import nltk
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("njvdnbus/personalised_opener-t5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("njvdnbus/personalised_opener-t5-large")

def use_model(text):
    inputs = ["" + text]
    inputs = tokenizer(inputs, truncation=True, return_tensors="pt")
    output = model.generate(**inputs, num_beams=1, do_sample=True, min_length=10, max_length=256)
    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    predicted_interests = nltk.sent_tokenize(decoded_output.strip())[0]
    return predicted_interests

text= "tennis"
print(use_model(text))

Do you think tennis is the most exciting sport out there?

Smaller model

Fine-tuned T5-large version can be found here.

njvdnbus/personalised_opener-t5-3b

Personalised opener

Model input

Example

Output

How to use in code

Smaller model

njvdnbus/
personalised_opener-t5-3b