diff --git "a/README.md" "b/README.md"
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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:533177
+- loss:Distillation
+base_model: jhu-clsp/ettin-encoder-17m
+datasets:
+- Speedsy/cleaned-ms-marco-bge-gemma-from-ligton
+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 jhu-clsp/ettin-encoder-17m
+ results:
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoClimateFEVER
+ type: NanoClimateFEVER
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.24
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.38
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.5
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.7
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.24
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.15333333333333332
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.11599999999999999
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.092
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.12833333333333333
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.21
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2533333333333333
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.36666666666666664
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.28763494301317366
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.3579841269841269
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.22941676804604197
+ 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: 0.7
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.86
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.9
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.94
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.7
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.6133333333333334
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.556
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.48
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.07180505985706782
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.16504755248565225
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2194302820279554
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.32972451635318606
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5813676616013099
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7881666666666667
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.4671775883716682
+ 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: 0.88
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.94
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.96
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.98
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.88
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.32666666666666666
+ 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: 0.8166666666666668
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.8933333333333333
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.93
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.96
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9050308205730978
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9106666666666665
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8792272727272727
+ 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: 0.42
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.6
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.66
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.74
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.42
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.26666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.212
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.132
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.2286904761904762
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.36584920634920637
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.4511190476190476
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.5564523809523809
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.4611987813833444
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5268571428571428
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.38520628807075186
+ 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: 0.88
+ 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: 0.88
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.5533333333333332
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.344
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.17999999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.44
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.83
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.86
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.858075938741974
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9333333333333332
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.800559405305322
+ 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: 0.52
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.66
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.7
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.8
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.52
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.22
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.14
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.08
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.52
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.66
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.7
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.8
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6531074122045695
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6071031746031745
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6169218803486538
+ 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: 0.44
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.54
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.64
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.66
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.44
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3466666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.332
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.26
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.04328552205779273
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.07677540422223056
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.1178452517343604
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.14031592190988035
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.32964183008765374
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5071904761904762
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.14909457042487737
+ 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: 0.52
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.78
+ 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: 0.52
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.26
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.16799999999999998
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.49
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.72
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.77
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.81
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6661035501816893
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6417460317460317
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6127876420077506
+ 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: 0.84
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.98
+ 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: 0.84
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.38666666666666655
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.244
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.12399999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.7440000000000001
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.9286666666666668
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.9593333333333334
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9626666666666668
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9075252606458076
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9106666666666667
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8834929814655215
+ 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: 0.4
+ 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: 0.4
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.31333333333333335
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.244
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.162
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.08366666666666667
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.19366666666666668
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.24966666666666662
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3306666666666666
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.3310802884457278
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5407222222222222
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.25548127218322214
+ 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: 0.14
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.56
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.64
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.76
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.14
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.18666666666666668
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.128
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.07600000000000001
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.14
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.56
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.64
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.76
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.457648969568352
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.3595714285714285
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.36839121630206756
+ 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: 0.62
+ 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: 0.88
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.62
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.27999999999999997
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.18799999999999997
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09799999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.595
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.77
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.845
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.87
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.7463564404006293
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7106666666666667
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.7060596028154851
+ 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: 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: 0.6666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.6285714285714286
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.5102040816326531
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.0512448639546046
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.13492382759077773
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.21028780790933668
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3208107900431349
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5860354215626813
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8656462585034013
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.42050847387780316
+ name: Maxsim Map@100
+ - task:
+ type: nano-beir
+ name: Nano BEIR
+ dataset:
+ name: NanoBEIR mean
+ type: NanoBEIR_mean
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.5657770800627943
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.749199372056515
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.7969230769230768
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.8538461538461538
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.5657770800627943
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3517948717948718
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.2698901098901099
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.18401569858712713
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.3348225068251237
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.5006355890241948
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.5543089017403102
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.6236387391737371
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5977544091084623
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.666178527821385
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5211019201497261
+ name: Maxsim Map@100
+---
+
+# PyLate model based on jhu-clsp/ettin-encoder-17m
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [train](https://huggingface.co/datasets/Speedsy/cleaned-ms-marco-bge-gemma-from-ligton) 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:** [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m)
+- **Document Length:** 300 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+ - [train](https://huggingface.co/datasets/Speedsy/cleaned-ms-marco-bge-gemma-from-ligton)
+
+
+
+### 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': 299, 'do_lower_case': False}) with Transformer model: ModernBertModel
+ (1): Dense({'in_features': 256, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
+
+#### Indexing documents
+
+First, load the ColBERT model and initialize the Voyager 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 Voyager index
+index = indexes.Voyager(
+ 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.Voyager(
+ 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,
+)
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Py Late Information Retrieval
+* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
+* Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator
+
+| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
+|:--------------------|:-----------------|:------------|:----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
+| MaxSim_accuracy@1 | 0.24 | 0.7 | 0.88 | 0.42 | 0.88 | 0.52 | 0.44 | 0.52 | 0.84 | 0.4 | 0.14 | 0.62 | 0.7551 |
+| MaxSim_accuracy@3 | 0.38 | 0.86 | 0.94 | 0.6 | 1.0 | 0.66 | 0.54 | 0.78 | 0.98 | 0.66 | 0.56 | 0.8 | 0.9796 |
+| MaxSim_accuracy@5 | 0.5 | 0.9 | 0.96 | 0.66 | 1.0 | 0.7 | 0.64 | 0.82 | 1.0 | 0.68 | 0.64 | 0.86 | 1.0 |
+| MaxSim_accuracy@10 | 0.7 | 0.94 | 0.98 | 0.74 | 1.0 | 0.8 | 0.66 | 0.86 | 1.0 | 0.78 | 0.76 | 0.88 | 1.0 |
+| MaxSim_precision@1 | 0.24 | 0.7 | 0.88 | 0.42 | 0.88 | 0.52 | 0.44 | 0.52 | 0.84 | 0.4 | 0.14 | 0.62 | 0.7551 |
+| MaxSim_precision@3 | 0.1533 | 0.6133 | 0.3267 | 0.2667 | 0.5533 | 0.22 | 0.3467 | 0.26 | 0.3867 | 0.3133 | 0.1867 | 0.28 | 0.6667 |
+| MaxSim_precision@5 | 0.116 | 0.556 | 0.208 | 0.212 | 0.344 | 0.14 | 0.332 | 0.168 | 0.244 | 0.244 | 0.128 | 0.188 | 0.6286 |
+| MaxSim_precision@10 | 0.092 | 0.48 | 0.108 | 0.132 | 0.18 | 0.08 | 0.26 | 0.09 | 0.124 | 0.162 | 0.076 | 0.098 | 0.5102 |
+| MaxSim_recall@1 | 0.1283 | 0.0718 | 0.8167 | 0.2287 | 0.44 | 0.52 | 0.0433 | 0.49 | 0.744 | 0.0837 | 0.14 | 0.595 | 0.0512 |
+| MaxSim_recall@3 | 0.21 | 0.165 | 0.8933 | 0.3658 | 0.83 | 0.66 | 0.0768 | 0.72 | 0.9287 | 0.1937 | 0.56 | 0.77 | 0.1349 |
+| MaxSim_recall@5 | 0.2533 | 0.2194 | 0.93 | 0.4511 | 0.86 | 0.7 | 0.1178 | 0.77 | 0.9593 | 0.2497 | 0.64 | 0.845 | 0.2103 |
+| MaxSim_recall@10 | 0.3667 | 0.3297 | 0.96 | 0.5565 | 0.9 | 0.8 | 0.1403 | 0.81 | 0.9627 | 0.3307 | 0.76 | 0.87 | 0.3208 |
+| **MaxSim_ndcg@10** | **0.2876** | **0.5814** | **0.905** | **0.4612** | **0.8581** | **0.6531** | **0.3296** | **0.6661** | **0.9075** | **0.3311** | **0.4576** | **0.7464** | **0.586** |
+| MaxSim_mrr@10 | 0.358 | 0.7882 | 0.9107 | 0.5269 | 0.9333 | 0.6071 | 0.5072 | 0.6417 | 0.9107 | 0.5407 | 0.3596 | 0.7107 | 0.8656 |
+| MaxSim_map@100 | 0.2294 | 0.4672 | 0.8792 | 0.3852 | 0.8006 | 0.6169 | 0.1491 | 0.6128 | 0.8835 | 0.2555 | 0.3684 | 0.7061 | 0.4205 |
+
+#### Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
+
+| Metric | Value |
+|:--------------------|:-----------|
+| MaxSim_accuracy@1 | 0.5658 |
+| MaxSim_accuracy@3 | 0.7492 |
+| MaxSim_accuracy@5 | 0.7969 |
+| MaxSim_accuracy@10 | 0.8538 |
+| MaxSim_precision@1 | 0.5658 |
+| MaxSim_precision@3 | 0.3518 |
+| MaxSim_precision@5 | 0.2699 |
+| MaxSim_precision@10 | 0.184 |
+| MaxSim_recall@1 | 0.3348 |
+| MaxSim_recall@3 | 0.5006 |
+| MaxSim_recall@5 | 0.5543 |
+| MaxSim_recall@10 | 0.6236 |
+| **MaxSim_ndcg@10** | **0.5978** |
+| MaxSim_mrr@10 | 0.6662 |
+| MaxSim_map@100 | 0.5211 |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### train
+
+* Dataset: [train](https://huggingface.co/datasets/Speedsy/cleaned-ms-marco-bge-gemma-from-ligton) at [63f51a3](https://huggingface.co/datasets/Speedsy/cleaned-ms-marco-bge-gemma-from-ligton/tree/63f51a32e72d981b44be6cace953edbb7dd2c4b0)
+* Size: 533,177 training samples
+* Columns: query_id, document_ids, and scores
+* Approximate statistics based on the first 1000 samples:
+ | | query_id | document_ids | scores |
+ |:--------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
+ | type | int | list | list |
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| | |
+* Samples:
+ | query_id | document_ids | scores |
+ |:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
+ | 237784 | [6366584, 4034101, 2325374, 6914618, 6042146, ...] | [0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...] |
+ | 904294 | [448408, 8743975, 49600, 7339401, 2714261, ...] | [0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...] |
+ | 412214 | [1006306, 4454048, 1949661, 4895656, 675880, ...] | [0.9999999994734676, 0.38790621123137803, 0.3747429039573546, 0.2990538871317199, 0.38420403106055895, ...] |
+* Loss: pylate.losses.distillation.Distillation
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `learning_rate`: 3e-05
+- `num_train_epochs`: 1
+- `bf16`: True
+
+#### All Hyperparameters
+Click to expand
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `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`: 1
+- `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`: 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
+- `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
+
+Click to expand
+
+| 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 |
+|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
+| 0.0030 | 100 | 0.0383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0060 | 200 | 0.0328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0090 | 300 | 0.0319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0120 | 400 | 0.0313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0150 | 500 | 0.0294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0180 | 600 | 0.0265 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0210 | 700 | 0.026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0240 | 800 | 0.0251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0270 | 900 | 0.0242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0300 | 1000 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0330 | 1100 | 0.0232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0360 | 1200 | 0.0236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0390 | 1300 | 0.0231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0420 | 1400 | 0.0227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0450 | 1500 | 0.0225 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0480 | 1600 | 0.0222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0510 | 1700 | 0.0218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0540 | 1800 | 0.022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0570 | 1900 | 0.0213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0600 | 2000 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0630 | 2100 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0660 | 2200 | 0.0209 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0690 | 2300 | 0.0204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0720 | 2400 | 0.0201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0750 | 2500 | 0.02 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0780 | 2600 | 0.0207 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0810 | 2700 | 0.0199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0840 | 2800 | 0.0198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0870 | 2900 | 0.0196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0900 | 3000 | 0.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0930 | 3100 | 0.0194 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0960 | 3200 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0990 | 3300 | 0.019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1020 | 3400 | 0.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1050 | 3500 | 0.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1080 | 3600 | 0.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1110 | 3700 | 0.0183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1140 | 3800 | 0.0185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1170 | 3900 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1200 | 4000 | 0.0188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1230 | 4100 | 0.019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1260 | 4200 | 0.018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1290 | 4300 | 0.0183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1320 | 4400 | 0.0178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1350 | 4500 | 0.018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1380 | 4600 | 0.0175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1410 | 4700 | 0.0172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1440 | 4800 | 0.0174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1470 | 4900 | 0.0176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1500 | 5000 | 0.017 | 0.2687 | 0.5353 | 0.8792 | 0.4640 | 0.8518 | 0.6460 | 0.3198 | 0.6059 | 0.9229 | 0.3192 | 0.3772 | 0.7166 | 0.5900 | 0.5767 |
+| 0.1530 | 5100 | 0.0173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1560 | 5200 | 0.0175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1590 | 5300 | 0.0173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1620 | 5400 | 0.0168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1650 | 5500 | 0.0164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1680 | 5600 | 0.0171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1710 | 5700 | 0.0169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1740 | 5800 | 0.0166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1770 | 5900 | 0.0169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1801 | 6000 | 0.0166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1831 | 6100 | 0.0164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1861 | 6200 | 0.0165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1891 | 6300 | 0.0162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1921 | 6400 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1951 | 6500 | 0.0163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1981 | 6600 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2011 | 6700 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2041 | 6800 | 0.0159 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2071 | 6900 | 0.0166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2101 | 7000 | 0.0158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2131 | 7100 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2161 | 7200 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2191 | 7300 | 0.0151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2221 | 7400 | 0.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2251 | 7500 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2281 | 7600 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2311 | 7700 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2341 | 7800 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2371 | 7900 | 0.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2401 | 8000 | 0.015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2431 | 8100 | 0.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2461 | 8200 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2491 | 8300 | 0.015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2521 | 8400 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2551 | 8500 | 0.0151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2581 | 8600 | 0.015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2611 | 8700 | 0.0151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2641 | 8800 | 0.0152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2671 | 8900 | 0.0152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2701 | 9000 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2731 | 9100 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2761 | 9200 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2791 | 9300 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2821 | 9400 | 0.0151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2851 | 9500 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2881 | 9600 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2911 | 9700 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2941 | 9800 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2971 | 9900 | 0.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3001 | 10000 | 0.0144 | 0.2775 | 0.5458 | 0.8872 | 0.4447 | 0.8686 | 0.6438 | 0.3179 | 0.6183 | 0.9151 | 0.3025 | 0.4000 | 0.7288 | 0.5733 | 0.5787 |
+| 0.3031 | 10100 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3061 | 10200 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3091 | 10300 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3121 | 10400 | 0.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3151 | 10500 | 0.0143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3181 | 10600 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3211 | 10700 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3241 | 10800 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3271 | 10900 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3301 | 11000 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3331 | 11100 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3361 | 11200 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3391 | 11300 | 0.0143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3421 | 11400 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3451 | 11500 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3481 | 11600 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3511 | 11700 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3541 | 11800 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3571 | 11900 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3601 | 12000 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3631 | 12100 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3661 | 12200 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3691 | 12300 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3721 | 12400 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3751 | 12500 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3781 | 12600 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3811 | 12700 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3841 | 12800 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3871 | 12900 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3901 | 13000 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3931 | 13100 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3961 | 13200 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3991 | 13300 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4021 | 13400 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4051 | 13500 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4081 | 13600 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4111 | 13700 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4141 | 13800 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4171 | 13900 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4201 | 14000 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4231 | 14100 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4261 | 14200 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4291 | 14300 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4321 | 14400 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4351 | 14500 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4381 | 14600 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4411 | 14700 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4441 | 14800 | 0.0134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
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+
+