README.md

metadata

license: apache-2.0
datasets:
  - AL-GR/AL-GR-v1
language:
  - zh
metrics:
  - accuracy
base_model:
  - google-t5/t5-base

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.

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

Model	HR@20	HR@100	HR@500	HR@1000
Forge-Qwen 2.5-0.5B-Base-s1	0.0506	0.1277	0.2602	0.3068
Forge-T5-Base-s1	0.0284	0.0689	0.1372	0.1557

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.

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