metadata

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, 11â€“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 15â€“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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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
            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 model finetuned from mixedbread-ai/mxbai-embed-large-v1 on the natural-questions dataset using the sentence-transformers 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 4096 dimensions
Similarity Function: Dot Product
Training Dataset:
- natural-questions
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO_128, NanoNFCorpus_128 and NanoNQ_128
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "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 SparseNanoBEIREvaluator with these parameters:

{
    "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 SparseInformationRetrievalEvaluator with these parameters:
```
{
    "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 SparseNanoBEIREvaluator with these parameters:

{
    "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 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 SparseNanoBEIREvaluator with these parameters:

{
    "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

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 99,000 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 10 tokens
mean: 11.71 tokens
max: 26 tokens

min: 4 tokens
mean: 131.81 tokens
max: 450 tokens

	query	answer
type	string	string
details	min: 10 tokens mean: 11.71 tokens max: 26 tokens	min: 4 tokens mean: 131.81 tokens max: 450 tokens

Samples:

query	answer
`who played the father in papa don't preach`	`Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.`
`where was the location of the battle of hastings`	`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.`
`how many puppies can a dog give birth to`	`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]`

Loss: CSRLoss with these parameters:

{
    "beta": 0.1,
    "gamma": 1.0,
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 10 tokens
mean: 11.69 tokens
max: 23 tokens

min: 15 tokens
mean: 134.01 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 10 tokens mean: 11.69 tokens max: 23 tokens	min: 15 tokens mean: 134.01 tokens max: 512 tokens

Samples:

query	answer
`where is the tiber river located in italy`	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.
`what kind of car does jay gatsby drive`	`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.`
`who sings if i can dream about you`	`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]`

Loss: CSRLoss with these parameters:

{
    "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

Click to expand

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

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.

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

@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

@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

@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}
}