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1_Dense/config.json

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+{"in_features": 128, "out_features": 128, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}

1_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84ba720a1b4d7ce9fb1aaf6fc056db332fa2ee90456b5d6cdeaf0528d9187283
+size 65624

README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:640000
+- loss:Distillation
+base_model: google/bert_uncased_L-2_H-128_A-2
+datasets:
+- lightonai/ms-marco-en-bge-gemma-unnormalized
+pipeline_tag: sentence-similarity
+library_name: PyLate
+license: apache-2.0
+metrics:
+- MaxSim_accuracy@1
+- MaxSim_accuracy@3
+- MaxSim_accuracy@5
+- MaxSim_accuracy@10
+- MaxSim_precision@1
+- MaxSim_precision@3
+- MaxSim_precision@5
+- MaxSim_precision@10
+- MaxSim_recall@1
+- MaxSim_recall@3
+- MaxSim_recall@5
+- MaxSim_recall@10
+- MaxSim_ndcg@10
+- MaxSim_mrr@10
+- MaxSim_map@100
+model-index:
+- name: ColBERT MUVERA Micro
+  results:
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoClimateFEVER
+      type: NanoClimateFEVER
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.26
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.36
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.4
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.58
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.26
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.12666666666666665
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.092
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.07800000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.11233333333333333
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.16066666666666665
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.184
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.3206666666666667
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.24408616743142095
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.33196825396825397
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.18128382432733356
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoDBPedia
+      type: NanoDBPedia
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.68
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.86
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.92
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.94
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.68
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.6066666666666667
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.56
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.502
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.05322585293904511
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.16789568954347403
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.22988072374930787
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.35043982767195947
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6003406576207015
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.7850000000000001
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.4687280514608297
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoFEVER
+      type: NanoFEVER
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.72
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.78
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.84
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.9
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.72
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2733333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.18
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.1
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.6866666666666668
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.7633333333333333
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.82
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.89
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.7955242043086649
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.7731666666666667
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7676133768765347
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoFiQA2018
+      type: NanoFiQA2018
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.3
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.54
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.58
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.66
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.3
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2333333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.17200000000000004
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.10800000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.1770793650793651
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.3453492063492064
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.4009047619047619
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.4740952380952381
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.38709436118795515
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.4288015873015872
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.3297000135708943
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoHotpotQA
+      type: NanoHotpotQA
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.94
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.94
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.98
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 1.0
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.94
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.5
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.31200000000000006
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.16599999999999995
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.47
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.75
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.78
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.83
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.8179728241272247
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9512222222222222
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7611883462001594
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.42
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.66
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.68
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.78
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.42
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.22
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.136
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.07800000000000001
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.42
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.66
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.68
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.78
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.5976880189340548
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5393809523809523
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5531015913611822
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoNFCorpus
+      type: NanoNFCorpus
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.46
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.58
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.62
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.68
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.46
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.38
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.324
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.272
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.04276439372638386
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.07977851865112022
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.11439841040272719
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.1391695106171535
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.34241148621124995
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5320000000000001
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.14897381866568696
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoNQ
+      type: NanoNQ
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.42
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.68
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.74
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.84
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.42
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.23333333333333328
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.15200000000000002
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.086
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.4
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.66
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.72
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.79
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6184738987111722
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5763888888888888
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5642312927870203
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoQuoraRetrieval
+      type: NanoQuoraRetrieval
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.8
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.92
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.94
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.96
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.8
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.3399999999999999
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.22399999999999998
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.11999999999999998
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.7239999999999999
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.8473333333333334
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.9006666666666666
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.9373333333333334
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.863105292852843
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8611904761904764
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.8312823701317842
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoSCIDOCS
+      type: NanoSCIDOCS
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.42
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.58
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.64
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.7
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.42
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2866666666666667
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.20799999999999996
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.138
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.085
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.17666666666666664
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.21366666666666667
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.2826666666666667
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.2889801789850345
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5005
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.21685607444339383
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoArguAna
+      type: NanoArguAna
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.2
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.44
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.5
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.64
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.2
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.14666666666666664
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.1
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.064
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.2
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.44
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.64
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.4151392430544827
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.3440555555555555
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.3521906424035335
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoSciFact
+      type: NanoSciFact
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.58
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.76
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.82
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.86
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.58
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2733333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.18
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09399999999999999
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.555
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.735
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.8
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.84
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.7153590631749926
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6798333333333333
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.6760413640032285
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoTouche2020
+      type: NanoTouche2020
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.7551020408163265
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 1.0
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 1.0
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 1.0
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.7551020408163265
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.6734693877551019
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.6000000000000001
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.5285714285714286
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.050375728116040484
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.13379303377518686
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.19744749683082305
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.3328396127707909
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.5927407647152685
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8639455782312924
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.4115661843314275
+      name: Maxsim Map@100
+  - task:
+      type: nano-beir
+      name: Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.5350078492935635
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.7000000000000001
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.743076923076923
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8107692307692307
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.5350078492935635
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.33026687598116167
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.24923076923076928
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.1795824175824176
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.3058804107585258
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.45537049602453755
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5031511327862271
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.5851700658324468
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.5599166277934665
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6282656549799407
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.4817505346586929
+      name: Maxsim Map@100
+---
+
+# ColBERT MUVERA Micro
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [msmarco-en-bge-gemma-unnormalized](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma-unnormalized) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+This model is trained with un-normalized scores, making it compatible with [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504).
+
+## Usage (txtai)
+
+This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
+
+_Note: txtai 9.0+ is required for late interaction model support_
+
+```python
+import txtai
+
+embeddings = txtai.Embeddings(
+  sparse="neuml/colbert-muvera-micro",
+  content=True
+)
+embeddings.index(documents())
+
+# Run a query
+embeddings.search("query to run")
+```
+
+Late interaction models excel as reranker pipelines.
+
+```python
+from txtai.pipeline import Reranker, Similarity
+
+similarity = Similarity(path="neuml/colbert-muvera-micro", lateencode=True)
+ranker = Reranker(embeddings, similarity)
+ranker("query to run")
+```
+
+## Usage (PyLate)
+
+Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).
+
+```python
+from pylate import rank, models
+
+queries = [
+    "query A",
+    "query B",
+]
+
+documents = [
+    ["document A", "document B"],
+    ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+    [1, 2],
+    [1, 3, 2],
+]
+
+model = models.ColBERT(
+    model_name_or_path="neuml/colbert-muvera-micro",
+)
+
+queries_embeddings = model.encode(
+    queries,
+    is_query=True,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+### Full Model Architecture
+
+```
+ColBERT(
+  (0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Dense({'in_features': 128, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Evaluation
+
+### BEIR Subset
+
+The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py). 
+
+Scores reported are `ndcg@10` and grouped into the following three categories.
+
+#### FULL multi-vector maxsim
+
+| Model             | Parameters | ArguAna  | NFCorpus | SciFact | Average |
+|:------------------|:-----------|:---------|:---------|:--------|:--------|
+| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.4440 | 0.3649 | 0.7423 | 0.5171 |
+| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4595 | 0.3165 | 0.6456 | 0.4739 |
+| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3947** | **0.3235** | **0.6676** | **0.4619** |
+| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4455 | 0.3502 | 0.7145 | 0.5034 |
+| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.4946 | 0.3717 | 0.7529 | 0.5397 |
+
+#### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper
+
+| Model             | Parameters | ArguAna  | NFCorpus | SciFact | Average |
+|:------------------|:-----------|:---------|:---------|:--------|:--------|
+| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0317 | 0.1135 | 0.0836 | 0.0763 |
+| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4562 | 0.3025 | 0.6278 | 0.4622 |
+| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3849** | **0.3095** | **0.6464** | **0.4469** |
+| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4451 | 0.3537 | 0.7148 | 0.5045 |
+| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0265 | 0.1052 | 0.0556 | 0.0624 | 
+
+#### MUVERA encoding only
+
+| Model             | Parameters | ArguAna  | NFCorpus | SciFact | Average |
+|:------------------|:-----------|:---------|:---------|:--------|:--------|
+| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0024 | 0.0201 | 0.0047 | 0.0091 |
+| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3463 | 0.2356 | 0.5002 | 0.3607 |
+| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.2795** | **0.2348** | **0.4875** | **0.3339** |
+| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.3850 | 0.2928 | 0.6357 | 0.4378 | 
+| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0003 | 0.0203 |0.0013 | 0.0073 | 
+
+_Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts._
+
+As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this [GitHub Issue](https://github.com/lightonai/pylate/issues/142) for more.
+
+**In reviewing the scores, this model is surprisingly and unreasonably competitive with the original ColBERT v2 model at only 3% of the size!**
+
+### Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| MaxSim_accuracy@1   | 0.535      |
+| MaxSim_accuracy@3   | 0.7        |
+| MaxSim_accuracy@5   | 0.7431     |
+| MaxSim_accuracy@10  | 0.8108     |
+| MaxSim_precision@1  | 0.535      |
+| MaxSim_precision@3  | 0.3303     |
+| MaxSim_precision@5  | 0.2492     |
+| MaxSim_precision@10 | 0.1796     |
+| MaxSim_recall@1     | 0.3059     |
+| MaxSim_recall@3     | 0.4554     |
+| MaxSim_recall@5     | 0.5032     |
+| MaxSim_recall@10    | 0.5852     |
+| **MaxSim_ndcg@10**  | **0.5599** |
+| MaxSim_mrr@10       | 0.6283     |
+| MaxSim_map@100      | 0.4818     |
+
+## Training Details
+
+### Training Hyperparameters
+
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `learning_rate`: 0.0003
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.05
+- `bf16`: True
+
+#### All Hyperparameters
+<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`: 32
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 0.0003
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Framework Versions
+- Python: 3.10.18
+- Sentence Transformers: 4.0.2
+- PyLate: 1.3.0
+- Transformers: 4.52.3
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## Citation
+
+### BibTeX
+
+#### 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
+@misc{PyLate,
+title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
+author={Chaffin, Antoine and Sourty, Raphaël},
+url={https://github.com/lightonai/pylate},
+year={2024}
+}
+```

added_tokens.json

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+  "[D] ": 30523,
+  "[Q] ": 30522
+}

config.json

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+  "architectures": [
+    "BertModel"
+  ],
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+  "hidden_act": "gelu",
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+  "initializer_range": 0.02,
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+  "layer_norm_eps": 1e-12,
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+  "model_type": "bert",
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+  "num_hidden_layers": 2,
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+  "transformers_version": "4.52.3",
+  "type_vocab_size": 2,
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+  "vocab_size": 30524
+}

config_sentence_transformers.json

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+  "__version__": {
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+    "transformers": "4.52.3",
+    "pytorch": "2.8.0+cu128"
+  },
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+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 300,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
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+    "\"",
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+    "'",
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+    "*",
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+    "@",
+    "[",
+    "\\",
+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ],
+  "do_query_expansion": true
+}

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+oid sha256:be7739300d0ea06e2463db6c50f2e790d25d4f629493fc68dd1e1a2a376f04e8
+size 17548936

modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 299,
+  "do_lower_case": false
+}

special_tokens_map.json

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+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[MASK]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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+  "added_tokens_decoder": {
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+      "lstrip": false,
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+}