基于lstm-crf的序列标注的模型代码

以下是使用TensorFlow实现基于LSTM-CRF的序列标注的模型代码示例：

import tensorflow as tf
from tensorflow.contrib.crf import crf_log_likelihood, viterbi_decode

class BiLSTM_CRF(object):
    def __init__(self, num_chars, num_tags, embedding_dim, hidden_dim, batch_size=64, learning_rate=0.001):
        self.num_chars = num_chars
        self.num_tags = num_tags
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        
        self.inputs = tf.placeholder(tf.int32, shape=[None, None], name='inputs')
        self.targets = tf.placeholder(tf.int32, shape=[None, None], name='targets')
        self.seq_length = tf.placeholder(tf.int32, shape=[None], name='seq_length')
        
        self._build_model()
        self._build_loss()
        self._build_optimizer()
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
        
    def _build_model(self):
        # 定义词嵌入层
        self.word_embeddings = tf.Variable(tf.random_uniform([self.num_chars, self.embedding_dim], -1.0, 1.0), name='word_embeddings')
        embeddings = tf.nn.embedding_lookup(self.word_embeddings, self.inputs)
        
        # 定义双向LSTM层
        cell_fw = tf.contrib.rnn.LSTMCell(self.hidden_dim)
        cell_bw = tf.contrib.rnn.LSTMCell(self.hidden_dim)
        (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, embeddings, dtype=tf.float32, sequence_length=self.seq_length)
        output = tf.concat([output_fw, output_bw], axis=-1)
        
        # 定义全连接层
        W = tf.get_variable('W', shape=[2*self.hidden_dim, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
        b = tf.get_variable('b', shape=[self.num_tags], initializer=tf.zeros_initializer())
        
        output = tf.reshape(output, [-1, 2*self.hidden_dim])
        logits = tf.matmul(output, W) + b
        self.logits = tf.reshape(logits, [-1, tf.shape(self.inputs)[1], self.num_tags])
        
        # 定义CRF层
        self.transition_params = tf.get_variable('transition_params', shape=[self.num_tags, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
        
    def _build_loss(self):
        log_likelihood, self.transition_params = crf_log_likelihood(inputs=self.logits, tag_indices=self.targets, sequence_lengths=self.seq_length, transition_params=self.transition_params)
        self.loss = tf.reduce_mean(-log_likelihood)
    
    def _build_optimizer(self):
        self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
        
    def train(self, inputs, targets, seq_length):
        feed_dict = {self.inputs: inputs, self.targets: targets, self.seq_length: seq_length}
        _, loss = self.sess.run([self.optimizer, self.loss], feed_dict=feed_dict)
        return loss
    
    def predict(self, inputs, seq_length):
        feed_dict = {self.inputs: inputs, self.seq_length: seq_length}
        logits, transition_params = self.sess.run([self.logits, self.transition_params], feed_dict=feed_dict)
        viterbi_sequences = []
        for logit, length in zip(logits, seq_length):
            logit = logit[:length]
            viterbi_seq, _ = viterbi_decode(logit, transition_params)
            viterbi_sequences.append(viterbi_seq)
        return viterbi_sequences

这里实现了一个包含词嵌入、双向LSTM、全连接和CRF层的模型，并使用Adam优化器进行训练。在训练过程中，需要传入输入序列、目标序列和序列长度；在预测过程中，只需要传入输入序列和序列长度即可得到预测的标注序列。