Feature Extraction
Transformers
PyTorch
Safetensors
fimhawkes
time-series
temporal-point-processes
hawkes-processes
scientific-ml
custom_code
Instructions to use FIM4Science/fim-pp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FIM4Science/fim-pp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FIM4Science/fim-pp", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FIM4Science/fim-pp", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3566a22f6e47332a5edc58f360dfd7637907519d54933e4ec024ece69404bab
- Size of remote file:
- 64.6 MB
- SHA256:
- cb9f416bc1059d11675a67d9693f7a094ebb4a8c0b6d1c3dfcc2a662280f2dca
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