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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: 0.26
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.42
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.5
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.26
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.15999999999999998
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.11600000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.092
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.13499999999999998
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.215
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: 0.29046566923016665
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3753571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2288474708316239
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: 0.72
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.86
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.94
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.72
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.62
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.5559999999999999
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.486
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.07317816591767387
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.16578829322639302
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.21602211788294917
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.33395660972359953
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5879701952218912
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7981904761904762
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4653911951587876
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: 0.20399999999999996
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: 0.9233333333333333
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.96
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.904750880683694
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9111904761904762
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8780844155844156
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: 0.44
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
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: 0.44
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.132
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2409126984126984
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.3758492063492064
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.4422857142857143
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5564523809523809
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4639924721325262
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5364682539682539
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.38776210596346167
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: 0.5466666666666665
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: 0.82
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: 0.8574760963060821
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9333333333333332
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.799964380447139
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: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
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: 0.15600000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
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: 0.78
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.82
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6644746347246161
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6150555555555556
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6240256785755237
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: 0.52
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: 0.32799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.25800000000000006
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.04328552205779273
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.0766231938568144
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.11774321091803387
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.14122996307881844
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3254678017637849
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5031904761904762
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.14596595568471235
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: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.78
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.16399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.088
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.47
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.72
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.75
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.8
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6545121111640885
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6283333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6016451583332388
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: 0.82
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: 0.82
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: 0.12599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7340000000000001
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9286666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9626666666666668
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.966
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.904268856532771
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8973333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8789129063746711
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: 0.64
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: 0.3066666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.23999999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.16
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.09166666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.18966666666666665
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.24566666666666662
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.32666666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3302289015435344
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5572222222222223
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2550652569095046
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: 0.18
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.76
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.18
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12400000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07600000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.18
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.62
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.76
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4645129865259301
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.370047619047619
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3781205313205313
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: 0.1
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: 0.88
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7484873429234673
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7096666666666667
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7040186906589345
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: 0.673469387755102
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: 0.673469387755102
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6598639455782312
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6285714285714286
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.044158696154151085
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.1348057247941035
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2118327372127382
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.31512231200199337
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5654862366863922
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8136054421768706
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.41218683538450235
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5671899529042386
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7507378335949764
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8015384615384614
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8584615384615385
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5671899529042386
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3517844060701203
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2695824175824175
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18369230769230768
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3372206473750499
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5007486988379373
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5559910600230336
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6250841999300097
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.597084168110688
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6653072562358275
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5199992754790036
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) <!-- at revision 987607455c61e7a5bbc85f7758e0512ea6d0ae4c -->
- **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)
<!-- - **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': 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,
)
```
<!--
### 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 | 0.26 | 0.72 | 0.88 | 0.44 | 0.88 | 0.52 | 0.44 | 0.5 | 0.82 | 0.44 | 0.18 | 0.62 | 0.6735 |
| MaxSim_accuracy@3 | 0.42 | 0.86 | 0.94 | 0.62 | 1.0 | 0.66 | 0.52 | 0.78 | 0.98 | 0.64 | 0.56 | 0.8 | 0.9796 |
| MaxSim_accuracy@5 | 0.5 | 0.92 | 0.96 | 0.66 | 1.0 | 0.78 | 0.64 | 0.8 | 1.0 | 0.68 | 0.62 | 0.86 | 1.0 |
| MaxSim_accuracy@10 | 0.74 | 0.94 | 0.98 | 0.74 | 1.0 | 0.82 | 0.66 | 0.84 | 1.0 | 0.8 | 0.76 | 0.88 | 1.0 |
| MaxSim_precision@1 | 0.26 | 0.72 | 0.88 | 0.44 | 0.88 | 0.52 | 0.44 | 0.5 | 0.82 | 0.44 | 0.18 | 0.62 | 0.6735 |
| MaxSim_precision@3 | 0.16 | 0.62 | 0.3267 | 0.2733 | 0.5467 | 0.22 | 0.3467 | 0.26 | 0.3867 | 0.3067 | 0.1867 | 0.28 | 0.6599 |
| MaxSim_precision@5 | 0.116 | 0.556 | 0.204 | 0.208 | 0.344 | 0.156 | 0.328 | 0.164 | 0.248 | 0.24 | 0.124 | 0.188 | 0.6286 |
| MaxSim_precision@10 | 0.092 | 0.486 | 0.108 | 0.132 | 0.18 | 0.082 | 0.258 | 0.088 | 0.126 | 0.16 | 0.076 | 0.1 | 0.5 |
| MaxSim_recall@1 | 0.135 | 0.0732 | 0.8167 | 0.2409 | 0.44 | 0.52 | 0.0433 | 0.47 | 0.734 | 0.0917 | 0.18 | 0.595 | 0.0442 |
| MaxSim_recall@3 | 0.215 | 0.1658 | 0.8933 | 0.3758 | 0.82 | 0.66 | 0.0766 | 0.72 | 0.9287 | 0.1897 | 0.56 | 0.77 | 0.1348 |
| MaxSim_recall@5 | 0.2533 | 0.216 | 0.9233 | 0.4423 | 0.86 | 0.78 | 0.1177 | 0.75 | 0.9627 | 0.2457 | 0.62 | 0.845 | 0.2118 |
| MaxSim_recall@10 | 0.3667 | 0.334 | 0.96 | 0.5565 | 0.9 | 0.82 | 0.1412 | 0.8 | 0.966 | 0.3267 | 0.76 | 0.88 | 0.3151 |
| **MaxSim_ndcg@10** | **0.2905** | **0.588** | **0.9048** | **0.464** | **0.8575** | **0.6645** | **0.3255** | **0.6545** | **0.9043** | **0.3302** | **0.4645** | **0.7485** | **0.5655** |
| MaxSim_mrr@10 | 0.3754 | 0.7982 | 0.9112 | 0.5365 | 0.9333 | 0.6151 | 0.5032 | 0.6283 | 0.8973 | 0.5572 | 0.37 | 0.7097 | 0.8136 |
| MaxSim_map@100 | 0.2288 | 0.4654 | 0.8781 | 0.3878 | 0.8 | 0.624 | 0.146 | 0.6016 | 0.8789 | 0.2551 | 0.3781 | 0.704 | 0.4122 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.5672 |
| MaxSim_accuracy@3 | 0.7507 |
| MaxSim_accuracy@5 | 0.8015 |
| MaxSim_accuracy@10 | 0.8585 |
| MaxSim_precision@1 | 0.5672 |
| MaxSim_precision@3 | 0.3518 |
| MaxSim_precision@5 | 0.2696 |
| MaxSim_precision@10 | 0.1837 |
| MaxSim_recall@1 | 0.3372 |
| MaxSim_recall@3 | 0.5007 |
| MaxSim_recall@5 | 0.556 |
| MaxSim_recall@10 | 0.6251 |
| **MaxSim_ndcg@10** | **0.5971** |
| MaxSim_mrr@10 | 0.6653 |
| MaxSim_map@100 | 0.52 |
<!--
## 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
#### 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: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | int | list | list |
| details | <ul><li>264: ~0.10%</li><li>836: ~0.10%</li><li>3582: ~0.10%</li><li>4599: ~0.10%</li><li>4645: ~0.10%</li><li>4853: ~0.10%</li><li>5154: ~0.10%</li><li>7504: ~0.10%</li><li>12254: ~0.10%</li><li>12283: ~0.10%</li><li>12916: ~0.10%</li><li>14049: ~0.10%</li><li>14587: ~0.10%</li><li>14828: ~0.10%</li><li>15674: ~0.10%</li><li>15813: ~0.10%</li><li>16728: ~0.10%</li><li>22006: ~0.10%</li><li>23675: ~0.10%</li><li>24199: ~0.10%</li><li>25323: ~0.10%</li><li>29213: ~0.10%</li><li>32344: ~0.10%</li><li>34604: ~0.10%</li><li>35424: ~0.10%</li><li>36148: ~0.10%</li><li>37826: ~0.10%</li><li>38185: ~0.10%</li><li>38242: ~0.10%</li><li>39839: ~0.10%</li><li>42077: ~0.10%</li><li>43614: ~0.10%</li><li>45073: ~0.10%</li><li>46289: ~0.10%</li><li>47507: ~0.10%</li><li>48005: ~0.10%</li><li>48785: ~0.10%</li><li>49216: ~0.10%</li><li>49636: ~0.10%</li><li>49970: ~0.10%</li><li>51145: ~0.10%</li><li>52075: ~0.10%</li><li>52094: ~0.10%</li><li>52725: ~0.10%</li><li>52788: ~0.10%</li><li>54210: ~0.10%</li><li>55032: ~0.10%</li><li>59546: ~0.10%</li><li>60941: ~0.10%</li><li>62649: ~0.10%</li><li>63333: ~0.10%</li><li>64197: ~0.10%</li><li>64879: ~0.10%</li><li>67608: ~0.10%</li><li>67627: ~0.10%</li><li>69463: ~0.10%</li><li>70002: ~0.10%</li><li>70429: ~0.10%</li><li>72166: ~0.10%</li><li>72518: ~0.10%</li><li>72607: ~0.10%</li><li>72791: ~0.10%</li><li>73325: ~0.10%</li><li>74078: ~0.10%</li><li>74857: ~0.10%</li><li>75323: ~0.10%</li><li>75816: ~0.10%</li><li>76929: ~0.10%</li><li>77306: ~0.10%</li><li>77845: ~0.10%</li><li>77889: ~0.10%</li><li>78077: ~0.10%</li><li>78256: ~0.10%</li><li>78401: ~0.10%</li><li>78798: ~0.10%</li><li>80329: ~0.10%</li><li>80871: ~0.10%</li><li>81089: ~0.10%</li><li>82179: ~0.10%</li><li>82883: ~0.10%</li><li>84168: ~0.10%</li><li>86891: ~0.10%</li><li>88535: ~0.10%</li><li>88953: ~0.10%</li><li>89346: ~0.10%</li><li>89386: ~0.10%</li><li>90699: ~0.10%</li><li>90795: ~0.10%</li><li>91367: ~0.10%</li><li>91795: ~0.10%</li><li>91834: ~0.10%</li><li>92070: ~0.10%</li><li>92523: ~0.10%</li><li>92753: ~0.10%</li><li>92787: ~0.10%</li><li>93317: ~0.10%</li><li>96382: ~0.10%</li><li>96455: ~0.10%</li><li>97274: ~0.10%</li><li>97603: ~0.10%</li><li>98140: ~0.10%</li><li>101205: ~0.10%</li><li>101305: ~0.10%</li><li>102707: ~0.10%</li><li>103074: ~0.10%</li><li>105437: ~0.10%</li><li>108207: ~0.10%</li><li>109776: ~0.10%</li><li>112056: ~0.10%</li><li>112955: ~0.10%</li><li>112977: ~0.10%</li><li>113635: ~0.10%</li><li>115551: ~0.10%</li><li>116098: ~0.10%</li><li>118057: ~0.10%</li><li>120298: ~0.10%</li><li>121703: ~0.10%</li><li>123429: ~0.10%</li><li>125043: ~0.10%</li><li>125979: ~0.10%</li><li>126851: ~0.10%</li><li>128218: ~0.10%</li><li>128804: ~0.10%</li><li>129598: ~0.10%</li><li>131299: ~0.10%</li><li>132114: ~0.10%</li><li>133553: ~0.10%</li><li>133696: ~0.10%</li><li>134460: ~0.10%</li><li>137602: ~0.10%</li><li>137679: ~0.10%</li><li>138121: ~0.10%</li><li>138260: ~0.10%</li><li>139039: ~0.10%</li><li>140651: ~0.10%</li><li>142305: ~0.10%</li><li>145653: ~0.10%</li><li>145683: ~0.10%</li><li>145763: ~0.10%</li><li>150202: ~0.10%</li><li>151135: ~0.10%</li><li>152307: ~0.10%</li><li>153693: ~0.10%</li><li>154470: ~0.10%</li><li>155587: ~0.10%</li><li>157602: ~0.10%</li><li>158565: ~0.10%</li><li>159177: ~0.10%</li><li>159224: ~0.10%</li><li>159341: ~0.10%</li><li>159892: ~0.10%</li><li>161881: ~0.10%</li><li>162414: ~0.10%</li><li>163765: ~0.10%</li><li>165888: ~0.10%</li><li>167756: ~0.10%</li><li>168048: ~0.10%</li><li>168894: ~0.10%</li><li>169991: ~0.10%</li><li>170731: ~0.10%</li><li>171705: ~0.10%</li><li>176165: ~0.10%</li><li>176798: ~0.10%</li><li>180259: ~0.10%</li><li>182660: ~0.10%</li><li>183930: ~0.10%</li><li>184045: ~0.10%</li><li>184676: ~0.10%</li><li>185294: ~0.10%</li><li>186475: ~0.10%</li><li>187155: ~0.10%</li><li>189571: ~0.10%</li><li>191383: ~0.10%</li><li>192165: ~0.10%</li><li>194207: ~0.10%</li><li>195056: ~0.10%</li><li>197377: 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~0.10%</li><li>647443: ~0.10%</li><li>648173: ~0.10%</li><li>649204: ~0.10%</li><li>649243: ~0.10%</li><li>650521: ~0.10%</li><li>650979: ~0.10%</li><li>652493: ~0.10%</li><li>654771: ~0.10%</li><li>656535: ~0.10%</li><li>658715: ~0.10%</li><li>659035: ~0.10%</li><li>659593: ~0.10%</li><li>660535: ~0.10%</li><li>662154: ~0.10%</li><li>662784: ~0.10%</li><li>663142: ~0.10%</li><li>664964: ~0.10%</li><li>665230: ~0.10%</li><li>666319: ~0.10%</li><li>666386: ~0.10%</li><li>666561: ~0.10%</li><li>668060: ~0.10%</li><li>668151: ~0.10%</li><li>669382: ~0.10%</li><li>670341: ~0.10%</li><li>673081: ~0.10%</li><li>673634: ~0.10%</li><li>673875: ~0.10%</li><li>673881: ~0.10%</li><li>674082: ~0.10%</li><li>674852: ~0.10%</li><li>675319: ~0.10%</li><li>675492: ~0.10%</li><li>676147: ~0.10%</li><li>676238: ~0.10%</li><li>676318: ~0.10%</li><li>677459: ~0.10%</li><li>677515: ~0.10%</li><li>679216: ~0.10%</li><li>679307: ~0.10%</li><li>680354: ~0.10%</li><li>681098: ~0.10%</li><li>681873: ~0.10%</li><li>683933: ~0.10%</li><li>684800: ~0.10%</li><li>685690: ~0.10%</li><li>685871: ~0.10%</li><li>686886: ~0.10%</li><li>689687: ~0.10%</li><li>689748: ~0.10%</li><li>693501: ~0.10%</li><li>694425: ~0.10%</li><li>694466: ~0.10%</li><li>695798: ~0.10%</li><li>698130: ~0.10%</li><li>700276: ~0.10%</li><li>702137: ~0.10%</li><li>703138: ~0.10%</li><li>704067: ~0.10%</li><li>704460: ~0.10%</li><li>706199: ~0.10%</li><li>706878: ~0.10%</li><li>707280: ~0.10%</li><li>710580: ~0.10%</li><li>710968: ~0.10%</li><li>713539: ~0.10%</li><li>713584: ~0.10%</li><li>714733: ~0.10%</li><li>718172: ~0.10%</li><li>719545: ~0.10%</li><li>720471: ~0.10%</li><li>720690: ~0.10%</li><li>723568: ~0.10%</li><li>724334: ~0.10%</li><li>724700: ~0.10%</li><li>727908: ~0.10%</li><li>730499: ~0.10%</li><li>730711: ~0.10%</li><li>733963: ~0.10%</li><li>734912: ~0.10%</li><li>736431: ~0.10%</li><li>738012: ~0.10%</li><li>742066: ~0.10%</li><li>745799: ~0.10%</li><li>746161: ~0.10%</li><li>748392: ~0.10%</li><li>748838: ~0.10%</li><li>749148: ~0.10%</li><li>751762: ~0.10%</li><li>752092: ~0.10%</li><li>752527: ~0.10%</li><li>753004: ~0.10%</li><li>755386: ~0.10%</li><li>756558: ~0.10%</li><li>756736: ~0.10%</li><li>758706: ~0.10%</li><li>759523: ~0.10%</li><li>760550: ~0.10%</li><li>762688: ~0.10%</li><li>762918: ~0.10%</li><li>765769: ~0.10%</li><li>766354: ~0.10%</li><li>768119: ~0.10%</li><li>769970: ~0.10%</li><li>773106: ~0.10%</li><li>775964: ~0.10%</li><li>776055: ~0.10%</li><li>777088: ~0.10%</li><li>777529: ~0.10%</li><li>779273: ~0.10%</li><li>781066: ~0.10%</li><li>781136: ~0.10%</li><li>782328: ~0.10%</li><li>783231: ~0.10%</li><li>784413: ~0.10%</li><li>785781: ~0.10%</li><li>786250: ~0.10%</li><li>786845: ~0.10%</li><li>788012: ~0.10%</li><li>792788: ~0.10%</li><li>793182: ~0.10%</li><li>794187: ~0.10%</li><li>796097: ~0.10%</li><li>796117: ~0.10%</li><li>797182: ~0.10%</li><li>798112: ~0.10%</li><li>798215: ~0.10%</li><li>802050: ~0.10%</li><li>802669: ~0.10%</li><li>804168: ~0.10%</li><li>804461: ~0.10%</li><li>805743: ~0.10%</li><li>806286: ~0.10%</li><li>808416: ~0.10%</li><li>808455: ~0.10%</li><li>810577: ~0.10%</li><li>811702: ~0.10%</li><li>811843: ~0.10%</li><li>812439: ~0.10%</li><li>815923: ~0.10%</li><li>816475: ~0.10%</li><li>818312: ~0.10%</li><li>818367: ~0.10%</li><li>818521: ~0.10%</li><li>819278: ~0.10%</li><li>819972: ~0.10%</li><li>820890: ~0.10%</li><li>821188: ~0.10%</li><li>821615: ~0.10%</li><li>822376: ~0.10%</li><li>823136: ~0.10%</li><li>823735: ~0.10%</li><li>829476: ~0.10%</li><li>830591: ~0.10%</li><li>832433: ~0.10%</li><li>832597: ~0.10%</li><li>835043: ~0.10%</li><li>835759: ~0.10%</li><li>837731: ~0.10%</li><li>837942: ~0.10%</li><li>839448: ~0.10%</li><li>840417: ~0.10%</li><li>843622: ~0.10%</li><li>844870: ~0.10%</li><li>846084: ~0.10%</li><li>846807: ~0.10%</li><li>847076: ~0.10%</li><li>847535: ~0.10%</li><li>847766: ~0.10%</li><li>847977: ~0.10%</li><li>848075: ~0.10%</li><li>848326: ~0.10%</li><li>852725: ~0.10%</li><li>853465: ~0.10%</li><li>856427: ~0.10%</li><li>857186: ~0.10%</li><li>858543: ~0.10%</li><li>858830: ~0.10%</li><li>861596: ~0.10%</li><li>863804: ~0.10%</li><li>865139: ~0.10%</li><li>866039: ~0.10%</li><li>866406: ~0.10%</li><li>867180: ~0.10%</li><li>868280: ~0.10%</li><li>872156: ~0.10%</li><li>872791: ~0.10%</li><li>872953: ~0.10%</li><li>872959: ~0.10%</li><li>875015: ~0.10%</li><li>876522: ~0.10%</li><li>878407: ~0.10%</li><li>878710: ~0.10%</li><li>878855: ~0.10%</li><li>880495: ~0.10%</li><li>884335: ~0.10%</li><li>884941: ~0.10%</li><li>885893: ~0.10%</li><li>886713: ~0.10%</li><li>887068: ~0.10%</li><li>887115: ~0.10%</li><li>887220: ~0.10%</li><li>887751: ~0.10%</li><li>888027: ~0.10%</li><li>888069: ~0.10%</li><li>888758: ~0.10%</li><li>890152: ~0.10%</li><li>891137: ~0.10%</li><li>891766: ~0.10%</li><li>891890: ~0.10%</li><li>892662: ~0.10%</li><li>892973: ~0.10%</li><li>893360: ~0.10%</li><li>893915: ~0.10%</li><li>893976: ~0.10%</li><li>894324: ~0.10%</li><li>895709: ~0.10%</li><li>897065: ~0.10%</li><li>898387: ~0.10%</li><li>898554: ~0.10%</li><li>899291: ~0.10%</li><li>899604: ~0.10%</li><li>900513: ~0.10%</li><li>900619: ~0.10%</li><li>901170: ~0.10%</li><li>903238: ~0.10%</li><li>904294: ~0.10%</li><li>904520: ~0.10%</li><li>904666: ~0.10%</li><li>904992: ~0.10%</li><li>907212: ~0.10%</li><li>908062: ~0.10%</li><li>908561: ~0.10%</li><li>909741: ~0.10%</li><li>911034: ~0.10%</li><li>911982: ~0.10%</li><li>913716: ~0.10%</li><li>914819: ~0.10%</li><li>915750: ~0.10%</li><li>915766: ~0.10%</li><li>916125: ~0.10%</li><li>916648: ~0.10%</li><li>917285: ~0.10%</li><li>918194: ~0.10%</li><li>920583: ~0.10%</li><li>924616: ~0.10%</li><li>926035: ~0.10%</li><li>927726: ~0.10%</li><li>929821: ~0.10%</li><li>930300: ~0.10%</li><li>930796: ~0.10%</li><li>931617: ~0.10%</li><li>932719: ~0.10%</li><li>933784: ~0.10%</li><li>934378: ~0.10%</li><li>935900: ~0.10%</li><li>936118: ~0.10%</li><li>936336: ~0.10%</li><li>937231: ~0.10%</li><li>938420: ~0.10%</li><li>939184: ~0.10%</li><li>939567: ~0.10%</li><li>941588: ~0.10%</li><li>944093: ~0.10%</li><li>944912: ~0.10%</li><li>945069: ~0.10%</li><li>945659: ~0.10%</li><li>946110: ~0.10%</li><li>950044: ~0.10%</li><li>954101: ~0.10%</li><li>954147: ~0.10%</li><li>958697: ~0.10%</li><li>959530: ~0.10%</li><li>961721: ~0.10%</li><li>963582: ~0.10%</li><li>964471: ~0.10%</li><li>965026: ~0.10%</li><li>966573: ~0.10%</li><li>967330: ~0.10%</li><li>968176: ~0.10%</li><li>968346: ~0.10%</li><li>970649: ~0.10%</li><li>970873: ~0.10%</li><li>971636: ~0.10%</li><li>971664: ~0.10%</li><li>972336: ~0.10%</li><li>973555: ~0.10%</li><li>974207: ~0.10%</li><li>976896: ~0.10%</li><li>981402: ~0.10%</li><li>983723: ~0.10%</li><li>984653: ~0.10%</li><li>985161: ~0.10%</li><li>987048: ~0.10%</li><li>987107: ~0.10%</li><li>987815: ~0.10%</li><li>993209: ~0.10%</li><li>994360: ~0.10%</li><li>994613: ~0.10%</li><li>1004305: ~0.10%</li><li>1004650: ~0.10%</li><li>1004849: ~0.10%</li><li>1005118: ~0.10%</li><li>1005614: ~0.10%</li><li>1005626: ~0.10%</li><li>1006835: ~0.10%</li><li>1009556: ~0.10%</li><li>1011008: ~0.10%</li><li>1014010: ~0.10%</li><li>1014030: ~0.10%</li><li>1016549: ~0.10%</li><li>1017016: ~0.10%</li><li>1017335: ~0.10%</li><li>1018386: ~0.10%</li><li>1020640: ~0.10%</li><li>1021041: ~0.10%</li><li>1021411: ~0.10%</li><li>1022998: ~0.10%</li><li>1025423: ~0.10%</li><li>1025767: ~0.10%</li><li>1026066: ~0.10%</li><li>1026434: ~0.10%</li><li>1027516: ~0.10%</li><li>1027703: ~0.10%</li><li>1028119: ~0.10%</li><li>1028642: ~0.10%</li><li>1031554: ~0.10%</li><li>1033639: ~0.10%</li><li>1033660: ~0.10%</li><li>1034832: ~0.10%</li><li>1035274: ~0.10%</li><li>1037432: ~0.10%</li><li>1037536: ~0.10%</li><li>1037759: ~0.10%</li><li>1039860: ~0.10%</li><li>1041131: ~0.10%</li><li>1041892: ~0.10%</li><li>1043066: ~0.10%</li><li>1044326: ~0.10%</li><li>1044905: ~0.10%</li><li>1047848: ~0.10%</li><li>1048062: ~0.10%</li><li>1048534: ~0.10%</li><li>1049477: ~0.10%</li><li>1049533: ~0.10%</li><li>1050531: ~0.10%</li><li>1050937: ~0.10%</li><li>1051747: ~0.10%</li><li>1052073: ~0.10%</li><li>1052617: ~0.10%</li><li>1054049: ~0.10%</li><li>1055142: ~0.10%</li><li>1056933: ~0.10%</li><li>1057358: ~0.10%</li><li>1057911: ~0.10%</li><li>1061411: ~0.10%</li><li>1062328: ~0.10%</li><li>1062485: ~0.10%</li><li>1062534: ~0.10%</li><li>1062794: ~0.10%</li><li>1063269: ~0.10%</li><li>1063467: ~0.10%</li><li>1064568: ~0.10%</li><li>1064868: ~0.10%</li><li>1065180: ~0.10%</li><li>1065481: ~0.10%</li><li>1067970: ~0.10%</li><li>1068479: ~0.10%</li><li>1070203: ~0.10%</li><li>1070708: ~0.10%</li><li>1071075: ~0.10%</li><li>1072038: ~0.10%</li><li>1072214: ~0.10%</li><li>1072525: ~0.10%</li><li>1073953: ~0.10%</li><li>1074885: ~0.10%</li><li>1075308: ~0.10%</li><li>1078872: ~0.10%</li><li>1079266: ~0.10%</li><li>1079736: ~0.10%</li><li>1080075: ~0.10%</li><li>1081716: ~0.10%</li><li>1137391: ~0.10%</li><li>1138530: ~0.10%</li><li>1139697: ~0.10%</li><li>1139738: ~0.10%</li><li>1140119: ~0.10%</li><li>1140869: ~0.10%</li><li>1141527: ~0.10%</li><li>1144693: ~0.10%</li><li>1145425: ~0.10%</li><li>1149162: ~0.10%</li><li>1149207: ~0.10%</li><li>1149985: ~0.10%</li><li>1150086: ~0.10%</li><li>1150731: ~0.10%</li><li>1151256: ~0.10%</li><li>1151403: ~0.10%</li><li>1152236: ~0.10%</li><li>1153693: ~0.10%</li><li>1154576: ~0.10%</li><li>1155859: ~0.10%</li><li>1156918: ~0.10%</li><li>1158007: ~0.10%</li><li>1158559: ~0.10%</li><li>1158952: ~0.10%</li><li>1159165: ~0.10%</li><li>1161242: ~0.10%</li><li>1163227: ~0.10%</li><li>1164078: ~0.10%</li><li>1165251: ~0.10%</li><li>1166023: ~0.10%</li><li>1166231: ~0.10%</li><li>1167002: ~0.10%</li><li>1168584: ~0.10%</li><li>1169844: ~0.10%</li><li>1170663: ~0.10%</li><li>1171580: ~0.10%</li><li>1172072: ~0.10%</li><li>1173371: ~0.10%</li><li>1173809: ~0.10%</li><li>1174049: ~0.10%</li><li>1175044: ~0.10%</li><li>1175745: ~0.10%</li><li>1176061: ~0.10%</li><li>1176414: ~0.10%</li><li>1177449: ~0.10%</li><li>1178311: ~0.10%</li><li>1179069: ~0.10%</li><li>1179450: ~0.10%</li><li>1181077: ~0.10%</li><li>1183293: ~0.10%</li><li>1184313: ~0.10%</li><li>1185090: ~0.10%</li><li>1185669: ~0.10%</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| <code>237784</code> | <code>[6366584, 4034101, 2325374, 6914618, 6042146, ...]</code> | <code>[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]</code> |
| <code>904294</code> | <code>[448408, 8743975, 49600, 7339401, 2714261, ...]</code> | <code>[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]</code> |
| <code>412214</code> | <code>[1006306, 4454048, 1949661, 4895656, 675880, ...]</code> | <code>[0.9999999994734676, 0.38790621123137803, 0.3747429039573546, 0.2990538871317199, 0.38420403106055895, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### 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
<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`: 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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoClimateFEVER_MaxSim_ndcg@10 | NanoDBPedia_MaxSim_ndcg@10 | NanoFEVER_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNFCorpus_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoQuoraRetrieval_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoArguAna_MaxSim_ndcg@10 | NanoSciFact_MaxSim_ndcg@10 | NanoTouche2020_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0030 | 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 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4471 | 14900 | 0.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4501 | 15000 | 0.0136 | 0.2846 | 0.5781 | 0.8899 | 0.4652 | 0.8629 | 0.6491 | 0.3255 | 0.6303 | 0.9427 | 0.3168 | 0.4224 | 0.7354 | 0.5816 | 0.5911 |
| 0.4531 | 15100 | 0.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4561 | 15200 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4591 | 15300 | 0.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4621 | 15400 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4651 | 15500 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4681 | 15600 | 0.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4711 | 15700 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4741 | 15800 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4771 | 15900 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4801 | 16000 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4831 | 16100 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4861 | 16200 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4891 | 16300 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4921 | 16400 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4951 | 16500 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4981 | 16600 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5011 | 16700 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5041 | 16800 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5071 | 16900 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5101 | 17000 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5131 | 17100 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5161 | 17200 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5191 | 17300 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5221 | 17400 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5251 | 17500 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5281 | 17600 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5311 | 17700 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5341 | 17800 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5372 | 17900 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5402 | 18000 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5432 | 18100 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5462 | 18200 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5492 | 18300 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5522 | 18400 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5552 | 18500 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5582 | 18600 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5612 | 18700 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5642 | 18800 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5672 | 18900 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5702 | 19000 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5732 | 19100 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5762 | 19200 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5792 | 19300 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5822 | 19400 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5852 | 19500 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5882 | 19600 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5912 | 19700 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5942 | 19800 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5972 | 19900 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6002 | 20000 | 0.0124 | 0.2803 | 0.5772 | 0.8907 | 0.4668 | 0.8676 | 0.6476 | 0.3364 | 0.6633 | 0.9129 | 0.3240 | 0.4412 | 0.7460 | 0.5781 | 0.5948 |
| 0.6032 | 20100 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6062 | 20200 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6092 | 20300 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6122 | 20400 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6152 | 20500 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6182 | 20600 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6212 | 20700 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6242 | 20800 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6272 | 20900 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6302 | 21000 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6332 | 21100 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6362 | 21200 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6392 | 21300 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6422 | 21400 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6452 | 21500 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6482 | 21600 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6512 | 21700 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6542 | 21800 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6572 | 21900 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6602 | 22000 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6632 | 22100 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6662 | 22200 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6692 | 22300 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6722 | 22400 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6752 | 22500 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6782 | 22600 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6812 | 22700 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6842 | 22800 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6872 | 22900 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6902 | 23000 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6932 | 23100 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6962 | 23200 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6992 | 23300 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7022 | 23400 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7052 | 23500 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7082 | 23600 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7112 | 23700 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7142 | 23800 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7172 | 23900 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7202 | 24000 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7232 | 24100 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7262 | 24200 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7292 | 24300 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7322 | 24400 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7352 | 24500 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7382 | 24600 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7412 | 24700 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7442 | 24800 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7472 | 24900 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7502 | 25000 | 0.012 | 0.2905 | 0.5880 | 0.9048 | 0.4640 | 0.8575 | 0.6645 | 0.3255 | 0.6545 | 0.9043 | 0.3302 | 0.4645 | 0.7485 | 0.5655 | 0.5971 |
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
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