torch word2vec +bilstm 分类

Torch word2vec和bilstm分类是两个非常有用的深度学习工具。Torch word2vec是一种用于词向量嵌入的技术，它将每个单词转换为一个向量表示，并在向量空间中对它们进行编码。这使得它们在机器学习问题中非常有用，例如自然语言处理和文本分类。Torch word2vec可以通过将文本语料库传递给模型来训练，从而为每个单词生成一个向量。这些向量在大多数情况下是稠密的，即它们具有相对较少的元素，而且它们的分布是连续的，这使得它们易于在机器学习问题中使用。

相反，bilstm是一种用于分类的技术。它使用双向长短时记忆网络（bi-directional Long Short-Term Memory），这是一种特殊的循环神经网络，它可以处理和预测分布式数据，例如自然语言。bilstm是一种有监督机器学习算法，它可以通过训练样本来进行分类。训练样本是具有标签的文本数据，例如文章或评论，每个文本都对应一个标签。通过运行bilstm并向其传递文本，它可以预测每个文本对应的标签。这种技术在分类任务中非常有用，例如情感分析，推荐算法和主题分类等。

将这两种技术结合使用可以非常有效地解决自然语言处理问题。例如，将Torch word2vec用于生成单词向量，然后将这些向量传递给bilstm进行分类。通过将这两种技术结合使用，可以使用bilstm对文本数据进行分类，同时利用Torch word2vec生成的向量表示来增强模型。这种技术已经被广泛用于自然语言处理和文本分类中，并且已经被证明是一种非常强大和有效的机器学习技术。

相关问题

bilstm-crf代码

以下是一个简单的BiLSTM-CRF模型的PyTorch实现：

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.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        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

    def _forward_alg(self, feats):
        init_alphas = torch.full((1, self.tagset_size), -10000.)
        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.

        forward_var = init_alphas

        for feat in feats:
            alphas_t = []
            for next_tag in range(self.tagset_size):
                emit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size)
                trans_score = self.transitions[next_tag].view(1, -1)
                next_tag_var = forward_var + trans_score + emit_score
                alphas_t.append(log_sum_exp(next_tag_var).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        alpha = log_sum_exp(terminal_var)
        return alpha

    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 + 1], tags[i]] + feat[tags[i + 1]]
        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
        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 forward(self, sentence):
        lstm_feats = self._get_lstm_features(sentence)
        score, tag_seq = self._viterbi_decode(lstm_feats)
        return score, tag_seq

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

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

    def init_hidden(self):
        return (torch.randn(2, 1, self.hidden_dim // 2),
                torch.randn(2, 1, self.hidden_dim // 2))
    
def log_sum_exp(vec):
    max_score = vec[0, argmax(vec)]
    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
    return max_score + \
        torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))

def argmax(vec):
    _, idx = torch.max(vec, 1)
    return idx.item()

其中，`START_TAG`和`STOP_TAG`是起始标记和结束标记。这里使用了`log_sum_exp`函数来处理数值上溢的问题，`argmax`函数用于取最大值的下标。这个模型可以用于序列标注任务，例如词性标注、命名实体识别等。

BiLSTM-CRF如何使用

BiLSTM-CRF是一种常用的命名实体识别方法，其使用双向LSTM进行特征提取，再使用CRF进行标注。具体步骤如下：

1.准备数据集，将文本中的实体标注出来，例如人名、地名、组织机构名等。

2.将文本转化为数字表示，例如使用词向量将每个词转化为向量。

3.使用BiLSTM对输入的词向量进行特征提取，得到每个词的特征向量。

4.将BiLSTM的输出作为CRF的输入，使用CRF进行标注，得到每个词的标签。

5.根据标签将文本中的实体识别出来。

以下是一个简单的BiLSTM-CRF的实现代码：

import torch
import torch.nn as nn
import torch.optim as optim

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.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_alg(self, feats):
        init_alphas = torch.full((1, self.tagset_size), -10000.)
        init_alphas[0][self.tag_to_ix['START_TAG']] = 0.

        forward_var = init_alphas

        for feat in feats:
            alphas_t = []
            for next_tag in range(self.tagset_size):
                emit_score = feat[next_tag].view(
                    1, -1).expand(1, self.tagset_size)
                trans_score = self.transitions[next_tag].view(1, -1)
                next_tag_var = forward_var + trans_score + emit_score
                alphas_t.append(self._log_sum_exp(next_tag_var).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix['STOP_TAG']]
        alpha = self._log_sum_exp(terminal_var)
        return alpha

    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 + 1], tags[i]] + feat[tags[i + 1]]
        score = score + self.transitions[self.tag_to_ix['STOP_TAG'], tags[-1]]
        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 = self._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 = self._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 _log_sum_exp(self, vec):
        max_score = vec[0, self._argmax(vec)]
        max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
        return max_score + \
            torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))

    def _argmax(self, vec):
        _, idx = torch.max(vec, 1)
        return idx.item()

    def neg_log_likelihood(self, sentence, tags):
        self.hidden = self.init_hidden()
        embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        tag_scores = self.hidden2tag(lstm_out)

        forward_score = self._forward_alg(tag_scores)
        gold_score = self._score_sentence(tag_scores, tags)
        return forward_score - gold_score

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

        score, tag_seq = self._viterbi_decode(tag_scores)
        return score, tag_seq