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
Point adapter to ModernVBERT/colmodernvbert-base
Browse files- adapter_config.json +1 -1
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "ModernVBERT/colmodernvbert",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "ModernVBERT/colmodernvbert-base",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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