--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: distilbert/distilbert-base-uncased widget: - text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower of the former World Trade Center in New York City. The introduction features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles. The rest of the video has several cuts to Durst and his bandmates hanging out of the Bentley as they drive about Manhattan. The song Ben Stiller is playing at the beginning is "My Generation" from the same album. The video also features scenes of Fred Durst with five girls dancing in a room. The video was filmed around the same time as the film Zoolander, which explains Stiller and Dorff's appearance. Fred Durst has a small cameo in that film. - text: 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release date once again, to February 9, 2018, in order to allow more time for post-production; months later, on August 25, the studio moved the release forward two weeks.[17] The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]' - text: who played the dj in the movie the warriors - text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with a growth hormone deficiency as a child. At age 13, he relocated to Spain to join Barcelona, who agreed to pay for his medical treatment. After a fast progression through Barcelona's youth academy, Messi made his competitive debut aged 17 in October 2004. Despite being injury-prone during his early career, he established himself as an integral player for the club within the next three years, finishing 2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year award, a feat he repeated the following year. His first uninterrupted campaign came in the 2008–09 season, during which he helped Barcelona achieve the first treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA World Player of the Year award by record voting margins. - text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik''s young wife runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 32.40901449048007 energy_consumed: 0.08337753469362151 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.285 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: splade-distilbert-base-uncased trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 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: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.1733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.12000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.52 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: 0.4954197868237354 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.41905555555555546 name: Dot Mrr@10 - type: dot_map@100 value: 0.43020916049077634 name: Dot Map@100 - type: query_active_dims value: 62.65999984741211 name: Query Active Dims - type: query_sparsity_ratio value: 0.9979470545885784 name: Query Sparsity Ratio - type: corpus_active_dims value: 110.4578628540039 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9963810411226655 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.26 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.26 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666669 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.26 name: Dot Recall@1 - type: dot_recall@3 value: 0.5 name: Dot Recall@3 - type: dot_recall@5 value: 0.64 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4944666703438861 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.41657936507936505 name: Dot Mrr@10 - type: dot_map@100 value: 0.42694690636460897 name: Dot Map@100 - type: query_active_dims value: 70.22000122070312 name: Query Active Dims - type: query_sparsity_ratio value: 0.9976993643529027 name: Query Sparsity Ratio - type: corpus_active_dims value: 125.49811553955078 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9958882735227197 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.46 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.52 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.272 name: Dot Precision@5 - type: dot_precision@10 value: 0.22399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.01892455420216294 name: Dot Recall@1 - type: dot_recall@3 value: 0.04889990251243477 name: Dot Recall@3 - type: dot_recall@5 value: 0.0672946061870769 name: Dot Recall@5 - type: dot_recall@10 value: 0.08887922550901164 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.26311322734975795 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3882460317460318 name: Dot Mrr@10 - type: dot_map@100 value: 0.11155968685488596 name: Dot Map@100 - type: query_active_dims value: 74.22000122070312 name: Query Active Dims - type: query_sparsity_ratio value: 0.9975683113419598 name: Query Sparsity Ratio - type: corpus_active_dims value: 152.51846313476562 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9950029990454503 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.48 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.31999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.264 name: Dot Precision@5 - type: dot_precision@10 value: 0.23200000000000004 name: Dot Precision@10 - type: dot_recall@1 value: 0.01967175630881205 name: Dot Recall@1 - type: dot_recall@3 value: 0.04958955768484856 name: Dot Recall@3 - type: dot_recall@5 value: 0.06588472678704523 name: Dot Recall@5 - type: dot_recall@10 value: 0.08890872761034473 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2726981353115194 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.42394444444444446 name: Dot Mrr@10 - type: dot_map@100 value: 0.11062543949876841 name: Dot Map@100 - type: query_active_dims value: 85.58000183105469 name: Query Active Dims - type: query_sparsity_ratio value: 0.9971961207708848 name: Query Sparsity Ratio - type: corpus_active_dims value: 182.97967529296875 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9940049906528744 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.36 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: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.34 name: Dot Recall@1 - type: dot_recall@3 value: 0.56 name: Dot Recall@3 - type: dot_recall@5 value: 0.64 name: Dot Recall@5 - type: dot_recall@10 value: 0.66 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5163228308253419 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.48788888888888876 name: Dot Mrr@10 - type: dot_map@100 value: 0.4744598045833104 name: Dot Map@100 - type: query_active_dims value: 46.939998626708984 name: Query Active Dims - type: query_sparsity_ratio value: 0.9984620929615783 name: Query Sparsity Ratio - type: corpus_active_dims value: 96.43376159667969 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9968405162965507 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.19333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.35 name: Dot Recall@1 - type: dot_recall@3 value: 0.55 name: Dot Recall@3 - type: dot_recall@5 value: 0.65 name: Dot Recall@5 - type: dot_recall@10 value: 0.66 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5211787059288393 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.49649999999999994 name: Dot Mrr@10 - type: dot_map@100 value: 0.48018058391724333 name: Dot Map@100 - type: query_active_dims value: 55.08000183105469 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981953999793246 name: Query Sparsity Ratio - type: corpus_active_dims value: 114.79106140136719 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9962390714435041 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5800000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6533333333333333 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.22888888888888884 name: Dot Precision@3 - type: dot_precision@5 value: 0.17600000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.12266666666666666 name: Dot Precision@10 - type: dot_recall@1 value: 0.212974851400721 name: Dot Recall@1 - type: dot_recall@3 value: 0.37629996750414496 name: Dot Recall@3 - type: dot_recall@5 value: 0.43576486872902565 name: Dot Recall@5 - type: dot_recall@10 value: 0.4962930751696706 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.42495194833294514 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4317301587301587 name: Dot Mrr@10 - type: dot_map@100 value: 0.33874288397632424 name: Dot Map@100 - type: query_active_dims value: 61.27333323160807 name: Query Active Dims - type: query_sparsity_ratio value: 0.9979924862973721 name: Query Sparsity Ratio - type: corpus_active_dims value: 119.80336252848308 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9960748521548889 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.4594348508634223 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6475039246467816 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7245525902668759 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7984615384615383 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4594348508634223 name: Dot Precision@1 - type: dot_precision@3 value: 0.29682888540031394 name: Dot Precision@3 - type: dot_precision@5 value: 0.23129042386185245 name: Dot Precision@5 - type: dot_precision@10 value: 0.1639026687598116 name: Dot Precision@10 - type: dot_recall@1 value: 0.2607784592309238 name: Dot Recall@1 - type: dot_recall@3 value: 0.41707679910314266 name: Dot Recall@3 - type: dot_recall@5 value: 0.4815762664814047 name: Dot Recall@5 - type: dot_recall@10 value: 0.5608540436995575 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5109231200022869 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5709977698038922 name: Dot Mrr@10 - type: dot_map@100 value: 0.4325529585492243 name: Dot Map@100 - type: query_active_dims value: 86.22103676429161 name: Query Active Dims - type: query_sparsity_ratio value: 0.9971751183813548 name: Query Sparsity Ratio - type: corpus_active_dims value: 137.4154827411358 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9954978218091496 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.26 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.36 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.48 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.26 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: 0.074 name: Dot Precision@10 - type: dot_recall@1 value: 0.12833333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.18833333333333332 name: Dot Recall@3 - type: dot_recall@5 value: 0.24666666666666665 name: Dot Recall@5 - type: dot_recall@10 value: 0.30333333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2564995235608964 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3453015873015872 name: Dot Mrr@10 - type: dot_map@100 value: 0.2062826189577625 name: Dot Map@100 - type: query_active_dims value: 86.26000213623047 name: Query Active Dims - type: query_sparsity_ratio value: 0.9971738417490259 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0489959716797 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9958046983824231 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.62 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.84 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.62 name: Dot Precision@1 - type: dot_precision@3 value: 0.54 name: Dot Precision@3 - type: dot_precision@5 value: 0.49200000000000005 name: Dot Precision@5 - type: dot_precision@10 value: 0.43400000000000005 name: Dot Precision@10 - type: dot_recall@1 value: 0.08260659025654458 name: Dot Recall@1 - type: dot_recall@3 value: 0.14565005878146683 name: Dot Recall@3 - type: dot_recall@5 value: 0.1854201572717294 name: Dot Recall@5 - type: dot_recall@10 value: 0.2804326420122478 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.534178112145825 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7352222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.41480896090579994 name: Dot Map@100 - type: query_active_dims value: 55.939998626708984 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981672236869567 name: Query Sparsity Ratio - type: corpus_active_dims value: 125.40165710449219 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9958914338148059 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.64 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.84 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.64 name: Dot Precision@1 - type: dot_precision@3 value: 0.2866666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.18799999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.10399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.6166666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8166666666666668 name: Dot Recall@3 - type: dot_recall@5 value: 0.8766666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9433333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7890721601412974 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7516904761904764 name: Dot Mrr@10 - type: dot_map@100 value: 0.7337194522253345 name: Dot Map@100 - type: query_active_dims value: 84.86000061035156 name: Query Active Dims - type: query_sparsity_ratio value: 0.9972197103528487 name: Query Sparsity Ratio - type: corpus_active_dims value: 142.34327697753906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9953363712411526 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666669 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.09199999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.13585714285714284 name: Dot Recall@1 - type: dot_recall@3 value: 0.28919047619047616 name: Dot Recall@3 - type: dot_recall@5 value: 0.33274603174603173 name: Dot Recall@5 - type: dot_recall@10 value: 0.4233015873015873 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.32546154855128656 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3704365079365079 name: Dot Mrr@10 - type: dot_map@100 value: 0.26492479686181064 name: Dot Map@100 - type: query_active_dims value: 65.44000244140625 name: Query Active Dims - type: query_sparsity_ratio value: 0.9978559726609854 name: Query Sparsity Ratio - type: corpus_active_dims value: 132.02734375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9956743547686915 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.74 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.74 name: Dot Precision@1 - type: dot_precision@3 value: 0.41999999999999993 name: Dot Precision@3 - type: dot_precision@5 value: 0.27599999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.156 name: Dot Precision@10 - type: dot_recall@1 value: 0.37 name: Dot Recall@1 - type: dot_recall@3 value: 0.63 name: Dot Recall@3 - type: dot_recall@5 value: 0.69 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7118024522387334 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8268888888888888 name: Dot Mrr@10 - type: dot_map@100 value: 0.6307915421731377 name: Dot Map@100 - type: query_active_dims value: 81.9000015258789 name: Query Active Dims - type: query_sparsity_ratio value: 0.9973166895509509 name: Query Sparsity Ratio - type: corpus_active_dims value: 142.991943359375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9953151188205434 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.86 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: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.86 name: Dot Precision@1 - type: dot_precision@3 value: 0.3666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.24799999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.132 name: Dot Precision@10 - type: dot_recall@1 value: 0.7706666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.8846666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9359999999999999 name: Dot Recall@5 - type: dot_recall@10 value: 0.9733333333333333 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.909591417031897 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.904190476190476 name: Dot Mrr@10 - type: dot_map@100 value: 0.8825369408369409 name: Dot Map@100 - type: query_active_dims value: 58.36000061035156 name: Query Active Dims - type: query_sparsity_ratio value: 0.9980879365503456 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.70333099365234 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9978801084138113 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 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: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.28 name: Dot Precision@3 - type: dot_precision@5 value: 0.236 name: Dot Precision@5 - type: dot_precision@10 value: 0.16799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.086 name: Dot Recall@1 - type: dot_recall@3 value: 0.17566666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.24466666666666664 name: Dot Recall@5 - type: dot_recall@10 value: 0.3446666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3317925768694159 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.533222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.25209583120462153 name: Dot Map@100 - type: query_active_dims value: 134.1999969482422 name: Query Active Dims - type: query_sparsity_ratio value: 0.9956031715828503 name: Query Sparsity Ratio - type: corpus_active_dims value: 164.88478088378906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9945978382516287 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.1 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: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.1 name: Dot Precision@1 - type: dot_precision@3 value: 0.1733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.1 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: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.44172833183312293 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.32852380952380955 name: Dot Mrr@10 - type: dot_map@100 value: 0.3339302930314127 name: Dot Map@100 - type: query_active_dims value: 152.0399932861328 name: Query Active Dims - type: query_sparsity_ratio value: 0.9950186752740275 name: Query Sparsity Ratio - type: corpus_active_dims value: 149.56478881835938 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9950997710235777 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.46 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 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: 0.46 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.425 name: Dot Recall@1 - type: dot_recall@3 value: 0.545 name: Dot Recall@3 - type: dot_recall@5 value: 0.59 name: Dot Recall@5 - type: dot_recall@10 value: 0.67 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5519450641329704 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5232698412698412 name: Dot Mrr@10 - type: dot_map@100 value: 0.5187507919958133 name: Dot Map@100 - type: query_active_dims value: 138.32000732421875 name: Query Active Dims - type: query_sparsity_ratio value: 0.9954681866416284 name: Query Sparsity Ratio - type: corpus_active_dims value: 166.03871154785156 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9945600317296425 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.6326530612244898 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8775510204081632 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6326530612244898 name: Dot Precision@1 - type: dot_precision@3 value: 0.6054421768707483 name: Dot Precision@3 - type: dot_precision@5 value: 0.5387755102040817 name: Dot Precision@5 - type: dot_precision@10 value: 0.43673469387755104 name: Dot Precision@10 - type: dot_recall@1 value: 0.04531781391284345 name: Dot Recall@1 - type: dot_recall@3 value: 0.12723496235073023 name: Dot Recall@3 - type: dot_recall@5 value: 0.18244054845345592 name: Dot Recall@5 - type: dot_recall@10 value: 0.2837929445033988 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5015858619400403 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.767201166180758 name: Dot Mrr@10 - type: dot_map@100 value: 0.3675943031666616 name: Dot Map@100 - type: query_active_dims value: 52.67346954345703 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982742458048799 name: Query Sparsity Ratio - type: corpus_active_dims value: 147.12759399414062 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9951796214535699 name: Corpus Sparsity Ratio --- # splade-distilbert-base-uncased trained on Natural Questions This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/distilbert/distilbert-base-uncased) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity") # Run inference sentences = [ 'is send in the clowns from a musical', 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]', 'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]', ] 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: `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:----------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 | | dot_accuracy@3 | 0.5 | 0.46 | 0.58 | 0.36 | 0.84 | 0.84 | 0.44 | 0.9 | 0.94 | 0.6 | 0.52 | 0.56 | 0.8776 | | dot_accuracy@5 | 0.64 | 0.48 | 0.68 | 0.48 | 0.9 | 0.9 | 0.52 | 0.94 | 0.98 | 0.72 | 0.62 | 0.6 | 0.9592 | | dot_accuracy@10 | 0.74 | 0.58 | 0.7 | 0.58 | 0.92 | 0.98 | 0.66 | 0.96 | 1.0 | 0.78 | 0.8 | 0.68 | 1.0 | | dot_precision@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 | | dot_precision@3 | 0.1667 | 0.32 | 0.1933 | 0.1333 | 0.54 | 0.2867 | 0.1667 | 0.42 | 0.3667 | 0.28 | 0.1733 | 0.2067 | 0.6054 | | dot_precision@5 | 0.128 | 0.264 | 0.136 | 0.108 | 0.492 | 0.188 | 0.128 | 0.276 | 0.248 | 0.236 | 0.124 | 0.14 | 0.5388 | | dot_precision@10 | 0.074 | 0.232 | 0.07 | 0.074 | 0.434 | 0.104 | 0.092 | 0.156 | 0.132 | 0.168 | 0.08 | 0.078 | 0.4367 | | dot_recall@1 | 0.26 | 0.0197 | 0.35 | 0.1283 | 0.0826 | 0.6167 | 0.1359 | 0.37 | 0.7707 | 0.086 | 0.1 | 0.425 | 0.0453 | | dot_recall@3 | 0.5 | 0.0496 | 0.55 | 0.1883 | 0.1457 | 0.8167 | 0.2892 | 0.63 | 0.8847 | 0.1757 | 0.52 | 0.545 | 0.1272 | | dot_recall@5 | 0.64 | 0.0659 | 0.65 | 0.2467 | 0.1854 | 0.8767 | 0.3327 | 0.69 | 0.936 | 0.2447 | 0.62 | 0.59 | 0.1824 | | dot_recall@10 | 0.74 | 0.0889 | 0.66 | 0.3033 | 0.2804 | 0.9433 | 0.4233 | 0.78 | 0.9733 | 0.3447 | 0.8 | 0.67 | 0.2838 | | **dot_ndcg@10** | **0.4945** | **0.2727** | **0.5212** | **0.2565** | **0.5342** | **0.7891** | **0.3255** | **0.7118** | **0.9096** | **0.3318** | **0.4417** | **0.5519** | **0.5016** | | dot_mrr@10 | 0.4166 | 0.4239 | 0.4965 | 0.3453 | 0.7352 | 0.7517 | 0.3704 | 0.8269 | 0.9042 | 0.5332 | 0.3285 | 0.5233 | 0.7672 | | dot_map@100 | 0.4269 | 0.1106 | 0.4802 | 0.2063 | 0.4148 | 0.7337 | 0.2649 | 0.6308 | 0.8825 | 0.2521 | 0.3339 | 0.5188 | 0.3676 | | query_active_dims | 70.22 | 85.58 | 55.08 | 86.26 | 55.94 | 84.86 | 65.44 | 81.9 | 58.36 | 134.2 | 152.04 | 138.32 | 52.6735 | | query_sparsity_ratio | 0.9977 | 0.9972 | 0.9982 | 0.9972 | 0.9982 | 0.9972 | 0.9979 | 0.9973 | 0.9981 | 0.9956 | 0.995 | 0.9955 | 0.9983 | | corpus_active_dims | 125.4981 | 182.9797 | 114.7911 | 128.049 | 125.4017 | 142.3433 | 132.0273 | 142.9919 | 64.7033 | 164.8848 | 149.5648 | 166.0387 | 147.1276 | | corpus_sparsity_ratio | 0.9959 | 0.994 | 0.9962 | 0.9958 | 0.9959 | 0.9953 | 0.9957 | 0.9953 | 0.9979 | 0.9946 | 0.9951 | 0.9946 | 0.9952 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.32 | | dot_accuracy@3 | 0.52 | | dot_accuracy@5 | 0.58 | | dot_accuracy@10 | 0.6533 | | dot_precision@1 | 0.32 | | dot_precision@3 | 0.2289 | | dot_precision@5 | 0.176 | | dot_precision@10 | 0.1227 | | dot_recall@1 | 0.213 | | dot_recall@3 | 0.3763 | | dot_recall@5 | 0.4358 | | dot_recall@10 | 0.4963 | | **dot_ndcg@10** | **0.425** | | dot_mrr@10 | 0.4317 | | dot_map@100 | 0.3387 | | query_active_dims | 61.2733 | | query_sparsity_ratio | 0.998 | | corpus_active_dims | 119.8034 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.4594 | | dot_accuracy@3 | 0.6475 | | dot_accuracy@5 | 0.7246 | | dot_accuracy@10 | 0.7985 | | dot_precision@1 | 0.4594 | | dot_precision@3 | 0.2968 | | dot_precision@5 | 0.2313 | | dot_precision@10 | 0.1639 | | dot_recall@1 | 0.2608 | | dot_recall@3 | 0.4171 | | dot_recall@5 | 0.4816 | | dot_recall@10 | 0.5609 | | **dot_ndcg@10** | **0.5109** | | dot_mrr@10 | 0.571 | | dot_map@100 | 0.4326 | | query_active_dims | 86.221 | | query_sparsity_ratio | 0.9972 | | corpus_active_dims | 137.4155 | | corpus_sparsity_ratio | 0.9955 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who played the father in papa don't preach | Alex McArthur Alex McArthur (born March 6, 1957) is an American actor. | | where was the location of the battle of hastings | Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory. | | how many puppies can a dog give birth to | Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | where is the tiber river located in italy | Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks. | | what kind of car does jay gatsby drive | Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry. | | who sings if i can dream about you | I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 12 - `per_device_eval_batch_size`: 12 - `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`: 12 - `per_device_eval_batch_size`: 12 - `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 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | |:-------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:| | 0.0242 | 200 | 4.7655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0485 | 400 | 0.168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0727 | 600 | 0.0672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0970 | 800 | 0.0533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1212 | 1000 | 0.0605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1455 | 1200 | 0.051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1697 | 1400 | 0.0244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 1600 | 0.0306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 1650 | - | 0.0220 | 0.4946 | 0.2654 | 0.4801 | 0.4134 | - | - | - | - | - | - | - | - | - | - | | 0.2182 | 1800 | 0.0246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2424 | 2000 | 0.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2667 | 2200 | 0.0322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2909 | 2400 | 0.0316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3152 | 2600 | 0.029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3394 | 2800 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3636 | 3000 | 0.0312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3879 | 3200 | 0.0232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4 | 3300 | - | 0.0155 | 0.4420 | 0.2753 | 0.5112 | 0.4095 | - | - | - | - | - | - | - | - | - | - | | 0.4121 | 3400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4364 | 3600 | 0.0233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 3800 | 0.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4848 | 4000 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5091 | 4200 | 0.0269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5333 | 4400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5576 | 4600 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 4800 | 0.0276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6 | 4950 | - | 0.0098 | 0.4901 | 0.2460 | 0.5124 | 0.4162 | - | - | - | - | - | - | - | - | - | - | | 0.6061 | 5000 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6303 | 5200 | 0.0223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6545 | 5400 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6788 | 5600 | 0.0254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7030 | 5800 | 0.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7273 | 6000 | 0.0271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7515 | 6200 | 0.02 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7758 | 6400 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.8** | **6600** | **0.0125** | **0.0233** | **0.4945** | **0.2727** | **0.5212** | **0.4294** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.8242 | 6800 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8485 | 7000 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8727 | 7200 | 0.0192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8970 | 7400 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9212 | 7600 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9455 | 7800 | 0.0205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9697 | 8000 | 0.0267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9939 | 8200 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 8250 | - | 0.0174 | 0.4954 | 0.2631 | 0.5163 | 0.4250 | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.4945 | 0.2727 | 0.5212 | 0.5109 | 0.2565 | 0.5342 | 0.7891 | 0.3255 | 0.7118 | 0.9096 | 0.3318 | 0.4417 | 0.5519 | 0.5016 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.083 kWh - **Carbon Emitted**: 0.032 kg of CO2 - **Hours Used**: 0.285 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 ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### SpladeLoss ```bibtex @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}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```