---
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`](https://huggingface.co/google-t5/t5-base) and fine-tuned on the **AL-GR/AL-GR-v1** dataset using the [FORGE](https://github.com/AL-GR/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:

```python
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**:
```json
"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](https://github.com/AL-GR/FORGE).