Sentence Similarity
ONNX
Safetensors
sentence-transformers
Transformers
English
PyLate
modernbert
feature-extraction
ColBERT
multi-vector
Generated from Trainer
text-embeddings-inference
Instructions to use mixedbread-ai/mxbai-edge-colbert-v0-32m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mixedbread-ai/mxbai-edge-colbert-v0-32m with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="mixedbread-ai/mxbai-edge-colbert-v0-32m") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Transformers
How to use mixedbread-ai/mxbai-edge-colbert-v0-32m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-edge-colbert-v0-32m") model = AutoModel.from_pretrained("mixedbread-ai/mxbai-edge-colbert-v0-32m", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07741af7943103613d630e4d1e83c9266177599cdb4075289e93876243a2c785
- Size of remote file:
- 128 MB
- SHA256:
- 9e6f0583917c283f725c3b88750087e3b741dc832b8591dd43acfe68725e89d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.