abstractive.py

# abstractive.py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "arousrihab/my-t5base-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def abstractive_summary(text, max_length_ratio=0.2, min_length_ratio=0.1):
    total_length = len(text.split())
    max_length = int(total_length * max_length_ratio)
    min_length = int(total_length * min_length_ratio)
    
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
    summary_ids = model.generate(inputs, max_length=max_length, min_length=min_length, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary