---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
base_model: google/bert_uncased_L-2_H-128_A-2
datasets:
- lightonai/ms-marco-en-bge-gemma-unnormalized
pipeline_tag: sentence-similarity
library_name: PyLate
license: apache-2.0
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: ColBERT MUVERA Micro
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.26
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.36
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.4
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.58
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.26
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.12666666666666665
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.092
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07800000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11233333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.16066666666666665
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.184
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3206666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.24408616743142095
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.33196825396825397
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.18128382432733356
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: 0.68
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.86
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.94
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.68
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6066666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.56
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.502
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05322585293904511
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.16789568954347403
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.22988072374930787
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.35043982767195947
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6003406576207015
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7850000000000001
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4687280514608297
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: 0.72
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.78
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.72
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.1
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.6866666666666668
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.7633333333333333
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.82
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.89
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7955242043086649
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7731666666666667
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7676133768765347
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: 0.3
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.54
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.58
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.66
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.17200000000000004
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10800000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.1770793650793651
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.3453492063492064
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.4009047619047619
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.4740952380952381
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.38709436118795515
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4288015873015872
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3297000135708943
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: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.94
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.31200000000000006
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.16599999999999995
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.47
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.75
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.78
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.83
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8179728241272247
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9512222222222222
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7611883462001594
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: 0.42
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.66
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.42
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.136
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07800000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.42
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.66
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.68
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.78
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5976880189340548
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5393809523809523
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5531015913611822
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: 0.46
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.58
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.68
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.46
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.38
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.324
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.272
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.04276439372638386
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.07977851865112022
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.11439841040272719
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.1391695106171535
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.34241148621124995
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5320000000000001
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.14897381866568696
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: 0.42
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: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.42
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.23333333333333328
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15200000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.086
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.4
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.66
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.72
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.79
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6184738987111722
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5763888888888888
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5642312927870203
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: 0.8
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: 0.96
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.8
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3399999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.22399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.11999999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7239999999999999
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.8473333333333334
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9006666666666666
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9373333333333334
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.863105292852843
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8611904761904764
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8312823701317842
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: 0.42
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.58
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: 0.42
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2866666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.138
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.085
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.17666666666666664
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.21366666666666667
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.2826666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2889801789850345
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5005
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.21685607444339383
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: 0.2
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.44
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.5
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.64
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.2
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.14666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.1
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.064
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.44
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.64
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4151392430544827
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3440555555555555
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3521906424035335
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: 0.58
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.76
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.82
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.555
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.735
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.8
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.84
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7153590631749926
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6798333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6760413640032285
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: 0.7551020408163265
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1.0
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7551020408163265
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6734693877551019
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6000000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5285714285714286
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.050375728116040484
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.13379303377518686
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.19744749683082305
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3328396127707909
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5927407647152685
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8639455782312924
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4115661843314275
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5350078492935635
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7000000000000001
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.743076923076923
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8107692307692307
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5350078492935635
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.33026687598116167
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24923076923076928
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.1795824175824176
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3058804107585258
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.45537049602453755
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5031511327862271
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5851700658324468
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5599166277934665
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6282656549799407
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4817505346586929
name: Maxsim Map@100
---
# ColBERT MUVERA Micro
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [msmarco-en-bge-gemma-unnormalized](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma-unnormalized) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
This model is trained with un-normalized scores, making it compatible with [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504).
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
_Note: txtai 9.0+ is required for late interaction model support_
```python
import txtai
embeddings = txtai.Embeddings(
sparse="neuml/colbert-muvera-micro",
content=True
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
Late interaction models excel as reranker pipelines.
```python
from txtai.pipeline import Reranker, Similarity
similarity = Similarity(path="neuml/colbert-muvera-micro", lateencode=True)
ranker = Reranker(embeddings, similarity)
ranker("query to run")
```
## Usage (PyLate)
Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="neuml/colbert-muvera-micro",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 128, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Evaluation
### BEIR Subset
The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py).
Scores reported are `ndcg@10` and grouped into the following three categories.
#### FULL multi-vector maxsim
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.4440 | 0.3649 | 0.7423 | 0.5171 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4595 | 0.3165 | 0.6456 | 0.4739 |
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3947** | **0.3235** | **0.6676** | **0.4619** |
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4455 | 0.3502 | 0.7145 | 0.5034 |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.4946 | 0.3717 | 0.7529 | 0.5397 |
#### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0317 | 0.1135 | 0.0836 | 0.0763 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4562 | 0.3025 | 0.6278 | 0.4622 |
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3849** | **0.3095** | **0.6464** | **0.4469** |
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4451 | 0.3537 | 0.7148 | 0.5045 |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0265 | 0.1052 | 0.0556 | 0.0624 |
#### MUVERA encoding only
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0024 | 0.0201 | 0.0047 | 0.0091 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3463 | 0.2356 | 0.5002 | 0.3607 |
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.2795** | **0.2348** | **0.4875** | **0.3339** |
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.3850 | 0.2928 | 0.6357 | 0.4378 |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0003 | 0.0203 |0.0013 | 0.0073 |
_Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts._
As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this [GitHub Issue](https://github.com/lightonai/pylate/issues/142) for more.
**In reviewing the scores, this model is surprisingly and unreasonably competitive with the original ColBERT v2 model at only 3% of the size!**
### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.535 |
| MaxSim_accuracy@3 | 0.7 |
| MaxSim_accuracy@5 | 0.7431 |
| MaxSim_accuracy@10 | 0.8108 |
| MaxSim_precision@1 | 0.535 |
| MaxSim_precision@3 | 0.3303 |
| MaxSim_precision@5 | 0.2492 |
| MaxSim_precision@10 | 0.1796 |
| MaxSim_recall@1 | 0.3059 |
| MaxSim_recall@3 | 0.4554 |
| MaxSim_recall@5 | 0.5032 |
| MaxSim_recall@10 | 0.5852 |
| **MaxSim_ndcg@10** | **0.5599** |
| MaxSim_mrr@10 | 0.6283 |
| MaxSim_map@100 | 0.4818 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `learning_rate`: 0.0003
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0003
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional