Instructions to use Mass-14/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mass-14/MNLP_M2_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Mass-14/MNLP_M2_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Mass-14/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("Mass-14/MNLP_M2_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 600ad20689ef3d9b5ac5bb95a91b495ca9931262f3f99d8b03fb6186c17b1f35
- Size of remote file:
- 1.34 GB
- SHA256:
- 899a39031aa9a6b636e59ffad35ebbcff6a78a2431d761b275f1b5dbab180ac1
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