Visual Document Retrieval
ColPali
Safetensors
sentence-transformers
English
vidore-experimental
vidore
multi-vector
Instructions to use ModernVBERT/colmodernvbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use ModernVBERT/colmodernvbert with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use ModernVBERT/colmodernvbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ModernVBERT/colmodernvbert") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "description": "Adapter matching colmvb_base__mvbhf__modelprefix__legacyproj.", | |
| "text_lora_source": "colmvb", | |
| "projection_lora_source": "colmvb__hf", | |
| "output_adapter": "colmvb__mvbhf__modelprefix__legacyproj" | |
| } | |