metadata
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- transformers
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: mxbai-edge-colbert-v0-17m
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.28
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.4
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.52
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.76
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.15333333333333332
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.132
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.114
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.13166666666666665
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19566666666666666
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.26899999999999996
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.4323333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.32110454808344563
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3874603174603174
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.24386041506572398
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.78
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.92
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.94
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.78
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6466666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6000000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.53
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11231081441624795
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19718498662932682
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2515039585287889
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3827585204510568
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6668364782038155
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8583333333333334
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.532138470469583
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.86
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.86
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3399999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21199999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10999999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7966666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.91
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.95
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.98
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9113009102891444
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9095238095238095
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8804077380952381
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.64
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.66
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.22399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.28257936507936504
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.45084920634920633
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5012857142857142
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5930079365079366
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5242333453411014
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5883888888888889
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4663825636525529
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.94
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.94
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5666666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.3559999999999999
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18599999999999994
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.47
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.85
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.89
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.93
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8918313878583112
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9666666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8388140096618357
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.58
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14800000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.58
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.68
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.74
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.82
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6902252545188936
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6501031746031746
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6593558218584534
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.6
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.64
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.4
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.3560000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.28
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.06472705697215374
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.09880268446365006
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.12166169350643057
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.14660598371037648
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3681157447334094
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5658333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.17231143133969085
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.58
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.72
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.82
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.24
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.17199999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.55
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.67
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.78
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7085689105698346
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6787142857142857
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6543090180774391
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.96
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.96
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13199999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8373333333333334
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9486666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9626666666666668
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9833333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9609623318470277
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.98
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9420639971139971
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.72
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.76
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.35999999999999993
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.284
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.192
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10166666666666666
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.22166666666666665
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.28966666666666663
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.39166666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.38777798626622473
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6133809523809525
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.30159944576020853
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.16
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.5
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.16
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.16666666666666663
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12400000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.16
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.62
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.82
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.47567106787289914
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3671111111111111
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.37145718958470925
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: MaxSim_accuracy@1
value: 0.72
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.84
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.72
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.29333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.695
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.82
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.84
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.85
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7943497079909279
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7799999999999998
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7769113522745599
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: MaxSim_accuracy@1
value: 0.8163265306122449
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9795918367346939
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9795918367346939
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.8163265306122449
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7210884353741496
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6530612244897959
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5510204081632653
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05492567388541453
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.14618659815433527
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.21615229246201462
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.35053940409516887
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6255925383730097
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8906705539358599
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4430753846973072
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.6274097331240187
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7645839874411303
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8076609105180533
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.883076923076923
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6274097331240187
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.36982731554160114
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.28438932496075353
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.19900156985871267
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3720674033605012
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5145402673535784
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5716874609320216
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6569419367767595
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6405054009190804
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7104758789962872
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5602066798193307
name: Maxsim Map@100
The crispy, lightweight ColBERT family from Mixedbread.
🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search, our multi-modal and multi-lingual search solution.
mxbai-edge-colbert-v0-17m
This model is a lightweight, 17 million parameter ColBERT with a projection dimension of 48. It is built on top of Ettin-17M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library.
Usage
To use this model, you first need to install PyLate:
# uv
uv add pylate
# uv + pip
uv pip install pylate
# pip
pip install -U pylate
Once installed, the model is immediately ready to use to generate representations and index documents:
from pylate import indexes, models, retrieve
# Step 1: Load the model
model = models.ColBERT(
model_name_or_path="mixedbread-ai/mxbai-edge-colbert-v0-17m",
)
# Step 2: Initialize an index (here, PLAID, for larger document collections)
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode your documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
That's all you need to do to encode a full collection! Your documents are indexed and ready to be queried:
# Step 5.1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
Reranking
Thanks to its extreme parameter efficiency, this model is particularly well-suited to being used as a re-ranker following an even more lightweight first stage retrieval, such as static embeding models. Re-ranking is just as straigthforward:
from pylate import rank, models
# Load the model
model = models.ColBERT(
model_name_or_path="mixedbread-ai/mxbai-edge-colbert-v0-17m",
)
# Define queries and documents
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
# Embed them
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
# Perform reranking
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
Evaluation
Results on BEIR
| Model | AVG | MS MARCO | SciFact | Touche | FiQA | TREC-COVID | NQ | DBPedia |
|---|---|---|---|---|---|---|---|---|
| Large Models (>100M) | ||||||||
| GTE-ModernColBERT-v1 | 0.547 | 0.453 | 0.763 | 0.312 | 0.453 | 0.836 | 0.618 | 0.480 |
| ColBERTv2 | 0.488 | 0.456 | 0.693 | 0.263 | 0.356 | 0.733 | 0.562 | 0.446 |
| Medium Models (<35M) | ||||||||
| mxbai-edge-colbert-v0-32m | 0.521 | 0.450 | 0.740 | 0.313 | 0.390 | 0.775 | 0.600 | 0.455 |
| answerai-colbert-small-v1 | 0.534 | 0.434 | 0.740 | 0.250 | 0.410 | 0.831 | 0.594 | 0.464 |
| bge-small-en-v1.5 | 0.517 | 0.408 | 0.713 | 0.260 | 0.403 | 0.759 | 0.502 | 0.400 |
| snowflake-s | 0.519 | 0.402 | 0.722 | 0.235 | 0.407 | 0.801 | 0.509 | 0.410 |
| Small Models (<25M) | ||||||||
| mxbai-edge-colbert-v0-17m | 0.490 | 0.416 | 0.719 | 0.316 | 0.326 | 0.713 | 0.551 | 0.410 |
| colbert-muvera-micro | 0.394 | 0.364 | 0.662 | 0.251 | 0.254 | 0.561 | 0.386 | 0.332 |
| all-MiniLM-L6-v2 | 0.419 | 0.365 | 0.645 | 0.169 | 0.369 | 0.472 | 0.439 | 0.323 |
Results on LongEmbed
| Model | AVG |
|---|---|
| Large Models (>100M) | |
| GTE-ModernColBERT-v1 (32k) | 0.898 |
| GTE-ModernColBERT-v1 (4k) | 0.809 |
| granite-embedding-english-r2 | 0.656 |
| ColBERTv2 | 0.428 |
| Medium Models (<50M) | |
| mxbai-edge-colbert-v0-32m (32k) | 0.849 |
| mxbai-edge-colbert-v0-32m (4k) | 0.783 |
| granite-embedding-small-english-r2 | 0.637 |
| answerai-colbert-small-v1 | 0.441 |
| bge-small-en-v1.5 | 0.312 |
| snowflake-arctic-embed-s | 0.356 |
| Small Models (<25M) | |
| mxbai-edge-colbert-v0-17m (32k) | 0.847 |
| mxbai-edge-colbert-v0-17m (4k) | 0.776 |
| all-MiniLM-L6-v2 | 0.298 |
| colbert-muvera-micro | 0.405 |
For more details on evaluations, please read our Tech Report.
Community
Please join our Discord Community and share your feedback and thoughts! We are here to help and also always happy to chat.
License
Apache 2.0
Citation
If you use our model, please cite the associated tech report:
Coming soon!
If you specifically use its projection heads, or discuss their effect, please cite our report on using different projections for ColBERT models:
@misc{clavie2025simpleprojectionvariantsimprove,
title={Simple Projection Variants Improve ColBERT Performance},
author={Benjamin Clavié and Sean Lee and Rikiya Takehi and Aamir Shakir and Makoto P. Kato},
year={2025},
eprint={2510.12327},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2510.12327},
}
Finally, if you use PyLate in your work, please cite PyLate itself:
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}