Time Series Forecasting
Transformers
PyTorch
Korean
jnu_tsb
feature-extraction
jnu-tsb
time-series
forecasting
chronos-2
polyglot-ko
korean
finance
covariates
r
reticulate
education
custom_code
Instructions to use HONGRIZON/JNU-TSB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HONGRIZON/JNU-TSB with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HONGRIZON/JNU-TSB", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from __future__ import annotations | |
| import argparse | |
| from pathlib import Path | |
| from huggingface_hub import HfApi, create_repo | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description="Upload JNU-TSB files to Hugging Face Hub") | |
| parser.add_argument("--repo_id", default="HONGRIZON/JNU-TSB", help="Hugging Face repo id") | |
| parser.add_argument("--private", action="store_true", help="Create/upload as a private repo") | |
| parser.add_argument("--folder", default=".", help="Folder to upload") | |
| args = parser.parse_args() | |
| folder = Path(args.folder).resolve() | |
| create_repo(args.repo_id, repo_type="model", private=args.private, exist_ok=True) | |
| api = HfApi() | |
| api.upload_folder( | |
| folder_path=str(folder), | |
| repo_id=args.repo_id, | |
| repo_type="model", | |
| ignore_patterns=[ | |
| ".git/*", | |
| "__pycache__/*", | |
| "*.pyc", | |
| "*.zip", | |
| ".venv/*", | |
| "venv/*", | |
| "env/*", | |
| "outputs/*", | |
| "checkpoints/*", | |
| "wandb/*", | |
| ], | |
| ) | |
| print(f"Uploaded {folder} to https://huggingface.co/{args.repo_id}") | |
| if __name__ == "__main__": | |
| main() | |