---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
base_model: NeuML/bert-hash-femto
datasets:
- lightonai/ms-marco-en-bge-gemma
pipeline_tag: sentence-similarity
library_name: PyLate
license: apache-2.0
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: ColBERT MUVERA Femto
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.14
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.32
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.36
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.52
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.14
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.11333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.07600000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.05600000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.085
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.165
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.19166666666666668
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.25233333333333335
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.19115874409066272
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.2408333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.1462389973257929
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.7
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.82
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.82
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.548
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.456
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.0728506527388449
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.13076941366456654
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.17827350013263704
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.2781635119304686
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5510945084552747
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7555555555555555
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.39128533545834626
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: 0.62
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.76
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.62
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.184
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.5766666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.7366666666666667
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.83
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.85
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7249306483092258
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6976666666666665
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.679664802101873
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: 0.28
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.34
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.44
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.48
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.13333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.10800000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.062
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.13555555555555557
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.19755555555555557
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2666349206349206
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.2994920634920635
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2502784944505909
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.33252380952380944
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.20907273372726215
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: 0.76
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.84
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.76
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.36666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.252
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.136
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.38
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.55
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.63
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.68
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6514325561331983
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8098333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5738665952275315
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: 0.32
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.48
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.6
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.16
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12000000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.32
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.48
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.6
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.7
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4946222844793249
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.43052380952380953
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4408050908765128
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: 0.32
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.42
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.52
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.62
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2866666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.26799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.21000000000000005
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.01921769353070746
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.03782391241260524
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.05411010345369293
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.09349869834347448
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2481257474345093
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3995793650793651
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.08737709081330662
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: 0.28
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.58
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.1733333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12000000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07600000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.26
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.49
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.56
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.7
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4828411530427104
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4289603174603174
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.41150699780701017
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: 0.74
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.84
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.88
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.74
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21199999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.11599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.674
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.784
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.8413333333333333
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.8626666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8016479127266055
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7995238095238095
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7733654571274
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: 0.3
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.44
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.52
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.62
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14400000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.092
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.061000000000000006
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.11100000000000002
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.14700000000000002
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.18799999999999997
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.198564235862039
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3978253968253968
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.13670583023266375
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: 0.24
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.28
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.36
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.14
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.07999999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.05600000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.036000000000000004
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.14
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.24
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.28
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.36
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2444065884095295
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.20804761904761904
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.21989999402599436
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: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.5
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.6
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.62
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07400000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.325
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.49
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.59
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.62
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4856083424090788
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.44449999999999995
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.44726079800650204
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: 0.6530612244897959
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: 0.6530612244897959
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6054421768707483
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.5673469387755103
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.45918367346938777
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.04308959031413618
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.11831839494199368
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.1804772716223025
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.2842813442856462
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5191595399345652
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8064625850340136
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.32574548665687825
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.4317739403453689
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.5753218210361067
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.6399686028257457
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7061538461538461
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.4317739403453689
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26554683411826263
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2150266875981162
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.14901412872841444
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2378753968312239
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.34854876486472214
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.41149967660335024
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.4744950475424349
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.44952851967210117
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5193719693005406
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3725227084143902
name: Maxsim Map@100
---
# ColBERT MUVERA Femto
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [neuml/bert-hash-femto](https://huggingface.co/neuml/bert-hash-femto) on the [msmarco-en-bge-gemma unnormalized split](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) dataset. It maps sentences & paragraphs to sequences of 50-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
This model is trained with un-normalized scores, making it compatible with [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504).
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
_Note: txtai 9.0+ is required for late interaction model support_
```python
import txtai
embeddings = txtai.Embeddings(
sparse="neuml/colbert-muvera-femto",
content=True
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
Late interaction models excel as reranker pipelines.
```python
from txtai.pipeline import Reranker, Similarity
similarity = Similarity(path="neuml/colbert-muvera-femto", lateencode=True)
ranker = Reranker(embeddings, similarity)
ranker("query to run")
```
## Usage (PyLate)
Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).
```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="neuml/colbert-muvera-femto",
)
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,
)
```
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertHashModel
(1): Dense({'in_features': 50, 'out_features': 50, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Evaluation
### BEIR Subset
The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py).
Scores reported are `ndcg@10` and grouped into the following three categories.
#### FULL multi-vector maxsim
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3165 | 0.1497 | 0.6456 | 0.3706 |
| [**ColBERT MUVERA Femto**](https://huggingface.co/neuml/colbert-muvera-femto) | **0.2M** | **0.2513** | **0.0870** | **0.4710** | **0.2698** |
| [ColBERT MUVERA Pico](https://huggingface.co/neuml/colbert-muvera-pico) | 0.4M | 0.3005 | 0.1117 | 0.6452 | 0.3525 |
| [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.3180 | 0.1262 | 0.6576 | 0.3673 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.3235 | 0.1244 | 0.6676 | 0.3718 |
#### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3025 | 0.1538 | 0.6278 | 0.3614 |
| [**ColBERT MUVERA Femto**](https://huggingface.co/neuml/colbert-muvera-femto) | **0.2M** | **0.2316** | **0.0858** | **0.4641** | **0.2605** |
| [ColBERT MUVERA Pico](https://huggingface.co/neuml/colbert-muvera-pico) | 0.4M | 0.2821 | 0.1004 | 0.6090 | 0.3305 |
| [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2996 | 0.1201 | 0.6249 | 0.3482 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.3095 | 0.1228 | 0.6464 | 0.3596 |
#### MUVERA encoding only
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.2356 | 0.1229 | 0.5002 | 0.2862 |
| [**ColBERT MUVERA Femto**](https://huggingface.co/neuml/colbert-muvera-femto) | **0.2M** | **0.1851** | **0.0411** | **0.3518** | **0.1927** |
| [ColBERT MUVERA Pico](https://huggingface.co/neuml/colbert-muvera-pico) | 0.4M | 0.1926 | 0.0564 | 0.4424 | 0.2305 |
| [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2355 | 0.0807 | 0.4904 | 0.2689 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.2348 | 0.0882 | 0.4875 | 0.2702 |
_Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts._
As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this [GitHub Issue](https://github.com/lightonai/pylate/issues/142) for more.
**This model is only 250K parameters with a file size of 950K. Keeping that in mind, it's surprising how decent the scores are!**
### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.4318 |
| MaxSim_accuracy@3 | 0.5753 |
| MaxSim_accuracy@5 | 0.64 |
| MaxSim_accuracy@10 | 0.7062 |
| MaxSim_precision@1 | 0.4318 |
| MaxSim_precision@3 | 0.2655 |
| MaxSim_precision@5 | 0.215 |
| MaxSim_precision@10 | 0.149 |
| MaxSim_recall@1 | 0.2379 |
| MaxSim_recall@3 | 0.3485 |
| MaxSim_recall@5 | 0.4115 |
| MaxSim_recall@10 | 0.4745 |
| **MaxSim_ndcg@10** | **0.4495** |
| MaxSim_mrr@10 | 0.5194 |
| MaxSim_map@100 | 0.3725 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `learning_rate`: 0.0003
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `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`: 0.0003
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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
- `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`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `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`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional