jieba深度学习代码
时间: 2023-08-21 09:07:09 浏览: 48
jieba本身不提供深度学习模型,但是可以结合其他深度学习框架如TensorFlow或PyTorch来完成中文分词任务。
以下是一种基于TensorFlow的深度学习分词代码:
1. 准备数据集:
首先需要准备一个中文分词的数据集,可以使用现成的数据集,如MSR语料库、人民日报语料库等,也可以自己制作。
2. 构建模型:
使用TensorFlow搭建深度学习分词模型,可以使用双向LSTM+CRF等经典模型。
```python
import tensorflow as tf
class BiLSTM_CRF(tf.keras.Model):
def __init__(self, vocab_size, num_labels, embedding_dim, hidden_dim):
super(BiLSTM_CRF, self).__init__()
self.embeddings = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.bi_lstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(hidden_dim, return_sequences=True))
self.dense = tf.keras.layers.Dense(num_labels)
self.transition_params = tf.Variable(tf.random.uniform(shape=(num_labels, num_labels)))
def call(self, inputs, training=False):
embeddings = self.embeddings(inputs)
mask = tf.cast(inputs != 0, dtype=tf.float32)
lstm_out = self.bi_lstm(embeddings, mask=mask)
logits = self.dense(lstm_out)
return logits
def loss(self, inputs, labels):
logits = self(inputs)
mask = tf.cast(inputs != 0, dtype=tf.float32)
log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(logits, labels, mask)
loss = tf.reduce_mean(-log_likelihood)
return loss
def predict(self, inputs):
logits = self(inputs)
mask = tf.cast(inputs != 0, dtype=tf.float32)
path, _ = tf.contrib.crf.viterbi_decode(logits, self.transition_params)
return path
```
3. 训练模型:
使用训练数据对模型进行训练,并保存训练好的模型。
```python
# 准备训练数据和测试数据
train_x, train_y = load_data('train.txt')
test_x, test_y = load_data('test.txt')
tokenizer = Tokenizer(lower=False, oov_token=None)
tokenizer.fit_on_texts(train_x)
train_x = tokenizer.texts_to_sequences(train_x)
test_x = tokenizer.texts_to_sequences(test_x)
train_x = pad_sequences(train_x, maxlen=MAX_SEQ_LENGTH, padding='post', truncating='post')
test_x = pad_sequences(test_x, maxlen=MAX_SEQ_LENGTH, padding='post', truncating='post')
train_y = pad_sequences(train_y, maxlen=MAX_SEQ_LENGTH, padding='post', truncating='post')
test_y = pad_sequences(test_y, maxlen=MAX_SEQ_LENGTH, padding='post', truncating='post')
vocab_size = len(tokenizer.word_index) + 1
num_labels = len(tag2idx)
train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)).shuffle(10000).batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.from_tensor_slices((test_x, test_y)).batch(BATCH_SIZE)
# 构建模型
model = BiLSTM_CRF(vocab_size, num_labels, EMBEDDING_DIM, HIDDEN_DIM)
# 训练模型
optimizer = tf.keras.optimizers.Adam(lr=LEARNING_RATE)
for epoch in range(NUM_EPOCHS):
total_loss = 0
for batch, (inputs, labels) in enumerate(train_dataset):
with tf.GradientTape() as tape:
loss = model.loss(inputs, labels)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
total_loss += loss
print('Epoch: {}, Loss: {}'.format(epoch+1, total_loss))
# 保存模型
model.save('model.h5')
```
4. 使用模型进行分词:
使用已经训练好的模型对新的文本进行分词。
```python
# 加载模型
model = tf.keras.models.load_model('model.h5', compile=False)
# 对新文本进行分词
text = '今天天气真好'
tokens = jieba.lcut(text)
token_ids = [tokenizer.word_index.get(token, 1) for token in tokens] # 1是OOV的编号
inputs = pad_sequences([token_ids], maxlen=MAX_SEQ_LENGTH, padding='post', truncating='post')
tags = model.predict(inputs)[0][:len(tokens)]
result = [(token, tag2label[tag]) for token, tag in zip(tokens, tags)]
print(result)
```
需要注意的是,深度学习模型需要大量的数据和计算资源进行训练,但是得到的分词效果通常比传统方法更好。
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