tensorflow - Adding a CRF layer to BiLSTM model in Keras (Jan 2021)

Question

i have searched through the internet to solve this problem, but no one seems to have a solution to it. I would like to improve my BiLSTM-model with a CRF layer, which is no longer supported by Keras obviously. How can i add this? I dont want to change my whole Keras and Tensorflow version for it. Please help me :) I have the latest Keras and Tensorflow versions.

    #vocab_size=4840, embedding is glove6B, max_seq_length=100
    model =  Sequential()
    model.add(Embedding(vocab_size, 300, weights=[embedding_vectors], input_length=max_seq_length, trainable= False))
    model.add(Bidirectional(LSTM(units=50, dropout=0.5, recurrent_dropout=0.5, return_sequences=True)))
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.4))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(n_tags, activation='softmax'))
    

    model.compile(loss="categorical_crossentropy", optimizer="rmsprop", metrics= ["categorical_accuracy"])
    model.summary()

Answer 1

等待大神解答