Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

2_CSRSparsity/config.json

@@ -0,0 +1 @@
+{"input_dim": 768, "hidden_dim": 3072, "k": 256, "k_aux": 512, "normalize": false, "dead_threshold": 30}

2_CSRSparsity/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3dcc951374b07cc3b2651c4ccd400b2b2c19c63b35ee7891d3e0ba835fe13a4
+size 9477512

README.md

@@ -0,0 +1,1867 @@
+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:99000
+- loss:CSRLoss
+base_model: microsoft/mpnet-base
+widget:
+- source_sentence: what is the difference between uae and saudi arabia
+  sentences:
+  - 'Monopoly Junior Players take turns in order, with the initial player determined
+    by age before the game: the youngest player goes first. Players are dealt an initial
+    amount Monopoly money depending on the total number of players playing: 20 in
+    a two-player game, 18 in a three-player game or 16 in a four-player game. A typical
+    turn begins with the rolling of the die and the player advancing their token clockwise
+    around the board the corresponding number of spaces. When the player lands on
+    an unowned space they must purchase the space from the bank for the amount indicated
+    on the board, and places a sold sign on the coloured band at the top of the space
+    to denote ownership. If a player lands on a space owned by an opponent the player
+    pays the opponent rent in the amount written on the board. If the opponent owns
+    both properties of the same colour the rent is doubled.'
+  - Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
+    continue to take somewhat differing stances on regional conflicts such the Yemeni
+    Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
+    which has fought against Saudi-backed forces, and the Syrian Civil War, where
+    the UAE has disagreed with Saudi support for Islamist movements.[4]
+  - Governors of states of India The governors and lieutenant-governors are appointed
+    by the President for a term of five years.
+- source_sentence: who came up with the seperation of powers
+  sentences:
+  - Separation of powers Aristotle first mentioned the idea of a "mixed government"
+    or hybrid government in his work Politics where he drew upon many of the constitutional
+    forms in the city-states of Ancient Greece. In the Roman Republic, the Roman Senate,
+    Consuls and the Assemblies showed an example of a mixed government according to
+    Polybius (Histories, Book 6, 11–13).
+  - Economy of New Zealand New Zealand's diverse market economy has a sizable service
+    sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing
+    industries include aluminium production, food processing, metal fabrication, wood
+    and paper products. Mining, manufacturing, electricity, gas, water, and waste
+    services accounted for 16.5% of GDP in 2013.[17] The primary sector continues
+    to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17]
+  - John Dalton John Dalton FRS (/ˈdɔːltən/; 6 September 1766 – 27 July 1844) was
+    an English chemist, physicist, and meteorologist. He is best known for proposing
+    the modern atomic theory and for his research into colour blindness, sometimes
+    referred to as Daltonism in his honour.
+- source_sentence: who was the first president of indian science congress meeting
+    held in kolkata in 1914
+  sentences:
+  - Nobody to Blame "Nobody to Blame" is a song recorded by American country music
+    artist Chris Stapleton. The song was released in November 2015 as the singer's
+    third single overall. Stapleton co-wrote the song with Barry Bales and Ronnie
+    Bowman. It became Stapleton's first top 10 single on the US Country Airplay chart.[2]
+    "Nobody to Blame" won Song of the Year at the ACM Awards.[3]
+  - Indian Science Congress Association The first meeting of the congress was held
+    from 15–17 January 1914 at the premises of the Asiatic Society, Calcutta. Honorable
+    justice Sir Ashutosh Mukherjee, the then Vice Chancellor of the University of
+    Calcutta presided over the Congress. One hundred and five scientists from different
+    parts of India and abroad attended it. Altogether 35 papers under 6 different
+    sections, namely Botany, Chemistry, Ethnography, Geology, Physics and Zoology
+    were presented.
+  - New Soul "New Soul" is a song by the French-Israeli R&B/soul singer Yael Naïm,
+    from her self-titled second album. The song gained popularity in the United States
+    following its use by Apple in an advertisement for their MacBook Air laptop. In
+    the song Naïm sings of being a new soul who has come into the world to learn "a
+    bit 'bout how to give and take." However, she finds that things are harder than
+    they seem. The song, also featured in the films The House Bunny and Wild Target,
+    features a prominent "la la la la" section as its hook. It remains Naïm's biggest
+    hit single in the U.S. to date, and her only one to reach the Top 40 of the Billboard
+    Hot 100.
+- source_sentence: who wrote get over it by the eagles
+  sentences:
+  - Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a
+    single after a fourteen-year breakup. It was also the first song written by bandmates
+    Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live
+    for the first time during their Hell Freezes Over tour in 1994. It returned the
+    band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the
+    Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks
+    chart. The song was not played live by the Eagles after the "Hell Freezes Over"
+    tour in 1994. It remains the group's last Top 40 hit in the U.S.
+  - Pokhran-II In 1980, the general elections marked the return of Indira Gandhi and
+    the nuclear program began to gain momentum under Ramanna in 1981. Requests for
+    additional nuclear tests were continued to be denied by the government when Prime
+    Minister Indira Gandhi saw Pakistan began exercising the brinkmanship, though
+    the nuclear program continued to advance.[7] Initiation towards hydrogen bomb
+    began as well as the launch of the missile programme began under Late president
+    Dr. Abdul Kalam, who was then an aerospace engineer.[7]
+  - R. Budd Dwyer Robert Budd Dwyer (November 21, 1939 – January 22, 1987) was the
+    30th State Treasurer of the Commonwealth of Pennsylvania. He served from 1971
+    to 1981 as a Republican member of the Pennsylvania State Senate representing the
+    state's 50th district. He then served as the 30th Treasurer of Pennsylvania from
+    January 20, 1981, until his death. On January 22, 1987, Dwyer called a news conference
+    in the Pennsylvania state capital of Harrisburg where he killed himself in front
+    of the gathered reporters, by shooting himself in the mouth with a .357 Magnum
+    revolver.[4] Dwyer's suicide was broadcast later that day to a wide television
+    audience across Pennsylvania.
+- source_sentence: who is cornelius in the book of acts
+  sentences:
+  - Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It
+    was included on Clapton's 1977 album Slowhand. Clapton wrote the song about Pattie
+    Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit
+    (then Marcy Levy) and Yvonne Elliman.
+  - Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their
+    head of story.[1] There he worked on all of their films produced up to 2006; this
+    included Toy Story (for which he received an Academy Award nomination) and A Bug's
+    Life, as the co-story writer and others as story supervisor. His final film was
+    Cars. He also voiced characters in many of the films, including Heimlich the caterpillar
+    in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in
+    Finding Nemo.[1]
+  - 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who
+    is considered by Christians to be one of the first Gentiles to convert to the
+    faith, as related in Acts of the Apostles.'
+datasets:
+- sentence-transformers/natural-questions
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+co2_eq_emissions:
+  emissions: 113.44094173179047
+  energy_consumed: 0.29184553136281904
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.773
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: SparseEncoder based on microsoft/mpnet-base
+  results:
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO 16
+      type: NanoMSMARCO_16
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.1
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.26
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.36
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.1
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.08666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.07200000000000001
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05000000000000001
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.26
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.36
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.272077335852507
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20234920634920633
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21758364304569
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus 16
+      type: NanoNFCorpus_16
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.08
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.14
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.24
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.32
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.08
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05999999999999999
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.08
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.005993249911183041
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.009403252754209558
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.013285393478414642
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.01646720008819819
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.06095056479011788
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.14072222222222222
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.015310893897400863
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ 16
+      type: NanoNQ_16
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.42
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.54
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.64
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13999999999999999
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.10800000000000003
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.064
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3867151912670764
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3266904761904762
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.3250246379519026
+      name: Cosine Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean 16
+      type: NanoBEIR_mean_16
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2733333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.38000000000000006
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.48666666666666664
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09555555555555555
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.08666666666666668
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05466666666666667
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.09533108330372768
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2231344175847365
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.29109513115947155
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.3721557333627327
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2399143639699004
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.22325396825396826
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.18597305829833113
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO 32
+      type: NanoMSMARCO_32
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.26
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.36
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.56
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.08666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.07200000000000001
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05600000000000001
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.26
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.36
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.56
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.33109644128066057
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2634444444444444
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.27935469743863556
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus 32
+      type: NanoNFCorpus_32
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.14
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.26
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.28
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.34
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.14
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.11333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.09600000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.007695869325666863
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.012313937822266688
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.01702903494334016
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.024165659145052122
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.10225707780728845
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2055238095238095
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.022577551502700435
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ 32
+      type: NanoNQ_32
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.32
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.46
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.58
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.68
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.32
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.15333333333333332
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11600000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.068
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.31
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.42
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.53
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.63
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4603957123337682
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4211904761904762
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.41127594932176303
+      name: Cosine Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean 32
+      type: NanoBEIR_mean_32
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.21333333333333335
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.32666666666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.4066666666666667
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5266666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.21333333333333335
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.11777777777777776
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.09466666666666668
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07133333333333335
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.16589862310855563
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.23077131260742223
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30234301164778005
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4047218863816841
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.29791641047390577
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2967195767195767
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23773606608769968
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO 64
+      type: NanoMSMARCO_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.16
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.38
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.46
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.16
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.12666666666666665
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.09200000000000001
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.06
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.16
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.38
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.46
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3545165496884908
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.27796031746031746
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.29572845389453484
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus 64
+      type: NanoNFCorpus_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.26
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.32
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.12666666666666665
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.088
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.009483451025013268
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.012904129822135095
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.036867855927155205
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.04756198673273659
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.11496239522394665
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.24210317460317454
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.0318282871881163
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ 64
+      type: NanoNQ_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.44
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.62
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.68
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.72
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.44
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.20666666666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07400000000000001
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.42
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.58
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.64
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.68
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.561884513825323
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5395555555555555
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5268055680783221
+      name: Cosine Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean 64
+      type: NanoBEIR_mean_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.26
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.42
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.48666666666666664
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5733333333333334
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.26
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.15333333333333332
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11733333333333335
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.074
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.19649448367500444
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.32430137660737834
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3789559519757184
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4425206622442455
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3437878195792535
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.35320634920634914
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2847874363869911
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO 128
+      type: NanoMSMARCO_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.34
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.46
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.68
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.11333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.09200000000000001
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.068
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.2
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.34
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.46
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.68
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4022072447482653
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.31815873015873014
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.33230553462724927
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus 128
+      type: NanoNFCorpus_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.14
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.34
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.38
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.52
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.14
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.16666666666666663
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.128
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.11399999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.0036955722371344803
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.021194355136532755
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.024553995602026958
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.043293677887263404
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.12666378888376595
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2537936507936508
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.03330968914510828
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ 128
+      type: NanoNQ_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.38
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.56
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.38
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.18666666666666665
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14400000000000002
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08199999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.35
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.53
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.66
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.76
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5527057053472701
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5072460317460317
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.4846991157483792
+      name: Cosine Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean 128
+      type: NanoBEIR_mean_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.24
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4133333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5133333333333333
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6666666666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.24
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.15555555555555553
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12133333333333335
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08800000000000001
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1845651907457115
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2970647850455109
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.381517998534009
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4944312259624211
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3605255796597671
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.35973280423280424
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2834381131735789
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO 256
+      type: NanoMSMARCO_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.26
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.48
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.52
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.68
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.26
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.15999999999999998
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.10400000000000001
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.068
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.26
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.48
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.52
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.68
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4651758219790261
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.39804761904761904
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.412474140043243
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNFCorpus 256
+      type: NanoNFCorpus_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.28
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.38
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.14666666666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.114
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.005516710448516594
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.011401609103753301
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.021271103372355084
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.0347182833647384
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.12628863554710404
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2575
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.033728487141126466
+      name: Cosine Map@100
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoNQ 256
+      type: NanoNQ_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.42
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.58
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.68
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.76
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.42
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.54
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.64
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.73
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5611650669716552
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5226904761904763
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5086922580864135
+      name: Cosine Map@100
+  - task:
+      type: sparse-nano-beir
+      name: Sparse Nano BEIR
+    dataset:
+      name: NanoBEIR mean 256
+      type: NanoBEIR_mean_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2866666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4466666666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5266666666666667
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6466666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2866666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.16666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.128
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08733333333333333
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.22183890348283888
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3438005363679178
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3937570344574517
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.48157276112157943
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3842098414992618
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3927460317460318
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.31829829509026103
+      name: Cosine Map@100
+---
+
+# SparseEncoder based on microsoft/mpnet-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): CSRSparsity({'input_dim': 768, 'hidden_dim': 3072, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("tomaarsen/sparse-mpnet-base-nq-fresh")
+# Run inference
+sentences = [
+    'who is cornelius in the book of acts',
+    'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
+    "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO_16`, `NanoNFCorpus_16` and `NanoNQ_16`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 16
+  }
+  ```
+
+| Metric              | NanoMSMARCO_16 | NanoNFCorpus_16 | NanoNQ_16  |
+|:--------------------|:---------------|:----------------|:-----------|
+| cosine_accuracy@1   | 0.1            | 0.08            | 0.18       |
+| cosine_accuracy@3   | 0.26           | 0.14            | 0.42       |
+| cosine_accuracy@5   | 0.36           | 0.24            | 0.54       |
+| cosine_accuracy@10  | 0.5            | 0.32            | 0.64       |
+| cosine_precision@1  | 0.1            | 0.08            | 0.18       |
+| cosine_precision@3  | 0.0867         | 0.06            | 0.14       |
+| cosine_precision@5  | 0.072          | 0.08            | 0.108      |
+| cosine_precision@10 | 0.05           | 0.05            | 0.064      |
+| cosine_recall@1     | 0.1            | 0.006           | 0.18       |
+| cosine_recall@3     | 0.26           | 0.0094          | 0.4        |
+| cosine_recall@5     | 0.36           | 0.0133          | 0.5        |
+| cosine_recall@10    | 0.5            | 0.0165          | 0.6        |
+| **cosine_ndcg@10**  | **0.2721**     | **0.061**       | **0.3867** |
+| cosine_mrr@10       | 0.2023         | 0.1407          | 0.3267     |
+| cosine_map@100      | 0.2176         | 0.0153          | 0.325      |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean_16`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "truncate_dim": 16
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.12       |
+| cosine_accuracy@3   | 0.2733     |
+| cosine_accuracy@5   | 0.38       |
+| cosine_accuracy@10  | 0.4867     |
+| cosine_precision@1  | 0.12       |
+| cosine_precision@3  | 0.0956     |
+| cosine_precision@5  | 0.0867     |
+| cosine_precision@10 | 0.0547     |
+| cosine_recall@1     | 0.0953     |
+| cosine_recall@3     | 0.2231     |
+| cosine_recall@5     | 0.2911     |
+| cosine_recall@10    | 0.3722     |
+| **cosine_ndcg@10**  | **0.2399** |
+| cosine_mrr@10       | 0.2233     |
+| cosine_map@100      | 0.186      |
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO_32`, `NanoNFCorpus_32` and `NanoNQ_32`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 32
+  }
+  ```
+
+| Metric              | NanoMSMARCO_32 | NanoNFCorpus_32 | NanoNQ_32  |
+|:--------------------|:---------------|:----------------|:-----------|
+| cosine_accuracy@1   | 0.18           | 0.14            | 0.32       |
+| cosine_accuracy@3   | 0.26           | 0.26            | 0.46       |
+| cosine_accuracy@5   | 0.36           | 0.28            | 0.58       |
+| cosine_accuracy@10  | 0.56           | 0.34            | 0.68       |
+| cosine_precision@1  | 0.18           | 0.14            | 0.32       |
+| cosine_precision@3  | 0.0867         | 0.1133          | 0.1533     |
+| cosine_precision@5  | 0.072          | 0.096           | 0.116      |
+| cosine_precision@10 | 0.056          | 0.09            | 0.068      |
+| cosine_recall@1     | 0.18           | 0.0077          | 0.31       |
+| cosine_recall@3     | 0.26           | 0.0123          | 0.42       |
+| cosine_recall@5     | 0.36           | 0.017           | 0.53       |
+| cosine_recall@10    | 0.56           | 0.0242          | 0.63       |
+| **cosine_ndcg@10**  | **0.3311**     | **0.1023**      | **0.4604** |
+| cosine_mrr@10       | 0.2634         | 0.2055          | 0.4212     |
+| cosine_map@100      | 0.2794         | 0.0226          | 0.4113     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean_32`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "truncate_dim": 32
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.2133     |
+| cosine_accuracy@3   | 0.3267     |
+| cosine_accuracy@5   | 0.4067     |
+| cosine_accuracy@10  | 0.5267     |
+| cosine_precision@1  | 0.2133     |
+| cosine_precision@3  | 0.1178     |
+| cosine_precision@5  | 0.0947     |
+| cosine_precision@10 | 0.0713     |
+| cosine_recall@1     | 0.1659     |
+| cosine_recall@3     | 0.2308     |
+| cosine_recall@5     | 0.3023     |
+| cosine_recall@10    | 0.4047     |
+| **cosine_ndcg@10**  | **0.2979** |
+| cosine_mrr@10       | 0.2967     |
+| cosine_map@100      | 0.2377     |
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO_64`, `NanoNFCorpus_64` and `NanoNQ_64`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 64
+  }
+  ```
+
+| Metric              | NanoMSMARCO_64 | NanoNFCorpus_64 | NanoNQ_64  |
+|:--------------------|:---------------|:----------------|:-----------|
+| cosine_accuracy@1   | 0.16           | 0.18            | 0.44       |
+| cosine_accuracy@3   | 0.38           | 0.26            | 0.62       |
+| cosine_accuracy@5   | 0.46           | 0.32            | 0.68       |
+| cosine_accuracy@10  | 0.6            | 0.4             | 0.72       |
+| cosine_precision@1  | 0.16           | 0.18            | 0.44       |
+| cosine_precision@3  | 0.1267         | 0.1267          | 0.2067     |
+| cosine_precision@5  | 0.092          | 0.12            | 0.14       |
+| cosine_precision@10 | 0.06           | 0.088           | 0.074      |
+| cosine_recall@1     | 0.16           | 0.0095          | 0.42       |
+| cosine_recall@3     | 0.38           | 0.0129          | 0.58       |
+| cosine_recall@5     | 0.46           | 0.0369          | 0.64       |
+| cosine_recall@10    | 0.6            | 0.0476          | 0.68       |
+| **cosine_ndcg@10**  | **0.3545**     | **0.115**       | **0.5619** |
+| cosine_mrr@10       | 0.278          | 0.2421          | 0.5396     |
+| cosine_map@100      | 0.2957         | 0.0318          | 0.5268     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean_64`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "truncate_dim": 64
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.26       |
+| cosine_accuracy@3   | 0.42       |
+| cosine_accuracy@5   | 0.4867     |
+| cosine_accuracy@10  | 0.5733     |
+| cosine_precision@1  | 0.26       |
+| cosine_precision@3  | 0.1533     |
+| cosine_precision@5  | 0.1173     |
+| cosine_precision@10 | 0.074      |
+| cosine_recall@1     | 0.1965     |
+| cosine_recall@3     | 0.3243     |
+| cosine_recall@5     | 0.379      |
+| cosine_recall@10    | 0.4425     |
+| **cosine_ndcg@10**  | **0.3438** |
+| cosine_mrr@10       | 0.3532     |
+| cosine_map@100      | 0.2848     |
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 128
+  }
+  ```
+
+| Metric              | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 |
+|:--------------------|:----------------|:-----------------|:-----------|
+| cosine_accuracy@1   | 0.2             | 0.14             | 0.38       |
+| cosine_accuracy@3   | 0.34            | 0.34             | 0.56       |
+| cosine_accuracy@5   | 0.46            | 0.38             | 0.7        |
+| cosine_accuracy@10  | 0.68            | 0.52             | 0.8        |
+| cosine_precision@1  | 0.2             | 0.14             | 0.38       |
+| cosine_precision@3  | 0.1133          | 0.1667           | 0.1867     |
+| cosine_precision@5  | 0.092           | 0.128            | 0.144      |
+| cosine_precision@10 | 0.068           | 0.114            | 0.082      |
+| cosine_recall@1     | 0.2             | 0.0037           | 0.35       |
+| cosine_recall@3     | 0.34            | 0.0212           | 0.53       |
+| cosine_recall@5     | 0.46            | 0.0246           | 0.66       |
+| cosine_recall@10    | 0.68            | 0.0433           | 0.76       |
+| **cosine_ndcg@10**  | **0.4022**      | **0.1267**       | **0.5527** |
+| cosine_mrr@10       | 0.3182          | 0.2538           | 0.5072     |
+| cosine_map@100      | 0.3323          | 0.0333           | 0.4847     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean_128`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "truncate_dim": 128
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.24       |
+| cosine_accuracy@3   | 0.4133     |
+| cosine_accuracy@5   | 0.5133     |
+| cosine_accuracy@10  | 0.6667     |
+| cosine_precision@1  | 0.24       |
+| cosine_precision@3  | 0.1556     |
+| cosine_precision@5  | 0.1213     |
+| cosine_precision@10 | 0.088      |
+| cosine_recall@1     | 0.1846     |
+| cosine_recall@3     | 0.2971     |
+| cosine_recall@5     | 0.3815     |
+| cosine_recall@10    | 0.4944     |
+| **cosine_ndcg@10**  | **0.3605** |
+| cosine_mrr@10       | 0.3597     |
+| cosine_map@100      | 0.2834     |
+
+#### Sparse Information Retrieval
+
+* Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 256
+  }
+  ```
+
+| Metric              | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 |
+|:--------------------|:----------------|:-----------------|:-----------|
+| cosine_accuracy@1   | 0.26            | 0.18             | 0.42       |
+| cosine_accuracy@3   | 0.48            | 0.28             | 0.58       |
+| cosine_accuracy@5   | 0.52            | 0.38             | 0.68       |
+| cosine_accuracy@10  | 0.68            | 0.5              | 0.76       |
+| cosine_precision@1  | 0.26            | 0.18             | 0.42       |
+| cosine_precision@3  | 0.16            | 0.1467           | 0.1933     |
+| cosine_precision@5  | 0.104           | 0.14             | 0.14       |
+| cosine_precision@10 | 0.068           | 0.114            | 0.08       |
+| cosine_recall@1     | 0.26            | 0.0055           | 0.4        |
+| cosine_recall@3     | 0.48            | 0.0114           | 0.54       |
+| cosine_recall@5     | 0.52            | 0.0213           | 0.64       |
+| cosine_recall@10    | 0.68            | 0.0347           | 0.73       |
+| **cosine_ndcg@10**  | **0.4652**      | **0.1263**       | **0.5612** |
+| cosine_mrr@10       | 0.398           | 0.2575           | 0.5227     |
+| cosine_map@100      | 0.4125          | 0.0337           | 0.5087     |
+
+#### Sparse Nano BEIR
+
+* Dataset: `NanoBEIR_mean_256`
+* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "truncate_dim": 256
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.2867     |
+| cosine_accuracy@3   | 0.4467     |
+| cosine_accuracy@5   | 0.5267     |
+| cosine_accuracy@10  | 0.6467     |
+| cosine_precision@1  | 0.2867     |
+| cosine_precision@3  | 0.1667     |
+| cosine_precision@5  | 0.128      |
+| cosine_precision@10 | 0.0873     |
+| cosine_recall@1     | 0.2218     |
+| cosine_recall@3     | 0.3438     |
+| cosine_recall@5     | 0.3938     |
+| cosine_recall@10    | 0.4816     |
+| **cosine_ndcg@10**  | **0.3842** |
+| cosine_mrr@10       | 0.3927     |
+| cosine_map@100      | 0.3183     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### natural-questions
+
+* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
+* Size: 99,000 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | answer                                                                              |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
+* Samples:
+  | query                                                         | answer                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+  |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>who played the father in papa don't preach</code>       | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code>                                                                                                                                                                                                                                                                                                                                                     |
+  | <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
+  | <code>how many puppies can a dog give birth to</code>         | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code>                                                                                                                                                                                                                                                      |
+* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters:
+  ```json
+  {
+      "beta": 0.1,
+      "gamma": 1,
+      "scale": 20.0
+  }
+  ```
+
+### Evaluation Dataset
+
+#### natural-questions
+
+* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
+* Size: 1,000 evaluation samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | answer                                                                               |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query                                                  | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+  |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
+  | <code>what kind of car does jay gatsby drive</code>    | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code>                                       |
+  | <code>who sings if i can dream about you</code>        | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code>                                                                                                                                                                                                                   |
+* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters:
+  ```json
+  {
+      "beta": 0.1,
+      "gamma": 1,
+      "scale": 20.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 4e-05
+- `weight_decay`: 0.0001
+- `adam_epsilon`: 6.25e-10
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 4e-05
+- `weight_decay`: 0.0001
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 6.25e-10
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_16_cosine_ndcg@10 | NanoNFCorpus_16_cosine_ndcg@10 | NanoNQ_16_cosine_ndcg@10 | NanoBEIR_mean_16_cosine_ndcg@10 | NanoMSMARCO_32_cosine_ndcg@10 | NanoNFCorpus_32_cosine_ndcg@10 | NanoNQ_32_cosine_ndcg@10 | NanoBEIR_mean_32_cosine_ndcg@10 | NanoMSMARCO_64_cosine_ndcg@10 | NanoNFCorpus_64_cosine_ndcg@10 | NanoNQ_64_cosine_ndcg@10 | NanoBEIR_mean_64_cosine_ndcg@10 | NanoMSMARCO_128_cosine_ndcg@10 | NanoNFCorpus_128_cosine_ndcg@10 | NanoNQ_128_cosine_ndcg@10 | NanoBEIR_mean_128_cosine_ndcg@10 | NanoMSMARCO_256_cosine_ndcg@10 | NanoNFCorpus_256_cosine_ndcg@10 | NanoNQ_256_cosine_ndcg@10 | NanoBEIR_mean_256_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:|
+| -1     | -1   | -             | -               | 0.0318                        | 0.0148                         | 0.0149                   | 0.0205                          | 0.0794                        | 0.0234                         | 0.0102                   | 0.0377                          | 0.0855                        | 0.0195                         | 0.0508                   | 0.0519                          | 0.1081                         | 0.0246                          | 0.0264                    | 0.0530                           | 0.1006                         | 0.0249                          | 0.0388                    | 0.0547                           |
+| 0.0646 | 200  | 0.7332        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.1293 | 400  | 0.2606        | 0.1970          | 0.2845                        | 0.0970                         | 0.3546                   | 0.2454                          | 0.3778                        | 0.1358                         | 0.3455                   | 0.2864                          | 0.3868                        | 0.1563                         | 0.3806                   | 0.3079                          | 0.3988                         | 0.1664                          | 0.4035                    | 0.3229                           | 0.4020                         | 0.1782                          | 0.4181                    | 0.3327                           |
+| 0.1939 | 600  | 0.2247        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.2586 | 800  | 0.1983        | 0.1750          | 0.2908                        | 0.0866                         | 0.3730                   | 0.2502                          | 0.3324                        | 0.1155                         | 0.4275                   | 0.2918                          | 0.3511                        | 0.1621                         | 0.4998                   | 0.3377                          | 0.3920                         | 0.1563                          | 0.5174                    | 0.3553                           | 0.4152                         | 0.1555                          | 0.5153                    | 0.3620                           |
+| 0.3232 | 1000 | 0.1822        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.3878 | 1200 | 0.1846        | 0.1594          | 0.2775                        | 0.0785                         | 0.3723                   | 0.2428                          | 0.2642                        | 0.1076                         | 0.4389                   | 0.2702                          | 0.3865                        | 0.1328                         | 0.4329                   | 0.3174                          | 0.3883                         | 0.1446                          | 0.5040                    | 0.3456                           | 0.3638                         | 0.1529                          | 0.4939                    | 0.3369                           |
+| 0.4525 | 1400 | 0.1669        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.5171 | 1600 | 0.1573        | 0.1452          | 0.2740                        | 0.0624                         | 0.3670                   | 0.2345                          | 0.3557                        | 0.0855                         | 0.4188                   | 0.2867                          | 0.4094                        | 0.1099                         | 0.5027                   | 0.3407                          | 0.3885                         | 0.1340                          | 0.4990                    | 0.3405                           | 0.4820                         | 0.1577                          | 0.5453                    | 0.3950                           |
+| 0.5818 | 1800 | 0.1502        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.6464 | 2000 | 0.1375        | 0.1255          | 0.2307                        | 0.0685                         | 0.3801                   | 0.2264                          | 0.2529                        | 0.0815                         | 0.4335                   | 0.2560                          | 0.3509                        | 0.0955                         | 0.4611                   | 0.3025                          | 0.3932                         | 0.1339                          | 0.4875                    | 0.3382                           | 0.4184                         | 0.1483                          | 0.4904                    | 0.3523                           |
+| 0.7111 | 2200 | 0.1359        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.7757 | 2400 | 0.1288        | 0.1184          | 0.2737                        | 0.0703                         | 0.3419                   | 0.2286                          | 0.3765                        | 0.0843                         | 0.4440                   | 0.3016                          | 0.3927                        | 0.1247                         | 0.5285                   | 0.3486                          | 0.3726                         | 0.1203                          | 0.5153                    | 0.3361                           | 0.4676                         | 0.1343                          | 0.5523                    | 0.3847                           |
+| 0.8403 | 2600 | 0.1235        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| 0.9050 | 2800 | 0.1168        | 0.1094          | 0.2751                        | 0.0710                         | 0.3602                   | 0.2354                          | 0.3227                        | 0.0966                         | 0.5046                   | 0.3080                          | 0.4112                        | 0.1129                         | 0.5268                   | 0.3503                          | 0.4077                         | 0.1259                          | 0.5253                    | 0.3530                           | 0.4642                         | 0.1238                          | 0.5726                    | 0.3869                           |
+| 0.9696 | 3000 | 0.1187        | -               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                             | -                              | -                        | -                               | -                              | -                               | -                         | -                                | -                              | -                               | -                         | -                                |
+| -1     | -1   | -             | -               | 0.2721                        | 0.0610                         | 0.3867                   | 0.2399                          | 0.3311                        | 0.1023                         | 0.4604                   | 0.2979                          | 0.3545                        | 0.1150                         | 0.5619                   | 0.3438                          | 0.4022                         | 0.1267                          | 0.5527                    | 0.3605                           | 0.4652                         | 0.1263                          | 0.5612                    | 0.3842                           |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.292 kWh
+- **Carbon Emitted**: 0.113 kg of CO2
+- **Hours Used**: 0.773 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 4.1.0.dev0
+- Transformers: 4.52.0.dev0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.1
+- Datasets: 3.3.2
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.0.dev0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0.dev0",
+    "transformers": "4.52.0.dev0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c28861893896a25094e805ad697c3c10c34aa1c24a6d71fbcd27a751df2b3d7
+size 437967672

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_CSRSparsity",
+    "type": "sentence_transformers.sparse_encoder.models.CSRSparsity"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}