kidist-amde / genret

Learning to Tokenize for Generative Retrieval (NeurIPS 2023)

Learning to Tokenize for Generative Retrieval

Code of the paper Learning to Tokenize for Generative Retrieval.

Environment

pytorch, transformers, accelerate, faiss, k_means_constrained

Dataset

NQ320K: unzip dataset/nq320k.zip

Other datasets coming soon.

Training and Evaluation

Code for GenRet on NQ320K:

python run.py --model_name t5-base --code_num 512 --max_length 3 --train_data dataset/nq320k/train.json --dev_data dataset/nq320k/dev.json --corpus_data dataset/nq320k/corpus_lite.json --save_path out/model

Code for generative retrieval baselines: baseline.py

Code for dense retrieval baselines: dpr.py

Cite

@article{Sun2023LearningTT,
  title={Learning to Tokenize for Generative Retrieval},
  author={Weiwei Sun and Lingyong Yan and Zheng Chen and Shuaiqiang Wang and Haichao Zhu and Pengjie Ren and Zhumin Chen and Dawei Yin and M. de Rijke and Zhaochun Ren},
  journal={ArXiv},
  year={2023},
  volume={abs/2304.04171},
}