Instructions to use StockCast/seperate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use StockCast/seperate with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://StockCast/seperate") - Notebooks
- Google Colab
- Kaggle
Download models/AMCL(PRAN)/forecast_next_7.csv from StockCast/seperate: direct link, hf CLI and curl.
- Browser
- Download file 105 Bytes
-
https://huggingface.co/StockCast/seperate/resolve/main/models/AMCL(PRAN)/forecast_next_7.csv
- Command line
-
hf download 'hf://StockCast/seperate/models/AMCL(PRAN)/forecast_next_7.csv'
-
curl -L -o forecast_next_7.csv 'https://huggingface.co/StockCast/seperate/resolve/main/models/AMCL(PRAN)/forecast_next_7.csv'
105 Bytes
| step_ahead,prediction | |
| 1,224.47797 | |
| 2,224.6253 | |
| 3,224.45248 | |
| 4,223.68971 | |
| 5,223.24765 | |
| 6,223.60503 | |
| 7,223.93602 | |