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tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000000
- loss:Contrastive
base_model: answerdotai/ModernBERT-base
datasets:
- bclavie/msmarco-10m-triplets
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on answerdotai/ModernBERT-base
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.24
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.5
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.56
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.24
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.136
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.12
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.23166666666666663
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.26733333333333337
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3456666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.28659074634439036
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.38899206349206344
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.22237332831909778
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: 0.88
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.72
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.608
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.514
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.08698943062762281
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.15785394286086044
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.25560775585402196
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.35140662490283503
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6310474103595507
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8023333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4766459270446468
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: 0.84
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.98
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.84
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.33333333333333326
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7866666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9266666666666667
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9566666666666667
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9633333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8973997659405684
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.905
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8618368368368369
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: 0.46
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.66
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.78
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.46
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.244
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.14
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2685793650793651
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.47251587301587306
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5722857142857143
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6081190476190477
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5290995192750131
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.579
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4613817582717077
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: 0.96
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: 0.49999999999999983
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.32799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.17199999999999996
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.44
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.75
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.82
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8190215640428958
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9195238095238095
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7542864635927516
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: 0.46
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.74
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.76
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.46
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.24666666666666665
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15200000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.46
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.74
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.76
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6683749348060735
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6068333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6153476502857308
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.46
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.56
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.46
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3533333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.29600000000000004
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.23600000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.043124364328590487
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.07617110540472594
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.0888523411805491
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.10700228981811344
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.30567453687850876
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.494
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.13042780558280634
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: 0.38
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: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.38
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: 0.092
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.37
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.62
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.73
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.84
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6117132890493197
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5528888888888889
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5368699134199134
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.9
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.98
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 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: 0.3933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7973333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9386666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9593333333333334
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9933333333333334
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9500644303763519
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.945
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9287460317460318
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.42
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.42
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.172
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.08866666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19066666666666662
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.24666666666666665
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3526666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.34178587115776565
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5458571428571429
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.254228684034207
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.14
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.48
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.66
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.14
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.15999999999999998
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.132
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.14
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.48
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.66
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.82
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.46517990763149614
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3529603174603174
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3567092878593359
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: 0.66
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.76
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: 0.66
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.176
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.625
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.745
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.79
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.83
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7435557624884191
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.720888888888889
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7171289121218996
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: 0.7346938775510204
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9591836734693877
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9591836734693877
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9591836734693877
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7346938775510204
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7006802721088434
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6285714285714286
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5122448979591837
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05035830341896132
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.14458823716252048
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.20896761189253432
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3315966017926261
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5870329612095947
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8401360544217688
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4348641866623347
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5611302982731554
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7460910518053375
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7983987441130298
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8583987441130299
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5611302982731554
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3559497645211931
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2732747252747253
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18709576138147568
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3289783177016312
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.4979842942392806
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5627471864009862
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6356249664717402
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6028108230430728
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6656472178615036
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.519295906598254
name: Maxsim Map@100
---
# PyLate model based on answerdotai/ModernBERT-base
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Document Length:** 512 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| MaxSim_accuracy@1 | 0.24 | 0.72 | 0.84 | 0.46 | 0.88 | 0.46 | 0.46 | 0.38 | 0.9 | 0.42 | 0.14 | 0.66 | 0.7347 |
| MaxSim_accuracy@3 | 0.5 | 0.88 | 0.98 | 0.66 | 0.96 | 0.74 | 0.52 | 0.66 | 0.98 | 0.62 | 0.48 | 0.76 | 0.9592 |
| MaxSim_accuracy@5 | 0.56 | 0.92 | 1.0 | 0.74 | 0.96 | 0.76 | 0.54 | 0.78 | 1.0 | 0.7 | 0.66 | 0.8 | 0.9592 |
| MaxSim_accuracy@10 | 0.72 | 0.92 | 1.0 | 0.78 | 0.98 | 0.86 | 0.56 | 0.9 | 1.0 | 0.82 | 0.82 | 0.84 | 0.9592 |
| MaxSim_precision@1 | 0.24 | 0.72 | 0.84 | 0.46 | 0.88 | 0.46 | 0.46 | 0.38 | 0.9 | 0.42 | 0.14 | 0.66 | 0.7347 |
| MaxSim_precision@3 | 0.18 | 0.6333 | 0.3333 | 0.3267 | 0.5 | 0.2467 | 0.3533 | 0.22 | 0.3933 | 0.3067 | 0.16 | 0.2733 | 0.7007 |
| MaxSim_precision@5 | 0.136 | 0.608 | 0.208 | 0.244 | 0.328 | 0.152 | 0.296 | 0.156 | 0.248 | 0.24 | 0.132 | 0.176 | 0.6286 |
| MaxSim_precision@10 | 0.09 | 0.514 | 0.106 | 0.14 | 0.172 | 0.086 | 0.236 | 0.092 | 0.136 | 0.172 | 0.082 | 0.094 | 0.5122 |
| MaxSim_recall@1 | 0.12 | 0.087 | 0.7867 | 0.2686 | 0.44 | 0.46 | 0.0431 | 0.37 | 0.7973 | 0.0887 | 0.14 | 0.625 | 0.0504 |
| MaxSim_recall@3 | 0.2317 | 0.1579 | 0.9267 | 0.4725 | 0.75 | 0.74 | 0.0762 | 0.62 | 0.9387 | 0.1907 | 0.48 | 0.745 | 0.1446 |
| MaxSim_recall@5 | 0.2673 | 0.2556 | 0.9567 | 0.5723 | 0.82 | 0.76 | 0.0889 | 0.73 | 0.9593 | 0.2467 | 0.66 | 0.79 | 0.209 |
| MaxSim_recall@10 | 0.3457 | 0.3514 | 0.9633 | 0.6081 | 0.86 | 0.86 | 0.107 | 0.84 | 0.9933 | 0.3527 | 0.82 | 0.83 | 0.3316 |
| **MaxSim_ndcg@10** | **0.2866** | **0.631** | **0.8974** | **0.5291** | **0.819** | **0.6684** | **0.3057** | **0.6117** | **0.9501** | **0.3418** | **0.4652** | **0.7436** | **0.587** |
| MaxSim_mrr@10 | 0.389 | 0.8023 | 0.905 | 0.579 | 0.9195 | 0.6068 | 0.494 | 0.5529 | 0.945 | 0.5459 | 0.353 | 0.7209 | 0.8401 |
| MaxSim_map@100 | 0.2224 | 0.4766 | 0.8618 | 0.4614 | 0.7543 | 0.6153 | 0.1304 | 0.5369 | 0.9287 | 0.2542 | 0.3567 | 0.7171 | 0.4349 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.5611 |
| MaxSim_accuracy@3 | 0.7461 |
| MaxSim_accuracy@5 | 0.7984 |
| MaxSim_accuracy@10 | 0.8584 |
| MaxSim_precision@1 | 0.5611 |
| MaxSim_precision@3 | 0.3559 |
| MaxSim_precision@5 | 0.2733 |
| MaxSim_precision@10 | 0.1871 |
| MaxSim_recall@1 | 0.329 |
| MaxSim_recall@3 | 0.498 |
| MaxSim_recall@5 | 0.5627 |
| MaxSim_recall@10 | 0.6356 |
| **MaxSim_ndcg@10** | **0.6028** |
| MaxSim_mrr@10 | 0.6656 |
| MaxSim_map@100 | 0.5193 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### msmarco-10m-triplets
* Dataset: [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2)
* Size: 10,000,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 31.95 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.91 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>the most important factor that influences k+ secretion is __________.</code> | <code>The regulation of K+ distribution between the intracellular and extracellular space is referred to as internal K+ balance. The most important factors regulating this movement under normal conditions are insulin and catecholamines (1).</code> | <code>They are both also important for secretion and flow of bile: 1 Cholecystokinin: The name of this hormone describes its effect on the biliary system-cholecysto = gallbladder and kinin = movement. 2 Secretin: This hormone is secreted in response to acid in the duodenum.</code> |
| <code>how much did the mackinac bridge cost to build</code> | <code>The cost to design the project was $3,500,000 (Steinman Company). The cost to construct the bridge was $70, 268,500. Two primary contractors were hired to build the bridge: American Bridge for superstructure - $44,532,900; and Merritt-Chapman and Scott of New York for the foundations - $25,735,600.</code> | <code>When your child needs a dental tooth bridge, you need to know the average cost so you can factor the price into your budget. Several factors affect the price of a bridge, which can run between $700 to $1,500 per tooth. If you have insurance or your child is covered by Medicaid, part of the cost may be covered.</code> |
| <code>when do concussion symptoms appear</code> | <code>Then you can get advice on what to do next. For milder symptoms, the doctor may recommend rest and ask you to watch your child closely for changes, such as a headache that gets worse. Symptoms of a concussion don't always show up right away, and can develop within 24 to 72 hours after an injury.</code> | <code>Concussion: A traumatic injury to soft tissue, usually the brain, as a result of a violent blow, shaking, or spinning. A brain concussion can cause immediate but temporary impairment of brain functions, such as thinking, vision, equilibrium, and consciousness. After a person has had a concussion, he or she is at increased risk for recurrence. Moreover, after a person has several concussions, less of a blow can cause injury, and the person can require more time to recover.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `learning_rate`: 3e-05
- `max_steps`: 50000
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `ddp_find_unused_parameters`: False
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `eval_on_start`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 50000
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
|