T5 Small for Conversation Summarization
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_checkpoint = "ahlad/t5-small-finetuned-samsum"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
input_text = """
Emma: Did you finish the book I lent you?
Liam: Yes, I couldn’t put it down! The twist at the end was insane.
Emma: I know, right? I didn’t see it coming at all. What did you think of the main character?
Liam: Honestly, I thought they were a bit frustrating at first, but they grew on me.
Emma: Same here. I loved how they developed by the end. Are you up for another book from the series?
Liam: Absolutely! Pass it my way.
"""
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Summary:", summary)
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