AL-GR/Forge-T5-Base-s1

Forge-T5-Base-s1

This model is initialized from the pre-trained `google-t5/t5-base` and fine-tuned on the AL-GR/AL-GR-v1 dataset using the FORGE framework for 4 training epochs.

More details can be found in the paper FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets.

Official Code: GitHub Repository

Evaluation Results on AL-GR/AL-GR-v1

Note: HR@K denotes Hit Rate at K — the proportion of test queries for which the correct answer appears in the top-K retrieved/generated results.

Usage

1. Download the Model

You can download this model locally using the huggingface_hub library:

import os
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"  # Optional: use mirror for faster download in some regions
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id='AL-GR/Forge-T5-Base-s1',
    local_dir='{YOUR_LOCAL_DIR}',  # Replace with your desired local path
    local_dir_use_symlinks=False,
)

2. Update Configuration

After downloading, update the configuration file used by the FORGE framework. Specifically, replace the load_checkpoint_from field in the JSON config file:

File: algr/config/generate_t5base_3layer_tiny.json

Update to:

"load_checkpoint_from": "{YOUR_LOCAL_DIR}"

Replace {YOUR_LOCAL_DIR} with the actual local path where you downloaded the model.

For more details about the training setup, dataset, or evaluation protocol, please refer to the FORGE framework repository.

Citation

If you find this work helpful, please cite the following paper:

@article{fu2025forge,
  title={FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets},
  author={Fu, Kairui and Zhang, Tao and Xiao, Shuwen and Wang, Ziyang and Zhang, Xinming and Zhang, Chenchi and Yan, Yuliang and Zheng, Junjun and others},
  journal={arXiv preprint arXiv:2509.20904},
  year={2025}
}