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language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
datasets:
- lightonai/ms-marco-en-bge-gemma
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
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: 0.42
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.56
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.76
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.24
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.14666666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.132
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.1
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11499999999999998
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.205
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2733333333333333
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3906666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2950902457523894
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.36876984126984125
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.22445703016815177
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: 0.76
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.92
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.94
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.76
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7199999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.64
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5359999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.103349775455209
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.2069476173044798
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.26630033614450777
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3798346720417632
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6745044425577195
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8420000000000001
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5354371280529658
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: 0.9
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.96
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: 0.9
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3399999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21599999999999994
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10999999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8366666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9233333333333333
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.97
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.98
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9294789232192022
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9366666666666665
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9025750915750915
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: 0.58
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.72
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.31999999999999995
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.244
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.36607936507936506
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.48507142857142854
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5518412698412698
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6031746031746031
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5639041299556308
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6375793650793651
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5136714023190043
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: 0.92
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1.0
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.92
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5533333333333332
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.352
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18199999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.46
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.83
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.88
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.91
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8735671033500391
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9533333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.819732728608772
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: 0.72
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.52
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.24
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15600000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.092
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.52
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.72
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.78
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.92
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7115365744941191
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6468571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6512663906142167
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.42666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.37200000000000005
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.28800000000000003
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.04445987936677032
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.08334318466845993
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.12387064834298472
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.15623137130300419
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3662101077105874
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5659126984126984
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.1629293985515298
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: 0.84
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: 0.52
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2866666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.17999999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.49
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.79
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.82
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.84
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.706413633867191
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6778571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6569910589410588
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.9
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.96
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.9
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.38666666666666655
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.25199999999999995
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7873333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9146666666666667
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.956
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9966666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9423484210846561
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9383333333333332
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9161729437229437
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: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.256
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10166666666666668
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.20266666666666666
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.26266666666666666
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3796666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.37448789415335676
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6007222222222223
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.28182998781809016
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.26
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.66
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.26
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.132
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.26
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.66
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.8
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5176675835157897
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4284920634920634
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.43500479781656254
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: MaxSim_accuracy@1
value: 0.74
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.82
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.88
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.74
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.29333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.19599999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09799999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.715
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.805
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.87
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.87
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8103600696147834
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7906666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.792673895287103
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: MaxSim_accuracy@1
value: 0.7755102040816326
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9591836734693877
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9795918367346939
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7755102040816326
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7210884353741496
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6326530612244898
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5306122448979592
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05246741937655717
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.1459745060885227
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.20856404158297343
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3416638417494836
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6056555459991261
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8646258503401361
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4446312449677973
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.6211930926216641
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7799372056514914
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8245839874411303
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8876923076923078
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6211930926216641
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.38059654631083195
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.28928100470957613
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.19789324960753532
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.37323254661112065
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5286156464076582
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5863520227624412
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6590695760206811
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6439403596365069
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7116781789638933
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5644133152648683
name: Maxsim Map@100
---
# PyLate
This is a [PyLate](https://github.com/lightonai/pylate) model trained on the [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) 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:** [Unknown](https://huggingface.co/unknown) -->
- **Document Length:** 512 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| MaxSim_accuracy@1 | 0.24 | 0.76 | 0.9 | 0.58 | 0.92 | 0.52 | 0.48 | 0.52 | 0.9 | 0.48 | 0.26 | 0.74 | 0.7755 |
| MaxSim_accuracy@3 | 0.42 | 0.92 | 0.96 | 0.68 | 1.0 | 0.72 | 0.62 | 0.84 | 0.96 | 0.68 | 0.56 | 0.82 | 0.9592 |
| MaxSim_accuracy@5 | 0.56 | 0.92 | 1.0 | 0.72 | 1.0 | 0.78 | 0.68 | 0.86 | 0.98 | 0.7 | 0.66 | 0.88 | 0.9796 |
| MaxSim_accuracy@10 | 0.76 | 0.94 | 1.0 | 0.78 | 1.0 | 0.92 | 0.74 | 0.88 | 1.0 | 0.84 | 0.8 | 0.88 | 1.0 |
| MaxSim_precision@1 | 0.24 | 0.76 | 0.9 | 0.58 | 0.92 | 0.52 | 0.48 | 0.52 | 0.9 | 0.48 | 0.26 | 0.74 | 0.7755 |
| MaxSim_precision@3 | 0.1467 | 0.72 | 0.34 | 0.32 | 0.5533 | 0.24 | 0.4267 | 0.2867 | 0.3867 | 0.3267 | 0.1867 | 0.2933 | 0.7211 |
| MaxSim_precision@5 | 0.132 | 0.64 | 0.216 | 0.244 | 0.352 | 0.156 | 0.372 | 0.18 | 0.252 | 0.256 | 0.132 | 0.196 | 0.6327 |
| MaxSim_precision@10 | 0.1 | 0.536 | 0.11 | 0.138 | 0.182 | 0.092 | 0.288 | 0.094 | 0.138 | 0.186 | 0.08 | 0.098 | 0.5306 |
| MaxSim_recall@1 | 0.115 | 0.1033 | 0.8367 | 0.3661 | 0.46 | 0.52 | 0.0445 | 0.49 | 0.7873 | 0.1017 | 0.26 | 0.715 | 0.0525 |
| MaxSim_recall@3 | 0.205 | 0.2069 | 0.9233 | 0.4851 | 0.83 | 0.72 | 0.0833 | 0.79 | 0.9147 | 0.2027 | 0.56 | 0.805 | 0.146 |
| MaxSim_recall@5 | 0.2733 | 0.2663 | 0.97 | 0.5518 | 0.88 | 0.78 | 0.1239 | 0.82 | 0.956 | 0.2627 | 0.66 | 0.87 | 0.2086 |
| MaxSim_recall@10 | 0.3907 | 0.3798 | 0.98 | 0.6032 | 0.91 | 0.92 | 0.1562 | 0.84 | 0.9967 | 0.3797 | 0.8 | 0.87 | 0.3417 |
| **MaxSim_ndcg@10** | **0.2951** | **0.6745** | **0.9295** | **0.5639** | **0.8736** | **0.7115** | **0.3662** | **0.7064** | **0.9423** | **0.3745** | **0.5177** | **0.8104** | **0.6057** |
| MaxSim_mrr@10 | 0.3688 | 0.842 | 0.9367 | 0.6376 | 0.9533 | 0.6469 | 0.5659 | 0.6779 | 0.9383 | 0.6007 | 0.4285 | 0.7907 | 0.8646 |
| MaxSim_map@100 | 0.2245 | 0.5354 | 0.9026 | 0.5137 | 0.8197 | 0.6513 | 0.1629 | 0.657 | 0.9162 | 0.2818 | 0.435 | 0.7927 | 0.4446 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.6212 |
| MaxSim_accuracy@3 | 0.7799 |
| MaxSim_accuracy@5 | 0.8246 |
| MaxSim_accuracy@10 | 0.8877 |
| MaxSim_precision@1 | 0.6212 |
| MaxSim_precision@3 | 0.3806 |
| MaxSim_precision@5 | 0.2893 |
| MaxSim_precision@10 | 0.1979 |
| MaxSim_recall@1 | 0.3732 |
| MaxSim_recall@3 | 0.5286 |
| MaxSim_recall@5 | 0.5864 |
| MaxSim_recall@10 | 0.6591 |
| **MaxSim_ndcg@10** | **0.6439** |
| MaxSim_mrr@10 | 0.7117 |
| MaxSim_map@100 | 0.5644 |
<!--
## 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/lightonai/ms-marco-en-bge-gemma) at [1a1ffe7](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma/tree/1a1ffe7cde403016be12ae532b249965b2293114)
* Size: 640,000 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | int | list | list |
| details | <ul><li>836: ~0.10%</li><li>3582: ~0.10%</li><li>4599: ~0.10%</li><li>4645: ~0.10%</li><li>4853: ~0.10%</li><li>5154: ~0.10%</li><li>7504: ~0.10%</li><li>12283: ~0.10%</li><li>12335: ~0.10%</li><li>12916: ~0.10%</li><li>14049: ~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>28517: ~0.10%</li><li>29213: ~0.10%</li><li>32344: ~0.10%</li><li>34071: ~0.10%</li><li>34604: ~0.10%</li><li>35424: ~0.10%</li><li>35445: ~0.10%</li><li>36148: ~0.10%</li><li>37078: ~0.10%</li><li>37826: ~0.10%</li><li>38185: ~0.10%</li><li>40855: ~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>48629: ~0.10%</li><li>48785: ~0.10%</li><li>49216: ~0.10%</li><li>49636: ~0.10%</li><li>49970: ~0.10%</li><li>52075: ~0.10%</li><li>52725: ~0.10%</li><li>54142: ~0.10%</li><li>54210: ~0.10%</li><li>55032: ~0.10%</li><li>59546: ~0.10%</li><li>60087: ~0.10%</li><li>60862: ~0.10%</li><li>60941: ~0.10%</li><li>61037: ~0.10%</li><li>61762: ~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>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>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>88282: ~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>92070: ~0.10%</li><li>92523: ~0.10%</li><li>92597: ~0.10%</li><li>92753: ~0.10%</li><li>92787: ~0.10%</li><li>96382: ~0.10%</li><li>96455: ~0.10%</li><li>97274: ~0.10%</li><li>97603: ~0.10%</li><li>100904: ~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>115280: ~0.10%</li><li>115551: ~0.10%</li><li>116098: ~0.10%</li><li>117658: ~0.10%</li><li>120255: ~0.10%</li><li>120298: ~0.10%</li><li>121437: ~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>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>138823: ~0.10%</li><li>139039: ~0.10%</li><li>140392: ~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>152675: ~0.10%</li><li>153693: ~0.10%</li><li>154470: ~0.10%</li><li>155587: ~0.10%</li><li>157602: ~0.10%</li><li>157779: ~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>168048: ~0.10%</li><li>168425: ~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>181243: ~0.10%</li><li>182102: ~0.10%</li><li>182660: ~0.10%</li><li>183426: ~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>188198: ~0.10%</li><li>191383: ~0.10%</li><li>192165: ~0.10%</li><li>193507: ~0.10%</li><li>194207: ~0.10%</li><li>195056: ~0.10%</li><li>197377: ~0.10%</li><li>198224: ~0.10%</li><li>198546: ~0.10%</li><li>202122: ~0.10%</li><li>203519: ~0.10%</li><li>206220: ~0.10%</li><li>209739: ~0.10%</li><li>210554: ~0.10%</li><li>212638: ~0.10%</li><li>213096: ~0.10%</li><li>213410: ~0.10%</li><li>214255: ~0.10%</li><li>217541: ~0.10%</li><li>219718: ~0.10%</li><li>220993: ~0.10%</li><li>223241: ~0.10%</li><li>224657: ~0.10%</li><li>227101: ~0.10%</li><li>227497: ~0.10%</li><li>227726: ~0.10%</li><li>228099: ~0.10%</li><li>228451: ~0.10%</li><li>230413: ~0.10%</li><li>231416: ~0.10%</li><li>233312: ~0.10%</li><li>234348: ~0.10%</li><li>235869: ~0.10%</li><li>237784: ~0.10%</li><li>240739: ~0.10%</li><li>246495: ~0.10%</li><li>246821: ~0.10%</li><li>248675: ~0.10%</li><li>249798: ~0.10%</li><li>249962: 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~0.10%</li><li>675492: ~0.10%</li><li>676147: ~0.10%</li><li>676238: ~0.10%</li><li>676318: ~0.10%</li><li>676431: ~0.10%</li><li>677459: ~0.10%</li><li>678468: ~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>684800: ~0.10%</li><li>685613: ~0.10%</li><li>685690: ~0.10%</li><li>686886: ~0.10%</li><li>689687: ~0.10%</li><li>689748: ~0.10%</li><li>694425: ~0.10%</li><li>694466: ~0.10%</li><li>698130: ~0.10%</li><li>702137: ~0.10%</li><li>703138: ~0.10%</li><li>704067: ~0.10%</li><li>704460: ~0.10%</li><li>705420: ~0.10%</li><li>706199: ~0.10%</li><li>706878: ~0.10%</li><li>708333: ~0.10%</li><li>710580: ~0.10%</li><li>710897: ~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>719580: ~0.10%</li><li>720471: ~0.10%</li><li>720690: ~0.10%</li><li>722394: ~0.10%</li><li>723568: ~0.10%</li><li>724334: ~0.10%</li><li>724700: ~0.10%</li><li>727908: ~0.10%</li><li>728088: ~0.10%</li><li>729096: ~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>738173: ~0.10%</li><li>739026: ~0.10%</li><li>739605: ~0.10%</li><li>740181: ~0.10%</li><li>742066: ~0.10%</li><li>742298: ~0.10%</li><li>745799: ~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>753568: ~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>763569: ~0.10%</li><li>763766: ~0.10%</li><li>765769: ~0.10%</li><li>766789: ~0.10%</li><li>768119: ~0.10%</li><li>768537: ~0.10%</li><li>773106: ~0.10%</li><li>775589: ~0.10%</li><li>775964: ~0.10%</li><li>776055: ~0.10%</li><li>777088: ~0.10%</li><li>777529: ~0.10%</li><li>778375: ~0.10%</li><li>781066: ~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>791857: ~0.10%</li><li>792788: ~0.10%</li><li>793182: ~0.10%</li><li>794187: ~0.10%</li><li>794308: ~0.10%</li><li>794318: ~0.10%</li><li>796097: ~0.10%</li><li>796117: ~0.10%</li><li>797182: ~0.10%</li><li>798215: ~0.10%</li><li>802050: ~0.10%</li><li>802669: ~0.10%</li><li>804168: ~0.10%</li><li>804253: ~0.10%</li><li>804461: ~0.10%</li><li>805743: ~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>815923: ~0.10%</li><li>816475: ~0.10%</li><li>818312: ~0.10%</li><li>818521: ~0.10%</li><li>819278: ~0.10%</li><li>820890: ~0.10%</li><li>821615: ~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>833053: ~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>840228: ~0.10%</li><li>840417: ~0.10%</li><li>841851: ~0.10%</li><li>843327: ~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>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>858377: ~0.10%</li><li>858543: ~0.10%</li><li>860426: ~0.10%</li><li>863804: ~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>882732: ~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>887751: ~0.10%</li><li>888027: ~0.10%</li><li>890152: ~0.10%</li><li>891137: ~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>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>902794: ~0.10%</li><li>903238: ~0.10%</li><li>904294: ~0.10%</li><li>904520: ~0.10%</li><li>904992: ~0.10%</li><li>907212: ~0.10%</li><li>908062: ~0.10%</li><li>908561: ~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>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>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>973555: ~0.10%</li><li>973851: ~0.10%</li><li>974207: ~0.10%</li><li>976896: ~0.10%</li><li>981402: ~0.10%</li><li>983723: ~0.10%</li><li>984358: ~0.10%</li><li>984653: ~0.10%</li><li>987107: ~0.10%</li><li>987167: ~0.10%</li><li>994360: ~0.10%</li><li>995049: ~0.10%</li><li>1002688: ~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>1005669: ~0.10%</li><li>1006835: ~0.10%</li><li>1011008: ~0.10%</li><li>1012299: ~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>1025341: ~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>1032300: ~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>1048534: ~0.10%</li><li>1049477: ~0.10%</li><li>1050531: ~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>1065481: ~0.10%</li><li>1065565: ~0.10%</li><li>1067970: ~0.10%</li><li>1068014: ~0.10%</li><li>1070203: ~0.10%</li><li>1070708: ~0.10%</li><li>1072038: ~0.10%</li><li>1072214: ~0.10%</li><li>1074885: ~0.10%</li><li>1075308: ~0.10%</li><li>1078872: ~0.10%</li><li>1078979: ~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>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>1150086: ~0.10%</li><li>1150398: ~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>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>1166023: ~0.10%</li><li>1166231: ~0.10%</li><li>1167002: ~0.10%</li><li>1169844: ~0.10%</li><li>1170663: ~0.10%</li><li>1171580: ~0.10%</li><li>1172072: ~0.10%</li><li>1172083: ~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>1176993: ~0.10%</li><li>1177449: ~0.10%</li><li>1178311: ~0.10%</li><li>1179029: ~0.10%</li><li>1179069: ~0.10%</li><li>1180579: ~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: 16 elements</li></ul> | <ul><li>size: 16 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| <code>685613</code> | <code>[7546874, 1176459, 197677, 2306318, 8541504, ...]</code> | <code>[0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...]</code> |
| <code>237784</code> | <code>[6366584, 4034101, 2325374, 6914618, 6042146, ...]</code> | <code>[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]</code> |
| <code>904294</code> | <code>[448408, 8743975, 49600, 7339401, 2714261, ...]</code> | <code>[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `learning_rate`: 4e-06
- `max_steps`: 20000
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `ddp_find_unused_parameters`: False
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `eval_on_start`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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`: 4e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 20000
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
|