--- 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: 'The term emergent literacy signals a belief that, in a literate society, young children even one and two year olds, are in the process of becoming literate”. ... Gray (1956:21) notes: Functional literacy is used for the training of adults to ''meet independently the reading and writing demands placed on them''.' - text: Rey is seemingly confirmed as being The Chosen One per a quote by a Lucasfilm production designer who worked on The Rise of Skywalker. - text: are union gun safes fireproof? - text: Fruit is an essential part of a healthy diet — and may aid weight loss. Most fruits are low in calories while high in nutrients and fiber, which can boost your fullness. Keep in mind that it's best to eat fruits whole rather than juiced. What's more, simply eating fruit is not the key to weight loss. - text: Treatment of suspected bacterial infection is with antibiotics, such as amoxicillin/clavulanate or doxycycline, given for 5 to 7 days for acute sinusitis and for up to 6 weeks for chronic sinusitis. 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 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 16.638146863146233 energy_consumed: 0.04280437678001716 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: 0.193 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: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.54 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5061981336542133 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43174603174603166 name: Dot Mrr@10 - type: dot_map@100 value: 0.44263003895418085 name: Dot Map@100 - type: query_active_dims value: 118.5999984741211 name: Query Active Dims - type: query_sparsity_ratio value: 0.996114278275535 name: Query Sparsity Ratio - type: corpus_active_dims value: 397.6775817871094 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9869707888805744 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.32 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: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.07200000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 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: 0.72 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5061402921245981 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43823809523809515 name: Dot Mrr@10 - type: dot_map@100 value: 0.4500866595693115 name: Dot Map@100 - type: query_active_dims value: 105.08000183105469 name: Query Active Dims - type: query_sparsity_ratio value: 0.996557237342538 name: Query Sparsity Ratio - type: corpus_active_dims value: 381.3874816894531 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9875045055471643 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.48 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.26 name: Dot Precision@3 - type: dot_precision@5 value: 0.23199999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.19599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.01204138289831077 name: Dot Recall@1 - type: dot_recall@3 value: 0.028423242145972874 name: Dot Recall@3 - type: dot_recall@5 value: 0.04013720529494631 name: Dot Recall@5 - type: dot_recall@10 value: 0.06944452178864681 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2238211925399539 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4057777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.07440414426513103 name: Dot Map@100 - type: query_active_dims value: 183.05999755859375 name: Query Active Dims - type: query_sparsity_ratio value: 0.9940023590341854 name: Query Sparsity Ratio - type: corpus_active_dims value: 823.3663940429688 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9730238387378621 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.3 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: 0.6 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.2733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.23199999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.21400000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.010708049564977435 name: Dot Recall@1 - type: dot_recall@3 value: 0.04042324214597287 name: Dot Recall@3 - type: dot_recall@5 value: 0.05817733939406678 name: Dot Recall@5 - type: dot_recall@10 value: 0.0849823575856454 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.24157503472859507 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3932222222222223 name: Dot Mrr@10 - type: dot_map@100 value: 0.08415340735361837 name: Dot Map@100 - type: query_active_dims value: 150.77999877929688 name: Query Active Dims - type: query_sparsity_ratio value: 0.9950599567925006 name: Query Sparsity Ratio - type: corpus_active_dims value: 807.0741577148438 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9735576253943109 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.11600000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.27 name: Dot Recall@1 - type: dot_recall@3 value: 0.48 name: Dot Recall@3 - type: dot_recall@5 value: 0.54 name: Dot Recall@5 - type: dot_recall@10 value: 0.64 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.45385561138570657 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.40454761904761893 name: Dot Mrr@10 - type: dot_map@100 value: 0.40238133013339067 name: Dot Map@100 - type: query_active_dims value: 108.23999786376953 name: Query Active Dims - type: query_sparsity_ratio value: 0.9964537055938743 name: Query Sparsity Ratio - type: corpus_active_dims value: 581.3165893554688 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9809541776634733 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.54 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.15333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.10800000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.23 name: Dot Recall@1 - type: dot_recall@3 value: 0.44 name: Dot Recall@3 - type: dot_recall@5 value: 0.51 name: Dot Recall@5 - type: dot_recall@10 value: 0.67 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4431148339670733 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3818015873015873 name: Dot Mrr@10 - type: dot_map@100 value: 0.3762054598208147 name: Dot Map@100 - type: query_active_dims value: 97.18000030517578 name: Query Active Dims - type: query_sparsity_ratio value: 0.996816067089143 name: Query Sparsity Ratio - type: corpus_active_dims value: 564.0422973632812 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9815201396578442 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: 0.3066666666666667 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.49333333333333335 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5800000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6733333333333333 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3066666666666667 name: Dot Precision@1 - type: dot_precision@3 value: 0.20222222222222222 name: Dot Precision@3 - type: dot_precision@5 value: 0.16133333333333333 name: Dot Precision@5 - type: dot_precision@10 value: 0.11333333333333334 name: Dot Precision@10 - type: dot_recall@1 value: 0.1940137942994369 name: Dot Recall@1 - type: dot_recall@3 value: 0.34947441404865764 name: Dot Recall@3 - type: dot_recall@5 value: 0.4200457350983155 name: Dot Recall@5 - type: dot_recall@10 value: 0.4831481739295489 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.39462497919329126 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4140238095238094 name: Dot Mrr@10 - type: dot_map@100 value: 0.3064718377842342 name: Dot Map@100 - type: query_active_dims value: 136.63333129882812 name: Query Active Dims - type: query_sparsity_ratio value: 0.9955234476345315 name: Query Sparsity Ratio - type: corpus_active_dims value: 565.0999949325504 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9814854860450642 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.35158555729984303 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5366091051805337 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.609105180533752 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7153218210361068 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.35158555729984303 name: Dot Precision@1 - type: dot_precision@3 value: 0.24084772370486657 name: Dot Precision@3 - type: dot_precision@5 value: 0.195861852433281 name: Dot Precision@5 - type: dot_precision@10 value: 0.14448037676609105 name: Dot Precision@10 - type: dot_recall@1 value: 0.18710365017134828 name: Dot Recall@1 - type: dot_recall@3 value: 0.3166600122342838 name: Dot Recall@3 - type: dot_recall@5 value: 0.38032257819651705 name: Dot Recall@5 - type: dot_recall@10 value: 0.4791896835492342 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.41388777461576925 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4636842715108021 name: Dot Mrr@10 - type: dot_map@100 value: 0.33650048535941457 name: Dot Map@100 - type: query_active_dims value: 195.48228298827937 name: Query Active Dims - type: query_sparsity_ratio value: 0.9935953645570972 name: Query Sparsity Ratio - type: corpus_active_dims value: 525.5023385946348 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9827828340674059 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.2 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.42 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.52 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.2 name: Dot Precision@1 - type: dot_precision@3 value: 0.14 name: Dot Precision@3 - type: dot_precision@5 value: 0.09200000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.06000000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.08833333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.18166666666666664 name: Dot Recall@3 - type: dot_recall@5 value: 0.19233333333333336 name: Dot Recall@5 - type: dot_recall@10 value: 0.2523333333333333 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2097369113981719 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2989603174603175 name: Dot Mrr@10 - type: dot_map@100 value: 0.16798141398273245 name: Dot Map@100 - type: query_active_dims value: 250.86000061035156 name: Query Active Dims - type: query_sparsity_ratio value: 0.9917810103987172 name: Query Sparsity Ratio - type: corpus_active_dims value: 643.326904296875 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9789225180428257 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: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.86 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: 0.4733333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.452 name: Dot Precision@5 - type: dot_precision@10 value: 0.39599999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.06769969786296744 name: Dot Recall@1 - type: dot_recall@3 value: 0.14199136819511296 name: Dot Recall@3 - type: dot_recall@5 value: 0.192778624550143 name: Dot Recall@5 - type: dot_recall@10 value: 0.2816492423802407 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4998791588316728 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7168571428571429 name: Dot Mrr@10 - type: dot_map@100 value: 0.3705445544087827 name: Dot Map@100 - type: query_active_dims value: 146.02000427246094 name: Query Active Dims - type: query_sparsity_ratio value: 0.9952159096955487 name: Query Sparsity Ratio - type: corpus_active_dims value: 481.7581481933594 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9842160360332429 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.08599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.7366666666666666 name: Dot Recall@5 - type: dot_recall@10 value: 0.7966666666666665 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6190748153469672 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5678888888888888 name: Dot Mrr@10 - type: dot_map@100 value: 0.5644736817593311 name: Dot Map@100 - type: query_active_dims value: 253.3800048828125 name: Query Active Dims - type: query_sparsity_ratio value: 0.9916984468618435 name: Query Sparsity Ratio - type: corpus_active_dims value: 749.9185180664062 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9754302300613852 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.42 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.46 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: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.09 name: Dot Precision@10 - type: dot_recall@1 value: 0.13933333333333334 name: Dot Recall@1 - type: dot_recall@3 value: 0.26035714285714284 name: Dot Recall@3 - type: dot_recall@5 value: 0.31182539682539684 name: Dot Recall@5 - type: dot_recall@10 value: 0.3924047619047619 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3071601294876744 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3309126984126985 name: Dot Mrr@10 - type: dot_map@100 value: 0.2510011498241125 name: Dot Map@100 - type: query_active_dims value: 85.69999694824219 name: Query Active Dims - type: query_sparsity_ratio value: 0.9971921893405333 name: Query Sparsity Ratio - type: corpus_active_dims value: 416.93829345703125 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9863397453162627 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.68 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.68 name: Dot Precision@1 - type: dot_precision@3 value: 0.35333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.24 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.34 name: Dot Recall@1 - type: dot_recall@3 value: 0.53 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.69 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6197567693807055 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7347142857142859 name: Dot Mrr@10 - type: dot_map@100 value: 0.540453368331375 name: Dot Map@100 - type: query_active_dims value: 152.5399932861328 name: Query Active Dims - type: query_sparsity_ratio value: 0.9950022936476596 name: Query Sparsity Ratio - type: corpus_active_dims value: 553.4066772460938 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9818685971677447 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.1733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.124 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.34666666666666673 name: Dot Recall@1 - type: dot_recall@3 value: 0.4706666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.5506666666666666 name: Dot Recall@5 - type: dot_recall@10 value: 0.7506666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5326024015174656 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4782936507936508 name: Dot Mrr@10 - type: dot_map@100 value: 0.4734890338060357 name: Dot Map@100 - type: query_active_dims value: 52.900001525878906 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982668238802871 name: Query Sparsity Ratio - type: corpus_active_dims value: 61.35552978515625 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9979897932709142 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.28 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: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.184 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.059666666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.12866666666666668 name: Dot Recall@3 - type: dot_recall@5 value: 0.18966666666666662 name: Dot Recall@5 - type: dot_recall@10 value: 0.2836666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2574919427490159 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.42540476190476184 name: Dot Mrr@10 - type: dot_map@100 value: 0.17688082476501285 name: Dot Map@100 - type: query_active_dims value: 197.1999969482422 name: Query Active Dims - type: query_sparsity_ratio value: 0.9935390866604993 name: Query Sparsity Ratio - type: corpus_active_dims value: 676.0037231445312 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9778519191683201 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.02 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.14 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.22 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.38 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.02 name: Dot Precision@1 - type: dot_precision@3 value: 0.04666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.044000000000000004 name: Dot Precision@5 - type: dot_precision@10 value: 0.038000000000000006 name: Dot Precision@10 - type: dot_recall@1 value: 0.02 name: Dot Recall@1 - type: dot_recall@3 value: 0.14 name: Dot Recall@3 - type: dot_recall@5 value: 0.22 name: Dot Recall@5 - type: dot_recall@10 value: 0.38 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.17464966621739791 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.11243650793650796 name: Dot Mrr@10 - type: dot_map@100 value: 0.11564322909400383 name: Dot Map@100 - type: query_active_dims value: 732.4600219726562 name: Query Active Dims - type: query_sparsity_ratio value: 0.9760022271812904 name: Query Sparsity Ratio - type: corpus_active_dims value: 648.47509765625 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9787538464826602 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.36 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: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.078 name: Dot Precision@10 - type: dot_recall@1 value: 0.335 name: Dot Recall@1 - type: dot_recall@3 value: 0.535 name: Dot Recall@3 - type: dot_recall@5 value: 0.575 name: Dot Recall@5 - type: dot_recall@10 value: 0.66 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5064687965907525 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4627222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.46039157929311386 name: Dot Map@100 - type: query_active_dims value: 276.20001220703125 name: Query Active Dims - type: query_sparsity_ratio value: 0.9909507891944489 name: Query Sparsity Ratio - type: corpus_active_dims value: 729.4652099609375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9761003469641264 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5306122448979592 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7959183673469388 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9183673469387755 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9591836734693877 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5306122448979592 name: Dot Precision@1 - type: dot_precision@3 value: 0.5510204081632653 name: Dot Precision@3 - type: dot_precision@5 value: 0.5102040816326532 name: Dot Precision@5 - type: dot_precision@10 value: 0.4122448979591837 name: Dot Precision@10 - type: dot_recall@1 value: 0.03493970479958239 name: Dot Recall@1 - type: dot_recall@3 value: 0.10780840584746129 name: Dot Recall@3 - type: dot_recall@5 value: 0.16707882245178216 name: Dot Recall@5 - type: dot_recall@10 value: 0.26709619093606257 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4628903176649091 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6864431486880467 name: Dot Mrr@10 - type: dot_map@100 value: 0.34320194766414486 name: Dot Map@100 - type: query_active_dims value: 37.81632614135742 name: Query Active Dims - type: query_sparsity_ratio value: 0.9987610141490939 name: Query Sparsity Ratio - type: corpus_active_dims value: 493.48040771484375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9838319766819067 name: Corpus Sparsity Ratio --- # splade-distilbert-base-uncased trained on GooAQ 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 [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) 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:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **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, 'architecture': '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-gooaq-peft-r128") # Run inference queries = [ "how many days for doxycycline to work on sinus infection?", ] documents = [ 'Treatment of suspected bacterial infection is with antibiotics, such as amoxicillin/clavulanate or doxycycline, given for 5 to 7 days for acute sinusitis and for up to 6 weeks for chronic sinusitis.', 'Most engagements typically have a cocktail dress code, calling for dresses at, or slightly above, knee-length and high heels. If your party states a different dress code, however, such as semi-formal or dressy-casual, you may need to dress up or down accordingly.', 'The average service life of a gas furnace is about 15 years, but the actual life span of an individual unit can vary greatly. There are a number of contributing factors that determine the age a furnace reaches: The quality of the equipment.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[85.3246, 22.8328, 29.6908]]) ``` ## 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 | 0.32 | 0.3 | 0.24 | 0.2 | 0.62 | 0.44 | 0.22 | 0.68 | 0.36 | 0.28 | 0.02 | 0.36 | 0.5306 | | dot_accuracy@3 | 0.5 | 0.46 | 0.46 | 0.38 | 0.78 | 0.66 | 0.42 | 0.78 | 0.52 | 0.52 | 0.14 | 0.56 | 0.7959 | | dot_accuracy@5 | 0.64 | 0.48 | 0.54 | 0.42 | 0.86 | 0.78 | 0.46 | 0.8 | 0.58 | 0.62 | 0.22 | 0.6 | 0.9184 | | dot_accuracy@10 | 0.72 | 0.6 | 0.72 | 0.52 | 0.92 | 0.84 | 0.54 | 0.88 | 0.78 | 0.78 | 0.38 | 0.66 | 0.9592 | | dot_precision@1 | 0.32 | 0.3 | 0.24 | 0.2 | 0.62 | 0.44 | 0.22 | 0.68 | 0.36 | 0.28 | 0.02 | 0.36 | 0.5306 | | dot_precision@3 | 0.1667 | 0.2733 | 0.1533 | 0.14 | 0.4733 | 0.22 | 0.1667 | 0.3533 | 0.1733 | 0.2067 | 0.0467 | 0.2067 | 0.551 | | dot_precision@5 | 0.128 | 0.232 | 0.108 | 0.092 | 0.452 | 0.156 | 0.144 | 0.24 | 0.124 | 0.184 | 0.044 | 0.132 | 0.5102 | | dot_precision@10 | 0.072 | 0.214 | 0.074 | 0.06 | 0.396 | 0.086 | 0.09 | 0.138 | 0.082 | 0.138 | 0.038 | 0.078 | 0.4122 | | dot_recall@1 | 0.32 | 0.0107 | 0.23 | 0.0883 | 0.0677 | 0.44 | 0.1393 | 0.34 | 0.3467 | 0.0597 | 0.02 | 0.335 | 0.0349 | | dot_recall@3 | 0.5 | 0.0404 | 0.44 | 0.1817 | 0.142 | 0.64 | 0.2604 | 0.53 | 0.4707 | 0.1287 | 0.14 | 0.535 | 0.1078 | | dot_recall@5 | 0.64 | 0.0582 | 0.51 | 0.1923 | 0.1928 | 0.7367 | 0.3118 | 0.6 | 0.5507 | 0.1897 | 0.22 | 0.575 | 0.1671 | | dot_recall@10 | 0.72 | 0.085 | 0.67 | 0.2523 | 0.2816 | 0.7967 | 0.3924 | 0.69 | 0.7507 | 0.2837 | 0.38 | 0.66 | 0.2671 | | **dot_ndcg@10** | **0.5061** | **0.2416** | **0.4431** | **0.2097** | **0.4999** | **0.6191** | **0.3072** | **0.6198** | **0.5326** | **0.2575** | **0.1746** | **0.5065** | **0.4629** | | dot_mrr@10 | 0.4382 | 0.3932 | 0.3818 | 0.299 | 0.7169 | 0.5679 | 0.3309 | 0.7347 | 0.4783 | 0.4254 | 0.1124 | 0.4627 | 0.6864 | | dot_map@100 | 0.4501 | 0.0842 | 0.3762 | 0.168 | 0.3705 | 0.5645 | 0.251 | 0.5405 | 0.4735 | 0.1769 | 0.1156 | 0.4604 | 0.3432 | | query_active_dims | 105.08 | 150.78 | 97.18 | 250.86 | 146.02 | 253.38 | 85.7 | 152.54 | 52.9 | 197.2 | 732.46 | 276.2 | 37.8163 | | query_sparsity_ratio | 0.9966 | 0.9951 | 0.9968 | 0.9918 | 0.9952 | 0.9917 | 0.9972 | 0.995 | 0.9983 | 0.9935 | 0.976 | 0.991 | 0.9988 | | corpus_active_dims | 381.3875 | 807.0742 | 564.0423 | 643.3269 | 481.7581 | 749.9185 | 416.9383 | 553.4067 | 61.3555 | 676.0037 | 648.4751 | 729.4652 | 493.4804 | | corpus_sparsity_ratio | 0.9875 | 0.9736 | 0.9815 | 0.9789 | 0.9842 | 0.9754 | 0.9863 | 0.9819 | 0.998 | 0.9779 | 0.9788 | 0.9761 | 0.9838 | #### 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 | 0.3067 | | dot_accuracy@3 | 0.4933 | | dot_accuracy@5 | 0.58 | | dot_accuracy@10 | 0.6733 | | dot_precision@1 | 0.3067 | | dot_precision@3 | 0.2022 | | dot_precision@5 | 0.1613 | | dot_precision@10 | 0.1133 | | dot_recall@1 | 0.194 | | dot_recall@3 | 0.3495 | | dot_recall@5 | 0.42 | | dot_recall@10 | 0.4831 | | **dot_ndcg@10** | **0.3946** | | dot_mrr@10 | 0.414 | | dot_map@100 | 0.3065 | | query_active_dims | 136.6333 | | query_sparsity_ratio | 0.9955 | | corpus_active_dims | 565.1 | | corpus_sparsity_ratio | 0.9815 | #### 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 | 0.3516 | | dot_accuracy@3 | 0.5366 | | dot_accuracy@5 | 0.6091 | | dot_accuracy@10 | 0.7153 | | dot_precision@1 | 0.3516 | | dot_precision@3 | 0.2408 | | dot_precision@5 | 0.1959 | | dot_precision@10 | 0.1445 | | dot_recall@1 | 0.1871 | | dot_recall@3 | 0.3167 | | dot_recall@5 | 0.3803 | | dot_recall@10 | 0.4792 | | **dot_ndcg@10** | **0.4139** | | dot_mrr@10 | 0.4637 | | dot_map@100 | 0.3365 | | query_active_dims | 195.4823 | | query_sparsity_ratio | 0.9936 | | corpus_active_dims | 525.5023 | | corpus_sparsity_ratio | 0.9828 | ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 99,000 training samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what are the 5 characteristics of a star? | Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness. | | are copic markers alcohol ink? | Copic Ink is alcohol-based and flammable. Keep away from direct sunlight and extreme temperatures. | | what is the difference between appellate term and appellate division? | Appellate terms An appellate term is an intermediate appellate court that hears appeals from the inferior courts within their designated counties or judicial districts, and are intended to ease the workload on the Appellate Division and provide a less expensive forum closer to the people. | * 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')", "document_regularizer_weight": 3e-05, "query_regularizer_weight": 5e-05 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 1,000 evaluation samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | should you take ibuprofen with high blood pressure? | In general, people with high blood pressure should use acetaminophen or possibly aspirin for over-the-counter pain relief. Unless your health care provider has said it's OK, you should not use ibuprofen, ketoprofen, or naproxen sodium. If aspirin or acetaminophen doesn't help with your pain, call your doctor. | | how old do you have to be to work in sc? | The general minimum age of employment for South Carolina youth is 14, although the state allows younger children who are performers to work in show business. If their families are agricultural workers, children younger than age 14 may also participate in farm labor. | | how to write a topic proposal for a research paper? | ['Write down the main topic of your paper. ... ', 'Write two or three short sentences under the main topic that explain why you chose that topic. ... ', 'Write a thesis sentence that states the angle and purpose of your research paper. ... ', 'List the items you will cover in the body of the paper that support your thesis statement.'] | * 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')", "document_regularizer_weight": 3e-05, "query_regularizer_weight": 5e-05 } ``` ### 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 - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_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 | |:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:| | 0.0323 | 100 | 81.7292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 200 | 4.3059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0970 | 300 | 0.8078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 400 | 0.4309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1616 | 500 | 0.3837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 600 | 0.282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1972 | 610 | - | 0.1867 | 0.4508 | 0.2059 | 0.3905 | 0.3491 | - | - | - | - | - | - | - | - | - | - | | 0.2262 | 700 | 0.2593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 800 | 0.2161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2909 | 900 | 0.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 1000 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3555 | 1100 | 0.2161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 1200 | 0.1835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3943 | 1220 | - | 0.1368 | 0.4567 | 0.2373 | 0.4209 | 0.3717 | - | - | - | - | - | - | - | - | - | - | | 0.4202 | 1300 | 0.1936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 1400 | 0.1689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4848 | 1500 | 0.1858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 1600 | 0.1639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5495 | 1700 | 0.1376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 1800 | 0.1677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.5915** | **1830** | **-** | **0.1138** | **0.5061** | **0.2416** | **0.4431** | **0.3969** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.6141 | 1900 | 0.1483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 2000 | 0.1513 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6787 | 2100 | 0.1449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 2200 | 0.193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7434 | 2300 | 0.1554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 2400 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7886 | 2440 | - | 0.1148 | 0.5084 | 0.2240 | 0.4428 | 0.3917 | - | - | - | - | - | - | - | - | - | - | | 0.8080 | 2500 | 0.1308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 2600 | 0.1284 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8727 | 2700 | 0.1309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 2800 | 0.1458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9373 | 2900 | 0.1351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 3000 | 0.1135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9858 | 3050 | - | 0.1068 | 0.5062 | 0.2238 | 0.4539 | 0.3946 | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.5061 | 0.2416 | 0.4431 | 0.4139 | 0.2097 | 0.4999 | 0.6191 | 0.3072 | 0.6198 | 0.5326 | 0.2575 | 0.1746 | 0.5065 | 0.4629 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.043 kWh - **Carbon Emitted**: 0.017 kg of CO2 - **Hours Used**: 0.193 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cu126 - 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} } ```