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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: 0.3
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.46
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.15999999999999998
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12800000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.145
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.20066666666666666
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.25566666666666665
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3723333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.29984094041575976
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.40457936507936504
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.23154243919711487
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.84
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.92
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.84
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6599999999999998
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6000000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.53
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11978017136836354
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19320640931807406
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2474564677729374
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.35362762531754766
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6642857997687286
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8766666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5056362918461486
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.86
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1.0
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.86
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.34666666666666657
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10799999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8066666666666668
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9566666666666667
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9566666666666667
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9733333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9143032727772558
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.92
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8848835412953059
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.72
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.33333333333333326
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.236
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.14
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.29724603174603176
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.49257142857142855
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5465079365079365
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6031746031746033
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5453834796894957
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.604079365079365
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.49074315182112516
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.9
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.96
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.96
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.9
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5266666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.32799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.17799999999999996
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.45
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.79
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.82
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.89
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8430810883372716
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9353571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.778500350140056
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14800000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08999999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.48
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.7
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.74
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.681981684088073
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6141031746031747
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6195014186409419
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.54
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.37333333333333335
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.36
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.29
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.024846700166746567
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.06745637325640307
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.1008052160248601
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.1497664943203363
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.34867256192135143
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5346031746031746
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.13572305233276538
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.54
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.86
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.54
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.17599999999999993
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.51
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.75
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.81
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7009621199364733
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6670238095238094
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6421027387645034
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.9
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.98
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.9
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.38666666666666655
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.24799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.7973333333333333
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9246666666666666
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9426666666666668
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9966666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9436609396356616
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9366666666666665
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9184467532467532
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.44
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.66
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.44
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.31999999999999995
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.236
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.166
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.09366666666666668
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19866666666666666
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.24366666666666664
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3396666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3404490877439103
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5581666666666668
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2561512796776031
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.22
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.64
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.22
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.1733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.128
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.22
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.52
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.64
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.8
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4988624746761941
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.40369047619047616
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.40858139686400563
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: MaxSim_accuracy@1
value: 0.7
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2866666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.184
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09799999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.675
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.785
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.825
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.87
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7836102750432731
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7577777777777777
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7575977078477077
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: MaxSim_accuracy@1
value: 0.7551020408163265
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9795918367346939
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9795918367346939
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9795918367346939
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7551020408163265
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7142857142857143
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6204081632653061
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5061224489795919
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05215472128680775
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.14371450561336085
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.20898774766999936
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3295518520522591
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5852674107635566
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8639455782312924
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.43897324704873364
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.6088540031397175
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.769199372056515
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8061224489795917
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8768916797488226
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6088540031397175
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3682783882783882
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.27695447409733126
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.19339403453689166
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.35936109932573973
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5171242602635334
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5644172334340307
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6490861980665189
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6269508565228465
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6982046049188907
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5437217975940587
name: Maxsim Map@100
---
# PyLate model based on answerdotai/ModernBERT-base
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Document Length:** 512 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:----------|:-------------------|:------------|:------------|:------------|:---------------|
| MaxSim_accuracy@1 | 0.3 | 0.84 | 0.86 | 0.5 | 0.9 | 0.48 | 0.48 | 0.54 | 0.9 | 0.44 | 0.22 | 0.7 | 0.7551 |
| MaxSim_accuracy@3 | 0.46 | 0.92 | 1.0 | 0.68 | 0.96 | 0.7 | 0.54 | 0.8 | 0.98 | 0.66 | 0.52 | 0.8 | 0.9796 |
| MaxSim_accuracy@5 | 0.54 | 0.92 | 1.0 | 0.72 | 0.96 | 0.74 | 0.62 | 0.86 | 0.98 | 0.68 | 0.64 | 0.84 | 0.9796 |
| MaxSim_accuracy@10 | 0.72 | 0.92 | 1.0 | 0.8 | 1.0 | 0.9 | 0.7 | 0.9 | 1.0 | 0.8 | 0.8 | 0.88 | 0.9796 |
| MaxSim_precision@1 | 0.3 | 0.84 | 0.86 | 0.5 | 0.9 | 0.48 | 0.48 | 0.54 | 0.9 | 0.44 | 0.22 | 0.7 | 0.7551 |
| MaxSim_precision@3 | 0.16 | 0.66 | 0.3467 | 0.3333 | 0.5267 | 0.2333 | 0.3733 | 0.2733 | 0.3867 | 0.32 | 0.1733 | 0.2867 | 0.7143 |
| MaxSim_precision@5 | 0.128 | 0.6 | 0.208 | 0.236 | 0.328 | 0.148 | 0.36 | 0.176 | 0.248 | 0.236 | 0.128 | 0.184 | 0.6204 |
| MaxSim_precision@10 | 0.094 | 0.53 | 0.108 | 0.14 | 0.178 | 0.09 | 0.29 | 0.096 | 0.138 | 0.166 | 0.08 | 0.098 | 0.5061 |
| MaxSim_recall@1 | 0.145 | 0.1198 | 0.8067 | 0.2972 | 0.45 | 0.48 | 0.0248 | 0.51 | 0.7973 | 0.0937 | 0.22 | 0.675 | 0.0522 |
| MaxSim_recall@3 | 0.2007 | 0.1932 | 0.9567 | 0.4926 | 0.79 | 0.7 | 0.0675 | 0.75 | 0.9247 | 0.1987 | 0.52 | 0.785 | 0.1437 |
| MaxSim_recall@5 | 0.2557 | 0.2475 | 0.9567 | 0.5465 | 0.82 | 0.74 | 0.1008 | 0.81 | 0.9427 | 0.2437 | 0.64 | 0.825 | 0.209 |
| MaxSim_recall@10 | 0.3723 | 0.3536 | 0.9733 | 0.6032 | 0.89 | 0.9 | 0.1498 | 0.86 | 0.9967 | 0.3397 | 0.8 | 0.87 | 0.3296 |
| **MaxSim_ndcg@10** | **0.2998** | **0.6643** | **0.9143** | **0.5454** | **0.8431** | **0.682** | **0.3487** | **0.701** | **0.9437** | **0.3404** | **0.4989** | **0.7836** | **0.5853** |
| MaxSim_mrr@10 | 0.4046 | 0.8767 | 0.92 | 0.6041 | 0.9354 | 0.6141 | 0.5346 | 0.667 | 0.9367 | 0.5582 | 0.4037 | 0.7578 | 0.8639 |
| MaxSim_map@100 | 0.2315 | 0.5056 | 0.8849 | 0.4907 | 0.7785 | 0.6195 | 0.1357 | 0.6421 | 0.9184 | 0.2562 | 0.4086 | 0.7576 | 0.439 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:----------|
| MaxSim_accuracy@1 | 0.6089 |
| MaxSim_accuracy@3 | 0.7692 |
| MaxSim_accuracy@5 | 0.8061 |
| MaxSim_accuracy@10 | 0.8769 |
| MaxSim_precision@1 | 0.6089 |
| MaxSim_precision@3 | 0.3683 |
| MaxSim_precision@5 | 0.277 |
| MaxSim_precision@10 | 0.1934 |
| MaxSim_recall@1 | 0.3594 |
| MaxSim_recall@3 | 0.5171 |
| MaxSim_recall@5 | 0.5644 |
| MaxSim_recall@10 | 0.6491 |
| **MaxSim_ndcg@10** | **0.627** |
| MaxSim_mrr@10 | 0.6982 |
| MaxSim_map@100 | 0.5437 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### msmarco-10m-triplets
* Dataset: [msmarco-10m-triplets](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2)
* Size: 10,000,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 31.95 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.91 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>the most important factor that influences k+ secretion is __________.</code> | <code>The regulation of K+ distribution between the intracellular and extracellular space is referred to as internal K+ balance. The most important factors regulating this movement under normal conditions are insulin and catecholamines (1).</code> | <code>They are both also important for secretion and flow of bile: 1 Cholecystokinin: The name of this hormone describes its effect on the biliary system-cholecysto = gallbladder and kinin = movement. 2 Secretin: This hormone is secreted in response to acid in the duodenum.</code> |
| <code>how much did the mackinac bridge cost to build</code> | <code>The cost to design the project was $3,500,000 (Steinman Company). The cost to construct the bridge was $70, 268,500. Two primary contractors were hired to build the bridge: American Bridge for superstructure - $44,532,900; and Merritt-Chapman and Scott of New York for the foundations - $25,735,600.</code> | <code>When your child needs a dental tooth bridge, you need to know the average cost so you can factor the price into your budget. Several factors affect the price of a bridge, which can run between $700 to $1,500 per tooth. If you have insurance or your child is covered by Medicaid, part of the cost may be covered.</code> |
| <code>when do concussion symptoms appear</code> | <code>Then you can get advice on what to do next. For milder symptoms, the doctor may recommend rest and ask you to watch your child closely for changes, such as a headache that gets worse. Symptoms of a concussion don't always show up right away, and can develop within 24 to 72 hours after an injury.</code> | <code>Concussion: A traumatic injury to soft tissue, usually the brain, as a result of a violent blow, shaking, or spinning. A brain concussion can cause immediate but temporary impairment of brain functions, such as thinking, vision, equilibrium, and consciousness. After a person has had a concussion, he or she is at increased risk for recurrence. Moreover, after a person has several concussions, less of a blow can cause injury, and the person can require more time to recover.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `learning_rate`: 3e-05
- `max_steps`: 50000
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `ddp_find_unused_parameters`: False
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `eval_on_start`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 50000
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
|