Instructions to use FIM4Science/fim-imp-temporal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FIM4Science/fim-imp-temporal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FIM4Science/fim-imp-temporal", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FIM4Science/fim-imp-temporal", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c1b0ba31c64acb0d880eafbb6fc8d5bbdf40b062ff2fe0d8d00a21d74fd9620
- Size of remote file:
- 106 MB
- SHA256:
- 2a32dacf09e6770fb21334bb98fd4b9c6a9b690e869339cd52c3cab826b75849
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