from transformers import pipeline

triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large')

# We need to use the tokenizer manually since we need special tokens.
extracted_text = triplet_extractor.tokenizer.batch_decode(triplet_extractor("Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic", return_tensors=True, return_text=False)[0]["generated_token_ids"]["output_ids"])

print(extracted_text[0])

# Function to parse the generated text and extract the triplets
def extract_triplets(text):
    triplets = []
    relation, subject, relation, object_ = '', '', '', ''
    text = text.strip()
    current = 'x'
    for token in text.replace("", "").replace("
        
         "
        , "").replace("", "").split():
        if token == "
       
        "
       :
            current = 't'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
                relation = ''
            subject = ''
        elif token == "
       
        "
       :
            current = 's'
            if relation != '':
                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
            object_ = ''
        elif token == "
       
        "
       :
            current = 'o'
            relation = ''
        else:
            if current == 't':
                subject += ' ' + token
            elif current == 's':
                object_ += ' ' + token
            elif current == 'o':
                relation += ' ' + token
    if subject != '' and relation != '' and object_ != '':
        triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
    return triplets
extracted_triplets = extract_triplets(extracted_text[0])
print(extracted_triplets)