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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: what is the difference between uae and saudi arabia
sentences:
- 'Monopoly Junior Players take turns in order, with the initial player determined
by age before the game: the youngest player goes first. Players are dealt an initial
amount Monopoly money depending on the total number of players playing: 20 in
a two-player game, 18 in a three-player game or 16 in a four-player game. A typical
turn begins with the rolling of the die and the player advancing their token clockwise
around the board the corresponding number of spaces. When the player lands on
an unowned space they must purchase the space from the bank for the amount indicated
on the board, and places a sold sign on the coloured band at the top of the space
to denote ownership. If a player lands on a space owned by an opponent the player
pays the opponent rent in the amount written on the board. If the opponent owns
both properties of the same colour the rent is doubled.'
- Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
continue to take somewhat differing stances on regional conflicts such the Yemeni
Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
which has fought against Saudi-backed forces, and the Syrian Civil War, where
the UAE has disagreed with Saudi support for Islamist movements.[4]
- Governors of states of India The governors and lieutenant-governors are appointed
by the President for a term of five years.
- source_sentence: who came up with the seperation of powers
sentences:
- Separation of powers Aristotle first mentioned the idea of a "mixed government"
or hybrid government in his work Politics where he drew upon many of the constitutional
forms in the city-states of Ancient Greece. In the Roman Republic, the Roman Senate,
Consuls and the Assemblies showed an example of a mixed government according to
Polybius (Histories, Book 6, 11–13).
- Economy of New Zealand New Zealand's diverse market economy has a sizable service
sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing
industries include aluminium production, food processing, metal fabrication, wood
and paper products. Mining, manufacturing, electricity, gas, water, and waste
services accounted for 16.5% of GDP in 2013.[17] The primary sector continues
to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17]
- John Dalton John Dalton FRS (/ˈdɔːltən/; 6 September 1766 – 27 July 1844) was
an English chemist, physicist, and meteorologist. He is best known for proposing
the modern atomic theory and for his research into colour blindness, sometimes
referred to as Daltonism in his honour.
- source_sentence: who was the first president of indian science congress meeting
held in kolkata in 1914
sentences:
- Nobody to Blame "Nobody to Blame" is a song recorded by American country music
artist Chris Stapleton. The song was released in November 2015 as the singer's
third single overall. Stapleton co-wrote the song with Barry Bales and Ronnie
Bowman. It became Stapleton's first top 10 single on the US Country Airplay chart.[2]
"Nobody to Blame" won Song of the Year at the ACM Awards.[3]
- Indian Science Congress Association The first meeting of the congress was held
from 15–17 January 1914 at the premises of the Asiatic Society, Calcutta. Honorable
justice Sir Ashutosh Mukherjee, the then Vice Chancellor of the University of
Calcutta presided over the Congress. One hundred and five scientists from different
parts of India and abroad attended it. Altogether 35 papers under 6 different
sections, namely Botany, Chemistry, Ethnography, Geology, Physics and Zoology
were presented.
- New Soul "New Soul" is a song by the French-Israeli R&B/soul singer Yael Naïm,
from her self-titled second album. The song gained popularity in the United States
following its use by Apple in an advertisement for their MacBook Air laptop. In
the song Naïm sings of being a new soul who has come into the world to learn "a
bit 'bout how to give and take." However, she finds that things are harder than
they seem. The song, also featured in the films The House Bunny and Wild Target,
features a prominent "la la la la" section as its hook. It remains Naïm's biggest
hit single in the U.S. to date, and her only one to reach the Top 40 of the Billboard
Hot 100.
- source_sentence: who wrote get over it by the eagles
sentences:
- Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a
single after a fourteen-year breakup. It was also the first song written by bandmates
Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live
for the first time during their Hell Freezes Over tour in 1994. It returned the
band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the
Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks
chart. The song was not played live by the Eagles after the "Hell Freezes Over"
tour in 1994. It remains the group's last Top 40 hit in the U.S.
- Pokhran-II In 1980, the general elections marked the return of Indira Gandhi and
the nuclear program began to gain momentum under Ramanna in 1981. Requests for
additional nuclear tests were continued to be denied by the government when Prime
Minister Indira Gandhi saw Pakistan began exercising the brinkmanship, though
the nuclear program continued to advance.[7] Initiation towards hydrogen bomb
began as well as the launch of the missile programme began under Late president
Dr. Abdul Kalam, who was then an aerospace engineer.[7]
- R. Budd Dwyer Robert Budd Dwyer (November 21, 1939 – January 22, 1987) was the
30th State Treasurer of the Commonwealth of Pennsylvania. He served from 1971
to 1981 as a Republican member of the Pennsylvania State Senate representing the
state's 50th district. He then served as the 30th Treasurer of Pennsylvania from
January 20, 1981, until his death. On January 22, 1987, Dwyer called a news conference
in the Pennsylvania state capital of Harrisburg where he killed himself in front
of the gathered reporters, by shooting himself in the mouth with a .357 Magnum
revolver.[4] Dwyer's suicide was broadcast later that day to a wide television
audience across Pennsylvania.
- source_sentence: who is cornelius in the book of acts
sentences:
- Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It
was included on Clapton's 1977 album Slowhand. Clapton wrote the song about Pattie
Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit
(then Marcy Levy) and Yvonne Elliman.
- Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their
head of story.[1] There he worked on all of their films produced up to 2006; this
included Toy Story (for which he received an Academy Award nomination) and A Bug's
Life, as the co-story writer and others as story supervisor. His final film was
Cars. He also voiced characters in many of the films, including Heimlich the caterpillar
in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in
Finding Nemo.[1]
- 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who
is considered by Christians to be one of the first Gentiles to convert to the
faith, as related in Acts of the Apostles.'
datasets:
- sentence-transformers/natural-questions
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: 113.44094173179047
energy_consumed: 0.29184553136281904
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: 0.773
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SparseEncoder based on microsoft/mpnet-base
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 16
type: NanoMSMARCO_16
metrics:
- type: cosine_accuracy@1
value: 0.1
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05000000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.272077335852507
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20234920634920633
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21758364304569
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 16
type: NanoNFCorpus_16
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24
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: 0.05999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.005993249911183041
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.009403252754209558
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.013285393478414642
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.01646720008819819
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.06095056479011788
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14072222222222222
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.015310893897400863
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 16
type: NanoNQ_16
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
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: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.064
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3867151912670764
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3266904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3250246379519026
name: Cosine Map@100
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 16
type: NanoBEIR_mean_16
metrics:
- type: cosine_accuracy@1
value: 0.12
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2733333333333334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.38000000000000006
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48666666666666664
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09555555555555555
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08666666666666668
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05466666666666667
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09533108330372768
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2231344175847365
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.29109513115947155
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3721557333627327
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2399143639699004
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22325396825396826
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18597305829833113
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 32
type: NanoMSMARCO_32
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: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.56
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.56
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.33109644128066057
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2634444444444444
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27935469743863556
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 32
type: NanoNFCorpus_32
metrics:
- type: cosine_accuracy@1
value: 0.14
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.28
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.007695869325666863
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.012313937822266688
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.01702903494334016
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.024165659145052122
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10225707780728845
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2055238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.022577551502700435
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 32
type: NanoNQ_32
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
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: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.31
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.53
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.63
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4603957123337682
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4211904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.41127594932176303
name: Cosine Map@100
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 32
type: NanoBEIR_mean_32
metrics:
- type: cosine_accuracy@1
value: 0.21333333333333335
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.32666666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4066666666666667
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5266666666666667
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.21333333333333335
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11777777777777776
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09466666666666668
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07133333333333335
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16589862310855563
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23077131260742223
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30234301164778005
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4047218863816841
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.29791641047390577
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2967195767195767
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23773606608769968
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 64
type: NanoMSMARCO_64
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.12666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.38
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3545165496884908
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.27796031746031746
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.29572845389453484
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 64
type: NanoNFCorpus_64
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: 0.12666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.088
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.009483451025013268
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.012904129822135095
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.036867855927155205
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.04756198673273659
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.11496239522394665
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24210317460317454
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.0318282871881163
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 64
type: NanoNQ_64
metrics:
- type: cosine_accuracy@1
value: 0.44
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.62
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.72
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.44
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: 0.07400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.42
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.58
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.64
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.561884513825323
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5395555555555555
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5268055680783221
name: Cosine Map@100
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 64
type: NanoBEIR_mean_64
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48666666666666664
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5733333333333334
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11733333333333335
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.074
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19649448367500444
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.32430137660737834
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3789559519757184
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4425206622442455
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3437878195792535
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35320634920634914
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2847874363869911
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 128
type: NanoMSMARCO_128
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.34
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4022072447482653
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.31815873015873014
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33230553462724927
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 128
type: NanoNFCorpus_128
metrics:
- type: cosine_accuracy@1
value: 0.14
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.34
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.38
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0036955722371344803
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.021194355136532755
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.024553995602026958
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.043293677887263404
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.12666378888376595
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2537936507936508
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.03330968914510828
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 128
type: NanoNQ_128
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.35
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.53
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.66
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5527057053472701
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5072460317460317
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4846991157483792
name: Cosine Map@100
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 128
type: NanoBEIR_mean_128
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4133333333333334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5133333333333333
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6666666666666666
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15555555555555553
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12133333333333335
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1845651907457115
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2970647850455109
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.381517998534009
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4944312259624211
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3605255796597671
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35973280423280424
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2834381131735789
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 256
type: NanoMSMARCO_256
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.52
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4651758219790261
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.39804761904761904
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.412474140043243
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 256
type: NanoNFCorpus_256
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.38
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.114
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.005516710448516594
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.011401609103753301
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.021271103372355084
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.0347182833647384
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.12628863554710404
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2575
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.033728487141126466
name: Cosine Map@100
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 256
type: NanoNQ_256
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.54
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.64
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.73
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5611650669716552
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5226904761904763
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5086922580864135
name: Cosine Map@100
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 256
type: NanoBEIR_mean_256
metrics:
- type: cosine_accuracy@1
value: 0.2866666666666667
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4466666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5266666666666667
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6466666666666667
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2866666666666667
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08733333333333333
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22183890348283888
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3438005363679178
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3937570344574517
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.48157276112157943
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3842098414992618
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3927460317460318
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.31829829509026103
name: Cosine Map@100
---
# SparseEncoder based on microsoft/mpnet-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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.
See [train_nq.py](train_nq.py) for the training script used for this model.
> [!WARNING]
> Warning:
> Sparse models in Sentence Transformers are still quite experimental.
## 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:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
<!-- - **License:** Unknown -->
### 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
```
SparseEncoder(
(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})
(2): CSRSparsity({'input_dim': 768, 'hidden_dim': 3072, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## 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/sparse-mpnet-base-nq-fresh")
# Run inference
sentences = [
'who is cornelius in the book of acts',
'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
]
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
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_16`, `NanoNFCorpus_16` and `NanoNQ_16`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 16
}
```
| Metric | NanoMSMARCO_16 | NanoNFCorpus_16 | NanoNQ_16 |
|:--------------------|:---------------|:----------------|:-----------|
| cosine_accuracy@1 | 0.1 | 0.08 | 0.18 |
| cosine_accuracy@3 | 0.26 | 0.14 | 0.42 |
| cosine_accuracy@5 | 0.36 | 0.24 | 0.54 |
| cosine_accuracy@10 | 0.5 | 0.32 | 0.64 |
| cosine_precision@1 | 0.1 | 0.08 | 0.18 |
| cosine_precision@3 | 0.0867 | 0.06 | 0.14 |
| cosine_precision@5 | 0.072 | 0.08 | 0.108 |
| cosine_precision@10 | 0.05 | 0.05 | 0.064 |
| cosine_recall@1 | 0.1 | 0.006 | 0.18 |
| cosine_recall@3 | 0.26 | 0.0094 | 0.4 |
| cosine_recall@5 | 0.36 | 0.0133 | 0.5 |
| cosine_recall@10 | 0.5 | 0.0165 | 0.6 |
| **cosine_ndcg@10** | **0.2721** | **0.061** | **0.3867** |
| cosine_mrr@10 | 0.2023 | 0.1407 | 0.3267 |
| cosine_map@100 | 0.2176 | 0.0153 | 0.325 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_16`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"truncate_dim": 16
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.12 |
| cosine_accuracy@3 | 0.2733 |
| cosine_accuracy@5 | 0.38 |
| cosine_accuracy@10 | 0.4867 |
| cosine_precision@1 | 0.12 |
| cosine_precision@3 | 0.0956 |
| cosine_precision@5 | 0.0867 |
| cosine_precision@10 | 0.0547 |
| cosine_recall@1 | 0.0953 |
| cosine_recall@3 | 0.2231 |
| cosine_recall@5 | 0.2911 |
| cosine_recall@10 | 0.3722 |
| **cosine_ndcg@10** | **0.2399** |
| cosine_mrr@10 | 0.2233 |
| cosine_map@100 | 0.186 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_32`, `NanoNFCorpus_32` and `NanoNQ_32`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 32
}
```
| Metric | NanoMSMARCO_32 | NanoNFCorpus_32 | NanoNQ_32 |
|:--------------------|:---------------|:----------------|:-----------|
| cosine_accuracy@1 | 0.18 | 0.14 | 0.32 |
| cosine_accuracy@3 | 0.26 | 0.26 | 0.46 |
| cosine_accuracy@5 | 0.36 | 0.28 | 0.58 |
| cosine_accuracy@10 | 0.56 | 0.34 | 0.68 |
| cosine_precision@1 | 0.18 | 0.14 | 0.32 |
| cosine_precision@3 | 0.0867 | 0.1133 | 0.1533 |
| cosine_precision@5 | 0.072 | 0.096 | 0.116 |
| cosine_precision@10 | 0.056 | 0.09 | 0.068 |
| cosine_recall@1 | 0.18 | 0.0077 | 0.31 |
| cosine_recall@3 | 0.26 | 0.0123 | 0.42 |
| cosine_recall@5 | 0.36 | 0.017 | 0.53 |
| cosine_recall@10 | 0.56 | 0.0242 | 0.63 |
| **cosine_ndcg@10** | **0.3311** | **0.1023** | **0.4604** |
| cosine_mrr@10 | 0.2634 | 0.2055 | 0.4212 |
| cosine_map@100 | 0.2794 | 0.0226 | 0.4113 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_32`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"truncate_dim": 32
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2133 |
| cosine_accuracy@3 | 0.3267 |
| cosine_accuracy@5 | 0.4067 |
| cosine_accuracy@10 | 0.5267 |
| cosine_precision@1 | 0.2133 |
| cosine_precision@3 | 0.1178 |
| cosine_precision@5 | 0.0947 |
| cosine_precision@10 | 0.0713 |
| cosine_recall@1 | 0.1659 |
| cosine_recall@3 | 0.2308 |
| cosine_recall@5 | 0.3023 |
| cosine_recall@10 | 0.4047 |
| **cosine_ndcg@10** | **0.2979** |
| cosine_mrr@10 | 0.2967 |
| cosine_map@100 | 0.2377 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_64`, `NanoNFCorpus_64` and `NanoNQ_64`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | NanoMSMARCO_64 | NanoNFCorpus_64 | NanoNQ_64 |
|:--------------------|:---------------|:----------------|:-----------|
| cosine_accuracy@1 | 0.16 | 0.18 | 0.44 |
| cosine_accuracy@3 | 0.38 | 0.26 | 0.62 |
| cosine_accuracy@5 | 0.46 | 0.32 | 0.68 |
| cosine_accuracy@10 | 0.6 | 0.4 | 0.72 |
| cosine_precision@1 | 0.16 | 0.18 | 0.44 |
| cosine_precision@3 | 0.1267 | 0.1267 | 0.2067 |
| cosine_precision@5 | 0.092 | 0.12 | 0.14 |
| cosine_precision@10 | 0.06 | 0.088 | 0.074 |
| cosine_recall@1 | 0.16 | 0.0095 | 0.42 |
| cosine_recall@3 | 0.38 | 0.0129 | 0.58 |
| cosine_recall@5 | 0.46 | 0.0369 | 0.64 |
| cosine_recall@10 | 0.6 | 0.0476 | 0.68 |
| **cosine_ndcg@10** | **0.3545** | **0.115** | **0.5619** |
| cosine_mrr@10 | 0.278 | 0.2421 | 0.5396 |
| cosine_map@100 | 0.2957 | 0.0318 | 0.5268 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_64`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.26 |
| cosine_accuracy@3 | 0.42 |
| cosine_accuracy@5 | 0.4867 |
| cosine_accuracy@10 | 0.5733 |
| cosine_precision@1 | 0.26 |
| cosine_precision@3 | 0.1533 |
| cosine_precision@5 | 0.1173 |
| cosine_precision@10 | 0.074 |
| cosine_recall@1 | 0.1965 |
| cosine_recall@3 | 0.3243 |
| cosine_recall@5 | 0.379 |
| cosine_recall@10 | 0.4425 |
| **cosine_ndcg@10** | **0.3438** |
| cosine_mrr@10 | 0.3532 |
| cosine_map@100 | 0.2848 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 |
|:--------------------|:----------------|:-----------------|:-----------|
| cosine_accuracy@1 | 0.2 | 0.14 | 0.38 |
| cosine_accuracy@3 | 0.34 | 0.34 | 0.56 |
| cosine_accuracy@5 | 0.46 | 0.38 | 0.7 |
| cosine_accuracy@10 | 0.68 | 0.52 | 0.8 |
| cosine_precision@1 | 0.2 | 0.14 | 0.38 |
| cosine_precision@3 | 0.1133 | 0.1667 | 0.1867 |
| cosine_precision@5 | 0.092 | 0.128 | 0.144 |
| cosine_precision@10 | 0.068 | 0.114 | 0.082 |
| cosine_recall@1 | 0.2 | 0.0037 | 0.35 |
| cosine_recall@3 | 0.34 | 0.0212 | 0.53 |
| cosine_recall@5 | 0.46 | 0.0246 | 0.66 |
| cosine_recall@10 | 0.68 | 0.0433 | 0.76 |
| **cosine_ndcg@10** | **0.4022** | **0.1267** | **0.5527** |
| cosine_mrr@10 | 0.3182 | 0.2538 | 0.5072 |
| cosine_map@100 | 0.3323 | 0.0333 | 0.4847 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_128`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.24 |
| cosine_accuracy@3 | 0.4133 |
| cosine_accuracy@5 | 0.5133 |
| cosine_accuracy@10 | 0.6667 |
| cosine_precision@1 | 0.24 |
| cosine_precision@3 | 0.1556 |
| cosine_precision@5 | 0.1213 |
| cosine_precision@10 | 0.088 |
| cosine_recall@1 | 0.1846 |
| cosine_recall@3 | 0.2971 |
| cosine_recall@5 | 0.3815 |
| cosine_recall@10 | 0.4944 |
| **cosine_ndcg@10** | **0.3605** |
| cosine_mrr@10 | 0.3597 |
| cosine_map@100 | 0.2834 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 |
|:--------------------|:----------------|:-----------------|:-----------|
| cosine_accuracy@1 | 0.26 | 0.18 | 0.42 |
| cosine_accuracy@3 | 0.48 | 0.28 | 0.58 |
| cosine_accuracy@5 | 0.52 | 0.38 | 0.68 |
| cosine_accuracy@10 | 0.68 | 0.5 | 0.76 |
| cosine_precision@1 | 0.26 | 0.18 | 0.42 |
| cosine_precision@3 | 0.16 | 0.1467 | 0.1933 |
| cosine_precision@5 | 0.104 | 0.14 | 0.14 |
| cosine_precision@10 | 0.068 | 0.114 | 0.08 |
| cosine_recall@1 | 0.26 | 0.0055 | 0.4 |
| cosine_recall@3 | 0.48 | 0.0114 | 0.54 |
| cosine_recall@5 | 0.52 | 0.0213 | 0.64 |
| cosine_recall@10 | 0.68 | 0.0347 | 0.73 |
| **cosine_ndcg@10** | **0.4652** | **0.1263** | **0.5612** |
| cosine_mrr@10 | 0.398 | 0.2575 | 0.5227 |
| cosine_map@100 | 0.4125 | 0.0337 | 0.5087 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_256`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2867 |
| cosine_accuracy@3 | 0.4467 |
| cosine_accuracy@5 | 0.5267 |
| cosine_accuracy@10 | 0.6467 |
| cosine_precision@1 | 0.2867 |
| cosine_precision@3 | 0.1667 |
| cosine_precision@5 | 0.128 |
| cosine_precision@10 | 0.0873 |
| cosine_recall@1 | 0.2218 |
| cosine_recall@3 | 0.3438 |
| cosine_recall@5 | 0.3938 |
| cosine_recall@10 | 0.4816 |
| **cosine_ndcg@10** | **0.3842** |
| cosine_mrr@10 | 0.3927 |
| cosine_map@100 | 0.3183 |
<!--
## 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
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1,
"scale": 20.0
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1,
"scale": 20.0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 4e-05
- `weight_decay`: 0.0001
- `adam_epsilon`: 6.25e-10
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 4e-05
- `weight_decay`: 0.0001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 6.25e-10
- `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}
- `tp_size`: 0
- `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
- `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 | NanoMSMARCO_16_cosine_ndcg@10 | NanoNFCorpus_16_cosine_ndcg@10 | NanoNQ_16_cosine_ndcg@10 | NanoBEIR_mean_16_cosine_ndcg@10 | NanoMSMARCO_32_cosine_ndcg@10 | NanoNFCorpus_32_cosine_ndcg@10 | NanoNQ_32_cosine_ndcg@10 | NanoBEIR_mean_32_cosine_ndcg@10 | NanoMSMARCO_64_cosine_ndcg@10 | NanoNFCorpus_64_cosine_ndcg@10 | NanoNQ_64_cosine_ndcg@10 | NanoBEIR_mean_64_cosine_ndcg@10 | NanoMSMARCO_128_cosine_ndcg@10 | NanoNFCorpus_128_cosine_ndcg@10 | NanoNQ_128_cosine_ndcg@10 | NanoBEIR_mean_128_cosine_ndcg@10 | NanoMSMARCO_256_cosine_ndcg@10 | NanoNFCorpus_256_cosine_ndcg@10 | NanoNQ_256_cosine_ndcg@10 | NanoBEIR_mean_256_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:|
| -1 | -1 | - | - | 0.0318 | 0.0148 | 0.0149 | 0.0205 | 0.0794 | 0.0234 | 0.0102 | 0.0377 | 0.0855 | 0.0195 | 0.0508 | 0.0519 | 0.1081 | 0.0246 | 0.0264 | 0.0530 | 0.1006 | 0.0249 | 0.0388 | 0.0547 |
| 0.0646 | 200 | 0.7332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 400 | 0.2606 | 0.1970 | 0.2845 | 0.0970 | 0.3546 | 0.2454 | 0.3778 | 0.1358 | 0.3455 | 0.2864 | 0.3868 | 0.1563 | 0.3806 | 0.3079 | 0.3988 | 0.1664 | 0.4035 | 0.3229 | 0.4020 | 0.1782 | 0.4181 | 0.3327 |
| 0.1939 | 600 | 0.2247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 800 | 0.1983 | 0.1750 | 0.2908 | 0.0866 | 0.3730 | 0.2502 | 0.3324 | 0.1155 | 0.4275 | 0.2918 | 0.3511 | 0.1621 | 0.4998 | 0.3377 | 0.3920 | 0.1563 | 0.5174 | 0.3553 | 0.4152 | 0.1555 | 0.5153 | 0.3620 |
| 0.3232 | 1000 | 0.1822 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 1200 | 0.1846 | 0.1594 | 0.2775 | 0.0785 | 0.3723 | 0.2428 | 0.2642 | 0.1076 | 0.4389 | 0.2702 | 0.3865 | 0.1328 | 0.4329 | 0.3174 | 0.3883 | 0.1446 | 0.5040 | 0.3456 | 0.3638 | 0.1529 | 0.4939 | 0.3369 |
| 0.4525 | 1400 | 0.1669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 1600 | 0.1573 | 0.1452 | 0.2740 | 0.0624 | 0.3670 | 0.2345 | 0.3557 | 0.0855 | 0.4188 | 0.2867 | 0.4094 | 0.1099 | 0.5027 | 0.3407 | 0.3885 | 0.1340 | 0.4990 | 0.3405 | 0.4820 | 0.1577 | 0.5453 | 0.3950 |
| 0.5818 | 1800 | 0.1502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 2000 | 0.1375 | 0.1255 | 0.2307 | 0.0685 | 0.3801 | 0.2264 | 0.2529 | 0.0815 | 0.4335 | 0.2560 | 0.3509 | 0.0955 | 0.4611 | 0.3025 | 0.3932 | 0.1339 | 0.4875 | 0.3382 | 0.4184 | 0.1483 | 0.4904 | 0.3523 |
| 0.7111 | 2200 | 0.1359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 2400 | 0.1288 | 0.1184 | 0.2737 | 0.0703 | 0.3419 | 0.2286 | 0.3765 | 0.0843 | 0.4440 | 0.3016 | 0.3927 | 0.1247 | 0.5285 | 0.3486 | 0.3726 | 0.1203 | 0.5153 | 0.3361 | 0.4676 | 0.1343 | 0.5523 | 0.3847 |
| 0.8403 | 2600 | 0.1235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 2800 | 0.1168 | 0.1094 | 0.2751 | 0.0710 | 0.3602 | 0.2354 | 0.3227 | 0.0966 | 0.5046 | 0.3080 | 0.4112 | 0.1129 | 0.5268 | 0.3503 | 0.4077 | 0.1259 | 0.5253 | 0.3530 | 0.4642 | 0.1238 | 0.5726 | 0.3869 |
| 0.9696 | 3000 | 0.1187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.2721 | 0.0610 | 0.3867 | 0.2399 | 0.3311 | 0.1023 | 0.4604 | 0.2979 | 0.3545 | 0.1150 | 0.5619 | 0.3438 | 0.4022 | 0.1267 | 0.5527 | 0.3605 | 0.4652 | 0.1263 | 0.5612 | 0.3842 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.292 kWh
- **Carbon Emitted**: 0.113 kg of CO2
- **Hours Used**: 0.773 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: 4.1.0.dev0
- Transformers: 4.52.0.dev0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## 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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