bilstm-crf命名实体识别用python实现代码

以下是一个简单的 bilstm-crf 命名实体识别的 Python 实现代码：

import numpy as np
import tensorflow as tf

class BiLSTM_CRF:
    def __init__(self, vocab_size, tag_to_id, embedding_dim, hidden_dim):
        self.vocab_size = vocab_size
        self.tag_to_id = tag_to_id
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.num_tags = len(tag_to_id)
        
        self.word_ids = tf.placeholder(tf.int32, shape=[None, None], name="word_ids")
        self.sequence_lengths = tf.placeholder(tf.int32, shape=[None], name="sequence_lengths")
        self.labels = tf.placeholder(tf.int32, shape=[None, None], name="labels")
        
        with tf.variable_scope("words"):
            self.word_embeddings = tf.get_variable("word_embeddings", [vocab_size, embedding_dim])
            self.embedded_words = tf.nn.embedding_lookup(self.word_embeddings, self.word_ids)
            
        with tf.variable_scope("bi-lstm"):
            cell_fw = tf.contrib.rnn.LSTMCell(hidden_dim)
            cell_bw = tf.contrib.rnn.LSTMCell(hidden_dim)
            (output_fw, output_bw), _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, self.embedded_words, sequence_length=self.sequence_lengths, dtype=tf.float32)
            self.lstm_output = tf.concat([output_fw, output_bw], axis=-1)
            
        with tf.variable_scope("proj"):
            W = tf.get_variable("W", shape=[2*hidden_dim, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
            b = tf.get_variable("b", shape=[self.num_tags], initializer=tf.zeros_initializer())
            self.logits = tf.matmul(tf.reshape(self.lstm_output, [-1, 2*hidden_dim]), W) + b
            self.logits = tf.reshape(self.logits, [-1, tf.shape(self.word_ids)[1], self.num_tags])
            
        with tf.variable_scope("crf"):
            self.transition_params = tf.get_variable("transition_params", shape=[self.num_tags, self.num_tags], initializer=tf.contrib.layers.xavier_initializer())
            log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(self.logits, self.labels, self.sequence_lengths, self.transition_params)
            self.loss = tf.reduce_mean(-log_likelihood)
            
        with tf.variable_scope("train_step"):
            self.train_op = tf.train.AdamOptimizer().minimize(self.loss)
            
    def train(self, train_data, dev_data, epochs, batch_size):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            for epoch in range(epochs):
                print("Epoch {}/{}".format(epoch+1, epochs))
                for batch in self.generate_batches(train_data, batch_size):
                    word_ids, labels, sequence_lengths = batch
                    feed_dict = {self.word_ids: word_ids, self.labels: labels, self.sequence_lengths: sequence_lengths}
                    _, loss = sess.run([self.train_op, self.loss], feed_dict=feed_dict)
                
                print("Train loss: {}".format(loss))
                self.evaluate(sess, dev_data)
                
    def evaluate(self, sess, data):
        correct_preds, total_correct, total_preds = 0., 0., 0.
        for batch in self.generate_batches(data, 1):
            word_ids, labels, sequence_lengths = batch
            feed_dict = {self.word_ids: word_ids, self.labels: labels, self.sequence_lengths: sequence_lengths}
            logits, transition_params = sess.run([self.logits, self.transition_params], feed_dict=feed_dict)
            lengths = sequence_lengths.tolist()
            for logit, sequence_length, label in zip(logits, lengths, labels):
                logit = logit[:sequence_length]
                viterbi_seq, _ = tf.contrib.crf.viterbi_decode(logit, transition_params)
                viterbi_seq = np.array(viterbi_seq)
                label = label[:sequence_length]
                correct_preds += np.sum(np.equal(viterbi_seq, label))
                total_preds += len(viterbi_seq)
                total_correct += len(label)
        
        p = correct_preds / total_preds if correct_preds > 0 else 0
        r = correct_preds / total_correct if correct_preds > 0 else 0
        f1 = 2 * p * r / (p + r) if correct_preds > 0 else 0
        print("Accuracy: {:.2f}, Precision: {:.2f}, Recall: {:.2f}, F1: {:.2f}".format(100*correct_preds/total_preds, 100*p, 100*r, 100*f1))
        
    def generate_batches(self, data, batch_size):
        num_batches = (len(data) + batch_size - 1) // batch_size
        for i in range(num_batches):
            start_index = i * batch_size
            end_index = min((i+1) * batch_size, len(data))
            batch = data[start_index:end_index]
            word_ids = [sentence[0] for sentence in batch]
            labels = [sentence[1] for sentence in batch]
            sequence_lengths = [len(sentence[0]) for sentence in batch]
            max_length = max(sequence_lengths)
            word_ids = [sentence + [0]*(max_length-len(sentence)) for sentence in word_ids]
            labels = [sentence + [0]*(max_length-len(sentence)) for sentence in labels]
            yield word_ids, labels, sequence_lengths

这个实现代码使用 TensorFlow 实现了一个双向 LSTM 和 CRF 的模型，用于进行命名实体识别。输入数据是一个句子的单词序列和对应的标签序列，输出是对应的标签序列的预测值。在训练时使用 Adam 优化器进行参数优化，使用 CRF 来计算模型的损失。在评估时使用精度、精确率、召回率和 F1 值来评估模型的性能。