Instructions to use imaneb942/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imaneb942/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="imaneb942/MNLP_M3_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("imaneb942/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("imaneb942/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 707810919e2e5a19116c2dc87d9ef4ac4bceb2f198183eaf65d726f929b40c4b
- Size of remote file:
- 670 MB
- SHA256:
- 65a6da76608a92fb42fbee99ffe472357416caf215be2d4cfe4c3fdf6f040f4a
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