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
Delete config.json
Browse files- config.json +0 -57
config.json
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{
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"model_type": "jnu_tsb",
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"architectures": [
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"JNUTSBModel"
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],
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"repo_id": "HONGRIZON/JNU-TSB",
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"display_name": "JNU-TSB",
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"full_name": "Jeju National University Time-Series Bridge",
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"nickname": "Time-Series Bridge / Time-Series Seungbin",
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"chronos_model_id": "amazon/chronos-2",
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"llm_model_id": "EleutherAI/polyglot-ko-1.3b",
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"route_name": "jnu-tsb",
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"quantile_levels": [
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0.1,
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0.5,
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0.9
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],
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"use_llm_extractor": true,
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"event_categories": [
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"earnings",
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"product",
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"macro",
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"regulation",
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"supply_chain",
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"competition",
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"other"
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],
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"covariate_columns": [
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"cov_earnings_count",
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"cov_product_count",
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"cov_macro_count",
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"cov_regulation_count",
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"cov_supply_chain_count",
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"cov_competition_count",
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"cov_other_count",
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"cov_sentiment_pos_count",
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"cov_sentiment_neg_count",
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"cov_sentiment_neu_count",
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"cov_news_count",
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"cov_sentiment_mean",
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"cov_confidence_mean",
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"cov_event_score"
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],
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"auto_map": {
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"AutoConfig": "configuration_jnu_tsb.JNUTSBConfig",
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"AutoModel": "modeling_jnu_tsb.JNUTSBModel"
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},
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"custom_pipelines": {
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"jnu-tsb": {
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"impl": "pipeline.JNUTSBPipeline",
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"pt": [
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"AutoModel"
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],
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"type": "multimodal"
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}
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}
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}
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