Instructions to use kyLELEng/patchtst-cross-sectional-return-forecast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kyLELEng/patchtst-cross-sectional-return-forecast with Transformers:
# Load model directly from transformers import AutoTokenizer, PatchTSTForPrediction tokenizer = AutoTokenizer.from_pretrained("kyLELEng/patchtst-cross-sectional-return-forecast") model = PatchTSTForPrediction.from_pretrained("kyLELEng/patchtst-cross-sectional-return-forecast") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 15, | |
| "best_validation_loss_so_far": 40.24222278594971, | |
| "resume_from_model_id": "JumpHigh/patchtst-cross-sectional-return-forecast", | |
| "resume_from_subfolder": "checkpoints/epoch-15" | |
| } |