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
File size: 902 Bytes
cf02581 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"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": "이 폴더의 내용물 전체를 Hugging Face repo 루트에 업로드하세요."
}
|