Instructions to use Youssefbou62/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Youssefbou62/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Youssefbou62/MNLP_M3_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Youssefbou62/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("Youssefbou62/MNLP_M3_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1182fc67ae242cce193c63e00e9535ee75b6b673eae972c8ad5291c7bd5138b
- Size of remote file:
- 670 MB
- SHA256:
- 520b60af7bcdf1443b149565fe579eeaa946ef3db2031e932d0cda2cbde3f4b0
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