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:
  - text: >-
      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]
  - text: >-
      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]
  - text: >-
      who was the first president of indian science congress meeting held in
      kolkata in 1914
  - text: >-
      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.
  - text: >-
      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
  - query_active_dims
  - query_sparsity_ratio
  - corpus_active_dims
  - corpus_sparsity_ratio
co2_eq_emissions:
  emissions: 66.56126466621346
  energy_consumed: 0.17123983068318005
  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: 0.564
  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 8
          type: NanoMSMARCO_8
        metrics:
          - type: dot_accuracy@1
            value: 0.12
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.24
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.28
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.3
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.12
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.07999999999999999
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.056000000000000015
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.030000000000000006
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.24
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.28
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.21196909248837792
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.18355555555555556
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.19168473018432397
            name: Dot Map@100
          - type: query_active_dims
            value: 8
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.998046875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 8
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.998046875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 8
          type: NanoBEIR_mean_8
        metrics:
          - type: dot_accuracy@1
            value: 0.12
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.24
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.28
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.3
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.12
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.07999999999999999
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.056000000000000015
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.030000000000000006
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.24
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.28
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.21196909248837792
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.18355555555555556
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.19168473018432397
            name: Dot Map@100
          - type: query_active_dims
            value: 8
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.998046875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 8
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.998046875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 16
          type: NanoMSMARCO_16
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.34
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.44
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.044000000000000004
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.22
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.34
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.44
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3259646473373541
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.28955555555555557
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.306813602994791
            name: Dot Map@100
          - type: query_active_dims
            value: 16
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.99609375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 16
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.99609375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 16
          type: NanoBEIR_mean_16
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.34
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.44
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.044000000000000004
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.22
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.34
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.44
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3259646473373541
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.28955555555555557
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.306813602994791
            name: Dot Map@100
          - type: query_active_dims
            value: 16
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.99609375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 16
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.99609375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 32
          type: NanoMSMARCO_32
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.36
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.36
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4175000854041106
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.36360317460317454
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.37705054554799494
            name: Dot Map@100
          - type: query_active_dims
            value: 32
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9921875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 32
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9921875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 32
          type: NanoBEIR_mean_32
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.36
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.36
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4175000854041106
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.36360317460317454
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.37705054554799494
            name: Dot Map@100
          - type: query_active_dims
            value: 32
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9921875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 32
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9921875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 64
          type: NanoMSMARCO_64
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.64
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06400000000000002
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.48
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.56
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.64
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4747516265872855
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4225
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.43804482701175623
            name: Dot Map@100
          - type: query_active_dims
            value: 64
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.984375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 64
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.984375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 64
          type: NanoBEIR_mean_64
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.64
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06400000000000002
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.48
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.56
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.64
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4747516265872855
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4225
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.43804482701175623
            name: Dot Map@100
          - type: query_active_dims
            value: 64
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.984375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 64
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.984375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 128
          type: NanoMSMARCO_128
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.64
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.128
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.54
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5165502329637498
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4448571428571429
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4609321037436295
            name: Dot Map@100
          - type: query_active_dims
            value: 128
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.96875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 128
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.96875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 128
          type: NanoBEIR_mean_128
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.64
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.128
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.54
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5165502329637498
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4448571428571429
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4609321037436295
            name: Dot Map@100
          - type: query_active_dims
            value: 128
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.96875
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 128
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.96875
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 256
          type: NanoMSMARCO_256
        metrics:
          - type: dot_accuracy@1
            value: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.74
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14800000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.34
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.74
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.84
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5842381969358662
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5026904761904762
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5098488479343186
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 256
          type: NanoBEIR_mean_256
        metrics:
          - type: dot_accuracy@1
            value: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.74
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14800000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.34
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.74
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.84
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5842381969358662
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5026904761904762
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5098488479343186
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.26
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.62
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.26
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20666666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.156
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.102
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12333333333333332
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.29333333333333333
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.34666666666666673
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.41566666666666663
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.33074042963512007
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.41507936507936505
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2605037455645458
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.78
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.92
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.96
            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: 0.68
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.6
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.49
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.08787178599815837
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.20076849643437242
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.2551529754028007
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.35977856932473445
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.631230472759085
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8546666666666668
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4715050434861439
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.82
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.94
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.96
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.98
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.82
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.32666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19999999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.10399999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7666666666666666
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.9066666666666667
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9266666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9433333333333332
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8786397520542688
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8795555555555555
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8474023961509473
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: dot_accuracy@1
            value: 0.46
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.64
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.46
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.22799999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13999999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.22924603174603175
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.4312936507936508
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.5035396825396825
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6116190476190476
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.505122448452203
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5688888888888889
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4305964674526582
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - 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: 0.96
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.98
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.78
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.48666666666666664
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.32799999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16999999999999996
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.39
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.73
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.82
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.85
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.7891312606021372
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8563333333333333
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7308084845910934
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - 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: 0.64
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.76
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.78
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            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: 0.078
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.38
            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: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5906197363202759
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.528
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5404706257099874
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.42
            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: 0.68
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.42
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3533333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.32
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.26799999999999996
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.044434174313891364
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.06886292486806139
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.10018663091887436
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.135993408976131
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3272577842417522
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5120238095238094
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1540609053707419
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - 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: 0.68
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.78
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.52
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.23333333333333336
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16399999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.088
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.5
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.65
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.73
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6523707439369819
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6238571428571428
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6127092058948297
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - 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: 0.94
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 1
            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: 0.264
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13799999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7773333333333332
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.912
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.986
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9966666666666666
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9408238851178163
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.935
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.9156785714285713
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: dot_accuracy@1
            value: 0.56
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.56
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.3
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.21
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.11866666666666668
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.2296666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.30966666666666665
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.43066666666666664
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4238434123293462
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6637142857142857
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.33702650955588553
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.28
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.82
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.84
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.28
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2733333333333334
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16799999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.092
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.28
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.82
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.84
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.92
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6320575399829071
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5360714285714285
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5398250835421888
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.7
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.86
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.7
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.24666666666666665
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.176
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09599999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.665
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.68
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.785
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.85
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.7512560957647406
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7302222222222224
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7208552252945762
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: dot_accuracy@1
            value: 0.6326530612244898
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8979591836734694
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9591836734693877
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6326530612244898
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5918367346938774
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5510204081632653
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.4489795918367347
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.04395130839858616
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.12411835933794488
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.18456901766491046
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.30287435988004324
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5113851766135886
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7748542274052478
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.375999626455593
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.5763579277864993
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7629199372056513
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8276295133437992
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.88
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.5763579277864993
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.35988487702773414
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.2774631083202512
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.18653689167974882
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3389617923428206
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.514362315238515
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.5805729466558668
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6443537476256377
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6126522106007865
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6829436096783036
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5336493761921356
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9375
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9375
            name: Corpus Sparsity Ratio

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 with 256 maximum active dimensions 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 (trained with 256 maximum active 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-no-reconstruction-2")
# Run inference
queries = [
    "who is cornelius in the book of acts",
]
documents = [
    '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]",
    '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.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[57.9578, 15.8308, 16.0606]])

Evaluation

Metrics

Sparse Information Retrieval

Dataset: NanoMSMARCO_8
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 8
}
```

Metric	Value
dot_accuracy@1	0.12
dot_accuracy@3	0.24
dot_accuracy@5	0.28
dot_accuracy@10	0.3
dot_precision@1	0.12
dot_precision@3	0.08
dot_precision@5	0.056
dot_precision@10	0.03
dot_recall@1	0.12
dot_recall@3	0.24
dot_recall@5	0.28
dot_recall@10	0.3
dot_ndcg@10	0.212
dot_mrr@10	0.1836
dot_map@100	0.1917
query_active_dims	8.0
query_sparsity_ratio	0.998
corpus_active_dims	8.0
corpus_sparsity_ratio	0.998

Sparse Nano BEIR

Dataset: NanoBEIR_mean_8

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 8
}

Metric	Value
dot_accuracy@1	0.12
dot_accuracy@3	0.24
dot_accuracy@5	0.28
dot_accuracy@10	0.3
dot_precision@1	0.12
dot_precision@3	0.08
dot_precision@5	0.056
dot_precision@10	0.03
dot_recall@1	0.12
dot_recall@3	0.24
dot_recall@5	0.28
dot_recall@10	0.3
dot_ndcg@10	0.212
dot_mrr@10	0.1836
dot_map@100	0.1917
query_active_dims	8.0
query_sparsity_ratio	0.998
corpus_active_dims	8.0
corpus_sparsity_ratio	0.998

Sparse Information Retrieval

Dataset: NanoMSMARCO_16
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 16
}
```

Metric	Value
dot_accuracy@1	0.22
dot_accuracy@3	0.34
dot_accuracy@5	0.4
dot_accuracy@10	0.44
dot_precision@1	0.22
dot_precision@3	0.1133
dot_precision@5	0.08
dot_precision@10	0.044
dot_recall@1	0.22
dot_recall@3	0.34
dot_recall@5	0.4
dot_recall@10	0.44
dot_ndcg@10	0.326
dot_mrr@10	0.2896
dot_map@100	0.3068
query_active_dims	16.0
query_sparsity_ratio	0.9961
corpus_active_dims	16.0
corpus_sparsity_ratio	0.9961

Sparse Nano BEIR

Dataset: NanoBEIR_mean_16

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 16
}

Metric	Value
dot_accuracy@1	0.22
dot_accuracy@3	0.34
dot_accuracy@5	0.4
dot_accuracy@10	0.44
dot_precision@1	0.22
dot_precision@3	0.1133
dot_precision@5	0.08
dot_precision@10	0.044
dot_recall@1	0.22
dot_recall@3	0.34
dot_recall@5	0.4
dot_recall@10	0.44
dot_ndcg@10	0.326
dot_mrr@10	0.2896
dot_map@100	0.3068
query_active_dims	16.0
query_sparsity_ratio	0.9961
corpus_active_dims	16.0
corpus_sparsity_ratio	0.9961

Sparse Information Retrieval

Dataset: NanoMSMARCO_32
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 32
}
```

Metric	Value
dot_accuracy@1	0.3
dot_accuracy@3	0.36
dot_accuracy@5	0.4
dot_accuracy@10	0.6
dot_precision@1	0.3
dot_precision@3	0.12
dot_precision@5	0.08
dot_precision@10	0.06
dot_recall@1	0.3
dot_recall@3	0.36
dot_recall@5	0.4
dot_recall@10	0.6
dot_ndcg@10	0.4175
dot_mrr@10	0.3636
dot_map@100	0.3771
query_active_dims	32.0
query_sparsity_ratio	0.9922
corpus_active_dims	32.0
corpus_sparsity_ratio	0.9922

Sparse Nano BEIR

Dataset: NanoBEIR_mean_32

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 32
}

Metric	Value
dot_accuracy@1	0.3
dot_accuracy@3	0.36
dot_accuracy@5	0.4
dot_accuracy@10	0.6
dot_precision@1	0.3
dot_precision@3	0.12
dot_precision@5	0.08
dot_precision@10	0.06
dot_recall@1	0.3
dot_recall@3	0.36
dot_recall@5	0.4
dot_recall@10	0.6
dot_ndcg@10	0.4175
dot_mrr@10	0.3636
dot_map@100	0.3771
query_active_dims	32.0
query_sparsity_ratio	0.9922
corpus_active_dims	32.0
corpus_sparsity_ratio	0.9922

Sparse Information Retrieval

Dataset: NanoMSMARCO_64
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 64
}
```

Metric	Value
dot_accuracy@1	0.32
dot_accuracy@3	0.48
dot_accuracy@5	0.56
dot_accuracy@10	0.64
dot_precision@1	0.32
dot_precision@3	0.16
dot_precision@5	0.112
dot_precision@10	0.064
dot_recall@1	0.32
dot_recall@3	0.48
dot_recall@5	0.56
dot_recall@10	0.64
dot_ndcg@10	0.4748
dot_mrr@10	0.4225
dot_map@100	0.438
query_active_dims	64.0
query_sparsity_ratio	0.9844
corpus_active_dims	64.0
corpus_sparsity_ratio	0.9844

Sparse Nano BEIR

Dataset: NanoBEIR_mean_64

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 64
}

Metric	Value
dot_accuracy@1	0.32
dot_accuracy@3	0.48
dot_accuracy@5	0.56
dot_accuracy@10	0.64
dot_precision@1	0.32
dot_precision@3	0.16
dot_precision@5	0.112
dot_precision@10	0.064
dot_recall@1	0.32
dot_recall@3	0.48
dot_recall@5	0.56
dot_recall@10	0.64
dot_ndcg@10	0.4748
dot_mrr@10	0.4225
dot_map@100	0.438
query_active_dims	64.0
query_sparsity_ratio	0.9844
corpus_active_dims	64.0
corpus_sparsity_ratio	0.9844

Sparse Information Retrieval

Dataset: NanoMSMARCO_128
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 128
}
```

Metric	Value
dot_accuracy@1	0.3
dot_accuracy@3	0.54
dot_accuracy@5	0.64
dot_accuracy@10	0.74
dot_precision@1	0.3
dot_precision@3	0.18
dot_precision@5	0.128
dot_precision@10	0.074
dot_recall@1	0.3
dot_recall@3	0.54
dot_recall@5	0.64
dot_recall@10	0.74
dot_ndcg@10	0.5166
dot_mrr@10	0.4449
dot_map@100	0.4609
query_active_dims	128.0
query_sparsity_ratio	0.9688
corpus_active_dims	128.0
corpus_sparsity_ratio	0.9688

Sparse Nano BEIR

Dataset: NanoBEIR_mean_128

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 128
}

Metric	Value
dot_accuracy@1	0.3
dot_accuracy@3	0.54
dot_accuracy@5	0.64
dot_accuracy@10	0.74
dot_precision@1	0.3
dot_precision@3	0.18
dot_precision@5	0.128
dot_precision@10	0.074
dot_recall@1	0.3
dot_recall@3	0.54
dot_recall@5	0.64
dot_recall@10	0.74
dot_ndcg@10	0.5166
dot_mrr@10	0.4449
dot_map@100	0.4609
query_active_dims	128.0
query_sparsity_ratio	0.9688
corpus_active_dims	128.0
corpus_sparsity_ratio	0.9688

Sparse Information Retrieval

Dataset: NanoMSMARCO_256
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 256
}
```

Metric	Value
dot_accuracy@1	0.34
dot_accuracy@3	0.6
dot_accuracy@5	0.74
dot_accuracy@10	0.84
dot_precision@1	0.34
dot_precision@3	0.2
dot_precision@5	0.148
dot_precision@10	0.084
dot_recall@1	0.34
dot_recall@3	0.6
dot_recall@5	0.74
dot_recall@10	0.84
dot_ndcg@10	0.5842
dot_mrr@10	0.5027
dot_map@100	0.5098
query_active_dims	256.0
query_sparsity_ratio	0.9375
corpus_active_dims	256.0
corpus_sparsity_ratio	0.9375

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco"
    ],
    "max_active_dims": 256
}

Metric	Value
dot_accuracy@1	0.34
dot_accuracy@3	0.6
dot_accuracy@5	0.74
dot_accuracy@10	0.84
dot_precision@1	0.34
dot_precision@3	0.2
dot_precision@5	0.148
dot_precision@10	0.084
dot_recall@1	0.34
dot_recall@3	0.6
dot_recall@5	0.74
dot_recall@10	0.84
dot_ndcg@10	0.5842
dot_mrr@10	0.5027
dot_map@100	0.5098
query_active_dims	256.0
query_sparsity_ratio	0.9375
corpus_active_dims	256.0
corpus_sparsity_ratio	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	0.26	0.78	0.82	0.46	0.78	0.38	0.42	0.52	0.9	0.56	0.28	0.7	0.6327
dot_accuracy@3	0.56	0.92	0.94	0.64	0.9	0.64	0.58	0.68	0.94	0.7	0.82	0.7	0.898
dot_accuracy@5	0.62	0.96	0.96	0.7	0.96	0.76	0.62	0.78	1.0	0.8	0.84	0.8	0.9592
dot_accuracy@10	0.74	1.0	0.98	0.76	0.98	0.78	0.68	0.82	1.0	0.92	0.92	0.86	1.0
dot_precision@1	0.26	0.78	0.82	0.46	0.78	0.38	0.42	0.52	0.9	0.56	0.28	0.7	0.6327
dot_precision@3	0.2067	0.68	0.3267	0.3	0.4867	0.2133	0.3533	0.2333	0.4	0.3667	0.2733	0.2467	0.5918
dot_precision@5	0.156	0.6	0.2	0.228	0.328	0.152	0.32	0.164	0.264	0.3	0.168	0.176	0.551
dot_precision@10	0.102	0.49	0.104	0.14	0.17	0.078	0.268	0.088	0.138	0.21	0.092	0.096	0.449
dot_recall@1	0.1233	0.0879	0.7667	0.2292	0.39	0.38	0.0444	0.5	0.7773	0.1187	0.28	0.665	0.044
dot_recall@3	0.2933	0.2008	0.9067	0.4313	0.73	0.64	0.0689	0.65	0.912	0.2297	0.82	0.68	0.1241
dot_recall@5	0.3467	0.2552	0.9267	0.5035	0.82	0.76	0.1002	0.73	0.986	0.3097	0.84	0.785	0.1846
dot_recall@10	0.4157	0.3598	0.9433	0.6116	0.85	0.78	0.136	0.78	0.9967	0.4307	0.92	0.85	0.3029
dot_ndcg@10	0.3307	0.6312	0.8786	0.5051	0.7891	0.5906	0.3273	0.6524	0.9408	0.4238	0.6321	0.7513	0.5114
dot_mrr@10	0.4151	0.8547	0.8796	0.5689	0.8563	0.528	0.512	0.6239	0.935	0.6637	0.5361	0.7302	0.7749
dot_map@100	0.2605	0.4715	0.8474	0.4306	0.7308	0.5405	0.1541	0.6127	0.9157	0.337	0.5398	0.7209	0.376
query_active_dims	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
query_sparsity_ratio	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
corpus_active_dims	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
corpus_sparsity_ratio	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	0.5764
dot_accuracy@3	0.7629
dot_accuracy@5	0.8276
dot_accuracy@10	0.88
dot_precision@1	0.5764
dot_precision@3	0.3599
dot_precision@5	0.2775
dot_precision@10	0.1865
dot_recall@1	0.339
dot_recall@3	0.5144
dot_recall@5	0.5806
dot_recall@10	0.6444
dot_ndcg@10	0.6127
dot_mrr@10	0.6829
dot_map@100	0.5336
query_active_dims	256.0
query_sparsity_ratio	0.9375
corpus_active_dims	256.0
corpus_sparsity_ratio	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": 3.0,
    "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": 3.0,
    "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
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_8_dot_ndcg@10	NanoBEIR_mean_8_dot_ndcg@10	NanoMSMARCO_16_dot_ndcg@10	NanoBEIR_mean_16_dot_ndcg@10	NanoMSMARCO_32_dot_ndcg@10	NanoBEIR_mean_32_dot_ndcg@10	NanoMSMARCO_64_dot_ndcg@10	NanoBEIR_mean_64_dot_ndcg@10	NanoMSMARCO_128_dot_ndcg@10	NanoBEIR_mean_128_dot_ndcg@10	NanoMSMARCO_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	-	-	0.2445	0.2445	0.3517	0.3517	0.5001	0.5001	0.5672	0.5672	0.6083	0.6083	0.6025	0.6025	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	100	0.1844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	200	0.1765	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	300	0.1581	0.1742	0.2187	0.2187	0.3538	0.3538	0.4677	0.4677	0.5313	0.5313	0.5713	0.5713	0.5932	0.5932	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	400	0.134	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	500	0.179	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	600	0.1414	0.2028	0.2075	0.2075	0.3395	0.3395	0.4250	0.4250	0.4930	0.4930	0.5670	0.5670	0.5534	0.5534	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	700	0.162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	800	0.1632	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	900	0.1684	0.1907	0.1784	0.1784	0.3429	0.3429	0.4207	0.4207	0.4764	0.4764	0.5705	0.5705	0.5861	0.5861	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	1000	0.1577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	1100	0.1249	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	1200	0.1494	0.1506	0.1993	0.1993	0.3459	0.3459	0.4185	0.4185	0.4925	0.4925	0.5248	0.5248	0.5880	0.5880	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	1300	0.1457	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	1400	0.1208	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	1500	0.1346	0.1349	0.2120	0.2120	0.3260	0.3260	0.4175	0.4175	0.4748	0.4748	0.5166	0.5166	0.5842	0.5842	-	-	-	-	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	-	-	-	-	-	-	-	-	-	-	-	-	0.3307	0.6312	0.8786	0.5051	0.7891	0.5906	0.3273	0.6524	0.9408	0.4238	0.6321	0.7513	0.5114	0.6127

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.171 kWh
Carbon Emitted: 0.067 kg of CO2
Hours Used: 0.563 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: 4.52.4
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}
}