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---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- embeddings
- retrieval
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
pipeline_tag: sentence-similarity
library_name: PyLate
license: apache-2.0
language:
- en
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: PyLate
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: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.28
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.19999999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.142
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.15833333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.35999999999999993
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.40399999999999997
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5263333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.42422936715942183
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4945555555555556
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3394857122449798
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: 0.84
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
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: 0.84
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7133333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6639999999999999
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5840000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.09765098476549273
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.21493936533978503
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.29263849542716386
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.4078048460655947
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7220216066014423
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8895238095238095
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5775350393915936
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: 0.98
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: 0.98
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.35999999999999993
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21999999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10999999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.9166666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.97
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.98
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.98
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9746887890888085
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.99
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9662597402597402
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: 0.54
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.72
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.54
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.144
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.30257936507936506
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.46840476190476193
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5460079365079364
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6121984126984128
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.543179419158261
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6155793650793651
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.48231826405604816
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: 0.98
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: 0.98
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6066666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.3679999999999999
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18599999999999994
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.49
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.91
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.92
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.93
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.928244418306152
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.99
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9025083961789844
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: 0.56
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15600000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.088
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.56
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.68
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.78
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.88
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7066072782610768
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6527142857142857
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6594095238095238
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: 0.56
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: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.42
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.368
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.298
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.06692907683596779
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.10206246733295422
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.12200662252749642
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.15528296036675676
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3873590885021362
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6126904761904761
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.18205866068341273
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: 0.62
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.62
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.172
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.58
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.76
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.79
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7400441315570866
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7189999999999999
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6963679394624304
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: 0.92
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: 0.92
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: 0.13599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7973333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9420000000000001
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9626666666666668
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9933333333333334
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9480099324300113
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9566666666666667
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9210518925518926
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: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
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: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.36666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.296
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.19999999999999996
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10466666666666669
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.22666666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.30266666666666664
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.40766666666666657
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.40113887814097937
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6128809523809524
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3131237586203457
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: 0.26
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.26
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.136
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.088
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.26
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.68
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.88
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5560482472286857
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.454095238095238
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4572284326784326
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: 0.74
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: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.74
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20399999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10199999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.715
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.83
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.91
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.91
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8258399595069874
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.804
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7954666666666667
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: 0.7755102040816326
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8979591836734694
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9795918367346939
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7755102040816326
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6802721088435373
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6612244897959183
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5122448979591837
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05163796594097508
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.1395393096723396
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.21812762563969756
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.33298037717516343
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5881232935062076
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8547619047619047
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4232821896377805
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.6581161695447411
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8029199372056516
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8522762951334379
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9061538461538462
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6581161695447411
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.39437990580847726
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.303171114599686
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.20678806907378336
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3923690302016769
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5510471208397314
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.6083164625719715
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6827384561260968
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6727334161113274
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7420360195360195
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5935458627878331
name: Maxsim Map@100
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/xn21ll7YRj0ZftBli3-T5.jpeg" width="600" height="auto">
[](https://lighton.ai)
[](https://www.linkedin.com/company/lighton/)
[](https://x.com/LightOnIO)
📄 [Paper](https://arxiv.org/abs/2602.16609) | 📝 [Blog](https://huggingface.co/blog/lightonai/colbert-zero) | 📚 [Collection](https://huggingface.co/collections/lightonai/colbert-zero)
</div>
# ColBERT-Zero
> 🎯 **TL;DR**: First large-scale fully pre-trained ColBERT model using only public data. Achieves **55.43 nDCG@10** on BEIR benchmark, outperforming GTE-ModernColBERT and GTE-ModernBERT trained on closed and stronger data. **New SOTA on BEIR for models <150M parameters**.
## Why ColBERT-Zero?
Late interaction (ColBERT / multi-vector) models have clear advantages in out-of-domain generalization, long-context handling, and reasoning-intensive retrieval. Yet they remain undertrained: current state-of-the-art ColBERT models (e.g, [GTE-ModernColBERT](https://huggingface.co/Alibaba-NLP/gte-modernbert-colbert) and [ColBERT-small](https://huggingface.co)) are simply built by bolting a small knowledge distillation step onto a strong dense (single-vector) model. Even recent efforts like [mxbai-edge-colbert-v0](https://huggingface.co/collections/mixedbread-ai/mxbai-edge-colbert-v0-series) perform all early training stages in a single-vector setting, only switching to the multi-vector objective at the very end.
**This leaves a lot of performance on the table.** ColBERT-Zero demonstrates that performing contrastive pre-training directly in the multi-vector setting, rather than treating it as an afterthought, unlocks a significantly higher performance ceiling. Trained exclusively on public data ([Nomic-embed](https://arxiv.org/abs/2402.01613) dataset mixture), [ColBERT-Zero](https://huggingface.co/lightonai/ColBERT-Zero) overcomes a 2.4-point data quality disadvantage to outperform models trained on proprietary, closed-source data. For detailed results, please have a look at our [blogpost](https://huggingface.co/blog/lightonai/colbert-zero/) and the [paper](https://arxiv.org/abs/2602.16609). All the [models](https://huggingface.co/collections/lightonai/colbert-zero) (including intermediate checkpoints) as well [training code](https://github.com/lightonai/pylate/tree/main/examples/train/ColBERT-zero) are released under an Apache 2.0 license.
## Controlled Comparison Design
We deliberately trained on the public [Nomic-embed](https://arxiv.org/abs/2402.01613) data mixture for a strategic reason: Nomic has already trained a dense ModernBERT model ([ModernBERT-embed](https://huggingface.co/nomic-ai/modernbert-embed-base)) on this exact data. This lets us compare dense vs. multi-vector training with the **same data, same base model ([ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)), and same pipeline**. The only variable is whether the contrastive phases are performed in the dense or multi-vector setting.
This design reveals a striking result: the dense baseline trained on Nomic data scores 52.89, while the one trained on GTE's proprietary data scores 55.33: a 2.4-point data quality gap. Despite this disadvantage, ColBERT-Zero's full multi-vector pre-training pipeline closes and surpasses this gap, reaching **55.43 nDCG@10**.
## The Three-Phase Training Pipeline
The development followed a three-phase pipeline, each providing a different type of learning signal:
### Phase 1 - Unsupervised Contrastive Pre-training
We began with the [nomic-embed-unsupervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data) dataset. Using [PyLate](https://lightonai.github.io/pylate/)'s **GradCache** implementation to scale per-GPU batch size without VRAM constraints, combined with **cross-GPU gathering** of representations, we reached effective batch sizes of **~16k**, required for unsupervised training to produce plausible in-batch hard negatives. Unlike dense training, the multi-vector objective allows the encoder to learn fine-grained token importance from the very first phase.
### Phase 2 - Supervised Contrastive Fine-tuning
We refined the model using the [nomic-embed-supervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-supervised-data). This stage introduced mined hard negatives: documents that are superficially similar to the query but not actually relevant. This allows teaching the model to handle nuance by prioritizing specific keywords and contextual tokens most indicative of a true match.
### Phase 3 - Knowledge Distillation (KD)
The final stage used the [ms-marco-en-bge](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) dataset. We leveraged a powerful Gemma-based model as a teacher, allowing our student models to learn to replicate complex reasoning scores via the efficient MaxSim operator.
## Key Findings
### 1. The Standard Recipe Leaves Performance on the Table
The KD-only approach (the current industry standard) scores 54.09, lagging behind full pre-training by **1.3 points**. A simple distillation step is insufficient for optimal multi-vector performance.
### 2. Supervised + KD Is the Efficiency Sweet Spot
By running a supervised contrastive step in the multi-vector setting before distillation, we reach **55.12 nDCG@10**, closing most of the gap with the fully pre-trained model (55.43). This costs **~40 GH200-hours instead of ~408**: roughly **10× cheaper for 99.4% of the performance**.
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/V1_hTZ0VnJHldfd3Ip-Jm.png" width="600" height="auto">
</div>
### 3. Prompt Alignment Is Non-Negotiable
Nomic's base models are pre-trained with asymmetric prompts (`search_query:` and `search_document:`). While ColBERT has its own asymmetric mechanism via `[Q]` and `[D]` markers, we found:
- **Stripping pre-training prompts during fine-tuning** causes significant performance degradation.
- **Adding prompts to a model not pre-trained with them** also hurts performance.
- **Even with perfect alignment**, prompts provide an intrinsic benefit: full ColBERT pre-training with prompts (55.43) vs. without prompts (54.61), no mismatch in either case, shows a meaningful 0.82-point gap.
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/uZoRA7SwisR-svi4lPDTi.png" width="600" height="auto">
</div>
**Why do prompts help?** Our leading hypothesis is that prompt tokens act as **implicit query expansion**: extra slots that don't carry specific meaning but let the model store global information about the sequence. The original ColBERT used `[PAD]` tokens for this purpose, but modern Flash Attention implementations broke this trick (masked tokens no longer produce usable embeddings). Explicit prompt tokens may be quietly re-enabling it.
**Practical takeaway:** Always align your prompts with the base model's pre-training setup. Misalignment is one of the easiest ways to silently lose performance. Note that this sensitivity decreases with stronger downstream fine-tuning: with enough training, the model can adapt to an initial mismatch.
## Model Lineup
### The Main Models (ColBERT-Zero)
`ColBERT-Zero` utilizes the full 3-phase pipeline with strict prompt alignment, **achieving 55.43 nDCG@10 on BEIR**, setting a new SOTA for models <150M parameters. We also provide `ColBERT-Zero-noprompts`, the same pipeline without asymmetric prompts, to study the impact of query expansion on multi-vector performance.
### The cheap-to-train ones (ModernColBERT-embed-base)
These models represent the practical sweet spot. By skipping the expensive unsupervised phase, `ModernColBERT-embed-base` (Supervised + KD) achieves ~97% of the flagship's performance at only ~10% of the compute cost. For reference, `ModernColBERT-embed-base-kd` performs only the distillation step on a supervised dense base.
### Intermediate Checkpoints
For researchers studying the incremental impact of each phase and prompt alignment, we release several ablation variants: `ColBERT-Zero-supervised`, `ColBERT-Zero-unsupervised` (and their `-noprompts` versions), and `ModernColBERT-embed-base-supervised`.
#### Full Performance on BEIR
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<style>
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.beir-wrap th, .beir-wrap td { padding: 7px 10px; text-align: center; border-bottom: 1px solid #e9ecef; }
.beir-wrap td:first-child, .beir-wrap th:first-child { text-align: left; min-width: 260px; }
.beir-wrap th { background: #1e293b; color: #fff; font-weight: 600; }
.beir-wrap th.avg-col { background: #f59e0b; color: #1e293b; font-weight: 700; }
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.beir-wrap strong { color: #0f172a; }
.beir-wrap tbody tr:not(.section-row):hover td { background: #f1f5f9; }
.beir-wrap tbody tr:not(.section-row):hover td.avg-col { background: #fde68a; }
.beir-wrap a { color: #3b82f6; text-decoration: none; }
.beir-wrap a:hover { text-decoration: underline; }
</style>
</head>
<body>
<div class="beir-wrap">
<table>
<thead>
<tr>
<th>Model</th>
<th class="avg-col">Avg</th>
<th>FiQA</th><th>NFCorpus</th><th>TREC-COVID</th><th>Touche</th><th>ArguAna</th><th>Quora</th><th>SCIDOCS</th><th>SciFact</th><th>NQ</th><th>ClimateFEVER</th><th>HotpotQA</th><th>DBPedia</th><th>CQADupstack</th><th>FEVER</th><th>MSMARCO</th>
</tr>
</thead>
<tbody>
<tr class="section-row"><td colspan="17">Baselines</td></tr>
<tr>
<td><a href="https://huggingface.co/nomic-ai/modernbert-embed-base-unsupervised">ModernBERT-embed-unsupervised</a></td>
<td class="avg-col">47.05</td>
<td>42.53</td><td>35.33</td><td>68.44</td><td>18.58</td><td>48.82</td><td>88.63</td><td>19.83</td><td>72.30</td><td>46.32</td><td>22.97</td><td>60.00</td><td>37.97</td><td>42.40</td><td>67.39</td><td>34.23</td>
</tr>
<tr>
<td><a href="https://huggingface.co/nomic-ai/modernbert-embed-base">ModernBERT-embed-supervised</a></td>
<td class="avg-col">52.89</td>
<td>40.59</td><td>33.40</td><td><strong>84.15</strong></td><td>31.91</td><td>48.96</td><td><strong>88.85</strong></td><td>18.59</td><td>69.63</td><td>62.15</td><td>35.67</td><td>67.11</td><td>41.50</td><td>42.08</td><td>87.35</td><td>41.47</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/GTE-ModernColBERT-v1">GTE-ModernColBERT</a></td>
<td class="avg-col">54.67</td>
<td>45.28</td><td><strong>37.93</strong></td><td>83.59</td><td>31.23</td><td>48.51</td><td>86.61</td><td>19.06</td><td>76.34</td><td>61.80</td><td>30.62</td><td>77.32</td><td>48.03</td><td>41.00</td><td>87.44</td><td>45.32</td>
</tr>
<tr>
<td><a href="https://huggingface.co/Alibaba-NLP/gte-modernbert-base">gte-modernbert-base</a></td>
<td class="avg-col">55.33</td>
<td><strong>48.81</strong></td><td>36.44</td><td>81.95</td><td>21.68</td><td><strong>72.68</strong></td><td>88.55</td><td>21.29</td><td><strong>77.40</strong></td><td>57.62</td><td><strong>37.74</strong></td><td>69.47</td><td>41.79</td><td>42.63</td><td><strong>91.03</strong></td><td>40.90</td>
</tr>
<tr class="section-row"><td colspan="17">KD from dense supervised</td></tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ModernColBERT-embed-base-kd-only">ModernColBERT-embed-base-kd-only</a></td>
<td class="avg-col">54.09</td>
<td>42.51</td><td>37.01</td><td>79.52</td><td>34.58</td><td>51.75</td><td>87.67</td><td>18.15</td><td>75.04</td><td>61.45</td><td>28.31</td><td>76.70</td><td>47.54</td><td>40.68</td><td>84.82</td><td>45.57</td>
</tr>
<tr class="section-row"><td colspan="17">Supervised + KD from dense unsupervised</td></tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ModernColBERT-embed-base-supervised">ModernColBERT-embed-base-supervised</a></td>
<td class="avg-col">50.72</td>
<td>40.09</td><td>35.56</td><td>71.12</td><td>25.53</td><td>44.27</td><td>86.96</td><td>18.19</td><td>73.78</td><td>58.89</td><td>32.95</td><td>71.49</td><td>43.23</td><td>42.55</td><td>70.51</td><td>45.72</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ModernColBERT-embed-base">ModernColBERT-embed-base</a></td>
<td class="avg-col">55.12</td>
<td>41.50</td><td>36.51</td><td>77.46</td><td>33.77</td><td>52.45</td><td>86.26</td><td>18.66</td><td>74.90</td><td>62.24</td><td>37.27</td><td><strong>80.07</strong></td><td><strong>48.27</strong></td><td>41.60</td><td>89.71</td><td><strong>46.17</strong></td>
</tr>
<tr class="section-row"><td colspan="17">ColBERT-Zero</td></tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero-unsupervised">Unsupervised</a></td>
<td class="avg-col">51.44</td>
<td>45.38</td><td>36.88</td><td>67.82</td><td>22.59</td><td>51.53</td><td>87.78</td><td>22.30</td><td>76.76</td><td>58.80</td><td>24.24</td><td>68.29</td><td>43.16</td><td><strong>45.76</strong></td><td>81.58</td><td>38.78</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero-supervised">Supervised</a></td>
<td class="avg-col">51.81</td>
<td>42.45</td><td>35.60</td><td>74.72</td><td>23.83</td><td>41.81</td><td>87.19</td><td>19.85</td><td>73.71</td><td>61.95</td><td>35.01</td><td>71.37</td><td>46.20</td><td>45.16</td><td>72.61</td><td>45.68</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero">Distilled</a></td>
<td class="avg-col"><strong>55.43</strong></td>
<td>42.62</td><td>37.28</td><td>78.69</td><td>36.13</td><td>53.07</td><td>85.24</td><td>19.88</td><td>76.50</td><td>61.66</td><td>35.72</td><td>79.41</td><td>47.48</td><td>41.34</td><td>90.59</td><td>45.80</td>
</tr>
<tr class="section-row"><td colspan="17">ColBERT-Zero-noprompts</td></tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero-unsupervised-noprompts">Unsupervised</a></td>
<td class="avg-col">51.70</td>
<td>45.31</td><td>34.72</td><td>73.55</td><td>23.26</td><td>52.56</td><td>88.15</td><td><strong>22.63</strong></td><td>76.10</td><td>59.18</td><td>24.24</td><td>66.66</td><td>42.61</td><td>45.56</td><td>81.88</td><td>39.15</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero-supervised-noprompts">Supervised</a></td>
<td class="avg-col">52.39</td>
<td>43.36</td><td>36.01</td><td>72.42</td><td>23.79</td><td>47.42</td><td>87.79</td><td>21.30</td><td>73.85</td><td><strong>62.25</strong></td><td>31.61</td><td>70.32</td><td>44.07</td><td>44.03</td><td>85.54</td><td>42.11</td>
</tr>
<tr>
<td><a href="https://huggingface.co/lightonai/ColBERT-Zero-noprompts">Distilled</a></td>
<td class="avg-col">54.61</td>
<td>43.14</td><td>36.60</td><td>78.60</td><td><strong>36.36</strong></td><td>49.49</td><td>88.05</td><td>19.13</td><td>76.42</td><td>61.73</td><td>32.70</td><td>76.99</td><td>47.69</td><td>40.21</td><td>85.97</td><td>46.01</td>
</tr>
</tbody>
</table>
</div>
</body>
</html>
## Limitations & Discussion
- **Data-specific findings.** We deliberately used the Nomic Embed data mixture for controlled comparison. Some observations (particularly around prompt sensitivity) may not generalize to different or stronger training configurations.
- **Scale vs. objective.** The gains from multi-vector pre-training likely reflect *more training time* in the multi-vector setting, rather than the contrastive objective itself. Performing KD alone at a larger scale might yield similar or superior results due to the higher quality of the distillation signal. Our study uses the conventional setup where training scale is inversely proportional to signal quality, reflecting the higher cost of generating high-quality labels.
- **Prompt sensitivity decreases with stronger fine-tuning.** When experimenting with stronger fine-tuning data (e.g., NV-Retriever), adding prompts on top of a model pre-trained without them did not degrade results the way it did with ColBERT-Zero. With enough downstream training, the model can adapt to an initial mismatch.
## Serving at Scale
For production deployment of ColBERT-Zero and other multi-vector models, check out [NextPlaid](https://github.com/lightonai/nextplaid) and [FastPlaid](https://github.com/lightonai/fastplaid), our production-grade engines for multi-vector retrieval.
## Resources
- 📦 **All checkpoints:** [HF Collection](https://huggingface.co/collections/lightonai/colbert-zero) - every phase, with and without prompts
- 💻 **Code:** [Training boilerplates](https://github.com/lightonai/pylate/tree/main/examples/train/ColBERT-zero)
- 📄 **Paper:** [ArXiv](https://arxiv.org/abs/2602.16609)
## Model Details
### Model Description
- **Model Type:** PyLate model
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Document Length:** 519 tokens
- **Query Length:** 39 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- train
<!-- - **Language:** Unknown -->
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### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 518, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
> [!WARNING]
> **Prompt alignment is critical for ColBERT-Zero models.** You **must** use `prompt_name="query"` when encoding queries and `prompt_name="document"` when encoding documents. ColBERT-Zero was pre-trained with asymmetric prompts (`search_query:` / `search_document:`), and stripping them causes significant performance.
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the 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
prompt_name="document", # ⚠️ Required for ColBERT-Zero! Do not omit.
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,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
[!WARNING]
Always pass prompt_name="query" for queries and prompt_name="document" for documents. Omitting these prompts will silently degrade retrieval quality.
```python
# Step 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
prompt_name="query", # ⚠️ Required for ColBERT-Zero! Do not omit.
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
> [!WARNING]
> Always pass `prompt_name="query"` for queries and `prompt_name="document"` for documents. Omitting these prompts will silently degrade retrieval quality.
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```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="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
prompt_name="query" # ⚠️ Required for ColBERT-Zero! Do not omit.
)
documents_embeddings = model.encode(
documents,
is_query=False,
prompt_name="document" # ⚠️ Required for ColBERT-Zero! Do not omit.
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
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<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:---------|:-------------------|:------------|:------------|:------------|:---------------|
| MaxSim_accuracy@1 | 0.28 | 0.84 | 0.98 | 0.54 | 0.98 | 0.56 | 0.56 | 0.62 | 0.92 | 0.5 | 0.26 | 0.74 | 0.7755 |
| MaxSim_accuracy@3 | 0.68 | 0.94 | 1.0 | 0.68 | 1.0 | 0.68 | 0.66 | 0.8 | 1.0 | 0.68 | 0.56 | 0.86 | 0.898 |
| MaxSim_accuracy@5 | 0.78 | 0.94 | 1.0 | 0.72 | 1.0 | 0.78 | 0.68 | 0.84 | 1.0 | 0.76 | 0.68 | 0.92 | 0.9796 |
| MaxSim_accuracy@10 | 0.88 | 0.96 | 1.0 | 0.78 | 1.0 | 0.88 | 0.74 | 0.88 | 1.0 | 0.86 | 0.88 | 0.92 | 1.0 |
| MaxSim_precision@1 | 0.28 | 0.84 | 0.98 | 0.54 | 0.98 | 0.56 | 0.56 | 0.62 | 0.92 | 0.5 | 0.26 | 0.74 | 0.7755 |
| MaxSim_precision@3 | 0.28 | 0.7133 | 0.36 | 0.32 | 0.6067 | 0.2267 | 0.42 | 0.2733 | 0.3933 | 0.3667 | 0.1867 | 0.3 | 0.6803 |
| MaxSim_precision@5 | 0.2 | 0.664 | 0.22 | 0.248 | 0.368 | 0.156 | 0.368 | 0.172 | 0.248 | 0.296 | 0.136 | 0.204 | 0.6612 |
| MaxSim_precision@10 | 0.142 | 0.584 | 0.11 | 0.144 | 0.186 | 0.088 | 0.298 | 0.098 | 0.136 | 0.2 | 0.088 | 0.102 | 0.5122 |
| MaxSim_recall@1 | 0.1583 | 0.0977 | 0.9167 | 0.3026 | 0.49 | 0.56 | 0.0669 | 0.58 | 0.7973 | 0.1047 | 0.26 | 0.715 | 0.0516 |
| MaxSim_recall@3 | 0.36 | 0.2149 | 0.97 | 0.4684 | 0.91 | 0.68 | 0.1021 | 0.76 | 0.942 | 0.2267 | 0.56 | 0.83 | 0.1395 |
| MaxSim_recall@5 | 0.404 | 0.2926 | 0.98 | 0.546 | 0.92 | 0.78 | 0.122 | 0.79 | 0.9627 | 0.3027 | 0.68 | 0.91 | 0.2181 |
| MaxSim_recall@10 | 0.5263 | 0.4078 | 0.98 | 0.6122 | 0.93 | 0.88 | 0.1553 | 0.86 | 0.9933 | 0.4077 | 0.88 | 0.91 | 0.333 |
| **MaxSim_ndcg@10** | **0.4242** | **0.722** | **0.9747** | **0.5432** | **0.9282** | **0.7066** | **0.3874** | **0.74** | **0.948** | **0.4011** | **0.556** | **0.8258** | **0.5881** |
| MaxSim_mrr@10 | 0.4946 | 0.8895 | 0.99 | 0.6156 | 0.99 | 0.6527 | 0.6127 | 0.719 | 0.9567 | 0.6129 | 0.4541 | 0.804 | 0.8548 |
| MaxSim_map@100 | 0.3395 | 0.5775 | 0.9663 | 0.4823 | 0.9025 | 0.6594 | 0.1821 | 0.6964 | 0.9211 | 0.3131 | 0.4572 | 0.7955 | 0.4233 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.6581 |
| MaxSim_accuracy@3 | 0.8029 |
| MaxSim_accuracy@5 | 0.8523 |
| MaxSim_accuracy@10 | 0.9062 |
| MaxSim_precision@1 | 0.6581 |
| MaxSim_precision@3 | 0.3944 |
| MaxSim_precision@5 | 0.3032 |
| MaxSim_precision@10 | 0.2068 |
| MaxSim_recall@1 | 0.3924 |
| MaxSim_recall@3 | 0.551 |
| MaxSim_recall@5 | 0.6083 |
| MaxSim_recall@10 | 0.6827 |
| **MaxSim_ndcg@10** | **0.6727** |
| MaxSim_mrr@10 | 0.742 |
| MaxSim_map@100 | 0.5935 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### train
* Dataset: train
* Size: 640,000 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | int | list | list |
| details | <ul><li>836: ~0.10%</li><li>3582: ~0.10%</li><li>4599: ~0.10%</li>...</ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| <code>685613</code> | <code>[7546874, 1176459, 197677, 2306318, 8541504, ...]</code> | <code>[0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...]</code> |
| <code>237784</code> | <code>[6366584, 4034101, 2325374, 6914618, 6042146, ...]</code> | <code>[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]</code> |
| <code>904294</code> | <code>[448408, 8743975, 49600, 7339401, 2714261, ...]</code> | <code>[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 2
- `learning_rate`: 4e-05
- `num_train_epochs`: 1.0
- `bf16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 2
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `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`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoClimateFEVER_MaxSim_ndcg@10 | NanoDBPedia_MaxSim_ndcg@10 | NanoFEVER_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNFCorpus_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoQuoraRetrieval_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoArguAna_MaxSim_ndcg@10 | NanoSciFact_MaxSim_ndcg@10 | NanoTouche2020_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0025 | 50 | 0.0192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0275 | 550 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0525 | 1050 | 0.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.075 | 1500 | 0.0145 | 0.4345 | 0.7035 | 0.9608 | 0.5361 | 0.9348 | 0.6818 | 0.3704 | 0.7291 | 0.9381 | 0.3923 | 0.5558 | 0.8060 | 0.5785 | 0.6632 |
| 0.0775 | 1550 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1025 | 2050 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1275 | 2550 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.15 | 3000 | 0.0128 | 0.4348 | 0.7184 | 0.9742 | 0.5474 | 0.9354 | 0.6925 | 0.3707 | 0.7316 | 0.9577 | 0.3986 | 0.5715 | 0.8156 | 0.6029 | 0.6732 |
| 0.1525 | 3050 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1775 | 3550 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2025 | 4050 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.225 | 4500 | 0.0119 | 0.4090 | 0.6891 | 0.9742 | 0.5301 | 0.9347 | 0.6903 | 0.3767 | 0.7251 | 0.9542 | 0.3935 | 0.5705 | 0.8245 | 0.5910 | 0.6664 |
| 0.2275 | 4550 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2525 | 5050 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2775 | 5550 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3 | 6000 | 0.0114 | 0.4342 | 0.7072 | 0.9698 | 0.5441 | 0.9302 | 0.7098 | 0.3777 | 0.7255 | 0.9533 | 0.4037 | 0.5621 | 0.8294 | 0.6033 | 0.6731 |
| 0.3025 | 6050 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3275 | 6550 | 0.0114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3525 | 7050 | 0.0112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.375 | 7500 | 0.0116 | 0.4160 | 0.7142 | 0.9722 | 0.5442 | 0.9281 | 0.6993 | 0.3749 | 0.7276 | 0.9494 | 0.4042 | 0.5444 | 0.8346 | 0.5940 | 0.6695 |
| 0.3775 | 7550 | 0.0111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4025 | 8050 | 0.0106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4275 | 8550 | 0.0107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.45 | 9000 | 0.0103 | 0.4267 | 0.7286 | 0.9722 | 0.5501 | 0.9325 | 0.7014 | 0.3794 | 0.7266 | 0.9487 | 0.4042 | 0.5635 | 0.8247 | 0.5986 | 0.6736 |
| 0.4525 | 9050 | 0.0108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4775 | 9550 | 0.0106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5025 | 10050 | 0.0101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.525 | 10500 | 0.0102 | 0.4293 | 0.7121 | 0.9731 | 0.5298 | 0.9270 | 0.7058 | 0.3716 | 0.7231 | 0.9452 | 0.4008 | 0.5605 | 0.8185 | 0.5808 | 0.6675 |
| 0.5275 | 10550 | 0.0103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5525 | 11050 | 0.0101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5775 | 11550 | 0.0098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 12000 | 0.01 | 0.4250 | 0.7261 | 0.9755 | 0.5208 | 0.9349 | 0.6825 | 0.3794 | 0.7314 | 0.9455 | 0.3970 | 0.5482 | 0.8161 | 0.5875 | 0.6669 |
| 0.6025 | 12050 | 0.0101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6275 | 12550 | 0.0098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6525 | 13050 | 0.0099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.675 | 13500 | 0.0096 | 0.4303 | 0.7139 | 0.9739 | 0.5517 | 0.9286 | 0.7090 | 0.3857 | 0.7466 | 0.9494 | 0.3902 | 0.5457 | 0.8178 | 0.5991 | 0.6725 |
| 0.6775 | 13550 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7025 | 14050 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7275 | 14550 | 0.0095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.75 | 15000 | 0.0096 | 0.4406 | 0.7261 | 0.9755 | 0.5440 | 0.9321 | 0.6973 | 0.3761 | 0.7283 | 0.9469 | 0.3958 | 0.5671 | 0.8162 | 0.5827 | 0.6714 |
| 0.7525 | 15050 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7775 | 15550 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8025 | 16050 | 0.009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.825 | 16500 | 0.0095 | 0.4313 | 0.7244 | 0.9717 | 0.5470 | 0.9280 | 0.7140 | 0.3872 | 0.7388 | 0.9487 | 0.3940 | 0.5719 | 0.8234 | 0.5910 | 0.6747 |
| 0.8275 | 16550 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8525 | 17050 | 0.0091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8775 | 17550 | 0.0091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9 | 18000 | 0.0091 | 0.4252 | 0.7205 | 0.9731 | 0.5461 | 0.9268 | 0.7029 | 0.3876 | 0.7445 | 0.9481 | 0.4054 | 0.5564 | 0.8258 | 0.5928 | 0.6735 |
| 0.9025 | 18050 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9275 | 18550 | 0.0091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9525 | 19050 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.975 | 19500 | 0.0091 | 0.4242 | 0.7220 | 0.9747 | 0.5432 | 0.9282 | 0.7066 | 0.3874 | 0.7400 | 0.9480 | 0.4011 | 0.5560 | 0.8258 | 0.5881 | 0.6727 |
| 0.9775 | 19550 | 0.0093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.13.0
- Sentence Transformers: 5.1.1
- PyLate: 1.3.4
- Transformers: 4.48.3
- PyTorch: 2.6.0
- Accelerate: 1.12.0
- Datasets: 4.4.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### ColBERT-Zero
```bibtex
@misc{chaffin2026colbertzeropretrainpretraincolbert,
title = {ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models},
author = {Antoine Chaffin and Luca Arnaboldi and Amélie Chatelain and Florent Krzakala},
year = {2026},
eprint = {2602.16609},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2602.16609},
}
```
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@inproceedings{DBLP:conf/cikm/ChaffinS25,
author = {Antoine Chaffin and
Rapha{"{e}}l Sourty},
editor = {Meeyoung Cha and
Chanyoung Park and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
booktitle = {Proceedings of the 34th {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
10-14, 2025},
pages = {6334--6339},
publisher = {{ACM}},
year = {2025},
url = {https://github.com/lightonai/pylate},
doi = {10.1145/3746252.3761608},
}
```
#### Nomic Embed
```bibtex
@article{DBLP:journals/tmlr/NussbaumMMD25,
author = {Zach Nussbaum and
John Xavier Morris and
Andriy Mulyar and
Brandon Duderstadt},
title = {Nomic Embed: Training a Reproducible Long Context Text Embedder},
journal = {Trans. Mach. Learn. Res.},
volume = {2025},
year = {2025},
url = {https://openreview.net/forum?id=IPmzyQSiQE},
timestamp = {Fri, 20 Jun 2025 14:19:48 +0200},
biburl = {https://dblp.org/rec/journals/tmlr/NussbaumMMD25.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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