Upload modernbert_colbert_contrastive

1_Dense/config.json

@@ -0,0 +1,7 @@
+{
+    "in_features": 768,
+    "out_features": 128,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity",
+    "use_residual": false
+}

1_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24b567136c275c7739e40343e3f0fb3e007ab6a935f228fd09dc27adc19a5b3e
+size 393304

README.md

@@ -0,0 +1,1166 @@
+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10000000
+- loss:Contrastive
+base_model: answerdotai/ModernBERT-base
+datasets:
+- bclavie/msmarco-10m-triplets
+pipeline_tag: sentence-similarity
+library_name: PyLate
+metrics:
+- MaxSim_accuracy@1
+- MaxSim_accuracy@3
+- MaxSim_accuracy@5
+- MaxSim_accuracy@10
+- MaxSim_precision@1
+- MaxSim_precision@3
+- MaxSim_precision@5
+- MaxSim_precision@10
+- MaxSim_recall@1
+- MaxSim_recall@3
+- MaxSim_recall@5
+- MaxSim_recall@10
+- MaxSim_ndcg@10
+- MaxSim_mrr@10
+- MaxSim_map@100
+model-index:
+- name: PyLate model based on answerdotai/ModernBERT-base
+  results:
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoClimateFEVER
+      type: NanoClimateFEVER
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.3
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.46
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.54
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.72
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.3
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.15999999999999998
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.12800000000000003
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09399999999999999
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.145
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.20066666666666666
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.25566666666666665
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.3723333333333333
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.29984094041575976
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.40457936507936504
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.23154243919711487
+      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: 0.92
+      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: 0.84
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.6599999999999998
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.6000000000000001
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.53
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.11978017136836354
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.19320640931807406
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.2474564677729374
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.35362762531754766
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6642857997687286
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8766666666666666
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5056362918461486
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoFEVER
+      type: NanoFEVER
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.86
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 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: 0.86
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.34666666666666657
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.20799999999999996
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.10799999999999997
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.8066666666666668
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.9566666666666667
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.9566666666666667
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.9733333333333333
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.9143032727772558
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.92
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.8848835412953059
+      name: Maxsim Map@100
+  - task:
+      type: py-late-information-retrieval
+      name: Py Late Information Retrieval
+    dataset:
+      name: NanoFiQA2018
+      type: NanoFiQA2018
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.5
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.68
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.72
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.5
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.33333333333333326
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.236
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.14
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.29724603174603176
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.49257142857142855
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5465079365079365
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.6031746031746033
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.5453834796894957
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.604079365079365
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.49074315182112516
+      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: 0.9
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.96
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.96
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 1.0
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.9
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.5266666666666666
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.32799999999999996
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.17799999999999996
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.45
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.79
+      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: 0.8430810883372716
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9353571428571428
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.778500350140056
+      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: 0.48
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.7
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.74
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.9
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.48
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2333333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.14800000000000002
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.08999999999999998
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.48
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.7
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.74
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.9
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.681981684088073
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6141031746031747
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.6195014186409419
+      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: 0.48
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.54
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.62
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.7
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.48
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.37333333333333335
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.36
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.29
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.024846700166746567
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.06745637325640307
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.1008052160248601
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.1497664943203363
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.34867256192135143
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5346031746031746
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.13572305233276538
+      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: 0.54
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.8
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.86
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.9
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.54
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2733333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.17599999999999993
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09599999999999997
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.51
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.75
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.81
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.86
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.7009621199364733
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6670238095238094
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.6421027387645034
+      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: 0.9
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.98
+      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: 0.9
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.38666666666666655
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.24799999999999997
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.13799999999999998
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.7973333333333333
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.9246666666666666
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.9426666666666668
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.9966666666666666
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.9436609396356616
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.9366666666666665
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.9184467532467532
+      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: 0.44
+      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: 0.8
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.44
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.31999999999999995
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.236
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.166
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.09366666666666668
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.19866666666666666
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.24366666666666664
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.3396666666666667
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.3404490877439103
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.5581666666666668
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.2561512796776031
+      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: 0.22
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.52
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.64
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.22
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.1733333333333333
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.128
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.08
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.22
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.52
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.64
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.8
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.4988624746761941
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.40369047619047616
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.40858139686400563
+      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: 0.7
+      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: 0.7
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.2866666666666666
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.184
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.09799999999999999
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.675
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.785
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.825
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.87
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.7836102750432731
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.7577777777777777
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.7575977078477077
+      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: 0.9795918367346939
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.9795918367346939
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.9795918367346939
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.7551020408163265
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.7142857142857143
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.6204081632653061
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.5061224489795919
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.05215472128680775
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.14371450561336085
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.20898774766999936
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.3295518520522591
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.5852674107635566
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.8639455782312924
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.43897324704873364
+      name: Maxsim Map@100
+  - task:
+      type: nano-beir
+      name: Nano BEIR
+    dataset:
+      name: NanoBEIR mean
+      type: NanoBEIR_mean
+    metrics:
+    - type: MaxSim_accuracy@1
+      value: 0.6088540031397175
+      name: Maxsim Accuracy@1
+    - type: MaxSim_accuracy@3
+      value: 0.769199372056515
+      name: Maxsim Accuracy@3
+    - type: MaxSim_accuracy@5
+      value: 0.8061224489795917
+      name: Maxsim Accuracy@5
+    - type: MaxSim_accuracy@10
+      value: 0.8768916797488226
+      name: Maxsim Accuracy@10
+    - type: MaxSim_precision@1
+      value: 0.6088540031397175
+      name: Maxsim Precision@1
+    - type: MaxSim_precision@3
+      value: 0.3682783882783882
+      name: Maxsim Precision@3
+    - type: MaxSim_precision@5
+      value: 0.27695447409733126
+      name: Maxsim Precision@5
+    - type: MaxSim_precision@10
+      value: 0.19339403453689166
+      name: Maxsim Precision@10
+    - type: MaxSim_recall@1
+      value: 0.35936109932573973
+      name: Maxsim Recall@1
+    - type: MaxSim_recall@3
+      value: 0.5171242602635334
+      name: Maxsim Recall@3
+    - type: MaxSim_recall@5
+      value: 0.5644172334340307
+      name: Maxsim Recall@5
+    - type: MaxSim_recall@10
+      value: 0.6490861980665189
+      name: Maxsim Recall@10
+    - type: MaxSim_ndcg@10
+      value: 0.6269508565228465
+      name: Maxsim Ndcg@10
+    - type: MaxSim_mrr@10
+      value: 0.6982046049188907
+      name: Maxsim Mrr@10
+    - type: MaxSim_map@100
+      value: 0.5437217975940587
+      name: Maxsim Map@100
+---
+
+# PyLate model based on answerdotai/ModernBERT-base
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+## Model Details
+
+### Model Description
+- **Model Type:** PyLate model
+- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Document Length:** 512 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+    - [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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': 512, '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
+```
+
+### 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
+    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:
+
+```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
+    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
+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,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## 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   | 0.3              | 0.84        | 0.86       | 0.5          | 0.9          | 0.48        | 0.48         | 0.54      | 0.9                | 0.44        | 0.22        | 0.7         | 0.7551         |
+| MaxSim_accuracy@3   | 0.46             | 0.92        | 1.0        | 0.68         | 0.96         | 0.7         | 0.54         | 0.8       | 0.98               | 0.66        | 0.52        | 0.8         | 0.9796         |
+| MaxSim_accuracy@5   | 0.54             | 0.92        | 1.0        | 0.72         | 0.96         | 0.74        | 0.62         | 0.86      | 0.98               | 0.68        | 0.64        | 0.84        | 0.9796         |
+| MaxSim_accuracy@10  | 0.72             | 0.92        | 1.0        | 0.8          | 1.0          | 0.9         | 0.7          | 0.9       | 1.0                | 0.8         | 0.8         | 0.88        | 0.9796         |
+| MaxSim_precision@1  | 0.3              | 0.84        | 0.86       | 0.5          | 0.9          | 0.48        | 0.48         | 0.54      | 0.9                | 0.44        | 0.22        | 0.7         | 0.7551         |
+| MaxSim_precision@3  | 0.16             | 0.66        | 0.3467     | 0.3333       | 0.5267       | 0.2333      | 0.3733       | 0.2733    | 0.3867             | 0.32        | 0.1733      | 0.2867      | 0.7143         |
+| MaxSim_precision@5  | 0.128            | 0.6         | 0.208      | 0.236        | 0.328        | 0.148       | 0.36         | 0.176     | 0.248              | 0.236       | 0.128       | 0.184       | 0.6204         |
+| MaxSim_precision@10 | 0.094            | 0.53        | 0.108      | 0.14         | 0.178        | 0.09        | 0.29         | 0.096     | 0.138              | 0.166       | 0.08        | 0.098       | 0.5061         |
+| MaxSim_recall@1     | 0.145            | 0.1198      | 0.8067     | 0.2972       | 0.45         | 0.48        | 0.0248       | 0.51      | 0.7973             | 0.0937      | 0.22        | 0.675       | 0.0522         |
+| MaxSim_recall@3     | 0.2007           | 0.1932      | 0.9567     | 0.4926       | 0.79         | 0.7         | 0.0675       | 0.75      | 0.9247             | 0.1987      | 0.52        | 0.785       | 0.1437         |
+| MaxSim_recall@5     | 0.2557           | 0.2475      | 0.9567     | 0.5465       | 0.82         | 0.74        | 0.1008       | 0.81      | 0.9427             | 0.2437      | 0.64        | 0.825       | 0.209          |
+| MaxSim_recall@10    | 0.3723           | 0.3536      | 0.9733     | 0.6032       | 0.89         | 0.9         | 0.1498       | 0.86      | 0.9967             | 0.3397      | 0.8         | 0.87        | 0.3296         |
+| **MaxSim_ndcg@10**  | **0.2998**       | **0.6643**  | **0.9143** | **0.5454**   | **0.8431**   | **0.682**   | **0.3487**   | **0.701** | **0.9437**         | **0.3404**  | **0.4989**  | **0.7836**  | **0.5853**     |
+| MaxSim_mrr@10       | 0.4046           | 0.8767      | 0.92       | 0.6041       | 0.9354       | 0.6141      | 0.5346       | 0.667     | 0.9367             | 0.5582      | 0.4037      | 0.7578      | 0.8639         |
+| MaxSim_map@100      | 0.2315           | 0.5056      | 0.8849     | 0.4907       | 0.7785       | 0.6195      | 0.1357       | 0.6421    | 0.9184             | 0.2562      | 0.4086      | 0.7576      | 0.439          |
+
+#### Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| MaxSim_accuracy@1   | 0.6089    |
+| MaxSim_accuracy@3   | 0.7692    |
+| MaxSim_accuracy@5   | 0.8061    |
+| MaxSim_accuracy@10  | 0.8769    |
+| MaxSim_precision@1  | 0.6089    |
+| MaxSim_precision@3  | 0.3683    |
+| MaxSim_precision@5  | 0.277     |
+| MaxSim_precision@10 | 0.1934    |
+| MaxSim_recall@1     | 0.3594    |
+| MaxSim_recall@3     | 0.5171    |
+| MaxSim_recall@5     | 0.5644    |
+| MaxSim_recall@10    | 0.6491    |
+| **MaxSim_ndcg@10**  | **0.627** |
+| MaxSim_mrr@10       | 0.6982    |
+| MaxSim_map@100      | 0.5437    |
+
+<!--
+## 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.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### msmarco-10m-triplets
+
+* Dataset: [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2)
+* Size: 10,000,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                           | negative                                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 31.95 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.91 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | query                                                                              | positive                                                                                                                                                                                                                                                                                                                 | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+  |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>the most important factor that influences k+ secretion is __________.</code> | <code>The regulation of K+ distribution between the intracellular and extracellular space is referred to as internal K+ balance. The most important factors regulating this movement under normal conditions are insulin and catecholamines (1).</code>                                                                  | <code>They are both also important for secretion and flow of bile: 1  Cholecystokinin: The name of this hormone describes its effect on the biliary system-cholecysto = gallbladder and kinin = movement. 2  Secretin: This hormone is secreted in response to acid in the duodenum.</code>                                                                                                                                                                                                               |
+  | <code>how much did the mackinac bridge cost to build</code>                        | <code>The cost to design the project was $3,500,000 (Steinman Company). The cost to construct the bridge was $70, 268,500. Two primary contractors were hired to build the bridge: American Bridge for superstructure - $44,532,900; and Merritt-Chapman and Scott of New York for the foundations - $25,735,600.</code> | <code>When your child needs a dental tooth bridge, you need to know the average cost so you can factor the price into your budget. Several factors affect the price of a bridge, which can run between $700 to $1,500 per tooth. If you have insurance or your child is covered by Medicaid, part of the cost may be covered.</code>                                                                                                                                                                      |
+  | <code>when do concussion symptoms appear</code>                                    | <code>Then you can get advice on what to do next. For milder symptoms, the doctor may recommend rest and ask you to watch your child closely for changes, such as a headache that gets worse. Symptoms of a concussion don't always show up right away, and can develop within 24 to 72 hours after an injury.</code>    | <code>Concussion: A traumatic injury to soft tissue, usually the brain, as a result of a violent blow, shaking, or spinning. A brain concussion can cause immediate but temporary impairment of brain functions, such as thinking, vision, equilibrium, and consciousness. After a person has had a concussion, he or she is at increased risk for recurrence. Moreover, after a person has several concussions, less of a blow can cause injury, and the person can require more time to recover.</code> |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 3e-05
+- `max_steps`: 50000
+- `fp16`: True
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 8
+- `ddp_find_unused_parameters`: False
+- `torch_compile`: True
+- `torch_compile_backend`: inductor
+- `eval_on_start`: 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`: 64
+- `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`: 3e-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`: 3.0
+- `max_steps`: 50000
+- `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`: False
+- `fp16`: True
+- `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`: True
+- `dataloader_num_workers`: 8
+- `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}
+- `parallelism_config`: 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
+- `hub_revision`: None
+- `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`: True
+- `torch_compile_backend`: inductor
+- `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`: True
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `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>
+

config.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
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