Instructions to use NFX74/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NFX74/MNLP_M2_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NFX74/MNLP_M2_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NFX74/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("NFX74/MNLP_M2_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72284d51aa5e12e4058fa8b32956f9037dd81fe8dbde39500566eea51b409099
- Size of remote file:
- 133 MB
- SHA256:
- 772487fa98b86cf51ec61e86b82e441b7ffe27b2a62179dae487bba07da68c76
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.