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
| { | |
| "repo_id": "HONGRIZON/JNU-TSB", | |
| "root_files": [ | |
| ".gitattributes", | |
| ".gitignore", | |
| "LICENSE", | |
| "MANIFEST.json", | |
| "NOTICE", | |
| "README.md", | |
| "__init__.py", | |
| "app.py", | |
| "config.json", | |
| "configuration_jnu_tsb.py", | |
| "data/sample_news.json", | |
| "data/sample_stock.csv", | |
| "docs/classroom_guide_ko.md", | |
| "docs/input_output_schema_ko.md", | |
| "docs/usage_ko.md", | |
| "event_extractor.py", | |
| "examples/python_automodel.py", | |
| "examples/python_quickstart.py", | |
| "examples/r_http_client.R", | |
| "examples/r_quickstart.R", | |
| "handler.py", | |
| "modeling_jnu_tsb.py", | |
| "pipeline.py", | |
| "pytorch_model.bin", | |
| "requirements.txt", | |
| "runtime.py", | |
| "tests/smoke_test.py", | |
| "upload_model_repo.py" | |
| ], | |
| "upload_command": "hf upload HONGRIZON/JNU-TSB . .", | |
| "note": "์ด ํด๋์ ๋ด์ฉ๋ฌผ ์ ์ฒด๋ฅผ Hugging Face repo ๋ฃจํธ์ ์ ๋ก๋ํ์ธ์." | |
| } | |