BiLSTM_CRF(word2idx, tag2idx, Config.embedding_dim, Config.bilstm_hidden_dim).cuda()

This code initializes a Bidirectional Long Short-Term Memory (BiLSTM) model with a Conditional Random Field (CRF) layer for named entity recognition (NER) or sequence labeling tasks.

The model takes as input a dictionary mapping words to their corresponding indices (word2idx) and a dictionary mapping tags to their corresponding indices (tag2idx). It also takes the embedding dimension and BiLSTM hidden dimension specified in the Config class.

The model is then moved to the GPU (cuda()) for faster training and inference.

相关问题

word2vec-bilstm-crf

Word2vec-BiLSTM-CRF是一种用于命名实体识别（NER）的神经网络模型。它结合了三个不同的层：Word2vec嵌入层、双向长短时记忆网络（BiLSTM）层和条件随机场（CRF）层。Word2vec嵌入层将每个单词转换为向量表示，BiLSTM层将这些向量作为输入并学习上下文信息，最后CRF层将BiLSTM层的输出作为输入并进行标记预测。

以下是Word2vec-BiLSTM-CRF模型的基本步骤：
1. 将每个单词转换为向量表示，这可以使用预训练的Word2vec模型来完成。
2. 将这些向量作为输入传递给BiLSTM层，该层将学习上下文信息并生成相应的输出。
3. 将BiLSTM层的输出作为输入传递给CRF层，该层将对每个标记进行预测，并使用Viterbi算法来找到最佳标记序列。

以下是一个使用Python和Keras实现Word2vec-BiLSTM-CRF模型的示例：

from keras.models import Model, Input
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
from keras_contrib.layers import CRF
import numpy as np

# 定义模型输入
input = Input(shape=(MAX_LEN,))
# 添加Word2vec嵌入层
model = Embedding(input_dim=len(word2idx), output_dim=EMBEDDING_DIM, input_length=MAX_LEN, weights=[embedding_matrix], trainable=False)(input)
# 添加双向LSTM层
model = Bidirectional(LSTM(units=HIDDEN_UNITS, return_sequences=True, recurrent_dropout=0.1))(model)
# 添加Dropout层
model = Dropout(0.1)(model)
# 添加全连接层
model = TimeDistributed(Dense(units=NUM_TAGS, activation="relu"))(model)
# 添加CRF层
crf = CRF(NUM_TAGS)
out = crf(model)

# 定义模型
model = Model(input, out)
# 编译模型
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])

# 训练模型
model.fit(X_train, np.array(y_train), batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=0.1, verbose=1)

# 预测标记
y_pred = model.predict(X_test)

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