Feature Extraction
Transformers
Safetensors
PyTorch
Keras
modern_tcn_probabilistic
time-series
forecasting
probabilistic-forecasting
multivariate
custom-model
custom_code
Instructions to use itsalissonsilva/modern-tcn-probabilistic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itsalissonsilva/modern-tcn-probabilistic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="itsalissonsilva/modern-tcn-probabilistic", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("itsalissonsilva/modern-tcn-probabilistic", trust_remote_code=True, device_map="auto") - Keras
How to use itsalissonsilva/modern-tcn-probabilistic 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://itsalissonsilva/modern-tcn-probabilistic") - Notebooks
- Google Colab
- Kaggle
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