Sentence Similarity
Safetensors
sentence-transformers
colbert-ai
xmod
colbert
multi-vector
passage-retrieval
Eval Results (legacy)
Instructions to use antoinelouis/colbert-xm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use antoinelouis/colbert-xm with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("antoinelouis/colbert-xm") 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) - Notebooks
- Google Colab
- Kaggle
ValueError: The state dictionary of the model you are trying to load is corrupted. Are you sure it was properly saved?
#3
by secain - opened
sha256 for my model file:9c74a0fabc96993d671aa8550eec103bd31ec62a5c50c570b41ec628a8985b3c
same with the repo
4141 base_model_expected_keys = list(model_to_load.state_dict().keys())
4142 if any(key in expected_keys_not_prefixed and key not in base_model_expected_keys for key in loaded_keys):
-> 4143 raise ValueError(
4144 "The state dictionary of the model you are trying to load is corrupted. Are you sure it was "
4145 "properly saved?"
4146 )
what's wrong?