ValueError: Error when checking target: expected activation_9 to have shape (74, 6) but got array with shape (75, 6)

I work on Named entity recognition (in the train each word has a label)
the number of labels is 6
i run the model

from keras.models import Sequential
from keras.layers import Dense, LSTM, InputLayer, Bidirectional, TimeDistributed, Embedding, Activation
from keras.optimizers import Adam
from keras import initializers

model = Sequential()
model.add(InputLayer(input_shape=(MAX_LENGTH, )))
model.add(Embedding(len(word2index), 128))
model.add(Conv1D(filters=32, kernel_size=2, activation='relu'))
model.add(Bidirectional(LSTM(256, return_sequences=True)))
# model.add(AttentionLayer(300,True,name='word_attention'))
model.add(TimeDistributed(Dense(len(tag2index))))

model.add(Activation('softmax'))
     
model.compile(loss='categorical_crossentropy',
              optimizer=Adam(0.001),
              metrics=['accuracy'])
     
model.summary()

the model is the following .

def to_categorical(sequences, categories):
    cat_sequences = []
    for s in sequences:
        cats = []
        for item in s:
            cats.append(np.zeros(categories))
            cats[-1][item] = 1.0
        cat_sequences.append(cats)
    return np.array(cat_sequences)
cat_train_tags_y = to_categorical(train_tags_y, len(tag2index))
model.fit(train_sentences_X, cat_train_tags_y, batch_size=128, epochs=20, validation_split=0.2)

When i execute the fit command the following error diplays: ValueError: Error when checking target: expected activation_9 to have shape (74, 6) but got array with shape (75, 6)

Convolutional layers reduce the spatial dimension of their inputs.

Since you're using a kernel_size=2, you are shortening the length of your data to original_length - 1 (from 75 to 74).

The solution is to use padding='same' in your convolutional layer, this way the system automatically adds a padding so the final length is the same as the input.