Instructions to use tedad09/DuplicateCrossEncoder-Riscaldamento with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tedad09/DuplicateCrossEncoder-Riscaldamento with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tedad09/DuplicateCrossEncoder-Riscaldamento")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tedad09/DuplicateCrossEncoder-Riscaldamento") model = AutoModelForSequenceClassification.from_pretrained("tedad09/DuplicateCrossEncoder-Riscaldamento") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97d85de6b0a010480cd84ca632d8bce97ced81135e5af0420151724828fb2116
- Size of remote file:
- 133 MB
- SHA256:
- c9ef093920d9a697964ef3200b1676dc0298ccb4071584d33a0fe637e0829195
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