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static-bert-uncased-gooaq-beir

Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
Generated from Trainer
dataset_size:3012496
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
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:MultipleNegativesRankingLoss
widget:
- source_sentence: how to sign legal documents as power of attorney?
sentences:
- 'After the principal''s name, write “by” and then sign your own name. Under or
after the signature line, indicate your status as POA by including any of the
following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.'
- '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap
Menu (...).'', ''Tap Export to SD card.'']'
- Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking
gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect
nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect
product for both cannabis and chocolate lovers, who appreciate a little twist.
- source_sentence: how to delete vdom in fortigate?
sentences:
- Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully
removed from the configuration.
- 'Both combination birth control pills and progestin-only pills may cause headaches
as a side effect. Additional side effects of birth control pills may include:
breast tenderness. nausea.'
- White cheese tends to show imperfections more readily and as consumers got more
used to yellow-orange cheese, it became an expected option. Today, many cheddars
are yellow. While most cheesemakers use annatto, some use an artificial coloring
agent instead, according to Sachs.
- source_sentence: where are earthquakes most likely to occur on earth?
sentences:
- Zelle in the Bank of the America app is a fast, safe, and easy way to send and
receive money with family and friends who have a bank account in the U.S., all
with no fees. Money moves in minutes directly between accounts that are already
enrolled with Zelle.
- It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft
travels at least 240,000 miles (386,400 kilometers) which is the distance between
Earth and the Moon.
- Most earthquakes occur along the edge of the oceanic and continental plates. The
earth's crust (the outer layer of the planet) is made up of several pieces, called
plates. The plates under the oceans are called oceanic plates and the rest are
continental plates.
- source_sentence: fix iphone is disabled connect to itunes without itunes?
sentences:
- 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.
- How Māui brought fire to the world. One evening, after eating a hearty meal, Māui
lay beside his fire staring into the flames. ... In the middle of the night, while
everyone was sleeping, Māui went from village to village and extinguished all
the fires until not a single fire burned in the world.
- Angry Orchard makes a variety of year-round craft cider styles, including Angry
Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of
culinary apples with dryness and bright acidity of bittersweet apples for a complex,
refreshing taste.
- source_sentence: how to reverse a video on tiktok that's not yours?
sentences:
- '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like
a clock. Open the Effects menu. ... '', ''At the end of the new list that appears,
tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then
see a preview of your new, reversed video appear on the screen.'']'
- Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial
investment range of $157,800 to $438,000. The initial cost of a franchise includes
several fees -- Unlock this franchise to better understand the costs such as training
and territory fees.
- Relative age is the age of a rock layer (or the fossils it contains) compared
to other layers. It can be determined by looking at the position of rock layers.
Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can
be determined by using radiometric dating.
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: 6.483463467240631
energy_consumed: 0.01667977902671103
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: 0.112
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.58
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.148
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10399999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10566666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22233333333333336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30566666666666664
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40399999999999997
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3021857757296797
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35745238095238085
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23166090256020686
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.52
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5133333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.48
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.43800000000000006
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04048260039152364
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10679067052991392
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16517406885695451
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.29331552217012935
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5008496215473859
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6488571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3752676117852694
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08599999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3966666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6466666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7766666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5890710274148659
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.546047619047619
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5325906780111076
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.36
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: 0.64
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.106
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1734126984126984
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.32126984126984126
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3737936507936508
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.47868253968253976
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.384612736899094
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.44405555555555554
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.32183898737919203
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.58
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.74
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.78
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.58
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3133333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21600000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.126
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.29
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.63
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5630232180814766
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.675079365079365
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.48992202928149226
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06000000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.52
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.41343867686046815
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3524603174603175
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3712333972436779
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
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: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.36
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.3440000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.264
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.019665573227317924
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07420738619382097
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.09536630802985016
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.11619353053313819
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3204704228749859
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48633333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12237170785886863
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.36
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.64
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.12
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.45
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.62
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3932776776815765
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33038888888888884
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33440957968177043
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.9
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.98
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: 0.9
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.38666666666666655
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23599999999999993
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13399999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8106666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.922
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9259999999999999
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9424143419536263
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9395238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9189180735930736
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.56
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.21333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16799999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.059666666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13166666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17266666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.26666666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.24548416934230666
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3969603174603174
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1751060490177909
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.12
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.64
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06400000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.48
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.64
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3703136948358056
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2856587301587301
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2982488157827007
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.076
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.425
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.58
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.685
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5455895863246394
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5181349206349206
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5100938735556383
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.6326530612244898
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9591836734693877
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6326530612244898
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.6326530612244897
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.5877551020408164
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.5163265306122449
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04221140303473122
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12126049151597706
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18889590300402684
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3304256352667907
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.563482462405376
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7857142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42991471736271825
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.4040502354788069
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5922448979591837
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6692307692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7692307692307693
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4040502354788069
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2794348508634223
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2233657770800628
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1671020408163265
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22103376474868755
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3635534658597092
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.41878691774496013
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5254577354604563
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.47186257015009897
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5205128205128206
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39319818639334664
name: Cosine Map@100
---
# Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** inf tokens
- **Output Dimensionality:** 1024 tokens
- **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): StaticEmbedding(
(embedding): EmbeddingBag(30522, 1024, mode='mean')
)
)
```
## 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/static-bert-uncased-gooaq-beir-4")
# Run inference
sentences = [
"how to reverse a video on tiktok that's not yours?",
'[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']',
'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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.*
-->
## 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 | 0.2 | 0.52 | 0.42 | 0.36 | 0.58 | 0.2 | 0.38 | 0.2 | 0.9 | 0.28 | 0.12 | 0.46 | 0.6327 |
| cosine_accuracy@3 | 0.42 | 0.7 | 0.68 | 0.48 | 0.74 | 0.5 | 0.54 | 0.36 | 0.98 | 0.44 | 0.4 | 0.5 | 0.9592 |
| cosine_accuracy@5 | 0.58 | 0.82 | 0.68 | 0.54 | 0.78 | 0.52 | 0.68 | 0.48 | 0.98 | 0.56 | 0.48 | 0.6 | 1.0 |
| cosine_accuracy@10 | 0.76 | 0.9 | 0.82 | 0.64 | 0.86 | 0.6 | 0.68 | 0.64 | 1.0 | 0.76 | 0.64 | 0.7 | 1.0 |
| cosine_precision@1 | 0.2 | 0.52 | 0.42 | 0.36 | 0.58 | 0.2 | 0.38 | 0.2 | 0.9 | 0.28 | 0.12 | 0.46 | 0.6327 |
| cosine_precision@3 | 0.1667 | 0.5133 | 0.2333 | 0.22 | 0.3133 | 0.1667 | 0.36 | 0.12 | 0.3867 | 0.2133 | 0.1333 | 0.1733 | 0.6327 |
| cosine_precision@5 | 0.148 | 0.48 | 0.14 | 0.16 | 0.216 | 0.104 | 0.344 | 0.096 | 0.236 | 0.168 | 0.096 | 0.128 | 0.5878 |
| cosine_precision@10 | 0.104 | 0.438 | 0.086 | 0.106 | 0.126 | 0.06 | 0.264 | 0.068 | 0.134 | 0.13 | 0.064 | 0.076 | 0.5163 |
| cosine_recall@1 | 0.1057 | 0.0405 | 0.3967 | 0.1734 | 0.29 | 0.2 | 0.0197 | 0.19 | 0.8107 | 0.0597 | 0.12 | 0.425 | 0.0422 |
| cosine_recall@3 | 0.2223 | 0.1068 | 0.6467 | 0.3213 | 0.47 | 0.5 | 0.0742 | 0.34 | 0.922 | 0.1317 | 0.4 | 0.47 | 0.1213 |
| cosine_recall@5 | 0.3057 | 0.1652 | 0.6467 | 0.3738 | 0.54 | 0.52 | 0.0954 | 0.45 | 0.926 | 0.1727 | 0.48 | 0.58 | 0.1889 |
| cosine_recall@10 | 0.404 | 0.2933 | 0.7767 | 0.4787 | 0.63 | 0.6 | 0.1162 | 0.62 | 0.99 | 0.2667 | 0.64 | 0.685 | 0.3304 |
| **cosine_ndcg@10** | **0.3022** | **0.5008** | **0.5891** | **0.3846** | **0.563** | **0.4134** | **0.3205** | **0.3933** | **0.9424** | **0.2455** | **0.3703** | **0.5456** | **0.5635** |
| cosine_mrr@10 | 0.3575 | 0.6489 | 0.546 | 0.4441 | 0.6751 | 0.3525 | 0.4863 | 0.3304 | 0.9395 | 0.397 | 0.2857 | 0.5181 | 0.7857 |
| cosine_map@100 | 0.2317 | 0.3753 | 0.5326 | 0.3218 | 0.4899 | 0.3712 | 0.1224 | 0.3344 | 0.9189 | 0.1751 | 0.2982 | 0.5101 | 0.4299 |
#### 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 | 0.4041 |
| cosine_accuracy@3 | 0.5922 |
| cosine_accuracy@5 | 0.6692 |
| cosine_accuracy@10 | 0.7692 |
| cosine_precision@1 | 0.4041 |
| cosine_precision@3 | 0.2794 |
| cosine_precision@5 | 0.2234 |
| cosine_precision@10 | 0.1671 |
| cosine_recall@1 | 0.221 |
| cosine_recall@3 | 0.3636 |
| cosine_recall@5 | 0.4188 |
| cosine_recall@10 | 0.5255 |
| **cosine_ndcg@10** | **0.4719** |
| cosine_mrr@10 | 0.5205 |
| cosine_map@100 | 0.3932 |
<!--
## 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: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</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": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
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: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</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": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
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`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 0.2
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `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`: 2048
- `per_device_eval_batch_size`: 2048
- `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`: 0.2
- `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`: 42
- `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
- `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
- `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 | - | - | 0.0726 | 0.3715 | 0.2100 | 0.1058 | 0.3196 | 0.3109 | 0.2221 | 0.1401 | 0.6737 | 0.1618 | 0.1183 | 0.4337 | 0.1331 | 0.2518 |
| 0.0007 | 1 | 35.3437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0682 | 100 | 16.3878 | 2.4139 | 0.2927 | 0.4729 | 0.5725 | 0.3235 | 0.5905 | 0.3674 | 0.2994 | 0.3324 | 0.9123 | 0.2326 | 0.3407 | 0.5618 | 0.5352 | 0.4488 |
| 0.1363 | 200 | 5.94 | 1.8298 | 0.2897 | 0.4880 | 0.5624 | 0.3447 | 0.5683 | 0.4311 | 0.3066 | 0.3502 | 0.9129 | 0.2533 | 0.3335 | 0.5696 | 0.5365 | 0.4575 |
| 0.2045 | 300 | 4.8307 | 1.5955 | 0.2780 | 0.4896 | 0.5746 | 0.3513 | 0.5815 | 0.4040 | 0.3125 | 0.3897 | 0.9190 | 0.2578 | 0.3556 | 0.5461 | 0.5401 | 0.4615 |
| 0.2727 | 400 | 4.33 | 1.4696 | 0.3113 | 0.4909 | 0.5920 | 0.3795 | 0.5836 | 0.3919 | 0.3201 | 0.4023 | 0.9355 | 0.2535 | 0.3419 | 0.5236 | 0.5524 | 0.4676 |
| 0.3408 | 500 | 4.0423 | 1.3887 | 0.3085 | 0.4966 | 0.5986 | 0.3794 | 0.5914 | 0.3914 | 0.3174 | 0.3590 | 0.9309 | 0.2441 | 0.3537 | 0.5311 | 0.5534 | 0.4658 |
| 0.4090 | 600 | 3.8422 | 1.3120 | 0.3034 | 0.5052 | 0.6075 | 0.3680 | 0.5834 | 0.4136 | 0.3122 | 0.3725 | 0.9257 | 0.2477 | 0.3583 | 0.5309 | 0.5646 | 0.4687 |
| 0.4772 | 700 | 3.6795 | 1.2693 | 0.2975 | 0.4988 | 0.5954 | 0.3785 | 0.5811 | 0.4160 | 0.3142 | 0.3908 | 0.9362 | 0.2471 | 0.3479 | 0.5520 | 0.5601 | 0.4704 |
| 0.5453 | 800 | 3.5367 | 1.2285 | 0.3011 | 0.4947 | 0.5829 | 0.3463 | 0.5689 | 0.4369 | 0.3224 | 0.3791 | 0.9310 | 0.2430 | 0.3663 | 0.5577 | 0.5585 | 0.4684 |
| 0.6135 | 900 | 3.4279 | 1.1963 | 0.3059 | 0.5027 | 0.5894 | 0.3674 | 0.5758 | 0.4126 | 0.3186 | 0.4066 | 0.9349 | 0.2456 | 0.3672 | 0.5560 | 0.5624 | 0.4727 |
| 0.6817 | 1000 | 3.3637 | 1.1652 | 0.3056 | 0.5022 | 0.5849 | 0.3702 | 0.5714 | 0.4238 | 0.3161 | 0.4007 | 0.9373 | 0.2430 | 0.3699 | 0.5618 | 0.5657 | 0.4733 |
| 0.7498 | 1100 | 3.2336 | 1.1312 | 0.3006 | 0.5038 | 0.5920 | 0.3884 | 0.5733 | 0.4241 | 0.3247 | 0.3974 | 0.9369 | 0.2431 | 0.3670 | 0.5644 | 0.5608 | 0.4751 |
| 0.8180 | 1200 | 3.1952 | 1.1132 | 0.3044 | 0.4987 | 0.5770 | 0.3630 | 0.5735 | 0.4259 | 0.3279 | 0.3955 | 0.9428 | 0.2416 | 0.3798 | 0.5659 | 0.5641 | 0.4739 |
| 0.8862 | 1300 | 3.1535 | 1.0926 | 0.2983 | 0.4968 | 0.5753 | 0.3812 | 0.5684 | 0.4108 | 0.3203 | 0.3965 | 0.9421 | 0.2428 | 0.3685 | 0.5608 | 0.5628 | 0.4711 |
| 0.9543 | 1400 | 3.0691 | 1.0862 | 0.3109 | 0.5008 | 0.5870 | 0.3761 | 0.5612 | 0.4121 | 0.3204 | 0.3947 | 0.9426 | 0.2414 | 0.3708 | 0.5456 | 0.5588 | 0.4709 |
| 1.0 | 1467 | - | - | 0.3022 | 0.5008 | 0.5891 | 0.3846 | 0.5630 | 0.4134 | 0.3205 | 0.3933 | 0.9424 | 0.2455 | 0.3703 | 0.5456 | 0.5635 | 0.4719 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.017 kWh
- **Carbon Emitted**: 0.006 kg of CO2
- **Hours Used**: 0.112 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: 3.3.0.dev0
- Transformers: 4.45.2
- PyTorch: 2.5.0.dev20240807+cu121
- Accelerate: 1.0.0
- Datasets: 2.20.0
- Tokenizers: 0.20.1-dev.0
## 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}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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