在nlp中训练数据，以便使用LSTM提取技能，但训练和验证精度未达到预期

from keras.models import Sequential
from keras.layers import LSTM, Dense, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer

# 假设我们有文本数据和对应的标签
texts = ["example sentence", "another example"]
labels = [0, 1]

# 文本预处理
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
data = pad_sequences(sequences)

# 构建模型
model = Sequential()
model.add(Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=50, input_length=data.shape[1]))
model.add(LSTM(100))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(data, labels, epochs=10, batch_size=32, validation_split=0.2)