Creating NER model with Keras and Python

I have made Keras model that detects if string value is Address, Company or Date. I have used only different company names, different date formants, and different street addresses for training. So each row in my dataset have between 1 and 5 words (some words can be numbers).

For preprocessing I have used vectorizers:

transformerVectoriser = ColumnTransformer(transformers=[('vector char', CountVectorizer(analyzer='char', ngram_range=(3, 6), max_features = 2000), 'text'),
                                                        ('vector word', CountVectorizer(analyzer='word', ngram_range=(1, 1), max_features = 4000), 'text')],
                                          remainder='passthrough') # Default is to drop untransformed columns


features = transformerVectoriser.fit_transform(features)

This is my model:

model = Sequential()
model.add(Dense(100, input_dim = features.shape[1], activation = 'relu')) # input layer requires input_dim param
model.add(Dense(200, activation = 'relu'))
model.add(Dense(100, activation = 'relu'))
model.add(Dense(50, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))

I have achieved accuracy of 93%. Is it possible to use that model for detecting where is that string (Address, Company or Date) in bigger text? I think that that kind of models is called NER models (named entity recognition).

My model takes string input and decides if its a company, person or address. String input is 1-5 words long.

For example if I have text:

text = "What do you think about Amazon and their customer policy?"

How can I detect start index and end index of "Amazon", or how can I extract only Amazon from this text? So required result should be:

start_index = 21
end_index = 27

Or:

result = 'Amazon'

I want to use my model to extract specific entities from text, in this case company, address or date. Basically I need some kind of location search thats based on Keras model, or searching for the pattern in large text.

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