from transformers import T5Tokenizer, T5ForConditionalGeneration

model = T5ForConditionalGeneration.from_pretrained("HuyTran1301/ABSOSUM_Phase2_v1.1_Test")
tokenizer = T5Tokenizer.from_pretrained("HuyTran1301/ABSOSUM_Phase2_v1.1_Test")

# Format input with special tokens
input_text = "<POST> Your question here </s> <ANS> Answer 1 </s> <ANS> Answer 2 </s>"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

# Generate summary
outputs = model.generate(input_ids, max_length=150)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)

Citation

If you use this model, please cite:

@misc{absosum_phase2_v1,
  title={ABSOSUM Phase 2: Weight-Aware Multi-Answer Summarization},
  author={Huy Tran},
  year={2025},
  url={https://huggingface.co/HuyTran1301/ABSOSUM_Phase2_v1.1_Test}
}

Training Date

November 28, 2025