metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:3011496
  - loss:SpladeLoss
base_model: Luyu/co-condenser-marco
widget:
  - source_sentence: how much percent of alcohol is in scotch?
    sentences:
      - >-
        Our 24-hour day comes from the ancient Egyptians who divided day-time
        into 10 hours they measured with devices such as shadow clocks, and
        added a twilight hour at the beginning and another one at the end of the
        day-time, says Lomb. "Night-time was divided in 12 hours, based on the
        observations of stars.
      - >-
        After distillation, a Scotch Whisky can be anywhere between 60-75% ABV,
        with American Whiskey rocketing right into the 90% region. Before being
        placed in casks, Scotch is usually diluted to around 63.5% ABV (68% for
        grain); welcome to the stage cask strength Whisky.
      - >-
        Money For Nothing. In season four Dominic West, the ostensible star of
        the series, requested a reduced role so that he could spend more time
        with his family in London. On the show it was explained that Jimmy
        McNulty had taken a patrol job which required less strenuous work.
  - source_sentence: what are the major causes of poor listening?
    sentences:
      - >-
        The four main causes of poor listening are due to not concentrating,
        listening too hard, jumping to conclusions and focusing on delivery and
        personal appearance. Sometimes we just don't feel attentive enough and
        hence don't concentrate.
      - >-
        That's called being idle. “System Idle Process” is the software that
        runs when the computer has absolutely nothing better to do. It has the
        lowest possible priority and uses as few resources as possible, so that
        if anything at all comes along for the CPU to work on, it can.
      - >-
        No alcohol wine: how it's made It's not easy. There are three main
        methods currently in use. Vacuum distillation sees alcohol and other
        volatiles removed at a relatively low temperature (25°C-30°C), with
        aromatics blended back in afterwards.
  - source_sentence: are jess and justin still together?
    sentences:
      - >-
        Download photos and videos to your device On your iPhone, iPad, or iPod
        touch, tap Settings > [your name] > iCloud > Photos. Then select
        Download and Keep Originals and import the photos to your computer. On
        your Mac, open the Photos app. Select the photos and videos you want to
        copy.
      - >-
        Later, Justin reunites with Jessica at prom and the two get back
        together. ... After a tearful goodbye to Jessica, the Jensens, and his
        friends, Justin dies just before graduation.
      - >-
        Incumbent president Muhammadu Buhari won his reelection bid, defeating
        his closest rival Atiku Abubakar by over 3 million votes. He was issued
        a Certificate of Return, and was sworn in on May 29, 2019, the former
        date of Democracy Day (Nigeria).
  - source_sentence: when humans are depicted in hindu art?
    sentences:
      - >-
        Answer: Humans are depicted in Hindu art often in sensuous and erotic
        postures.
      - >-
        Bettas are carnivores. They require foods high in animal protein. Their
        preferred diet in nature includes insects and insect larvae. In
        captivity, they thrive on a varied diet of pellets or flakes made from
        fish meal, as well as frozen or freeze-dried bloodworms.
      - >-
        An active continental margin is found on the leading edge of the
        continent where it is crashing into an oceanic plate. ... Passive
        continental margins are found along the remaining coastlines.
  - source_sentence: what is the difference between 18 and 20 inch tires?
    sentences:
      - >-
        ['Alienware m17 R3. The best gaming laptop overall offers big power in
        slim, redesigned chassis. ... ', 'Dell G3 15. ... ', 'Asus ROG Zephyrus
        G14. ... ', 'Lenovo Legion Y545. ... ', 'Alienware Area 51m. ... ',
        'Asus ROG Mothership. ... ', 'Asus ROG Strix Scar III. ... ', 'HP Omen
        17 (2019)']
      - >-
        So extracurricular activities are just activities that you do outside of
        class. The Common App says that extracurricular activities "include
        arts, athletics, clubs, employment, personal commitments, and other
        pursuits."
      - >-
        The only real difference is a 20" rim would be more likely to be
        damaged, as you pointed out. Beyond looks, there is zero benefit for the
        20" rim. Also, just the availability of tires will likely be much more
        limited for the larger rim. ... Tire selection is better for 18" wheels
        than 20" wheels.
datasets:
  - sentence-transformers/gooaq
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
co2_eq_emissions:
  emissions: 1032.3672234821006
  energy_consumed: 2.655934941117104
  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: 9.368
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: splade-cocondenser trained on GooAQ
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.18
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.38
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.52
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.18
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.14
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.12
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.115
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.19833333333333333
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.2683333333333333
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.33233333333333326
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.25936082036566754
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3062460317460317
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.20719224548153503
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.6
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.78
            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: 0.6
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.52
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5120000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.452
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.05522786915214328
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.11018533697480869
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.1586380992797861
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.28717168510493385
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5254041819320687
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6988888888888889
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3939831545534725
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.74
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.88
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.94
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.94
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.74
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.29333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19999999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.102
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7166666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8266666666666667
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9166666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9233333333333333
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8283955451135206
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8096666666666669
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7933820346320346
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: dot_accuracy@1
            value: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.52
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20666666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.15200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09599999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.1855793650793651
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.31376984126984125
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.35210317460317453
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.42468253968253966
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3556599197720009
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4181904761904762
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3060313184828012
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: dot_accuracy@1
            value: 0.72
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.86
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.94
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.72
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.4266666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.2799999999999999
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14999999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.36
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.64
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.75
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6950779198152243
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7948333333333334
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6244374457422245
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.24
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            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: 0.24
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.1733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.24
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.62
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4858300241520006
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.40521428571428575
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.42175748899886834
            name: Dot Map@100
      - 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: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.42
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3466666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.32799999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.28200000000000003
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.046687705999640026
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.09790588476953502
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.12000426530930396
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.16155008782514965
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3519230892919392
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5292380952380953
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.16799707461195798
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14400000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.42
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.56
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.65
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.79
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5968041069603208
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5449682539682539
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5360944900687548
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: dot_accuracy@1
            value: 0.8
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.94
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.98
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.8
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.37999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.24799999999999997
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13399999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.6973333333333332
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8946666666666667
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.946
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.99
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8922979605477963
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8785238095238094
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8493405677655678
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: dot_accuracy@1
            value: 0.36
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            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: 0.36
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.26666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.22399999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16599999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.074
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.16666666666666669
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.23166666666666663
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.34266666666666656
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.32123645548157265
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5074126984126984
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.23675914234249176
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.14
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.14
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666663
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.12
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.14
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.5
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.6
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4389511719056823
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3431904761904762
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.35224302854950346
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.48
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.68
            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: 0.48
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14800000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08599999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.455
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.65
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.68
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.76
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.61443063378869
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5731666666666667
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5696919873212444
            name: Dot Map@100
      - 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: 0.7959183673469388
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8367346938775511
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9591836734693877
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6326530612244898
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5374149659863945
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5020408163265306
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.4326530612244897
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.043721411012674946
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.11111388641462987
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.1725353206760411
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.28394925382833736
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4877365323610393
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7339002267573695
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3590109302813293
            name: Dot Map@100
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.46866562009419144
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6627629513343798
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7305180533751963
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8137833594976451
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.46866562009419144
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.29928833071690214
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.23861852433281003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.1706656200941915
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.27301664240337103
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.4299467909817038
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.4935344251180747
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5788989922903304
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5271621816528863
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5802646084074655
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.447532377602445
            name: Dot Map@100

splade-cocondenser trained on GooAQ

This is a SPLADE Sparse Encoder model finetuned from Luyu/co-condenser-marco on the gooaq dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: Luyu/co-condenser-marco
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- gooaq
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): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

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/splade-cocondenser-gooaq")
# Run inference
sentences = [
    'what is the difference between 18 and 20 inch tires?',
    'The only real difference is a 20" rim would be more likely to be damaged, as you pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the availability of tires will likely be much more limited for the larger rim. ... Tire selection is better for 18" wheels than 20" wheels.',
    'So extracurricular activities are just activities that you do outside of class. The Common App says that extracurricular activities "include arts, athletics, clubs, employment, personal commitments, and other pursuits."',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

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	0.18	0.6	0.74	0.34	0.72	0.24	0.42	0.44	0.8	0.36	0.14	0.48	0.6327
dot_accuracy@3	0.38	0.78	0.88	0.46	0.86	0.52	0.6	0.6	0.94	0.62	0.5	0.68	0.7959
dot_accuracy@5	0.52	0.84	0.94	0.52	0.9	0.62	0.66	0.68	0.98	0.7	0.6	0.7	0.8367
dot_accuracy@10	0.62	0.92	0.94	0.62	0.94	0.74	0.76	0.82	1.0	0.76	0.74	0.76	0.9592
dot_precision@1	0.18	0.6	0.74	0.34	0.72	0.24	0.42	0.44	0.8	0.36	0.14	0.48	0.6327
dot_precision@3	0.14	0.52	0.2933	0.2067	0.4267	0.1733	0.3467	0.2	0.38	0.2667	0.1667	0.2333	0.5374
dot_precision@5	0.12	0.512	0.2	0.152	0.28	0.124	0.328	0.144	0.248	0.224	0.12	0.148	0.502
dot_precision@10	0.08	0.452	0.102	0.096	0.15	0.074	0.282	0.09	0.134	0.166	0.074	0.086	0.4327
dot_recall@1	0.115	0.0552	0.7167	0.1856	0.36	0.24	0.0467	0.42	0.6973	0.074	0.14	0.455	0.0437
dot_recall@3	0.1983	0.1102	0.8267	0.3138	0.64	0.52	0.0979	0.56	0.8947	0.1667	0.5	0.65	0.1111
dot_recall@5	0.2683	0.1586	0.9167	0.3521	0.7	0.62	0.12	0.65	0.946	0.2317	0.6	0.68	0.1725
dot_recall@10	0.3323	0.2872	0.9233	0.4247	0.75	0.74	0.1616	0.79	0.99	0.3427	0.74	0.76	0.2839
dot_ndcg@10	0.2594	0.5254	0.8284	0.3557	0.6951	0.4858	0.3519	0.5968	0.8923	0.3212	0.439	0.6144	0.4877
dot_mrr@10	0.3062	0.6989	0.8097	0.4182	0.7948	0.4052	0.5292	0.545	0.8785	0.5074	0.3432	0.5732	0.7339
dot_map@100	0.2072	0.394	0.7934	0.306	0.6244	0.4218	0.168	0.5361	0.8493	0.2368	0.3522	0.5697	0.359

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	0.4687
dot_accuracy@3	0.6628
dot_accuracy@5	0.7305
dot_accuracy@10	0.8138
dot_precision@1	0.4687
dot_precision@3	0.2993
dot_precision@5	0.2386
dot_precision@10	0.1707
dot_recall@1	0.273
dot_recall@3	0.4299
dot_recall@5	0.4935
dot_recall@10	0.5789
dot_ndcg@10	0.5272
dot_mrr@10	0.5803
dot_map@100	0.4475

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,011,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.87 tokens
max: 23 tokens

min: 14 tokens
mean: 60.09 tokens
max: 201 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.87 tokens max: 23 tokens	min: 14 tokens mean: 60.09 tokens max: 201 tokens

Samples:

question	answer
`what is the difference between clay and mud mask?`	`The main difference between the two is that mud is a skin-healing agent, while clay is a cosmetic, drying agent. Clay masks are most useful for someone who has oily skin and is prone to breakouts of acne and blemishes.`
`myki how much on card?`	`A full fare myki card costs $6 and a concession, seniors or child myki costs $3. For more information about how to use your myki, visit ptv.vic.gov.au or call 1800 800 007.`
`how to find out if someone blocked your phone number on iphone?`	`If you get a notification like "Message Not Delivered" or you get no notification at all, that's a sign of a potential block. Next, you could try calling the person. If the call goes right to voicemail or rings once (or a half ring) then goes to voicemail, that's further evidence you may have been blocked.`

Loss: SpladeLoss with these parameters:

{'loss': SparseMultipleNegativesRankingLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
  (cross_entropy_loss): CrossEntropyLoss()
), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
), 'query_regularizer': FlopsLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
)}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.88 tokens
max: 22 tokens

min: 14 tokens
mean: 61.03 tokens
max: 127 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.88 tokens max: 22 tokens	min: 14 tokens mean: 61.03 tokens max: 127 tokens

Samples:

question	answer
`how do i program my directv remote with my tv?`	`['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']`
`are rodrigues fruit bats nocturnal?`	`Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.`
`why does your heart rate increase during exercise bbc bitesize?`	`During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.`

Loss: SpladeLoss with these parameters:

{'loss': SparseMultipleNegativesRankingLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
  (cross_entropy_loss): CrossEntropyLoss()
), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
), 'query_regularizer': FlopsLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None})
  )
)}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-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: 16
per_device_eval_batch_size: 16
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	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
0.0213	4000	0.3968	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0425	8000	0.054	0.0224	0.2847	0.5628	0.8027	0.3260	0.6627	0.5252	0.3028	0.5467	0.7301	0.2563	0.3150	0.5072	0.4771	0.4846
0.0638	12000	0.0468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	16000	0.0394	0.0137	0.1908	0.5269	0.7778	0.3464	0.6510	0.5374	0.3086	0.5719	0.7901	0.2900	0.3661	0.5473	0.4839	0.4914
0.1063	20000	0.035	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1275	24000	0.0402	0.0142	0.1971	0.5098	0.6363	0.3715	0.6979	0.5442	0.3555	0.5223	0.7881	0.3008	0.3401	0.5963	0.4795	0.4877
0.1488	28000	0.0286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1700	32000	0.0289	0.0209	0.2097	0.5169	0.7501	0.3622	0.6629	0.5151	0.3239	0.5322	0.8189	0.3121	0.3045	0.5318	0.4748	0.4858
0.1913	36000	0.0241	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2125	40000	0.0243	0.0166	0.2150	0.4990	0.6614	0.3184	0.6564	0.5499	0.2924	0.5506	0.8177	0.2755	0.3214	0.5292	0.4605	0.4729
0.2338	44000	0.021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2550	48000	0.0205	0.0045	0.2210	0.5328	0.5836	0.3180	0.6990	0.5365	0.2860	0.5529	0.8704	0.2860	0.4025	0.6107	0.4314	0.4870
0.2763	52000	0.0181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2975	56000	0.018	0.0129	0.2131	0.5543	0.7181	0.3645	0.6852	0.5199	0.3232	0.5970	0.8914	0.2980	0.4618	0.5037	0.4592	0.5069
0.3188	60000	0.0176	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3400	64000	0.018	0.0141	0.2607	0.4594	0.7357	0.3597	0.6538	0.5082	0.3070	0.4944	0.8569	0.3252	0.4125	0.5243	0.4489	0.4882
0.3613	68000	0.016	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3825	72000	0.0143	0.0082	0.2737	0.5459	0.7570	0.3845	0.6806	0.5035	0.3408	0.5338	0.8608	0.2888	0.3096	0.6163	0.4709	0.5051
0.4038	76000	0.0148	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4250	80000	0.0135	0.0211	0.2267	0.4964	0.7829	0.3579	0.6758	0.4954	0.3195	0.5164	0.8698	0.2745	0.3012	0.6260	0.4426	0.4912
0.4463	84000	0.0132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4675	88000	0.012	0.0270	0.2442	0.5741	0.8005	0.3372	0.7019	0.5064	0.3109	0.6238	0.8988	0.2805	0.3875	0.5590	0.4396	0.5126
0.4888	92000	0.0126	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5100	96000	0.0127	0.0201	0.2948	0.5384	0.7822	0.3800	0.6947	0.5237	0.3674	0.5646	0.8843	0.2873	0.3825	0.5898	0.4812	0.5208
0.5313	100000	0.0113	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5525	104000	0.0112	0.0057	0.2318	0.5091	0.8362	0.3649	0.6829	0.4695	0.3442	0.5403	0.8920	0.2696	0.3787	0.6109	0.4384	0.5053
0.5738	108000	0.0094	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5951	112000	0.0095	0.0101	0.2325	0.5184	0.7349	0.3672	0.6673	0.4474	0.3196	0.5647	0.8866	0.2938	0.3345	0.5744	0.4609	0.4925
0.6163	116000	0.0096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6376	120000	0.01	0.0084	0.2362	0.4989	0.8299	0.3595	0.6820	0.5200	0.3286	0.6138	0.8959	0.3088	0.4139	0.5808	0.4833	0.5194
0.6588	124000	0.0103	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6801	128000	0.0082	0.0115	0.2402	0.5127	0.7943	0.3828	0.6796	0.4925	0.3337	0.5848	0.8956	0.2880	0.3962	0.5981	0.4634	0.5124
0.7013	132000	0.0085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7226	136000	0.0087	0.0125	0.2444	0.5258	0.7659	0.3397	0.6939	0.4942	0.3330	0.5573	0.8866	0.2789	0.3829	0.5305	0.4699	0.5002
0.7438	140000	0.0092	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7651	144000	0.0084	0.0071	0.2376	0.5247	0.8359	0.3551	0.6987	0.4440	0.3230	0.5973	0.8875	0.3052	0.4243	0.5601	0.4865	0.5138
0.7863	148000	0.0082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8076	152000	0.0073	0.0036	0.2379	0.5045	0.8240	0.3389	0.7027	0.4895	0.3373	0.5893	0.8878	0.2870	0.3998	0.5728	0.4735	0.5112
0.8288	156000	0.0069	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8501	160000	0.0076	0.0024	0.2594	0.5254	0.8284	0.3557	0.6951	0.4858	0.3519	0.5968	0.8923	0.3212	0.439	0.6144	0.4877	0.5272
0.8713	164000	0.0062	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8926	168000	0.0061	0.0084	0.2580	0.5068	0.8307	0.3629	0.7095	0.5132	0.3373	0.5577	0.8803	0.3041	0.4438	0.5802	0.4668	0.5193
0.9138	172000	0.0067	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9351	176000	0.0072	0.0076	0.2627	0.4988	0.8192	0.3587	0.7072	0.4968	0.3488	0.5746	0.8794	0.3049	0.4671	0.5872	0.4739	0.5215
0.9563	180000	0.0049	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9776	184000	0.0056	0.0067	0.2672	0.4954	0.8207	0.3473	0.7148	0.4997	0.3479	0.5798	0.8778	0.3115	0.4557	0.5884	0.4753	0.5216
0.9988	188000	0.005	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.2594	0.5254	0.8284	0.3557	0.6951	0.4858	0.3519	0.5968	0.8923	0.3212	0.4390	0.6144	0.4877	0.5272

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 2.656 kWh
Carbon Emitted: 1.032 kg of CO2
Hours Used: 9.368 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 4.2.0.dev0
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.5.1
Datasets: 2.21.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}