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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
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: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- row_non_zero_mean_query
- row_sparsity_mean_query
- row_non_zero_mean_corpus
- row_sparsity_mean_corpus
co2_eq_emissions:
emissions: 53.0254354591015
energy_consumed: 0.1364166777096632
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: 0.398
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 128
type: NanoMSMARCO_128
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.2
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5700548121129412
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5031904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.514501390584724
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 128
type: NanoNFCorpus_128
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.32
name: Dot Precision@3
- type: dot_precision@5
value: 0.28400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.22399999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.020619614054435857
name: Dot Recall@1
- type: dot_recall@3
value: 0.07638129396550794
name: Dot Recall@3
- type: dot_recall@5
value: 0.09086567610708625
name: Dot Recall@5
- type: dot_recall@10
value: 0.10949508245462748
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2705576989448532
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43883333333333324
name: Dot Mrr@10
- type: dot_map@100
value: 0.11570301194076318
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 128
type: NanoNQ_128
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.43
name: Dot Recall@1
- type: dot_recall@3
value: 0.54
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.73
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5760476804950475
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5402222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.5348788301685897
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 128
type: NanoBEIR_mean_128
metrics:
- type: dot_accuracy@1
value: 0.36666666666666664
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5866666666666666
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6533333333333333
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7333333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36666666666666664
name: Dot Precision@1
- type: dot_precision@3
value: 0.23777777777777778
name: Dot Precision@3
- type: dot_precision@5
value: 0.18266666666666667
name: Dot Precision@5
- type: dot_precision@10
value: 0.128
name: Dot Precision@10
- type: dot_recall@1
value: 0.27020653801814526
name: Dot Recall@1
- type: dot_recall@3
value: 0.405460431321836
name: Dot Recall@3
- type: dot_recall@5
value: 0.4502885587023621
name: Dot Recall@5
- type: dot_recall@10
value: 0.5398316941515425
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4722200638509473
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4940820105820105
name: Dot Mrr@10
- type: dot_map@100
value: 0.38836107756469235
name: Dot Map@100
- type: row_non_zero_mean_query
value: 128.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.96875
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 128.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.96875
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 256
type: NanoMSMARCO_256
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6020044872439759
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5252142857142856
name: Dot Mrr@10
- type: dot_map@100
value: 0.5321764898130005
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 256
type: NanoNFCorpus_256
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.34666666666666657
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.023916206387792894
name: Dot Recall@1
- type: dot_recall@3
value: 0.060605496737713836
name: Dot Recall@3
- type: dot_recall@5
value: 0.08375989700258081
name: Dot Recall@5
- type: dot_recall@10
value: 0.14574397353137197
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3186443185167164
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5101904761904763
name: Dot Mrr@10
- type: dot_map@100
value: 0.1354214218643388
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 256
type: NanoNQ_256
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.23333333333333336
name: Dot Precision@3
- type: dot_precision@5
value: 0.15600000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.71
name: Dot Recall@5
- type: dot_recall@10
value: 0.79
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6113177400510434
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5685238095238094
name: Dot Mrr@10
- type: dot_map@100
value: 0.5538446726220486
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 256
type: NanoBEIR_mean_256
metrics:
- type: dot_accuracy@1
value: 0.39999999999999997
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6333333333333334
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7200000000000001
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8066666666666666
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.39999999999999997
name: Dot Precision@1
- type: dot_precision@3
value: 0.2644444444444444
name: Dot Precision@3
- type: dot_precision@5
value: 0.20800000000000005
name: Dot Precision@5
- type: dot_precision@10
value: 0.14733333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.267972068795931
name: Dot Recall@1
- type: dot_recall@3
value: 0.45353516557923795
name: Dot Recall@3
- type: dot_recall@5
value: 0.517919965667527
name: Dot Recall@5
- type: dot_recall@10
value: 0.5919146578437907
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5106555152705786
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5346428571428571
name: Dot Mrr@10
- type: dot_map@100
value: 0.407147528099796
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.16
name: Dot Precision@3
- type: dot_precision@5
value: 0.128
name: Dot Precision@5
- type: dot_precision@10
value: 0.1
name: Dot Precision@10
- type: dot_recall@1
value: 0.16
name: Dot Recall@1
- type: dot_recall@3
value: 0.205
name: Dot Recall@3
- type: dot_recall@5
value: 0.255
name: Dot Recall@5
- type: dot_recall@10
value: 0.3833333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.31822361752418216
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.41229365079365077
name: Dot Mrr@10
- type: dot_map@100
value: 0.2533758500528694
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.66
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.88
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.66
name: Dot Precision@1
- type: dot_precision@3
value: 0.6466666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.6040000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.49
name: Dot Precision@10
- type: dot_recall@1
value: 0.06909677601128397
name: Dot Recall@1
- type: dot_recall@3
value: 0.17837135105230828
name: Dot Recall@3
- type: dot_recall@5
value: 0.26249987826636084
name: Dot Recall@5
- type: dot_recall@10
value: 0.35073086886185734
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6034573399856589
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7786666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.44336900502358395
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.3066666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.19599999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7266666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.8566666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.9066666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.9266666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8474860667472335
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8490000000000001
name: Dot Mrr@10
- type: dot_map@100
value: 0.8124727372162975
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.28
name: Dot Precision@3
- type: dot_precision@5
value: 0.2
name: Dot Precision@5
- type: dot_precision@10
value: 0.12399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.1779126984126984
name: Dot Recall@1
- type: dot_recall@3
value: 0.3990714285714285
name: Dot Recall@3
- type: dot_recall@5
value: 0.45465079365079364
name: Dot Recall@5
- type: dot_recall@10
value: 0.5628412698412698
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.44217413756349744
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.503095238095238
name: Dot Mrr@10
- type: dot_map@100
value: 0.3726712950424665
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.5
name: Dot Precision@3
- type: dot_precision@5
value: 0.33599999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.17599999999999993
name: Dot Precision@10
- type: dot_recall@1
value: 0.39
name: Dot Recall@1
- type: dot_recall@3
value: 0.75
name: Dot Recall@3
- type: dot_recall@5
value: 0.84
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8076193908022954
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8510000000000001
name: Dot Mrr@10
- type: dot_map@100
value: 0.7532702446589332
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.2333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.38
name: Dot Recall@1
- type: dot_recall@3
value: 0.7
name: Dot Recall@3
- type: dot_recall@5
value: 0.74
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6105756359135982
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5418571428571429
name: Dot Mrr@10
- type: dot_map@100
value: 0.5510242257742258
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.36
name: Dot Precision@3
- type: dot_precision@5
value: 0.32
name: Dot Precision@5
- type: dot_precision@10
value: 0.258
name: Dot Precision@10
- type: dot_recall@1
value: 0.04221121382565747
name: Dot Recall@1
- type: dot_recall@3
value: 0.07831185452988602
name: Dot Recall@3
- type: dot_recall@5
value: 0.09530060099380368
name: Dot Recall@5
- type: dot_recall@10
value: 0.12471139152233171
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.31877595732776315
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49883333333333324
name: Dot Mrr@10
- type: dot_map@100
value: 0.14727865014045124
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.52
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.72
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.52
name: Dot Precision@1
- type: dot_precision@3
value: 0.24
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.51
name: Dot Recall@1
- type: dot_recall@3
value: 0.67
name: Dot Recall@3
- type: dot_recall@5
value: 0.7
name: Dot Recall@5
- type: dot_recall@10
value: 0.77
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6459385405932947
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6175238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.6086016240895907
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.9
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.98
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.4
name: Dot Precision@3
- type: dot_precision@5
value: 0.25999999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.13999999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.7906666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.9353333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.966
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9507875725473174
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9395238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.9286047619047619
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3466666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.28800000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.20800000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.09466666666666669
name: Dot Recall@1
- type: dot_recall@3
value: 0.21566666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.29766666666666663
name: Dot Recall@5
- type: dot_recall@10
value: 0.4266666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4003633964698161
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5814126984126983
name: Dot Mrr@10
- type: dot_map@100
value: 0.3235984936558747
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.86
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.26666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.17199999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.32
name: Dot Recall@1
- type: dot_recall@3
value: 0.8
name: Dot Recall@3
- type: dot_recall@5
value: 0.86
name: Dot Recall@5
- type: dot_recall@10
value: 0.94
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6564774175565204
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.562579365079365
name: Dot Mrr@10
- type: dot_map@100
value: 0.56506105006105
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.64
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.72
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.64
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.17199999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.615
name: Dot Recall@1
- type: dot_recall@3
value: 0.69
name: Dot Recall@3
- type: dot_recall@5
value: 0.775
name: Dot Recall@5
- type: dot_recall@10
value: 0.83
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7238166818627989
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.696888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.6903857890475537
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.5714285714285714
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8367346938775511
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8979591836734694
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5714285714285714
name: Dot Precision@1
- type: dot_precision@3
value: 0.5374149659863945
name: Dot Precision@3
- type: dot_precision@5
value: 0.5306122448979592
name: Dot Precision@5
- type: dot_precision@10
value: 0.43061224489795913
name: Dot Precision@10
- type: dot_recall@1
value: 0.03877084212205675
name: Dot Recall@1
- type: dot_recall@3
value: 0.10977308661269546
name: Dot Recall@3
- type: dot_recall@5
value: 0.1862486001524683
name: Dot Recall@5
- type: dot_recall@10
value: 0.2846992525980098
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4824099438070076
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7269517330741821
name: Dot Mrr@10
- type: dot_map@100
value: 0.34839943189604633
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.5470329670329671
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7474411302982732
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8013814756671901
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8676923076923077
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5470329670329671
name: Dot Precision@1
- type: dot_precision@3
value: 0.34800627943485085
name: Dot Precision@3
- type: dot_precision@5
value: 0.2697394034536892
name: Dot Precision@5
- type: dot_precision@10
value: 0.1832778649921507
name: Dot Precision@10
- type: dot_recall@1
value: 0.33192242541320743
name: Dot Recall@1
- type: dot_recall@3
value: 0.506784183648691
name: Dot Recall@3
- type: dot_recall@5
value: 0.5645410158766739
name: Dot Recall@5
- type: dot_recall@10
value: 0.6384345730377028
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.600623515284691
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6584327950960606
name: Dot Mrr@10
- type: dot_map@100
value: 0.5229317814279774
name: Dot Map@100
- type: row_non_zero_mean_query
value: 256.0
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9375
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 256.0
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9375
name: Row Sparsity Mean Corpus
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 1024, 'hidden_dim': 4096, '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 SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq")
# 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, 4096)
# 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_128`, `NanoNFCorpus_128` and `NanoNQ_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 |
|:-------------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.36 | 0.3 | 0.44 |
| dot_accuracy@3 | 0.6 | 0.58 | 0.58 |
| dot_accuracy@5 | 0.66 | 0.64 | 0.66 |
| dot_accuracy@10 | 0.78 | 0.66 | 0.76 |
| dot_precision@1 | 0.36 | 0.3 | 0.44 |
| dot_precision@3 | 0.2 | 0.32 | 0.1933 |
| dot_precision@5 | 0.132 | 0.284 | 0.132 |
| dot_precision@10 | 0.078 | 0.224 | 0.082 |
| dot_recall@1 | 0.36 | 0.0206 | 0.43 |
| dot_recall@3 | 0.6 | 0.0764 | 0.54 |
| dot_recall@5 | 0.66 | 0.0909 | 0.6 |
| dot_recall@10 | 0.78 | 0.1095 | 0.73 |
| **dot_ndcg@10** | **0.5701** | **0.2706** | **0.576** |
| dot_mrr@10 | 0.5032 | 0.4388 | 0.5402 |
| dot_map@100 | 0.5145 | 0.1157 | 0.5349 |
| row_non_zero_mean_query | 128.0 | 128.0 | 128.0 |
| row_sparsity_mean_query | 0.9688 | 0.9688 | 0.9688 |
| row_non_zero_mean_corpus | 128.0 | 128.0 | 128.0 |
| row_sparsity_mean_corpus | 0.9688 | 0.9688 | 0.9688 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_128`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"max_active_dims": 128
}
```
| Metric | Value |
|:-------------------------|:-----------|
| dot_accuracy@1 | 0.3667 |
| dot_accuracy@3 | 0.5867 |
| dot_accuracy@5 | 0.6533 |
| dot_accuracy@10 | 0.7333 |
| dot_precision@1 | 0.3667 |
| dot_precision@3 | 0.2378 |
| dot_precision@5 | 0.1827 |
| dot_precision@10 | 0.128 |
| dot_recall@1 | 0.2702 |
| dot_recall@3 | 0.4055 |
| dot_recall@5 | 0.4503 |
| dot_recall@10 | 0.5398 |
| **dot_ndcg@10** | **0.4722** |
| dot_mrr@10 | 0.4941 |
| dot_map@100 | 0.3884 |
| row_non_zero_mean_query | 128.0 |
| row_sparsity_mean_query | 0.9688 |
| row_non_zero_mean_corpus | 128.0 |
| row_sparsity_mean_corpus | 0.9688 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 |
|:-------------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.36 | 0.4 | 0.44 |
| dot_accuracy@3 | 0.64 | 0.56 | 0.7 |
| dot_accuracy@5 | 0.76 | 0.64 | 0.76 |
| dot_accuracy@10 | 0.84 | 0.76 | 0.82 |
| dot_precision@1 | 0.36 | 0.4 | 0.44 |
| dot_precision@3 | 0.2133 | 0.3467 | 0.2333 |
| dot_precision@5 | 0.152 | 0.316 | 0.156 |
| dot_precision@10 | 0.084 | 0.27 | 0.088 |
| dot_recall@1 | 0.36 | 0.0239 | 0.42 |
| dot_recall@3 | 0.64 | 0.0606 | 0.66 |
| dot_recall@5 | 0.76 | 0.0838 | 0.71 |
| dot_recall@10 | 0.84 | 0.1457 | 0.79 |
| **dot_ndcg@10** | **0.602** | **0.3186** | **0.6113** |
| dot_mrr@10 | 0.5252 | 0.5102 | 0.5685 |
| dot_map@100 | 0.5322 | 0.1354 | 0.5538 |
| row_non_zero_mean_query | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_query | 0.9375 | 0.9375 | 0.9375 |
| row_non_zero_mean_corpus | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_corpus | 0.9375 | 0.9375 | 0.9375 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_256`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"max_active_dims": 256
}
```
| Metric | Value |
|:-------------------------|:-----------|
| dot_accuracy@1 | 0.4 |
| dot_accuracy@3 | 0.6333 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.8067 |
| dot_precision@1 | 0.4 |
| dot_precision@3 | 0.2644 |
| dot_precision@5 | 0.208 |
| dot_precision@10 | 0.1473 |
| dot_recall@1 | 0.268 |
| dot_recall@3 | 0.4535 |
| dot_recall@5 | 0.5179 |
| dot_recall@10 | 0.5919 |
| **dot_ndcg@10** | **0.5107** |
| dot_mrr@10 | 0.5346 |
| dot_map@100 | 0.4071 |
| row_non_zero_mean_query | 256.0 |
| row_sparsity_mean_query | 0.9375 |
| row_non_zero_mean_corpus | 256.0 |
| row_sparsity_mean_corpus | 0.9375 |
#### Sparse Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:-------------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| dot_accuracy@1 | 0.32 | 0.66 | 0.78 | 0.4 | 0.78 | 0.38 | 0.4 | 0.52 | 0.9 | 0.44 | 0.32 | 0.64 | 0.5714 |
| dot_accuracy@3 | 0.44 | 0.88 | 0.9 | 0.58 | 0.9 | 0.7 | 0.58 | 0.72 | 0.98 | 0.68 | 0.8 | 0.72 | 0.8367 |
| dot_accuracy@5 | 0.52 | 0.94 | 0.94 | 0.62 | 0.94 | 0.74 | 0.64 | 0.76 | 0.98 | 0.78 | 0.86 | 0.8 | 0.898 |
| dot_accuracy@10 | 0.66 | 0.94 | 0.96 | 0.76 | 1.0 | 0.82 | 0.72 | 0.8 | 1.0 | 0.84 | 0.94 | 0.84 | 1.0 |
| dot_precision@1 | 0.32 | 0.66 | 0.78 | 0.4 | 0.78 | 0.38 | 0.4 | 0.52 | 0.9 | 0.44 | 0.32 | 0.64 | 0.5714 |
| dot_precision@3 | 0.16 | 0.6467 | 0.3067 | 0.28 | 0.5 | 0.2333 | 0.36 | 0.24 | 0.4 | 0.3467 | 0.2667 | 0.2467 | 0.5374 |
| dot_precision@5 | 0.128 | 0.604 | 0.196 | 0.2 | 0.336 | 0.148 | 0.32 | 0.152 | 0.26 | 0.288 | 0.172 | 0.172 | 0.5306 |
| dot_precision@10 | 0.1 | 0.49 | 0.1 | 0.124 | 0.176 | 0.082 | 0.258 | 0.086 | 0.14 | 0.208 | 0.094 | 0.094 | 0.4306 |
| dot_recall@1 | 0.16 | 0.0691 | 0.7267 | 0.1779 | 0.39 | 0.38 | 0.0422 | 0.51 | 0.7907 | 0.0947 | 0.32 | 0.615 | 0.0388 |
| dot_recall@3 | 0.205 | 0.1784 | 0.8567 | 0.3991 | 0.75 | 0.7 | 0.0783 | 0.67 | 0.9353 | 0.2157 | 0.8 | 0.69 | 0.1098 |
| dot_recall@5 | 0.255 | 0.2625 | 0.9067 | 0.4547 | 0.84 | 0.74 | 0.0953 | 0.7 | 0.966 | 0.2977 | 0.86 | 0.775 | 0.1862 |
| dot_recall@10 | 0.3833 | 0.3507 | 0.9267 | 0.5628 | 0.88 | 0.82 | 0.1247 | 0.77 | 1.0 | 0.4267 | 0.94 | 0.83 | 0.2847 |
| **dot_ndcg@10** | **0.3182** | **0.6035** | **0.8475** | **0.4422** | **0.8076** | **0.6106** | **0.3188** | **0.6459** | **0.9508** | **0.4004** | **0.6565** | **0.7238** | **0.4824** |
| dot_mrr@10 | 0.4123 | 0.7787 | 0.849 | 0.5031 | 0.851 | 0.5419 | 0.4988 | 0.6175 | 0.9395 | 0.5814 | 0.5626 | 0.6969 | 0.727 |
| dot_map@100 | 0.2534 | 0.4434 | 0.8125 | 0.3727 | 0.7533 | 0.551 | 0.1473 | 0.6086 | 0.9286 | 0.3236 | 0.5651 | 0.6904 | 0.3484 |
| row_non_zero_mean_query | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_query | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
| row_non_zero_mean_corpus | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 |
| row_sparsity_mean_corpus | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
]
}
```
| Metric | Value |
|:-------------------------|:-----------|
| dot_accuracy@1 | 0.547 |
| dot_accuracy@3 | 0.7474 |
| dot_accuracy@5 | 0.8014 |
| dot_accuracy@10 | 0.8677 |
| dot_precision@1 | 0.547 |
| dot_precision@3 | 0.348 |
| dot_precision@5 | 0.2697 |
| dot_precision@10 | 0.1833 |
| dot_recall@1 | 0.3319 |
| dot_recall@3 | 0.5068 |
| dot_recall@5 | 0.5645 |
| dot_recall@10 | 0.6384 |
| **dot_ndcg@10** | **0.6006** |
| dot_mrr@10 | 0.6584 |
| dot_map@100 | 0.5229 |
| row_non_zero_mean_query | 256.0 |
| row_sparsity_mean_query | 0.9375 |
| row_non_zero_mean_corpus | 256.0 |
| row_sparsity_mean_corpus | 0.9375 |
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## 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/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### 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/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 4e-05
- `num_train_epochs`: 1
- `bf16`: True
- `load_best_model_at_end`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 0.0
- `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`: 0.0
- `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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:----------:|:-------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:|
| -1 | -1 | - | - | 0.5920 | 0.2869 | 0.6003 | 0.4930 | 0.5785 | 0.3370 | 0.6392 | 0.5183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 100 | 0.3598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.3648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.3272 | 0.3362 | 0.5728 | 0.2771 | 0.5552 | 0.4684 | 0.5932 | 0.3225 | 0.6162 | 0.5107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 400 | 0.3534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.3423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.3878** | **600** | **0.3601** | **0.3204** | **0.5672** | **0.2679** | **0.5813** | **0.4721** | **0.611** | **0.3195** | **0.6453** | **0.5253** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.4525 | 700 | 0.3279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.3235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.3359 | 0.3098 | 0.5840 | 0.2496 | 0.5808 | 0.4715 | 0.6014 | 0.3208 | 0.6265 | 0.5162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 1000 | 0.3215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.3394 | 0.3065 | 0.5838 | 0.2449 | 0.5739 | 0.4676 | 0.6022 | 0.3227 | 0.6069 | 0.5106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 1300 | 0.331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.3188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 0.3225 | 0.3034 | 0.5701 | 0.2706 | 0.5760 | 0.4722 | 0.6020 | 0.3186 | 0.6113 | 0.5107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3182 | 0.6035 | 0.8475 | 0.4422 | 0.8076 | 0.6106 | 0.3188 | 0.6459 | 0.9508 | 0.4004 | 0.6565 | 0.7238 | 0.4824 | 0.6006 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.136 kWh
- **Carbon Emitted**: 0.053 kg of CO2
- **Hours Used**: 0.398 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: 4.2.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- 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",
}
```
#### CSRLoss
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
year={2025},
eprint={2503.01776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.01776},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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