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stsb-bert-tiny-lora

Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
Generated from Trainer
dataset_size:3012496
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:MultipleNegativesRankingLoss
base_model: sentence-transformers-testing/stsb-bert-tiny-safetensors
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: 9.679189270737199
energy_consumed: 0.024901310697493708
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: 0.15
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: stsb-bert-tiny adapter finetuned on GooAQ pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.14
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.26
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05600000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.056666666666666664
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08666666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.11166666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.17833333333333332
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1412311142763055
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.19938095238095235
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.11363345611144926
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.62
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.34
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.344
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.29
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.02634308391586433
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.06038926804951766
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.10265977040056268
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.19610280190566398
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.34151812101104584
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5504126984126985
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21133731615809154
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.12
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.22
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.36
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.044000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.036000000000000004
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.18
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.22
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.34
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21218661613500586
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.17491269841269838
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18857101300669993
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.28
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.032
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.044000000000000004
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.06199999999999999
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.12488888888888887
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.15574603174603174
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10395695406287388
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10821428571428571
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08041090092126037
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.36
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: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36
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: 0.07800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.31
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.35
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.39
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3504958855767756
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4476349206349205
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.29308037158200173
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.36
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.064
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.036000000000000004
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.36
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21417075898440763
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16666666666666663
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19159156983842277
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44
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: 0.07999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.00377949106046741
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.007274949456892388
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.012714784638321257
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.019303285579015287
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.09870502263453415
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2538809523809524
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.018928657854150332
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.42
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.042
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.17
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.19
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2051878697694875
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1506904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16101738947158584
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.22399999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.624
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7719999999999999
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.866
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8993333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7992844609162323
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7798333333333335
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7635205205527187
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.12
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.036000000000000004
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.09466666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.13466666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1348403477257659
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24209523809523809
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.10255365352032365
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2375425714519515
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1856666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1985205474177431
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.32
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.064
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.034
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.28
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19370675821369307
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16466666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1653693334513147
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.20408163265306123
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5102040816326531
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7551020408163265
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8775510204081632
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.20408163265306123
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25170068027210885
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25306122448979596
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.24489795918367346
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.014397370082893721
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.04876234248655414
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.0792610922160282
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.14648888406884147
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2485959675297849
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4082118561710398
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16376385142142616
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.20646781789638935
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.33924646781789636
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.41039246467817886
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5121193092621665
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.20646781789638935
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1419256933542648
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11762009419152278
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08822291993720567
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10809127782506864
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.19128922256356135
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2362967591905488
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.30153648743329886
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25241711140675877
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2947898009020458
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2040229677928606
name: Cosine Map@100
---
# stsb-bert-tiny adapter finetuned on GooAQ pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
This model was trained using [train_script.py](train_script.py).
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) <!-- at revision f3cb857cba53019a20df283396bcca179cf051a4 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 128 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': 128, '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("sentence-transformers-testing/stsb-bert-tiny-lora")
# 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, 128]
# 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>
-->
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### 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 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 |
| cosine_accuracy@3 | 0.22 | 0.62 | 0.18 | 0.1 | 0.52 | 0.26 | 0.26 | 0.18 | 0.82 | 0.26 | 0.26 | 0.22 | 0.5102 |
| cosine_accuracy@5 | 0.26 | 0.72 | 0.22 | 0.2 | 0.54 | 0.32 | 0.3 | 0.2 | 0.88 | 0.32 | 0.32 | 0.3 | 0.7551 |
| cosine_accuracy@10 | 0.38 | 0.86 | 0.36 | 0.28 | 0.62 | 0.36 | 0.44 | 0.42 | 0.94 | 0.4 | 0.4 | 0.32 | 0.8776 |
| cosine_precision@1 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 |
| cosine_precision@3 | 0.08 | 0.34 | 0.06 | 0.04 | 0.2067 | 0.0867 | 0.12 | 0.06 | 0.32 | 0.12 | 0.0867 | 0.0733 | 0.2517 |
| cosine_precision@5 | 0.056 | 0.344 | 0.044 | 0.048 | 0.14 | 0.064 | 0.096 | 0.04 | 0.224 | 0.092 | 0.064 | 0.064 | 0.2531 |
| cosine_precision@10 | 0.05 | 0.29 | 0.036 | 0.032 | 0.078 | 0.036 | 0.08 | 0.042 | 0.118 | 0.066 | 0.04 | 0.034 | 0.2449 |
| cosine_recall@1 | 0.0567 | 0.0263 | 0.12 | 0.044 | 0.18 | 0.06 | 0.0038 | 0.08 | 0.624 | 0.036 | 0.08 | 0.08 | 0.0144 |
| cosine_recall@3 | 0.0867 | 0.0604 | 0.18 | 0.062 | 0.31 | 0.26 | 0.0073 | 0.17 | 0.772 | 0.0747 | 0.26 | 0.195 | 0.0488 |
| cosine_recall@5 | 0.1117 | 0.1027 | 0.22 | 0.1249 | 0.35 | 0.32 | 0.0127 | 0.19 | 0.866 | 0.0947 | 0.32 | 0.28 | 0.0793 |
| cosine_recall@10 | 0.1783 | 0.1961 | 0.34 | 0.1557 | 0.39 | 0.36 | 0.0193 | 0.4 | 0.8993 | 0.1347 | 0.4 | 0.3 | 0.1465 |
| **cosine_ndcg@10** | **0.1412** | **0.3415** | **0.2122** | **0.104** | **0.3505** | **0.2142** | **0.0987** | **0.2052** | **0.7993** | **0.1348** | **0.2375** | **0.1937** | **0.2486** |
| cosine_mrr@10 | 0.1994 | 0.5504 | 0.1749 | 0.1082 | 0.4476 | 0.1667 | 0.2539 | 0.1507 | 0.7798 | 0.2421 | 0.1857 | 0.1647 | 0.4082 |
| cosine_map@100 | 0.1136 | 0.2113 | 0.1886 | 0.0804 | 0.2931 | 0.1916 | 0.0189 | 0.161 | 0.7635 | 0.1026 | 0.1985 | 0.1654 | 0.1638 |
#### 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 | 0.2065 |
| cosine_accuracy@3 | 0.3392 |
| cosine_accuracy@5 | 0.4104 |
| cosine_accuracy@10 | 0.5121 |
| cosine_precision@1 | 0.2065 |
| cosine_precision@3 | 0.1419 |
| cosine_precision@5 | 0.1176 |
| cosine_precision@10 | 0.0882 |
| cosine_recall@1 | 0.1081 |
| cosine_recall@3 | 0.1913 |
| cosine_recall@5 | 0.2363 |
| cosine_recall@10 | 0.3015 |
| **cosine_ndcg@10** | **0.2524** |
| cosine_mrr@10 | 0.2948 |
| cosine_map@100 | 0.204 |
<!--
## 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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
- `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`: 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 | - | - | 0.1174 | 0.3053 | 0.1405 | 0.0440 | 0.2821 | 0.2297 | 0.0773 | 0.1708 | 0.7830 | 0.1181 | 0.2017 | 0.1447 | 0.1642 | 0.2138 |
| 0.0010 | 1 | 3.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0256 | 25 | 3.6146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0512 | 50 | 3.6074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0768 | 75 | 3.5997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1024 | 100 | 3.5737 | 2.0205 | 0.1178 | 0.3061 | 0.1477 | 0.0461 | 0.2837 | 0.2291 | 0.0804 | 0.1713 | 0.7791 | 0.1205 | 0.2049 | 0.1534 | 0.1731 | 0.2164 |
| 0.1279 | 125 | 3.5644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1535 | 150 | 3.4792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1791 | 175 | 3.4743 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2047 | 200 | 3.4169 | 1.9114 | 0.1336 | 0.3084 | 0.1446 | 0.0604 | 0.2965 | 0.2350 | 0.0847 | 0.1650 | 0.7806 | 0.1270 | 0.2141 | 0.1633 | 0.1835 | 0.2228 |
| 0.2303 | 225 | 3.3535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2559 | 250 | 3.3336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2815 | 275 | 3.3038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3071 | 300 | 3.2576 | 1.8114 | 0.1359 | 0.3260 | 0.1733 | 0.0752 | 0.3167 | 0.2323 | 0.0851 | 0.1753 | 0.7843 | 0.1266 | 0.2218 | 0.1752 | 0.2012 | 0.2330 |
| 0.3327 | 325 | 3.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3582 | 350 | 3.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3838 | 375 | 3.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4094 | 400 | 3.1412 | 1.7379 | 0.1389 | 0.3298 | 0.1930 | 0.0934 | 0.3261 | 0.2310 | 0.0852 | 0.1760 | 0.7850 | 0.1349 | 0.2235 | 0.1863 | 0.2118 | 0.2396 |
| 0.4350 | 425 | 3.0782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 450 | 3.0948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4862 | 475 | 3.0696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5118 | 500 | 3.0641 | 1.6850 | 0.1373 | 0.3307 | 0.1945 | 0.0937 | 0.3301 | 0.2365 | 0.0931 | 0.1950 | 0.7933 | 0.1359 | 0.2231 | 0.1885 | 0.2289 | 0.2447 |
| 0.5374 | 525 | 3.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5629 | 550 | 2.9927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5885 | 575 | 2.9796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6141 | 600 | 2.9624 | 1.6475 | 0.1397 | 0.3321 | 0.2058 | 0.0999 | 0.3422 | 0.2276 | 0.1014 | 0.1901 | 0.7971 | 0.1393 | 0.2258 | 0.1918 | 0.2342 | 0.2482 |
| 0.6397 | 625 | 2.9508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6653 | 650 | 2.958 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6909 | 675 | 2.9428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7165 | 700 | 2.9589 | 1.6209 | 0.1425 | 0.3344 | 0.2061 | 0.1050 | 0.3427 | 0.2295 | 0.1001 | 0.1868 | 0.7955 | 0.1342 | 0.2298 | 0.1922 | 0.2343 | 0.2487 |
| 0.7421 | 725 | 2.9152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7677 | 750 | 2.9056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7932 | 775 | 2.9111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8188 | 800 | 2.9107 | 1.6037 | 0.1415 | 0.3401 | 0.2064 | 0.1053 | 0.3523 | 0.2153 | 0.1001 | 0.1934 | 0.7976 | 0.1340 | 0.2302 | 0.1946 | 0.2461 | 0.2505 |
| 0.8444 | 825 | 2.8675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8700 | 850 | 2.9175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8956 | 875 | 2.8592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9212 | 900 | 2.86 | 1.5941 | 0.1411 | 0.3415 | 0.2180 | 0.1048 | 0.3506 | 0.2210 | 0.0987 | 0.2052 | 0.7988 | 0.1349 | 0.2302 | 0.1946 | 0.2464 | 0.2528 |
| 0.9468 | 925 | 2.8603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9724 | 950 | 2.8909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9980 | 975 | 2.8819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 977 | - | - | 0.1412 | 0.3415 | 0.2122 | 0.1040 | 0.3505 | 0.2142 | 0.0987 | 0.2052 | 0.7993 | 0.1348 | 0.2375 | 0.1937 | 0.2486 | 0.2524 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.025 kWh
- **Carbon Emitted**: 0.010 kg of CO2
- **Hours Used**: 0.15 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: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.0
- 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",
}
```
#### 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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