sentence-transformers
ONNX
Safetensors
English
bert
ColBERT
multi-vector
RAGatouille
passage-retrieval
Instructions to use answerdotai/answerai-colbert-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use answerdotai/answerai-colbert-small-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("answerdotai/answerai-colbert-small-v1") 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
Onnx models lack linear layer
#6
by jmzzomg - opened
Models in onnx directory produce outputs of shape (x, 384) instead of (x, 96), like with the torch counterpart.
However, vespa_colbert.onnx produce outputs of the correct shape
Is it a bug, or is it supposed to be this way?
Thank you
Whoops no this isn't intended behaviour! My guess as to what happened is that @Xenova converted the weights as a normal BERTModel, without wrapping the linear downcasting layer at the end. Can you confirm @Xenova ? Is there any way I can assist you in generating proper ones?
Thanks for flagging @jmzzomg !