继承自 `nn.Module` 的类 `BiLSTM_CRF`，它包含一个双向LSTM层和一个CRF层。在初始化函数中，我们定义了各种超参数和模型参数。在 `forward` 函数中，我们首先对输入的句子进行词嵌入，然后将其输入到双向LSTM中，将输出传递到一个线性层中，最后得到一个标签空间。在 `neg_log_likelihood` 函数中，我们计算了负对数似然损失，该损失可以用来训练模型。在 `forward_decode` 函数中，我们使用 Viterbi 算法解码标签序列。