README.md

---
datasets:
- microsoft/ms_marco
language:
- en
base_model:
- google-t5/t5-base
pipeline_tag: text2text-generation
---


# Question decomposer Based t5 and Seq2seq

Example: What is the capital of France and when it entablish ?

- What is the capital of France ?
- When was the capital of France entablish ?

Checkout my demo here 👉🏻 [demo](https://huggingface.co/spaces/thenHung/Demo-question-decomposer)

## How to Usage

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Set device
device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model_path = "thenHung/question_decomposer_t5"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to(device)
model.eval()

# Decompose question
question = "Who is taller between John and Mary?"
input_text = f"decompose question: {question}"
input_ids = tokenizer(
    input_text,
    max_length=128,
    padding="max_length",
    truncation=True,
    return_tensors="pt"
).input_ids.to(device)

with torch.no_grad():
    outputs = model.generate(
        input_ids,
        max_length=128,
        num_beams=4,
        early_stopping=True
    )

# Decode output
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
sub_questions = decoded_output.split(" [SEP] ")

# Print sub-questions
print(sub_questions)
# ['What is the height of John?', 'What is the height of Mary?']
```