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: 608 Bytes
cf02581 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | from __future__ import annotations
from typing import Any, Dict
try:
from .runtime import JNUTSBRuntime
except ImportError: # pragma: no cover
from runtime import JNUTSBRuntime
class EndpointHandler:
"""Hugging Face Inference Endpoint custom handler."""
def __init__(self, model_dir: str, **kwargs: Any) -> None:
self.runtime = JNUTSBRuntime.from_config_dir(model_dir)
def __call__(self, data: Dict[str, Any]) -> Any:
inputs = data.get("inputs", data)
parameters = data.get("parameters", {})
return self.runtime.predict(inputs=inputs, **parameters)
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