代码如下，由于初学Python水平有限，未能确定问题所在，望指正，谢谢大家！
from keras.models import Sequential
from keras.layers import Embedding, Bidirectional, LSTM
from keras_contrib.layers import CRF

https://github.com/keras-team/keras-contrib

import process_data
import pickle
from keras.utils.vis_utils import plot_model
from keras.optimizers import SGD,Adam

EMBED_DIM = 200
BiRNN_UNITS = 200

def create_model(train=True):

if train:
    (train_x, train_y), (vocab, chunk_tags) = process_data.load_data()
    with open('model/config.pkl', 'wb') as inp:
        pickle.dump((vocab, chunk_tags),inp)
else:
    with open('model/config.pkl', 'rb') as inp:
        (vocab, chunk_tags) = pickle.load(inp)

model = Sequential()
model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  
model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
crf = CRF(len(chunk_tags), sparse_target=True)
model.add(crf)
model.summary()
plot_model(model, to_file="model.png",show_shapes=True)
model.compile(loss=crf.loss_function,optimizer=Adam(lr=0.01), metrics=[crf.accuracy])


if train:
    return model, (train_x, train_y)
else:
    return model, (vocab, chunk_tags)

keras_contrib这个包你没安装吧，导致crf找不到，你可以看下你最上面导入库的红色波浪线，鼠标放上去会提示你install安装

原因
按照提示，CRF没有安装或者 keras_contrib安装不正确或者没有安装导致的吧

from keras_contrib.layers import CRF

另外，就是注意你的第三方库的版本是否跟你的demo代码版本一致。

解决方法

下载安装

git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install

或者使用 pip手动安装

pip install git+https://www.github.com/keras-team/keras-contrib.git 

参考：

python - ImportError: No module named 'keras_contrib' - Stack Overflow https://stackoverflow.com/questions/49791178/importerror-no-module-named-keras-contrib

如何使用crf Demo（不确定是否有用）:

how to use CRF in tensorflow keras? - Stack Overflow https://stackoverflow.com/questions/58498107/how-to-use-crf-in-tensorflow-keras

如有问题及时沟通

使用 https://github.com/keras-team/keras-contrib实现的crf layer，
安装 keras-contrib

pip install git+https://www.github.com/keras-team/keras-contrib.git

代码示例：

# coding: utf-8
from keras.models import Sequential
from keras.layers import Embedding
from keras.layers import LSTM
from keras.layers import Bidirectional
from keras.layers import Dense
from keras.layers import TimeDistributed
from keras.layers import Dropout
from keras_contrib.layers.crf import CRF
from keras_contrib.utils import save_load_utils

VOCAB_SIZE = 2500
EMBEDDING_OUT_DIM = 128
TIME_STAMPS = 100
HIDDEN_UNITS = 200
DROPOUT_RATE = 0.3
NUM_CLASS = 5

def build_embedding_bilstm2_crf_model():
 """
 带embedding的双向LSTM + crf
 """
 model = Sequential()
 model.add(Embedding(VOCAB_SIZE, output_dim=EMBEDDING_OUT_DIM, input_length=TIME_STAMPS))
 model.add(Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True)))
 model.add(Dropout(DROPOUT_RATE))
 model.add(Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True)))
 model.add(Dropout(DROPOUT_RATE))
 model.add(TimeDistributed(Dense(NUM_CLASS)))
 crf_layer = CRF(NUM_CLASS)
 model.add(crf_layer)
 model.compile('rmsprop', loss=crf_layer.loss_function, metrics=[crf_layer.accuracy])
 return model

def save_embedding_bilstm2_crf_model(model, filename):
 save_load_utils.save_all_weights(model,filename)

def load_embedding_bilstm2_crf_model(filename):
 model = build_embedding_bilstm2_crf_model()
 save_load_utils.load_all_weights(model, filename)
 return model

if __name__ == '__main__':
 model = build_embedding_bilstm2_crf_model()