Instructions to use chungimungi/PatchTST-2-input-channels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chungimungi/PatchTST-2-input-channels with Transformers:
# Load model directly from transformers import AutoTokenizer, PatchTSTForPrediction tokenizer = AutoTokenizer.from_pretrained("chungimungi/PatchTST-2-input-channels") model = PatchTSTForPrediction.from_pretrained("chungimungi/PatchTST-2-input-channels") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
CHANGED
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@@ -40,7 +40,7 @@
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"positional_dropout": 0.0,
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"positional_encoding_type": "sincos",
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"pre_norm": true,
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"prediction_length":
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"random_mask_ratio": 0.5,
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"scaling": "std",
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"seed_number": null,
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"positional_dropout": 0.0,
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"positional_encoding_type": "sincos",
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"pre_norm": true,
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+
"prediction_length": 10000,
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"random_mask_ratio": 0.5,
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"scaling": "std",
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"seed_number": null,
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