Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
multi-vector
ColBERT
text-embeddings-inference
Instructions to use mixedbread-ai/mxbai-colbert-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mixedbread-ai/mxbai-colbert-large-v1 with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("mixedbread-ai/mxbai-colbert-large-v1") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Inference
- Notebooks
- Google Colab
- Kaggle
[Using the pre-trained model to get inference via CrossEncoders]
#3 opened about 2 years ago
by
EltonLobo
Question about the mask of query
2
#1 opened over 2 years ago
by
kagaii