---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- multi-vector
- colbert
- late-interaction
- generated_from_trainer
- dataset_size:50000
- loss:MultiVectorMultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
- text: The job of a physical education teacher is to promote students' physical fitness
through exercise and sport activities. A bachelor's degree in physical education
is required along with hands-on student teaching experience. Those who teach in
public schools must become licensed. Show me 10 popular schools.
- text: The Handheld Braille Labeler, model 2891, is a braille labeler designed for
use by individuals who are blind or deaf blind or have low vision. This lightweight
plastic braille label gun has both braille and print characters on its character
selection dial.
- text: surname meaning, fontana
- text: garlic did originate near Siberia, Russia so powder was made also at russia
at american factory. Garlic is native to central Asia, but its use spread across
the world more than 5000 years ago, before recorded history. It was worshipped
by the Egyptians and fed to workers building the Gread Pyramid at Giza, about
2600 BC. Greek athletes ate it to build their strength.
- text: "With a 50-50 ratio of marijuana to tobacco, the cost of producing a pack\
\ of 20 pre-rolled joints could be brought down to just a little more than $20â\x80\
\x94so a $40 pack at the store. It isnâ\x80\x99t as easy as it seems, though.\
\ The government has a vested interest in producing income from the selling of\
\ marijuana.nother solution: mix the marijuana with tobacco. If marijuana cigarettes\
\ were to be mixed with tobacco, at a 50-50 ratio, it would bring the cost down\
\ significantly. Many tobacco farmers will wholesale a pound of their product\
\ for less than $2."
datasets:
- sentence-transformers/msmarco-bm25
pipeline_tag: feature-extraction
library_name: sentence-transformers
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 ModernBERT-base trained on MS MARCO triplets
results:
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: maxsim_accuracy@1
value: 0.3
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.4
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.5
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: 0.13333333333333333
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.1
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.06600000000000002
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.3
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.4
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.5
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.66
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.4527581784932048
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.39018253968253963
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.40322961689375353
name: Maxsim Map@100
- type: maxsim_accuracy@1
value: 0.32
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.46
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.6
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.15333333333333332
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.10800000000000001
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.06000000000000001
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.32
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.46
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.54
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.6
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.45502112234851405
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.4092777777777778
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.43050860789683937
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: maxsim_accuracy@1
value: 0.22
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.48
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.58
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.22
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.15999999999999998
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.12
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07600000000000001
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: 0.53
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.68
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.44041050127975234
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.3815555555555556
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.3659063680027385
name: Maxsim Map@100
- type: maxsim_accuracy@1
value: 0.28
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.6
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.18
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.128
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.08
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.26
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.48
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.57
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.7
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.4738978154793734
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.414015873015873
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.40376142487928957
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: maxsim_accuracy@1
value: 0.28
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.42
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.44
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.54
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.16666666666666669
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.12000000000000002
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07600000000000001
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.17085714285714285
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.2609047619047619
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.2989047619047619
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.36923809523809525
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.30837985877391133
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.3637222222222222
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.26018546740220616
name: Maxsim Map@100
- type: maxsim_accuracy@1
value: 0.3
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.36
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.46
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.5
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.15333333333333332
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.124
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.068
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.19085714285714286
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.24957142857142856
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.3074047619047619
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.34140476190476193
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.295377235646036
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.34672222222222215
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.2605135618174357
name: Maxsim Map@100
- task:
type: multi-vector-nano-beir
name: Multi Vector Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: maxsim_accuracy@1
value: 0.26666666666666666
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.43333333333333335
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.5066666666666667
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.6466666666666666
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.26666666666666666
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.15333333333333335
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.11333333333333334
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07266666666666667
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.2236190476190476
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.366968253968254
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.442968253968254
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.5697460317460318
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.40051617951562285
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.3784867724867725
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.3431071507662327
name: Maxsim Map@100
- type: maxsim_accuracy@1
value: 0.39908948194662486
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.581098901098901
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.6536577708006279
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.7491679748822607
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.39908948194662486
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.25862899005756146
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.203723704866562
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.14720251177394034
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.23062992515826716
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.37294339343615923
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.4321351116772938
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.519316239197735
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.4580842641524777
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.507998205875757
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.3854174628797499
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: maxsim_accuracy@1
value: 0.16
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.22
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.36
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.6
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.16
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.07333333333333333
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.07600000000000001
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07400000000000001
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.065
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.09666666666666666
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.1733333333333333
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.3123333333333333
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.20328688856846505
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.25064285714285706
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.14046408140724229
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
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: 0.8
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.4599999999999999
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.4159999999999999
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.376
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.07193848165362521
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.12336786289481484
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.1697367247949505
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.23906302016037423
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.46049338115280875
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.6635555555555556
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.31549502875205054
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: maxsim_accuracy@1
value: 0.56
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.88
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.30666666666666664
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.19199999999999995
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.10399999999999998
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.5166666666666666
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.8433333333333333
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.8733333333333333
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.9433333333333332
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.7533164734382702
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.711
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.6819194884124758
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: maxsim_accuracy@1
value: 0.68
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.8
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.82
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.68
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.3
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.204
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.11599999999999998
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.34
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.45
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.51
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.58
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.5518930389479023
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.7415555555555555
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.45883358284049924
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: maxsim_accuracy@1
value: 0.32
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.46
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.46
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.58
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.32
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.28
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.22599999999999998
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.020608387079507632
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.05831411005123257
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.07257884522927437
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.1022383828974261
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.26736600238438624
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.3999365079365079
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.11817323882706855
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
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: 0.9
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.94
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.72
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.3533333333333333
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.23199999999999996
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.12599999999999997
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.6106666666666666
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.812
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.8686666666666667
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.9259999999999999
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.8212615451918094
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.7992222222222223
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.7831050306138172
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: maxsim_accuracy@1
value: 0.34
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.7
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.34
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.26666666666666666
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.22
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.15
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.07166666666666666
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.16566666666666663
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.22866666666666663
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.30966666666666665
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.29635887359636653
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.464047619047619
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.22414235511948863
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: maxsim_accuracy@1
value: 0.12
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.46
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.56
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.12
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.1533333333333333
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.11200000000000003
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07800000000000001
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.12
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.46
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.56
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.78
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.4323319619267822
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.3235793650793651
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.332455902068624
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: maxsim_accuracy@1
value: 0.4
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.58
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.64
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.4
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.2
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.136
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.07400000000000001
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.375
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.54
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.58
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.64
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.5279305999807071
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.4907142857142857
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.5004343033880488
name: Maxsim Map@100
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: maxsim_accuracy@1
value: 0.40816326530612246
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.7142857142857143
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.8775510204081632
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.9591836734693877
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.40816326530612246
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.44217687074829926
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.42040816326530617
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.38163265306122446
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.03578501546719726
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.10934404648592841
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.16403611987583339
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.27707161127465835
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.4165604953207882
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.5897068351149983
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.3606204114138682
name: Maxsim Map@100
---
# ColBERT ModernBERT-base trained on MS MARCO triplets
This is a [Multi-Vector Encoder](https://www.sbert.net/docs/multi_vector_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps inputs to sequences of 128-dimensional token-level vectors and scores them with late interaction (MaxSim), useful for semantic search with late interaction.
## Model Details
### Model Description
- **Model Type:** Multi-Vector Encoder
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 128 dimensions
- **Similarity Function:** maxsim
- **Supported Modality:** Text
- **Training Dataset:**
- [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Multi-Vector Encoder Documentation](https://www.sbert.net/docs/multi_vector_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Multi-Vector Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=multi-vector)
### Full Model Architecture
```
MultiVectorEncoder(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
(2): MultiVectorMask({'skiplist_words': [], 'keep_only_token_ids': None})
(3): Normalize({'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import MultiVectorEncoder
# Download from the 🤗 Hub
model = MultiVectorEncoder("tomaarsen/multivector-ModernBERT-base-msmarco-contrastive")
# Run inference: each input becomes a sequence of per-token vectors (variable length).
queries = [
'what does marijuana cost per joint',
]
documents = [
'With a 50-50 ratio of marijuana to tobacco, the cost of producing a pack of 20 pre-rolled joints could be brought down to just a little more than $20â\x80\x94so a $40 pack at the store. It isnâ\x80\x99t as easy as it seems, though. The government has a vested interest in producing income from the selling of marijuana.nother solution: mix the marijuana with tobacco. If marijuana cigarettes were to be mixed with tobacco, at a 50-50 ratio, it would bring the cost down significantly. Many tobacco farmers will wholesale a pound of their product for less than $2.',
'What does a dime,dub,eigth,quarter,and a zip of marijuana look like and cost?',
'In January of 1980, residents decided to incorporate by an overwhelming margin. The Town of Farragut was incorporated on January 16, 1980, with the first board of Mayor and Alderman elected on April 1, 1980.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings[0].shape, document_embeddings[0].shape)
# (8, 128) (129, 128)
# Get the MaxSim similarity scores
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 7.4345, 3.4202, -0.0586]])
```
## Evaluation
### Metrics
#### Multi Vector Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNQ`, `NanoFiQA2018`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [MultiVectorInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/multi_vector_encoder/evaluation.html#sentence_transformers.multi_vector_encoder.evaluation.MultiVectorInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNQ | NanoFiQA2018 | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoHotpotQA | NanoNFCorpus | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:------------|:-----------|:-------------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------|
| maxsim_accuracy@1 | 0.32 | 0.28 | 0.3 | 0.16 | 0.58 | 0.56 | 0.68 | 0.32 | 0.72 | 0.34 | 0.12 | 0.4 | 0.4082 |
| maxsim_accuracy@3 | 0.46 | 0.52 | 0.36 | 0.22 | 0.72 | 0.88 | 0.8 | 0.46 | 0.84 | 0.56 | 0.46 | 0.56 | 0.7143 |
| maxsim_accuracy@5 | 0.54 | 0.6 | 0.46 | 0.36 | 0.8 | 0.92 | 0.82 | 0.46 | 0.9 | 0.62 | 0.56 | 0.58 | 0.8776 |
| maxsim_accuracy@10 | 0.6 | 0.74 | 0.5 | 0.6 | 0.84 | 0.98 | 0.88 | 0.58 | 0.94 | 0.7 | 0.78 | 0.64 | 0.9592 |
| maxsim_precision@1 | 0.32 | 0.28 | 0.3 | 0.16 | 0.58 | 0.56 | 0.68 | 0.32 | 0.72 | 0.34 | 0.12 | 0.4 | 0.4082 |
| maxsim_precision@3 | 0.1533 | 0.18 | 0.1533 | 0.0733 | 0.46 | 0.3067 | 0.3 | 0.32 | 0.3533 | 0.2667 | 0.1533 | 0.2 | 0.4422 |
| maxsim_precision@5 | 0.108 | 0.128 | 0.124 | 0.076 | 0.416 | 0.192 | 0.204 | 0.28 | 0.232 | 0.22 | 0.112 | 0.136 | 0.4204 |
| maxsim_precision@10 | 0.06 | 0.08 | 0.068 | 0.074 | 0.376 | 0.104 | 0.116 | 0.226 | 0.126 | 0.15 | 0.078 | 0.074 | 0.3816 |
| maxsim_recall@1 | 0.32 | 0.26 | 0.1909 | 0.065 | 0.0719 | 0.5167 | 0.34 | 0.0206 | 0.6107 | 0.0717 | 0.12 | 0.375 | 0.0358 |
| maxsim_recall@3 | 0.46 | 0.48 | 0.2496 | 0.0967 | 0.1234 | 0.8433 | 0.45 | 0.0583 | 0.812 | 0.1657 | 0.46 | 0.54 | 0.1093 |
| maxsim_recall@5 | 0.54 | 0.57 | 0.3074 | 0.1733 | 0.1697 | 0.8733 | 0.51 | 0.0726 | 0.8687 | 0.2287 | 0.56 | 0.58 | 0.164 |
| maxsim_recall@10 | 0.6 | 0.7 | 0.3414 | 0.3123 | 0.2391 | 0.9433 | 0.58 | 0.1022 | 0.926 | 0.3097 | 0.78 | 0.64 | 0.2771 |
| **maxsim_ndcg@10** | **0.455** | **0.4739** | **0.2954** | **0.2033** | **0.4605** | **0.7533** | **0.5519** | **0.2674** | **0.8213** | **0.2964** | **0.4323** | **0.5279** | **0.4166** |
| maxsim_mrr@10 | 0.4093 | 0.414 | 0.3467 | 0.2506 | 0.6636 | 0.711 | 0.7416 | 0.3999 | 0.7992 | 0.464 | 0.3236 | 0.4907 | 0.5897 |
| maxsim_map@100 | 0.4305 | 0.4038 | 0.2605 | 0.1405 | 0.3155 | 0.6819 | 0.4588 | 0.1182 | 0.7831 | 0.2241 | 0.3325 | 0.5004 | 0.3606 |
#### Multi Vector Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [MultiVectorNanoBEIREvaluator](https://sbert.net/docs/package_reference/multi_vector_encoder/evaluation.html#sentence_transformers.multi_vector_encoder.evaluation.MultiVectorNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nq",
"fiqa2018"
],
"dataset_id": "sentence-transformers/NanoBEIR-en"
}
```
| Metric | Value |
|:--------------------|:-----------|
| maxsim_accuracy@1 | 0.2667 |
| maxsim_accuracy@3 | 0.4333 |
| maxsim_accuracy@5 | 0.5067 |
| maxsim_accuracy@10 | 0.6467 |
| maxsim_precision@1 | 0.2667 |
| maxsim_precision@3 | 0.1533 |
| maxsim_precision@5 | 0.1133 |
| maxsim_precision@10 | 0.0727 |
| maxsim_recall@1 | 0.2236 |
| maxsim_recall@3 | 0.367 |
| maxsim_recall@5 | 0.443 |
| maxsim_recall@10 | 0.5697 |
| **maxsim_ndcg@10** | **0.4005** |
| maxsim_mrr@10 | 0.3785 |
| maxsim_map@100 | 0.3431 |
#### Multi Vector Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [MultiVectorNanoBEIREvaluator](https://sbert.net/docs/package_reference/multi_vector_encoder/evaluation.html#sentence_transformers.multi_vector_encoder.evaluation.MultiVectorNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
],
"dataset_id": "sentence-transformers/NanoBEIR-en"
}
```
| Metric | Value |
|:--------------------|:-----------|
| maxsim_accuracy@1 | 0.3991 |
| maxsim_accuracy@3 | 0.5811 |
| maxsim_accuracy@5 | 0.6537 |
| maxsim_accuracy@10 | 0.7492 |
| maxsim_precision@1 | 0.3991 |
| maxsim_precision@3 | 0.2586 |
| maxsim_precision@5 | 0.2037 |
| maxsim_precision@10 | 0.1472 |
| maxsim_recall@1 | 0.2306 |
| maxsim_recall@3 | 0.3729 |
| maxsim_recall@5 | 0.4321 |
| maxsim_recall@10 | 0.5193 |
| **maxsim_ndcg@10** | **0.4581** |
| maxsim_mrr@10 | 0.508 |
| maxsim_map@100 | 0.3854 |
## Training Details
### Training Dataset
#### msmarco-bm25
* Dataset: [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) at [ce8a493](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25/tree/ce8a493a65af5e872c3c92f72a89e2e99e175f02)
* Size: 50,000 training samples
* Columns: query, positive, and negative
* Approximate statistics based on the first 100 samples:
| | query | positive | negative |
|:---------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| modality | text | text | text |
| details | - min: 5 tokens
- mean: 9.2 tokens
- max: 23 tokens
| - min: 24 tokens
- mean: 85.21 tokens
- max: 234 tokens
| - min: 24 tokens
- mean: 78.79 tokens
- max: 189 tokens
|
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| how many days to renew philippine passport to usa | The United States requires non-citizens to keep a foreign passport that is valid for six months beyond their date of departure. If you are in the United States legally, then you can renew your Philippine passport at the consulate general's office in Los Angeles. | How much does it cost to renew a Philippines passport? Philippine Passport Fees for Renewal is P 950 for 15 working days and 1,200 for 7 working days. This is according to the Department of Freign Affairs website. |
| which sexually transmitted diseases can lead to infections inside joint spaces? | Gonorrhea is a sexually transmitted disease (STD) that can infect both men and women. It can cause infections in the genitals, rectum, and throat.It is a very common infection, especially among young people ages 15-24 years.omen with gonorrhea are at risk of developing serious complications from the infection, even if they donât have any symptoms. Symptoms in women can include: 1 Painful or burning sensation when urinating; 2 Increased vaginal discharge; 3 Vaginal bleeding between periods. | STDs and Infertility. Sexually transmitted diseases, STDs, also called sexually transmitted infections or STIs, can cause immediate, annoying symptoms with long-lasting, serious repercussions. Few people realize that these sexually transmitted diseases can cause damage that may eventually lead to infertility. |
| when was eviva amore constructed | Nasher Sculpture Center Press Images Back of the garden, Nasher Sculpture Center; photo by Tim Hursley. Mark di Suvero, Eviva Amore, 2001 in gardens of Nasher Sculpture Center; photo by Tim Hursley. Jaume Plensa, The Long Night (From Ausias March to Vincent Andres Andrés) , estelles, estellés 2007 At Nasher; sculpture center Photo By. tim hursley | Richard Serra, My Curves Are Not Mad, 1987 and Augustus Rodin, Eve, 1881 (cast before 1932) at Nasher Sculpture Center; photo by Tim Hursley. Mark di Suvero, Eviva Amore, 2001 at dusk in gardens of Nasher Sculpture Center; photo by Tim Hursley. Jeremy Strick, Director of the Nasher Sculpture. |
* Loss: [MultiVectorMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/multi_vector_encoder/losses.html#multivectormultiplenegativesrankingloss) with these parameters:
```json
{
"score_metric": "colbert_scores",
"scale": 1.0,
"score_mini_batch_size": null,
"size_average": true,
"gather_across_devices": false
}
```
### Evaluation Dataset
#### msmarco-bm25
* Dataset: [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) at [ce8a493](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25/tree/ce8a493a65af5e872c3c92f72a89e2e99e175f02)
* Size: 1,000 evaluation samples
* Columns: query, positive, and negative
* Approximate statistics based on the first 100 samples:
| | query | positive | negative |
|:---------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| modality | text | text | text |
| details | - min: 4 tokens
- mean: 9.15 tokens
- max: 19 tokens
| - min: 39 tokens
- mean: 82.03 tokens
- max: 199 tokens
| - min: 27 tokens
- mean: 81.62 tokens
- max: 157 tokens
|
* Samples:
| query | positive | negative |
|:------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| what is chor means | ⢠CHORE (noun) The noun CHORE has 1 sense: 1. a specific piece of work required to be done as a duty or for a specific fee. Familiarity information: CHORE used as a noun is very rare. | Any two different languages and not just English and other language. Example 1. Chore (pronounced as cHor) means 'a routine task' in English language. Whereas Chor {à¤à¥à¤°} (also pronounced as CHor) means a thief or a burglar in both Hindi and Marathi language. |
| how is gravity measured | The gravity of Earth, which is denoted by g, refers to the acceleration that the Earth imparts to objects on or near its surface due to gravity. In SI units this acceleration is measured in metres per second squared (in symbols, m/s2 or m·sâ2) or equivalently in newtons per kilogram (N/kg or N·kgâ1). | When the wort is first added to the yeast, the specific gravity of the mixture is measured. Later, the specific gravity may be measured again to determine how much alcohol is in the beer, and to know when to stop the fermentation.hen the wort is first added to the yeast, the specific gravity of the mixture is measured. Later, the specific gravity may be measured again to determine how much alcohol is in the beer, and to know when to stop the fermentation. |
| salary of doctor during fellowship | Average fellowship salary and wage. The median expected salary for a Fellowship physician in the United States averages to about $150,353 per annum and an average hourly wage is around $20 per hour. fellowship physician in USA receives an average yearly salary ranging from between $34,225 â $59,542. In addition, a yearly bonus of around $4,888 will be included as part of the annual salary package. | Medical Fellowship Salary. Medical Fellowship average salary is $55,008, median salary is $- with a salary range from $- to $-.Medical Fellowship salaries are collected from government agencies and companies. Each salary is associated with a real job position.Medical Fellowship salary statistics is not exclusive and is for reference only.They are presented as is and updated regularly.edical Fellowship salaries are collected from government agencies and companies. Each salary is associated with a real job position. Medical Fellowship salary statistics is not exclusive and is for reference only. They are presented as is and updated regularly. |
* Loss: [MultiVectorMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/multi_vector_encoder/losses.html#multivectormultiplenegativesrankingloss) with these parameters:
```json
{
"score_metric": "colbert_scores",
"scale": 1.0,
"score_mini_batch_size": null,
"size_average": true,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `num_train_epochs`: 1
- `learning_rate`: 3e-05
- `warmup_steps`: 0.05
- `bf16`: True
- `per_device_eval_batch_size`: 32
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `per_device_train_batch_size`: 32
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 3e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.05
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 32
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
- `max_length`: None
Click to expand
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_maxsim_ndcg@10 | NanoNQ_maxsim_ndcg@10 | NanoFiQA2018_maxsim_ndcg@10 | NanoBEIR_mean_maxsim_ndcg@10 | NanoClimateFEVER_maxsim_ndcg@10 | NanoDBPedia_maxsim_ndcg@10 | NanoFEVER_maxsim_ndcg@10 | NanoHotpotQA_maxsim_ndcg@10 | NanoNFCorpus_maxsim_ndcg@10 | NanoQuoraRetrieval_maxsim_ndcg@10 | NanoSCIDOCS_maxsim_ndcg@10 | NanoArguAna_maxsim_ndcg@10 | NanoSciFact_maxsim_ndcg@10 | NanoTouche2020_maxsim_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:---------------------------:|:----------------------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|
| -1 | -1 | - | - | 0.1739 | 0.1797 | 0.1710 | 0.1749 | - | - | - | - | - | - | - | - | - | - |
| 0.0102 | 16 | 3.7538 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0205 | 32 | 3.3661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0307 | 48 | 2.5871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0409 | 64 | 1.9409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0512 | 80 | 1.5994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0614 | 96 | 1.4578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0717 | 112 | 1.3978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0819 | 128 | 1.2065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0921 | 144 | 1.1288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1004 | 157 | - | 1.0105 | 0.3962 | 0.3703 | 0.2569 | 0.3412 | - | - | - | - | - | - | - | - | - | - |
| 0.1024 | 160 | 1.0619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1126 | 176 | 1.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1228 | 192 | 0.9993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1331 | 208 | 0.9477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1433 | 224 | 0.8920 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1536 | 240 | 0.9153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1638 | 256 | 0.8994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1740 | 272 | 0.8754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1843 | 288 | 0.8445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1945 | 304 | 0.8697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2009 | 314 | - | 0.8284 | 0.4284 | 0.4444 | 0.2827 | 0.3852 | - | - | - | - | - | - | - | - | - | - |
| 0.2047 | 320 | 0.8569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2150 | 336 | 0.8456 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2252 | 352 | 0.8400 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2354 | 368 | 0.8554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2457 | 384 | 0.8155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2559 | 400 | 0.8120 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2662 | 416 | 0.7631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2764 | 432 | 0.8388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2866 | 448 | 0.8001 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2969 | 464 | 0.7797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3013 | 471 | - | 0.7752 | 0.4347 | 0.3954 | 0.2937 | 0.3746 | - | - | - | - | - | - | - | - | - | - |
| 0.3071 | 480 | 0.7782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3173 | 496 | 0.7381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3276 | 512 | 0.7863 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3378 | 528 | 0.8055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3480 | 544 | 0.7434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3583 | 560 | 0.7354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3685 | 576 | 0.7944 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3788 | 592 | 0.7828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3890 | 608 | 0.7276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3992 | 624 | 0.7484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4018 | 628 | - | 0.7316 | 0.4197 | 0.4419 | 0.2871 | 0.3829 | - | - | - | - | - | - | - | - | - | - |
| 0.4095 | 640 | 0.7728 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4197 | 656 | 0.7389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4299 | 672 | 0.7296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4402 | 688 | 0.7094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4504 | 704 | 0.7304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4607 | 720 | 0.6726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4709 | 736 | 0.6522 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4811 | 752 | 0.6913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4914 | 768 | 0.6558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5016 | 784 | 0.6472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5022 | 785 | - | 0.6360 | 0.3996 | 0.3904 | 0.3067 | 0.3656 | - | - | - | - | - | - | - | - | - | - |
| 0.5118 | 800 | 0.6718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5221 | 816 | 0.6167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5323 | 832 | 0.6957 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5425 | 848 | 0.6781 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5528 | 864 | 0.6126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5630 | 880 | 0.7008 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5733 | 896 | 0.5734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5835 | 912 | 0.6099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5937 | 928 | 0.6329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6027 | 942 | - | 0.6093 | 0.4213 | 0.4733 | 0.3191 | 0.4045 | - | - | - | - | - | - | - | - | - | - |
| 0.6040 | 944 | 0.5956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6142 | 960 | 0.5947 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6244 | 976 | 0.5665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6347 | 992 | 0.6213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6449 | 1008 | 0.5896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6552 | 1024 | 0.5584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6654 | 1040 | 0.6183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6756 | 1056 | 0.5957 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6859 | 1072 | 0.5814 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6961 | 1088 | 0.5643 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.7031** | **1099** | **-** | **0.5899** | **0.455** | **0.4739** | **0.2954** | **0.4081** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.7063 | 1104 | 0.6579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7166 | 1120 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7268 | 1136 | 0.6088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7370 | 1152 | 0.5648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7473 | 1168 | 0.5743 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7575 | 1184 | 0.5905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7678 | 1200 | 0.5474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7780 | 1216 | 0.6153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7882 | 1232 | 0.6152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7985 | 1248 | 0.5790 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8036 | 1256 | - | 0.5776 | 0.4383 | 0.4559 | 0.3231 | 0.4058 | - | - | - | - | - | - | - | - | - | - |
| 0.8087 | 1264 | 0.6067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8189 | 1280 | 0.6050 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8292 | 1296 | 0.5423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8394 | 1312 | 0.5930 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8496 | 1328 | 0.5682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8599 | 1344 | 0.5487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8701 | 1360 | 0.5960 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8804 | 1376 | 0.5687 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8906 | 1392 | 0.5542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9008 | 1408 | 0.5451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9040 | 1413 | - | 0.5711 | 0.4252 | 0.4364 | 0.3032 | 0.3883 | - | - | - | - | - | - | - | - | - | - |
| 0.9111 | 1424 | 0.5537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9213 | 1440 | 0.6169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9315 | 1456 | 0.5889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9418 | 1472 | 0.5735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9520 | 1488 | 0.5574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9623 | 1504 | 0.5436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9725 | 1520 | 0.5563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9827 | 1536 | 0.5849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9930 | 1552 | 0.5657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 1563 | - | 0.5687 | 0.4528 | 0.4404 | 0.3084 | 0.4005 | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.4550 | 0.4739 | 0.2954 | 0.4581 | 0.2033 | 0.4605 | 0.7533 | 0.5519 | 0.2674 | 0.8213 | 0.2964 | 0.4323 | 0.5279 | 0.4166 |
* The bold row denotes the saved checkpoint.