Hugging Face's logo

tomaarsen
/
mpnet-base-gooaq-cmnrl-mrl

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:3012496
loss:MatryoshkaLoss
loss:CachedMultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
mpnet-base-gooaq-cmnrl-mrl / README.md
tomaarsen's picture
tomaarsen HF staff
Add new SentenceTransformer model
60d0fcf verified
preview code
|
raw
history blame contribute delete
57.8 kB
---
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: microsoft/mpnet-base
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: 901.0176370050929
energy_consumed: 2.3180164676412596
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: 5.999
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on GooAQ triplets
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
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: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12833333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23566666666666664
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2523333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3423333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2832168283343785
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3685714285714285
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22816684702715823
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.56
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.88
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.56
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.436
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.37800000000000006
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05411706752798353
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12035295895525228
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15928246254162917
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23697530489351543
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4605652479922868
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6701666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.313461519912651
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.62
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.84
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.62
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27999999999999997
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.172
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.092
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5766666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7866666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8066666666666668
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8666666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7421816204572005
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7256349206349206
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6984857882513162
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.4
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
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: 0.4
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.188
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.24385714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.37612698412698414
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.429515873015873
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5025952380952381
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43956943866243664
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48483333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39610909278538586
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: 0.72
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.78
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31999999999999995
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.204
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.51
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.59
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5463522282651155
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6749126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4777656892588857
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.54
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.82
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5254388867327386
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.43241269841269836
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.44192370495002076
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
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: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3533333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.29600000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.22999999999999995
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.024846889440892198
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.050109275117862714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.06353201637623539
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.08853093525637233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2784279013606366
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48200000000000004
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1099281411687893
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.64
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.44
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.63
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.67
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6103091812374759
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5662380952380953
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5687228298733515
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.92
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: 0.92
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.40666666666666657
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25599999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13399999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7973333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9453333333333334
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9593333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9893333333333334
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9468303023215506
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.948888888888889
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9245031746031745
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21600000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.148
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.22266666666666668
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3046666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.29180682575954126
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4679126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20981154821773768
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
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: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.24
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.82
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5108280876289467
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.413579365079365
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42352200577200577
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.52
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.64
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22666666666666668
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.485
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.61
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.705
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.73
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6181538011380482
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5913333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5833669046006453
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.5102040816326531
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8163265306122449
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8571428571428571
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5102040816326531
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.510204081632653
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.47346938775510194
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.41020408163265304
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03893285013079613
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11588553532033441
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17562928121209787
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2858043118244373
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4588632608031716
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6822238419177193
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.36126308261178003
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.47001569858712716
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6520251177394034
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7182417582417582
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8061224489795917
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.47001569858712716
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2971951857666143
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2259591836734694
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.15663108320251176
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2814682525607806
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4269339553990077
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.48722766408814117
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5643773684668895
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5163495085148867
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5775929206847574
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4413100253102233
name: Cosine Map@100
---
# MPNet base trained on GooAQ triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **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: MPNetModel
(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/mpnet-base-gooaq-cmnrl-mrl")
# 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, 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.*
-->
## 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 | 0.26 | 0.56 | 0.62 | 0.4 | 0.6 | 0.26 | 0.42 | 0.46 | 0.92 | 0.34 | 0.24 | 0.52 | 0.5102 |
| cosine_accuracy@3 | 0.46 | 0.78 | 0.82 | 0.52 | 0.72 | 0.54 | 0.52 | 0.64 | 0.98 | 0.54 | 0.5 | 0.64 | 0.8163 |
| cosine_accuracy@5 | 0.5 | 0.82 | 0.84 | 0.6 | 0.78 | 0.7 | 0.54 | 0.68 | 0.98 | 0.64 | 0.68 | 0.72 | 0.8571 |
| cosine_accuracy@10 | 0.62 | 0.88 | 0.9 | 0.68 | 0.84 | 0.82 | 0.64 | 0.8 | 1.0 | 0.76 | 0.82 | 0.74 | 0.9796 |
| cosine_precision@1 | 0.26 | 0.56 | 0.62 | 0.4 | 0.6 | 0.26 | 0.42 | 0.46 | 0.92 | 0.34 | 0.24 | 0.52 | 0.5102 |
| cosine_precision@3 | 0.1733 | 0.5 | 0.28 | 0.26 | 0.32 | 0.18 | 0.3533 | 0.2267 | 0.4067 | 0.26 | 0.1667 | 0.2267 | 0.5102 |
| cosine_precision@5 | 0.116 | 0.436 | 0.172 | 0.188 | 0.204 | 0.14 | 0.296 | 0.144 | 0.256 | 0.216 | 0.136 | 0.16 | 0.4735 |
| cosine_precision@10 | 0.082 | 0.378 | 0.092 | 0.112 | 0.118 | 0.082 | 0.23 | 0.084 | 0.134 | 0.148 | 0.082 | 0.084 | 0.4102 |
| cosine_recall@1 | 0.1283 | 0.0541 | 0.5767 | 0.2439 | 0.3 | 0.26 | 0.0248 | 0.44 | 0.7973 | 0.07 | 0.24 | 0.485 | 0.0389 |
| cosine_recall@3 | 0.2357 | 0.1204 | 0.7867 | 0.3761 | 0.48 | 0.54 | 0.0501 | 0.63 | 0.9453 | 0.16 | 0.5 | 0.61 | 0.1159 |
| cosine_recall@5 | 0.2523 | 0.1593 | 0.8067 | 0.4295 | 0.51 | 0.7 | 0.0635 | 0.67 | 0.9593 | 0.2227 | 0.68 | 0.705 | 0.1756 |
| cosine_recall@10 | 0.3423 | 0.237 | 0.8667 | 0.5026 | 0.59 | 0.82 | 0.0885 | 0.76 | 0.9893 | 0.3047 | 0.82 | 0.73 | 0.2858 |
| **cosine_ndcg@10** | **0.2832** | **0.4606** | **0.7422** | **0.4396** | **0.5464** | **0.5254** | **0.2784** | **0.6103** | **0.9468** | **0.2918** | **0.5108** | **0.6182** | **0.4589** |
| cosine_mrr@10 | 0.3686 | 0.6702 | 0.7256 | 0.4848 | 0.6749 | 0.4324 | 0.482 | 0.5662 | 0.9489 | 0.4679 | 0.4136 | 0.5913 | 0.6822 |
| cosine_map@100 | 0.2282 | 0.3135 | 0.6985 | 0.3961 | 0.4778 | 0.4419 | 0.1099 | 0.5687 | 0.9245 | 0.2098 | 0.4235 | 0.5834 | 0.3613 |
#### 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 | 0.47 |
| cosine_accuracy@3 | 0.652 |
| cosine_accuracy@5 | 0.7182 |
| cosine_accuracy@10 | 0.8061 |
| cosine_precision@1 | 0.47 |
| cosine_precision@3 | 0.2972 |
| cosine_precision@5 | 0.226 |
| cosine_precision@10 | 0.1566 |
| cosine_recall@1 | 0.2815 |
| cosine_recall@3 | 0.4269 |
| cosine_recall@5 | 0.4872 |
| cosine_recall@10 | 0.5644 |
| **cosine_ndcg@10** | **0.5163** |
| cosine_mrr@10 | 0.5776 |
| cosine_map@100 | 0.4413 |
<!--
## 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`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 8e-05
- `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`: 8e-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`: 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
- `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 | - | - | 0.0419 | 0.1123 | 0.0389 | 0.0309 | 0.0746 | 0.1310 | 0.0311 | 0.0397 | 0.6607 | 0.0638 | 0.2616 | 0.1097 | 0.1098 | 0.1312 |
| 0.0007 | 1 | 41.9671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0682 | 100 | 12.4237 | 1.0176 | 0.3022 | 0.4597 | 0.7934 | 0.4621 | 0.5280 | 0.4849 | 0.2517 | 0.5561 | 0.8988 | 0.3144 | 0.5708 | 0.5755 | 0.4514 | 0.5115 |
| 0.1363 | 200 | 3.0536 | 0.6917 | 0.2883 | 0.4588 | 0.7773 | 0.4272 | 0.5264 | 0.5494 | 0.2538 | 0.5837 | 0.9303 | 0.2945 | 0.5493 | 0.5795 | 0.4547 | 0.5133 |
| 0.2045 | 300 | 2.2724 | 0.5954 | 0.2944 | 0.4606 | 0.7825 | 0.4522 | 0.5247 | 0.5069 | 0.2554 | 0.5636 | 0.9177 | 0.2861 | 0.5560 | 0.5562 | 0.4667 | 0.5095 |
| 0.2727 | 400 | 1.933 | 0.5171 | 0.3027 | 0.4841 | 0.7050 | 0.4406 | 0.4877 | 0.5406 | 0.2768 | 0.6014 | 0.9463 | 0.2989 | 0.5725 | 0.6151 | 0.4680 | 0.5184 |
| 0.3408 | 500 | 1.7806 | 0.4745 | 0.3034 | 0.4857 | 0.7537 | 0.4435 | 0.5661 | 0.5529 | 0.2733 | 0.5878 | 0.9470 | 0.3016 | 0.5377 | 0.6073 | 0.4682 | 0.5252 |
| 0.4090 | 600 | 1.6253 | 0.4392 | 0.3018 | 0.4790 | 0.7502 | 0.4617 | 0.5478 | 0.5411 | 0.2812 | 0.6220 | 0.9443 | 0.2916 | 0.5210 | 0.5900 | 0.4644 | 0.5228 |
| 0.4772 | 700 | 1.5136 | 0.4312 | 0.3175 | 0.4846 | 0.7481 | 0.4168 | 0.5761 | 0.5222 | 0.2825 | 0.6142 | 0.9415 | 0.2888 | 0.5373 | 0.5754 | 0.4675 | 0.5210 |
| 0.5453 | 800 | 1.4454 | 0.4022 | 0.3017 | 0.4756 | 0.7307 | 0.4494 | 0.5484 | 0.5184 | 0.2821 | 0.6182 | 0.9440 | 0.2834 | 0.5191 | 0.6071 | 0.4694 | 0.5191 |
| 0.6135 | 900 | 1.3711 | 0.3886 | 0.2945 | 0.4602 | 0.7463 | 0.4529 | 0.5433 | 0.5457 | 0.2730 | 0.5972 | 0.9449 | 0.2776 | 0.5183 | 0.6018 | 0.4716 | 0.5175 |
| 0.6817 | 1000 | 1.3295 | 0.3688 | 0.2811 | 0.4720 | 0.7275 | 0.4342 | 0.5581 | 0.5418 | 0.2809 | 0.6087 | 0.9421 | 0.2823 | 0.5138 | 0.5729 | 0.4662 | 0.5140 |
| 0.7498 | 1100 | 1.267 | 0.3637 | 0.2815 | 0.4666 | 0.7168 | 0.4346 | 0.5348 | 0.5317 | 0.2789 | 0.6056 | 0.9450 | 0.2775 | 0.5117 | 0.6116 | 0.4583 | 0.5119 |
| 0.8180 | 1200 | 1.2542 | 0.3514 | 0.2882 | 0.4659 | 0.7275 | 0.4308 | 0.5585 | 0.5373 | 0.2788 | 0.5950 | 0.9433 | 0.2767 | 0.5241 | 0.6141 | 0.4655 | 0.5158 |
| 0.8862 | 1300 | 1.2146 | 0.3427 | 0.2932 | 0.4638 | 0.7118 | 0.4453 | 0.5636 | 0.5363 | 0.2788 | 0.6098 | 0.9481 | 0.2825 | 0.5160 | 0.6238 | 0.4619 | 0.5181 |
| 0.9543 | 1400 | 1.1892 | 0.3378 | 0.2809 | 0.4610 | 0.7319 | 0.4353 | 0.5397 | 0.5295 | 0.2828 | 0.6029 | 0.9474 | 0.2931 | 0.5078 | 0.6182 | 0.4602 | 0.5147 |
| 1.0 | 1467 | - | - | 0.2832 | 0.4606 | 0.7422 | 0.4396 | 0.5464 | 0.5254 | 0.2784 | 0.6103 | 0.9468 | 0.2918 | 0.5108 | 0.6182 | 0.4589 | 0.5163 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 2.318 kWh
- **Carbon Emitted**: 0.901 kg of CO2
- **Hours Used**: 5.999 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: 1.1.1
- 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->