Dont Know Response

🚀 Natural Don't Know Response Model

This model is fine-tuned on Google's T5 to generate natural "Don't Know" responses, leveraging dependency-rule based data and the Quora Question Pairs dataset.

🚀 Quick Start

This model is fine-tuned on Google's T5 using a combination of a dependency-rule based data and Quora Question Pairs(QQP) dataset for the Don't Know Response Generation task.

📚 Documentation

• Paper: Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
• Github Repo: https://github.com/kaustubhdhole/natural-dont-know

💻 Usage Examples

Basic Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer
model_name = "ashish-shrivastava/dont-know-response"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)

input = "Where can I find good Italian food ?"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded_output) # I'm not sure where you can get good quality Italian food.

🔧 Technical Details

Hyperparameters

n_epochs = 2
base_LM_model = "T5-base"
max_seq_len = 256
learning_rate = 3e-4
adam_epsilon = 1e-8
train_batch_size = 6

📄 License

BibTeX entry and citation info

@misc{shrivastava2020saying,
      title={Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries}, 
      author={Ashish Shrivastava and Kaustubh Dhole and Abhinav Bhatt and Sharvani Raghunath},
      year={2020},
      eprint={2012.01873},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}