metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:3011496
  - loss:SpladeLoss
base_model: distilbert/distilbert-base-uncased
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: 520.0942452560889
  energy_consumed: 1.3380282202203462
  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: 3.894
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: splade-distilbert-base-uncased trained on GooAQ
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.38
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.48
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.54
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.13333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.10800000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.066
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.11666666666666665
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.17066666666666663
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.21566666666666662
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.254
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2261790676778388
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3172460317460317
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.18142380833103508
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.52
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.68
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.72
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.86
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.52
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.42666666666666664
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.39599999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.3600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.04167451005436889
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.11687345791837826
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.1514949553474152
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.24492588020184664
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4347755184129968
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6113253968253968
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3307600412459809
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.68
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.92
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.94
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.68
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3066666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18799999999999997
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09999999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.6566666666666666
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8566666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.8766666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9166666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8076442104958218
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7872222222222223
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7674244773257931
            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: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.68
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19199999999999995
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.1459126984126984
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.36649206349206354
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.39607142857142863
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5118174603174603
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.40298090899579636
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4699365079365079
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.32554676606566874
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: dot_accuracy@1
            value: 0.74
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.92
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.96
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.74
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.44666666666666655
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.3
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.158
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.37
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.67
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.75
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.79
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.735492134090369
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8236666666666668
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6711040184164847
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.26
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.42
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.78
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.26
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.13999999999999999
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.26
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.42
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.56
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.48566582103432865
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.39584126984126977
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4043469063460482
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.4
            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: 0.66
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.34
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.316
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.24999999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.02367568043139258
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07666946243603708
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09651550012847633
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.11965782081153208
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3006693982141158
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.48066666666666663
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.130037101713329
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.21333333333333335
            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: 0.36
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.57
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.77
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5618580490206411
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5104126984126982
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.49679904702760114
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: dot_accuracy@1
            value: 0.82
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.96
            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: 0.82
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.23599999999999993
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13199999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7340000000000001
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.9113333333333332
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.922
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9833333333333333
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9062363336812763
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8922222222222222
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8721868432072424
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            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: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.24
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.188
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.148
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.08066666666666666
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.14966666666666667
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.19466666666666665
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3036666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.29127983049304745
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4865
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.21683445069332832
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.1
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.58
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.72
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.1
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11599999999999999
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07200000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.1
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.48
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.58
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.72
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4187747413908095
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.32185714285714284
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3357998070888097
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.54
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.66
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.72
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.78
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.54
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.15600000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08799999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.505
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.635
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.69
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.77
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6442911439119196
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.616
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.603482712383199
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: dot_accuracy@1
            value: 0.6530612244897959
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8979591836734694
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8979591836734694
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9795918367346939
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6530612244897959
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.6326530612244898
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5346938775510205
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.46122448979591835
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.045158988646388926
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.13170444661082067
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.1831114698285018
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.29974279420697125
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5207054661668302
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7654680919987042
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3842460294701173
            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: 0.4640816326530612
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6613814756671901
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7152276295133438
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8061224489795917
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4640816326530612
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3014861329147044
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.2300533751962324
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16301726844583989
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.26457091365729607
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.42731328952235625
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.482784104144294
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5741392786311136
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5181963556604454
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5752588397996561
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4399993853318952
            name: Dot Map@100

splade-distilbert-base-uncased trained on GooAQ

This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased 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: distilbert/distilbert-base-uncased
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: DistilBertForMaskedLM 
  (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-distilbert-base-uncased-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	0.22	0.52	0.68	0.34	0.74	0.26	0.4	0.38	0.82	0.38	0.1	0.54	0.6531
dot_accuracy@3	0.38	0.68	0.9	0.6	0.9	0.42	0.56	0.6	0.96	0.56	0.48	0.66	0.898
dot_accuracy@5	0.48	0.72	0.92	0.6	0.92	0.56	0.62	0.7	0.96	0.62	0.58	0.72	0.898
dot_accuracy@10	0.54	0.86	0.94	0.68	0.96	0.78	0.66	0.82	1.0	0.76	0.72	0.78	0.9796
dot_precision@1	0.22	0.52	0.68	0.34	0.74	0.26	0.4	0.38	0.82	0.38	0.1	0.54	0.6531
dot_precision@3	0.1333	0.4267	0.3067	0.2733	0.4467	0.14	0.34	0.2133	0.3733	0.24	0.16	0.2333	0.6327
dot_precision@5	0.108	0.396	0.188	0.192	0.3	0.112	0.316	0.148	0.236	0.188	0.116	0.156	0.5347
dot_precision@10	0.066	0.36	0.1	0.12	0.158	0.078	0.25	0.086	0.132	0.148	0.072	0.088	0.4612
dot_recall@1	0.1167	0.0417	0.6567	0.1459	0.37	0.26	0.0237	0.36	0.734	0.0807	0.1	0.505	0.0452
dot_recall@3	0.1707	0.1169	0.8567	0.3665	0.67	0.42	0.0767	0.57	0.9113	0.1497	0.48	0.635	0.1317
dot_recall@5	0.2157	0.1515	0.8767	0.3961	0.75	0.56	0.0965	0.66	0.922	0.1947	0.58	0.69	0.1831
dot_recall@10	0.254	0.2449	0.9167	0.5118	0.79	0.78	0.1197	0.77	0.9833	0.3037	0.72	0.77	0.2997
dot_ndcg@10	0.2262	0.4348	0.8076	0.403	0.7355	0.4857	0.3007	0.5619	0.9062	0.2913	0.4188	0.6443	0.5207
dot_mrr@10	0.3172	0.6113	0.7872	0.4699	0.8237	0.3958	0.4807	0.5104	0.8922	0.4865	0.3219	0.616	0.7655
dot_map@100	0.1814	0.3308	0.7674	0.3255	0.6711	0.4043	0.13	0.4968	0.8722	0.2168	0.3358	0.6035	0.3842

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	0.4641
dot_accuracy@3	0.6614
dot_accuracy@5	0.7152
dot_accuracy@10	0.8061
dot_precision@1	0.4641
dot_precision@3	0.3015
dot_precision@5	0.2301
dot_precision@10	0.163
dot_recall@1	0.2646
dot_recall@3	0.4273
dot_recall@5	0.4828
dot_recall@10	0.5741
dot_ndcg@10	0.5182
dot_mrr@10	0.5753
dot_map@100	0.44

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: 18 characters
mean: 43.42 characters
max: 96 characters

min: 54 characters
mean: 252.96 characters
max: 426 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.42 characters max: 96 characters	min: 54 characters mean: 252.96 characters max: 426 characters

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: DistilBertForMaskedLM 
    (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: DistilBertForMaskedLM 
    (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: DistilBertForMaskedLM 
    (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: 18 characters
mean: 43.17 characters
max: 98 characters

min: 51 characters
mean: 254.12 characters
max: 360 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.17 characters max: 98 characters	min: 51 characters mean: 254.12 characters max: 360 characters

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: DistilBertForMaskedLM 
    (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: DistilBertForMaskedLM 
    (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: DistilBertForMaskedLM 
    (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: 32
per_device_eval_batch_size: 32
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: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 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	2000	0.75	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0425	4000	0.071	0.0924	0.1931	0.2903	0.5966	0.3079	0.6182	0.3378	0.1867	0.3781	0.3784	0.1966	0.2325	0.4148	0.5139	0.3573
0.0638	6000	0.0578	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0850	8000	0.0511	0.0589	0.1826	0.2911	0.5719	0.3820	0.6818	0.2417	0.2032	0.2925	0.4541	0.2090	0.2306	0.5240	0.5183	0.3679
0.1063	10000	0.0464	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1275	12000	0.0458	0.0795	0.1978	0.2958	0.6206	0.3664	0.6673	0.2691	0.1872	0.2327	0.6770	0.2008	0.3288	0.5384	0.5017	0.3911
0.1488	14000	0.0427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1700	16000	0.0392	0.0581	0.2785	0.4104	0.8125	0.3832	0.7265	0.5093	0.2688	0.6075	0.7879	0.2760	0.3342	0.5722	0.5301	0.4998
0.1913	18000	0.039	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2125	20000	0.0366	0.0472	0.2319	0.3466	0.7349	0.3774	0.7174	0.4061	0.2189	0.4166	0.7486	0.2364	0.3560	0.5907	0.5211	0.4541
0.2338	22000	0.0312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2550	24000	0.0322	0.0543	0.2169	0.4469	0.7618	0.4014	0.6831	0.4412	0.2707	0.5253	0.8104	0.2621	0.3581	0.6006	0.5037	0.4832
0.2763	26000	0.0292	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2975	28000	0.03	0.0529	0.2257	0.5070	0.7976	0.4014	0.7442	0.5165	0.3216	0.5799	0.8483	0.3318	0.3206	0.5665	0.5149	0.5135
0.3188	30000	0.0294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3400	32000	0.0291	0.0512	0.1754	0.4539	0.8196	0.3903	0.7372	0.4689	0.2948	0.5548	0.8643	0.2791	0.4040	0.5229	0.5055	0.4977
0.3613	34000	0.0284	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3825	36000	0.0268	0.0404	0.2566	0.4462	0.8142	0.3737	0.7281	0.4418	0.2568	0.5135	0.8305	0.2749	0.3775	0.5485	0.5228	0.4912
0.4038	38000	0.0262	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4250	40000	0.0238	0.0416	0.2464	0.5235	0.8004	0.4016	0.7418	0.4483	0.2915	0.5771	0.8538	0.2523	0.3536	0.6227	0.4967	0.5084
0.4463	42000	0.0253	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4675	44000	0.0224	0.0360	0.2080	0.5100	0.8317	0.3775	0.7223	0.4447	0.2789	0.5586	0.8324	0.3151	0.4005	0.6089	0.5119	0.5077
0.4888	46000	0.0225	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5100	48000	0.0209	0.0232	0.2386	0.5045	0.8204	0.3746	0.7390	0.4662	0.2963	0.5380	0.8580	0.3292	0.4010	0.6336	0.5214	0.5170
0.5313	50000	0.0225	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5525	52000	0.0205	0.0380	0.2237	0.5114	0.7952	0.3583	0.6979	0.4310	0.2816	0.5364	0.8747	0.2703	0.4009	0.5947	0.5038	0.4984
0.5738	54000	0.0204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5950	56000	0.0204	0.0365	0.2316	0.4676	0.8134	0.3754	0.7280	0.4536	0.2927	0.5205	0.8662	0.2859	0.3589	0.6281	0.5069	0.5022
0.6163	58000	0.0199	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6376	60000	0.0196	0.0365	0.2233	0.4897	0.8149	0.3385	0.7395	0.4778	0.2725	0.5365	0.8610	0.2836	0.4031	0.5380	0.5146	0.4995
0.6588	62000	0.0187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6801	64000	0.0184	0.0453	0.2333	0.4792	0.7881	0.3653	0.7402	0.5062	0.3008	0.5607	0.8922	0.2857	0.4039	0.5972	0.5217	0.5134
0.7013	66000	0.0182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7226	68000	0.0162	0.0323	0.2341	0.4678	0.8283	0.3855	0.7567	0.5229	0.3297	0.5445	0.8909	0.2787	0.3917	0.5904	0.5115	0.5179
0.7438	70000	0.0195	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7651	72000	0.0181	0.0243	0.2082	0.4374	0.7487	0.4010	0.7245	0.4712	0.3179	0.5168	0.8721	0.2794	0.4312	0.5801	0.5129	0.5001
0.7863	74000	0.0171	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8076	76000	0.0164	0.0284	0.2153	0.4654	0.7985	0.4027	0.7528	0.4871	0.3267	0.5385	0.9092	0.2997	0.3852	0.5979	0.5001	0.5138
0.8288	78000	0.0169	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8501	80000	0.0168	0.0244	0.2032	0.4466	0.7855	0.4042	0.7396	0.4971	0.2946	0.5485	0.9071	0.2983	0.3919	0.5862	0.5149	0.5091
0.8713	82000	0.0144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8926	84000	0.0155	0.0229	0.2262	0.4348	0.8076	0.403	0.7355	0.4857	0.3007	0.5619	0.9062	0.2913	0.4188	0.6443	0.5207	0.5182
0.9138	86000	0.0144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9351	88000	0.0149	0.0211	0.2259	0.4212	0.8098	0.3938	0.7309	0.4665	0.3051	0.5301	0.9061	0.2881	0.4086	0.6390	0.5199	0.5111
0.9563	90000	0.013	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9776	92000	0.0143	0.0231	0.2288	0.4224	0.8176	0.4130	0.7332	0.4807	0.3033	0.5424	0.9007	0.2772	0.4215	0.6354	0.5170	0.5149
0.9988	94000	0.0129	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.2262	0.4348	0.8076	0.4030	0.7355	0.4857	0.3007	0.5619	0.9062	0.2913	0.4188	0.6443	0.5207	0.5182

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 1.338 kWh
Carbon Emitted: 0.520 kg of CO2
Hours Used: 3.894 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},
}