[Python] How can I use BERT for address matching problem?

I am building an address matching algorithm. The main problem is that previous models like Conditional Random fields (CRF)from Paserator and Averaged Perceptron from Libpostal do not match address entities correctly.

I am using the free sample from AddressBases premium https://www.ordnancesurvey.co.uk/business-government/products/addressbase-premium.

I want when I parse an address to the algorithm like this:

bert.parser('FLAT ABC 7-9 TEDWORTH SQUARE LONDON SW3 4DU')



it will return the parsed tokens with high precision

('BuildingName', '7-9'),
('StreetName', 'TEDWORTH SQUARE'),
('TownName', 'LONDON'),
('Postcode', 'SW3 4DU')])


I have reviewed AddressNet, Usaddress, Deepmatcher and chinese address with (BERT) https://huggingface.co/cola/chinese-address-ner

I am looking for something in English with BERT (RNN,lSTM) for this problem.

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