Instructions to use Magolor/deepex-ranking-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Magolor/deepex-ranking-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Magolor/deepex-ranking-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Magolor/deepex-ranking-model") model = AutoModel.from_pretrained("Magolor/deepex-ranking-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2850f5ed1a19d75a7a29af817a516801c7de234e36c9e2bc41e2f4866c8c547e
- Size of remote file:
- 433 MB
- SHA256:
- f1666ae08c719efaea5a2317ca1b28820cc29f8d6cc52ab14825b4faaa98cee9
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