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bert-base-uncased-gooaq

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:3012496
loss:MatryoshkaLoss
loss:CachedMultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: are the sequels better than the prequels?
sentences:
- '[''Automatically.'', ''When connected to car Bluetooth and,'', ''Manually.'']'
- The prequels are also not scared to take risks, making movies which are very different
from the original trilogy. The sequel saga, on the other hand, are technically
better made films, the acting is more consistent, the CGI is better and the writing
is stronger, however it falls down in many other places.
- While both public and private sectors use budgets as a key planning tool, public
bodies balance budgets, while private sector firms use budgets to predict operating
results. The public sector budget matches expenditures on mandated assets and
services with receipts of public money such as taxes and fees.
- source_sentence: are there bbqs at lake leschenaultia?
sentences:
- Vestavia Hills. The hummingbird, or, el zunzún as they are often called in the
Caribbean, have such a nickname because of their quick movements. The ruby-throated
hummingbird, the most commonly seen hummingbird in Alabama, is the inspiration
for this restaurant.
- Common causes of abdominal tenderness Abdominal tenderness is generally a sign
of inflammation or other acute processes in one or more organs. The organs are
located around the tender area. Acute processes mean sudden pressure caused by
something. For example, twisted or blocked organs can cause point tenderness.
- ​Located on 168 hectares of nature reserve, Lake Leschenaultia is the perfect
spot for a family day out in the Perth Hills. The Lake offers canoeing, swimming,
walk and cycle trails, as well as picnic, BBQ and camping facilities. ... There
are picnic tables set amongst lovely Wandoo trees.
- source_sentence: how much folic acid should you take prenatal?
sentences:
- Folic acid is a pregnancy superhero! Taking a prenatal vitamin with the recommended
400 micrograms (mcg) of folic acid before and during pregnancy can help prevent
birth defects of your baby's brain and spinal cord. Take it every day and go ahead
and have a bowl of fortified cereal, too.
- '[''You must be unemployed through no fault of your own, as defined by Virginia
law.'', ''You must have earned at least a minimum amount in wages before you were
unemployed.'', ''You must be able and available to work, and you must be actively
seeking employment.'']'
- Wallpaper is printed in batches of rolls. It is important to have the same batch
number, to ensure colours match exactly. The batch number is usually located on
the wallpaper label close to the pattern number. Remember batch numbers also apply
to white wallpapers, as different batches can be different shades of white.
- source_sentence: what is the difference between minerals and electrolytes?
sentences:
- 'North: Just head north of Junk Junction like so. South: Head below Lucky Landing.
East: You''re basically landing between Lonely Lodge and the Racetrack. West:
The sign is west of Snobby Shores.'
- The fasting glucose tolerance test is the simplest and fastest way to measure
blood glucose and diagnose diabetes. Fasting means that you have had nothing to
eat or drink (except water) for 8 to 12 hours before the test.
- In other words, the term “electrolyte” typically implies ionized minerals dissolved
within water and beverages. Electrolytes are typically minerals, whereas minerals
may or may not be electrolytes.
- source_sentence: how can i download youtube videos with internet download manager?
sentences:
- '[''Go to settings and then click on extensions (top left side in chrome).'',
''Minimise your browser and open the location (folder) where IDM is installed.
... '', ''Find the file “IDMGCExt. ... '', ''Drag this file to your chrome browser
and drop to install the IDM extension.'']'
- Coca-Cola might rot your teeth and load your body with sugar and calories, but
it's actually an effective and safe first line of treatment for some stomach blockages,
researchers say.
- To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
Click on the "Erase iPhone" option and confirm your selection. Wait for a while
as the "Find My iPhone" feature will remotely erase your iOS device. Needless
to say, it will also disable its lock.
datasets:
- sentence-transformers/gooaq
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
co2_eq_emissions:
emissions: 242.52371141034885
energy_consumed: 0.623932244779674
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 1.619
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: bert-base-uncased adapter finetuned on GooAQ pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.56
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13166666666666665
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20833333333333337
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24166666666666664
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.29666666666666663
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25516520961338873
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3378809523809523
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20756281994556017
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.54
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.84
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.54
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4866666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4440000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3899999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.046781664425339056
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11117774881295754
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15829952609979633
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2554819210350403
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4644109757573673
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6797460317460318
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3253011706807197
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.54
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.54
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.184
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09599999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.53
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7766666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8566666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8866666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7348538316509182
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6961904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6788071339639872
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11474603174603175
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22874603174603172
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3166031746031746
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3986031746031745
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2925721974861802
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3385
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2372091627126374
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.6
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.74
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.88
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2866666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.192
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.118
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.43
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.48
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.59
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5291588954628265
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6639365079365079
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.45230644038161627
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.66
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.28
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.66
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.46795689507567784
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4079126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42763462709531985
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.56
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.244
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.184
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.02092621665706462
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.053426190783308986
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.06393651269284006
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.08045448545888809
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23067635403503162
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.39788888888888885
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.09661097314535905
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.62
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.38
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.51
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.71
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5386606354769653
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.490547619047619
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.48961052316839493
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.84
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.94
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.84
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.38666666666666655
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.24799999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7573333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.912
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.946
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9793333333333334
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9157663307482551
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9009999999999999
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8893741502029173
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.184
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.126
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.054000000000000006
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12866666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18966666666666668
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.25866666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.24181947685643387
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3803571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18652061021747493
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.74
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.58
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.74
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.84
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5045313323048141
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3963333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.40074428294573644
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.62
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.64
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.605
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.64
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5380316349319392
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5056666666666666
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5079821472790408
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.4489795918367347
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8979591836734694
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9183673469387755
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4489795918367347
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4965986394557823
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.45714285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.38979591836734706
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03475887574057735
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11109807516506923
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1656210426064535
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2684807614936963
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43233093716838594
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6532555879494653
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33493945959592186
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.4053061224489796
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6213814756671899
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6891051805337519
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7676609105180533
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4053061224489796
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2694819466248038
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20962637362637365
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14567660910518054
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.24693944527453943
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3915472856287718
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4541123273847895
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5280272058403178
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4727642081975526
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5268627619545987
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.40266180779497585
name: Cosine Map@100
---
# bert-base-uncased adapter finetuned on GooAQ pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq")
# Run inference
sentences = [
'how can i download youtube videos with internet download manager?',
"['Go to settings and then click on extensions (top left side in chrome).', 'Minimise your browser and open the location (folder) where IDM is installed. ... ', 'Find the file “IDMGCExt. ... ', 'Drag this file to your chrome browser and drop to install the IDM extension.']",
"Coca-Cola might rot your teeth and load your body with sugar and calories, but it's actually an effective and safe first line of treatment for some stomach blockages, researchers say.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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.*
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.24 | 0.54 | 0.54 | 0.24 | 0.6 | 0.28 | 0.32 | 0.38 | 0.84 | 0.26 | 0.16 | 0.42 | 0.449 |
| cosine_accuracy@3 | 0.42 | 0.8 | 0.82 | 0.4 | 0.68 | 0.48 | 0.48 | 0.54 | 0.94 | 0.46 | 0.58 | 0.58 | 0.898 |
| cosine_accuracy@5 | 0.46 | 0.84 | 0.9 | 0.5 | 0.74 | 0.54 | 0.5 | 0.62 | 0.98 | 0.6 | 0.74 | 0.62 | 0.9184 |
| cosine_accuracy@10 | 0.56 | 0.92 | 0.92 | 0.6 | 0.88 | 0.66 | 0.56 | 0.74 | 1.0 | 0.68 | 0.84 | 0.64 | 0.9796 |
| cosine_precision@1 | 0.24 | 0.54 | 0.54 | 0.24 | 0.6 | 0.28 | 0.32 | 0.38 | 0.84 | 0.26 | 0.16 | 0.42 | 0.449 |
| cosine_precision@3 | 0.16 | 0.4867 | 0.2733 | 0.16 | 0.2867 | 0.16 | 0.3067 | 0.18 | 0.3867 | 0.2067 | 0.1933 | 0.2067 | 0.4966 |
| cosine_precision@5 | 0.108 | 0.444 | 0.184 | 0.14 | 0.192 | 0.108 | 0.244 | 0.128 | 0.248 | 0.184 | 0.148 | 0.14 | 0.4571 |
| cosine_precision@10 | 0.07 | 0.39 | 0.096 | 0.088 | 0.118 | 0.066 | 0.184 | 0.076 | 0.13 | 0.126 | 0.084 | 0.076 | 0.3898 |
| cosine_recall@1 | 0.1317 | 0.0468 | 0.53 | 0.1147 | 0.3 | 0.28 | 0.0209 | 0.38 | 0.7573 | 0.054 | 0.16 | 0.4 | 0.0348 |
| cosine_recall@3 | 0.2083 | 0.1112 | 0.7767 | 0.2287 | 0.43 | 0.48 | 0.0534 | 0.51 | 0.912 | 0.1287 | 0.58 | 0.56 | 0.1111 |
| cosine_recall@5 | 0.2417 | 0.1583 | 0.8567 | 0.3166 | 0.48 | 0.54 | 0.0639 | 0.6 | 0.946 | 0.1897 | 0.74 | 0.605 | 0.1656 |
| cosine_recall@10 | 0.2967 | 0.2555 | 0.8867 | 0.3986 | 0.59 | 0.66 | 0.0805 | 0.71 | 0.9793 | 0.2587 | 0.84 | 0.64 | 0.2685 |
| **cosine_ndcg@10** | **0.2552** | **0.4644** | **0.7349** | **0.2926** | **0.5292** | **0.468** | **0.2307** | **0.5387** | **0.9158** | **0.2418** | **0.5045** | **0.538** | **0.4323** |
| cosine_mrr@10 | 0.3379 | 0.6797 | 0.6962 | 0.3385 | 0.6639 | 0.4079 | 0.3979 | 0.4905 | 0.901 | 0.3804 | 0.3963 | 0.5057 | 0.6533 |
| cosine_map@100 | 0.2076 | 0.3253 | 0.6788 | 0.2372 | 0.4523 | 0.4276 | 0.0966 | 0.4896 | 0.8894 | 0.1865 | 0.4007 | 0.508 | 0.3349 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4053 |
| cosine_accuracy@3 | 0.6214 |
| cosine_accuracy@5 | 0.6891 |
| cosine_accuracy@10 | 0.7677 |
| cosine_precision@1 | 0.4053 |
| cosine_precision@3 | 0.2695 |
| cosine_precision@5 | 0.2096 |
| cosine_precision@10 | 0.1457 |
| cosine_recall@1 | 0.2469 |
| cosine_recall@3 | 0.3915 |
| cosine_recall@5 | 0.4541 |
| cosine_recall@10 | 0.528 |
| **cosine_ndcg@10** | **0.4728** |
| cosine_mrr@10 | 0.5269 |
| cosine_map@100 | 0.4027 |
<!--
## 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
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> |
| <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
| <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> |
| <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> |
| <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `batch_sampler`: no_duplicates
#### 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`: 1024
- `per_device_eval_batch_size`: 1024
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0 | 0 | - | - | 0.1046 | 0.2182 | 0.1573 | 0.0575 | 0.2597 | 0.1602 | 0.0521 | 0.0493 | 0.7310 | 0.1320 | 0.2309 | 0.1240 | 0.0970 | 0.1826 |
| 0.0010 | 1 | 28.4268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0256 | 25 | 24.7252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0512 | 50 | 13.3628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0768 | 75 | 7.843 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1024 | 100 | 5.7393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1279 | 125 | 4.6576 | 2.3368 | 0.2890 | 0.4610 | 0.7408 | 0.2882 | 0.5446 | 0.4091 | 0.2179 | 0.4664 | 0.9079 | 0.2394 | 0.5433 | 0.5003 | 0.4318 | 0.4646 |
| 0.1535 | 150 | 4.0846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1791 | 175 | 3.7129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2047 | 200 | 3.4899 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2303 | 225 | 3.3263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2559 | 250 | 3.2013 | 1.6545 | 0.2622 | 0.4744 | 0.7456 | 0.2934 | 0.5371 | 0.4326 | 0.2290 | 0.5157 | 0.9130 | 0.2577 | 0.5189 | 0.5155 | 0.4302 | 0.4712 |
| 0.2815 | 275 | 2.9109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3071 | 300 | 2.9064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3327 | 325 | 2.8215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3582 | 350 | 2.7893 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3838 | 375 | 2.6663 | 1.4146 | 0.2629 | 0.4657 | 0.7330 | 0.2853 | 0.5299 | 0.4346 | 0.2311 | 0.5216 | 0.9172 | 0.2513 | 0.5133 | 0.5429 | 0.4287 | 0.4706 |
| 0.4094 | 400 | 2.6672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4350 | 425 | 2.5587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 450 | 2.5001 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4862 | 475 | 2.4476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5118 | 500 | 2.4127 | 1.2843 | 0.2565 | 0.4668 | 0.7289 | 0.2838 | 0.5392 | 0.4599 | 0.2284 | 0.5238 | 0.9021 | 0.2416 | 0.4971 | 0.5349 | 0.4320 | 0.4688 |
| 0.5374 | 525 | 2.414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5629 | 550 | 2.3723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5885 | 575 | 2.3418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6141 | 600 | 2.2862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6397 | 625 | 2.207 | 1.2078 | 0.2613 | 0.4542 | 0.7382 | 0.2817 | 0.5230 | 0.4664 | 0.2282 | 0.5266 | 0.9095 | 0.2453 | 0.5127 | 0.5414 | 0.4239 | 0.4702 |
| 0.6653 | 650 | 2.2305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6909 | 675 | 2.2409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7165 | 700 | 2.2001 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7421 | 725 | 2.1923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7677 | 750 | 2.195 | 1.1538 | 0.2549 | 0.4671 | 0.7333 | 0.2804 | 0.5265 | 0.4659 | 0.2321 | 0.5331 | 0.9086 | 0.2429 | 0.5070 | 0.5430 | 0.4369 | 0.4717 |
| 0.7932 | 775 | 2.1826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8188 | 800 | 2.1754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8444 | 825 | 2.1141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8700 | 850 | 2.1572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8956 | 875 | 2.1126 | 1.1256 | 0.2505 | 0.4622 | 0.7293 | 0.2857 | 0.5286 | 0.4823 | 0.2308 | 0.5397 | 0.9158 | 0.2412 | 0.5050 | 0.5365 | 0.4387 | 0.4728 |
| 0.9212 | 900 | 2.0755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9468 | 925 | 2.1032 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9724 | 950 | 2.1211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9980 | 975 | 2.0826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 977 | - | - | 0.2552 | 0.4644 | 0.7349 | 0.2926 | 0.5292 | 0.4680 | 0.2307 | 0.5387 | 0.9158 | 0.2418 | 0.5045 | 0.5380 | 0.4323 | 0.4728 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.624 kWh
- **Carbon Emitted**: 0.243 kg of CO2
- **Hours Used**: 1.619 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.20.3
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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