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mpnet-base-nq-cgist-triplet-mask-initial

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:50000
loss:CachedGISTEmbedLoss
Eval Results
Model card Files
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Community
mpnet-base-nq-cgist-triplet-mask-initial / README.md
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 ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:50000
- loss:CachedGISTEmbedLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: what does the accounts receivable turnover measure?
sentences:
- The accounts receivable turnover ratio is an accounting measure used to quantify
a company's effectiveness in collecting its receivables or money owed by clients.
The ratio shows how well a company uses and manages the credit it extends to customers
and how quickly that short-term debt is collected or is paid.
- Capital budgeting, and investment appraisal, is the planning process used to determine
whether an organization's long term investments such as new machinery, replacement
of machinery, new plants, new products, and research development projects are
worth the funding of cash through the firm's capitalization structure ( ...
- The accounts receivable turnover ratio is an accounting measure used to quantify
a company's effectiveness in collecting its receivables or money owed by clients.
The ratio shows how well a company uses and manages the credit it extends to customers
and how quickly that short-term debt is collected or is paid.
- source_sentence: does gabapentin cause liver problems?
sentences:
- Gabapentin has no appreciable liver metabolism, yet, suspected cases of gabapentin-induced
hepatotoxicity have been reported. Per literature review, two cases of possible
gabapentin-induced liver injury have been reported.
- Strongholds are a type of story mission which only unlocks after enough progression
through the game. There are three Stronghold's during the first section of progression
through The Division 2. You'll need to complete the first two and have reached
level 30 before being able to unlock the final Stronghold.
- The most-common side effects attributed to Gabapentin include mild sedation, ataxia,
and occasional diarrhea. Sedation can be minimized by tapering from a smaller
starting dose to the desired dose. When treating seizures, it is ideal to wean
off the drug to reduce the risk of withdrawal seizures.
- source_sentence: how long should you wait to give blood after eating?
sentences:
- Until the bleeding has stopped it is natural to taste blood or to see traces of
blood in your saliva. You may stop using gauze after the flow stops – usually
around 8 hours after surgery.
- Before donation The first and most important rule—never donate blood on an empty
stomach. “Eat a wholesome meal about 2-3 hours before donating to keep your blood
sugar stable," says Dr Chaturvedi. The timing of the meal is important too. You
need to allow the food to be digested properly before the blood is drawn.
- While grid computing involves virtualizing computing resources to store massive
amounts of data, whereas cloud computing is where an application doesn't access
resources directly, rather it accesses them through a service over the internet.
...
- source_sentence: what is the difference between chicken francese and chicken marsala?
sentences:
- Chicken is the species name, equivalent to our “human.” Rooster is an adult male,
equivalent to “man.” Hen is an adult female, equivalent to “woman.” Cockerel is
a juvenile male, equivalent to “boy/young man.”
- What is 99 kg in pounds? - 99 kg is equal to 218.26 pounds.
- The difference between the two is for Francese, the chicken breast is first dipped
in flour, then into a beaten egg mixture, before being cooked. For piccata, the
chicken is first dipped in egg and then in flour. Both are then simmered in a
lemony butter sauce, but the piccata sauce includes capers.”
- source_sentence: what energy is released when coal is burned?
sentences:
- When coal is burned, it reacts with the oxygen in the air. This chemical reaction
converts the stored solar energy into thermal energy, which is released as heat.
But it also produces carbon dioxide and methane.
- When coal is burned it releases a number of airborne toxins and pollutants. They
include mercury, lead, sulfur dioxide, nitrogen oxides, particulates, and various
other heavy metals.
- Squad Building Challenges allow you to exchange sets of players for coins, packs,
and special items in FUT 20. Each of these challenges come with specific requirements,
such as including players from certain teams. ... Live SBCs are time-limited challenges
which often give out unique, high-rated versions of players.
datasets:
- tomaarsen/gooaq-hard-negatives
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: 40.416471447949384
energy_consumed: 0.10397803831199579
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: 0.273
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on Natural Questions pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.72
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11599999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.092
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09333333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.22666666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.36733333333333335
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.27334708954752546
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36268253968253966
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2030635178169122
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.44
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.66
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.74
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.44
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.38666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.37200000000000005
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.34600000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03040608825384637
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07615846857205867
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.11999750129731426
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2193590803164296
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.388302838821125
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5594126984126983
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.278242819200185
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
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: 0.58
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: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.37
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.57
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.66
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5161962245159489
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.47610317460317453
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.47704035866094685
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.26
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: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
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: 0.09799999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13433333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3226904761904762
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3653571428571428
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.43073809523809525
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.34083869249027804
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3756904761904761
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2830059503847294
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.21333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.094
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.17
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.32
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.47
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3823677764194786
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4719365079365079
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3071047037648779
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.12
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.34
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.66
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.66
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.36933631896924085
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.27802380952380945
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2903297758489702
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.44
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.188
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13799999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.012173283062756207
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.02074558886495681
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.026655271941004092
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.03744446828268134
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.16981476360614464
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3736904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.04845896733139879
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.36
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.56
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.43
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.53
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.33694488555577967
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.28549206349206346
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2889544490538325
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.82
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.92
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.96
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.82
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.244
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13399999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7206666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.862
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8993333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9566666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8834196907213419
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8638888888888888
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8576174787744555
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: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.66
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2533333333333333
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: 0.07066666666666668
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15766666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.22266666666666668
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.30566666666666664
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2911964961614548
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4294920634920634
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23186478719609563
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.62
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.124
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.54
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.62
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.84
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.49499964917078587
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3864126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3939847733965381
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.36
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.325
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.585
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.45625099735000324
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4282142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42683234725999136
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.5306122448979592
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7346938775510204
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8367346938775511
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9591836734693877
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5306122448979592
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.45578231292517
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4081632653061224
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3489795918367347
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03881638827876476
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10112189874472176
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.14360203271733188
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2368499712298808
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.402135622609889
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6536767087787496
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3149629356234365
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.3438932496075353
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5349764521193093
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.601287284144427
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7122448979591838
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3438932496075353
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23454735740450025
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18432025117739403
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13407535321821037
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1857996738150283
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3257986999260677
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3834060473445738
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4845429447487502
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4080885419953074
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.45728587625526396
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.338574066485567
name: Cosine Map@100
---
# MPNet base trained on Natural Questions pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) 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-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives)
- **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-nq-cgist-triplet-mask")
# Run inference
sentences = [
'what energy is released when coal is burned?',
'When coal is burned, it reacts with the oxygen in the air. This chemical reaction converts the stored solar energy into thermal energy, which is released as heat. But it also produces carbon dioxide and methane.',
'When coal is burned it releases a number of airborne toxins and pollutants. They include mercury, lead, sulfur dioxide, nitrogen oxides, particulates, and various other heavy metals.',
]
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]
```
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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 | 0.22 | 0.44 | 0.38 | 0.26 | 0.34 | 0.12 | 0.32 | 0.16 | 0.82 | 0.34 | 0.18 | 0.36 | 0.5306 |
| cosine_accuracy@3 | 0.44 | 0.66 | 0.54 | 0.5 | 0.58 | 0.34 | 0.42 | 0.36 | 0.9 | 0.48 | 0.54 | 0.46 | 0.7347 |
| cosine_accuracy@5 | 0.5 | 0.74 | 0.58 | 0.52 | 0.64 | 0.54 | 0.44 | 0.46 | 0.92 | 0.54 | 0.62 | 0.48 | 0.8367 |
| cosine_accuracy@10 | 0.72 | 0.82 | 0.68 | 0.58 | 0.74 | 0.66 | 0.48 | 0.56 | 0.96 | 0.66 | 0.84 | 0.6 | 0.9592 |
| cosine_precision@1 | 0.22 | 0.44 | 0.38 | 0.26 | 0.34 | 0.12 | 0.32 | 0.16 | 0.82 | 0.34 | 0.18 | 0.36 | 0.5306 |
| cosine_precision@3 | 0.1667 | 0.3867 | 0.18 | 0.22 | 0.2133 | 0.1133 | 0.22 | 0.12 | 0.3733 | 0.2533 | 0.18 | 0.1667 | 0.4558 |
| cosine_precision@5 | 0.116 | 0.372 | 0.12 | 0.16 | 0.144 | 0.108 | 0.188 | 0.092 | 0.244 | 0.216 | 0.124 | 0.104 | 0.4082 |
| cosine_precision@10 | 0.092 | 0.346 | 0.07 | 0.098 | 0.094 | 0.066 | 0.138 | 0.058 | 0.134 | 0.148 | 0.084 | 0.066 | 0.349 |
| cosine_recall@1 | 0.0933 | 0.0304 | 0.37 | 0.1343 | 0.17 | 0.12 | 0.0122 | 0.15 | 0.7207 | 0.0707 | 0.18 | 0.325 | 0.0388 |
| cosine_recall@3 | 0.195 | 0.0762 | 0.52 | 0.3227 | 0.32 | 0.34 | 0.0207 | 0.34 | 0.862 | 0.1577 | 0.54 | 0.44 | 0.1011 |
| cosine_recall@5 | 0.2267 | 0.12 | 0.57 | 0.3654 | 0.36 | 0.54 | 0.0267 | 0.43 | 0.8993 | 0.2227 | 0.62 | 0.46 | 0.1436 |
| cosine_recall@10 | 0.3673 | 0.2194 | 0.66 | 0.4307 | 0.47 | 0.66 | 0.0374 | 0.53 | 0.9567 | 0.3057 | 0.84 | 0.585 | 0.2368 |
| **cosine_ndcg@10** | **0.2733** | **0.3883** | **0.5162** | **0.3408** | **0.3824** | **0.3693** | **0.1698** | **0.3369** | **0.8834** | **0.2912** | **0.495** | **0.4563** | **0.4021** |
| cosine_mrr@10 | 0.3627 | 0.5594 | 0.4761 | 0.3757 | 0.4719 | 0.278 | 0.3737 | 0.2855 | 0.8639 | 0.4295 | 0.3864 | 0.4282 | 0.6537 |
| cosine_map@100 | 0.2031 | 0.2782 | 0.477 | 0.283 | 0.3071 | 0.2903 | 0.0485 | 0.289 | 0.8576 | 0.2319 | 0.394 | 0.4268 | 0.315 |
#### 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 | 0.3439 |
| cosine_accuracy@3 | 0.535 |
| cosine_accuracy@5 | 0.6013 |
| cosine_accuracy@10 | 0.7122 |
| cosine_precision@1 | 0.3439 |
| cosine_precision@3 | 0.2345 |
| cosine_precision@5 | 0.1843 |
| cosine_precision@10 | 0.1341 |
| cosine_recall@1 | 0.1858 |
| cosine_recall@3 | 0.3258 |
| cosine_recall@5 | 0.3834 |
| cosine_recall@10 | 0.4845 |
| **cosine_ndcg@10** | **0.4081** |
| cosine_mrr@10 | 0.4573 |
| cosine_map@100 | 0.3386 |
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## Training Details
### Training Dataset
#### gooaq-hard-negatives
* Dataset: [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 50,000 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.53 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 59.79 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 58.76 tokens</li><li>max: 143 tokens</li></ul> |
* Samples:
| question | answer | negative |
|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the difference between calories from fat and total fat?</code> | <code>Fat has more than twice as many calories per gram as carbohydrates and proteins. A gram of fat has about 9 calories, while a gram of carbohydrate or protein has about 4 calories. In other words, you could eat twice as much carbohydrates or proteins as fat for the same amount of calories.</code> | <code>Fat has more than twice as many calories per gram as carbohydrates and proteins. A gram of fat has about 9 calories, while a gram of carbohydrate or protein has about 4 calories. In other words, you could eat twice as much carbohydrates or proteins as fat for the same amount of calories.</code> |
| <code>what is the difference between return transcript and account transcript?</code> | <code>A tax return transcript usually meets the needs of lending institutions offering mortgages and student loans. ... Tax Account Transcript - shows basic data such as return type, marital status, adjusted gross income, taxable income and all payment types. It also shows changes made after you filed your original return.</code> | <code>Trial balance is not a financial statement whereas a balance sheet is a financial statement. Trial balance is solely used for internal purposes whereas a balance sheet is used for purposes other than internal i.e. external. In a trial balance, each and every account is divided into debit (dr.) and credit (cr.)</code> |
| <code>how long does my dog need to fast before sedation?</code> | <code>Now, guidelines are aimed towards 6-8 hours before surgery. This pre-op fasting time is much more beneficial for your pets because you have enough food in there to neutralize the stomach acid, preventing it from coming up the esophagus that causes regurgitation under anesthetic.</code> | <code>Try not to let your pooch rapidly wolf down his/her food! Do not let the dog play or exercise (e.g. go for a walk) for at least two hours after having a meal. Ensure continuous fresh water is available to avoid your pet gulping down a large amount after eating.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
(2): Normalize()
), 'temperature': 0.01}
```
### Evaluation Dataset
#### gooaq-hard-negatives
* Dataset: [gooaq-hard-negatives](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives) at [87594a1](https://huggingface.co/datasets/tomaarsen/gooaq-hard-negatives/tree/87594a1e6c58e88b5843afa9da3a97ffd75d01c2)
* Size: 10,048,700 evaluation samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.61 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 58.16 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.98 tokens</li><li>max: 157 tokens</li></ul> |
* Samples:
| question | answer | negative |
|:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>how is height width and length written?</code> | <code>The Graphics' industry standard is width by height (width x height). Meaning that when you write your measurements, you write them from your point of view, beginning with the width.</code> | <code>The Graphics' industry standard is width by height (width x height). Meaning that when you write your measurements, you write them from your point of view, beginning with the width. That's important.</code> |
| <code>what is the difference between pork shoulder and loin?</code> | <code>All the recipes I've found for pulled pork recommends a shoulder/butt. Shoulders take longer to cook than a loin, because they're tougher. Loins are lean, while shoulders have marbled fat inside.</code> | <code>They are extracted from the loin, which runs from the hip to the shoulder, and it has a small strip of meat called the tenderloin. Unlike other pork, this pork chop is cut from four major sections, which are the shoulder, also known as the blade chops, ribs chops, loin chops, and the last, which is the sirloin chops.</code> |
| <code>is the yin yang symbol religious?</code> | <code>The ubiquitous yin-yang symbol holds its roots in Taoism/Daoism, a Chinese religion and philosophy. The yin, the dark swirl, is associated with shadows, femininity, and the trough of a wave; the yang, the light swirl, represents brightness, passion and growth.</code> | <code>Yin energy is in the calm colors around you, in the soft music, in the soothing sound of a water fountain, or the relaxing images of water. Yang (active energy) is the feng shui energy expressed in strong, vibrant sounds and colors, bright lights, upward moving energy, tall plants, etc.</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
(2): Normalize()
), 'temperature': 0.01}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
#### 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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `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`: batch_sampler
- `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 |
|:-----:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.04 | 1 | 11.5143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 5 | 9.4399 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 10 | 5.5951 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 15 | 3.7416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 20 | 2.8021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 25 | 2.2003 | 1.3332 | 0.2733 | 0.3883 | 0.5162 | 0.3408 | 0.3824 | 0.3693 | 0.1698 | 0.3369 | 0.8834 | 0.2912 | 0.4950 | 0.4563 | 0.4021 | 0.4081 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.104 kWh
- **Carbon Emitted**: 0.040 kg of CO2
- **Hours Used**: 0.273 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: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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",
}
```
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