--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 53.0273650168183 energy_consumed: 0.13642164181511365 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: 0.41 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6074833126260415 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5392698412698412 name: Dot Mrr@10 - type: dot_map@100 value: 0.5478391044500884 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 128 type: NanoNFCorpus_128 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.28 name: Dot Precision@5 - type: dot_precision@10 value: 0.24600000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.045132854073603 name: Dot Recall@1 - type: dot_recall@3 value: 0.06751477851868476 name: Dot Recall@3 - type: dot_recall@5 value: 0.08765169300408888 name: Dot Recall@5 - type: dot_recall@10 value: 0.12035202437952344 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3037747903284991 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5081904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.13867493157888547 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 128 type: NanoNQ_128 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.45 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.67 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6337677207897237 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5932936507936507 name: Dot Mrr@10 - type: dot_map@100 value: 0.5761859932841973 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.43333333333333335 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6200000000000001 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6866666666666665 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7799999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.43333333333333335 name: Dot Precision@1 - type: dot_precision@3 value: 0.25333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.19066666666666668 name: Dot Precision@5 - type: dot_precision@10 value: 0.13933333333333334 name: Dot Precision@10 - type: dot_recall@1 value: 0.2917109513578677 name: Dot Recall@1 - type: dot_recall@3 value: 0.44917159283956165 name: Dot Recall@3 - type: dot_recall@5 value: 0.49255056433469635 name: Dot Recall@5 - type: dot_recall@10 value: 0.5834506747931745 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5150086079147548 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5469179894179893 name: Dot Mrr@10 - type: dot_map@100 value: 0.42090000977105707 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6405150998246686 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5768809523809523 name: Dot Mrr@10 - type: dot_map@100 value: 0.5851061967133396 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 256 type: NanoNFCorpus_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37333333333333324 name: Dot Precision@3 - type: dot_precision@5 value: 0.324 name: Dot Precision@5 - type: dot_precision@10 value: 0.248 name: Dot Precision@10 - type: dot_recall@1 value: 0.045123947439696374 name: Dot Recall@1 - type: dot_recall@3 value: 0.08083248635236362 name: Dot Recall@3 - type: dot_recall@5 value: 0.0993952531376598 name: Dot Recall@5 - type: dot_recall@10 value: 0.1259275313458498 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3181127342430942 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5041666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.15847418838222901 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 256 type: NanoNQ_256 metrics: - type: dot_accuracy@1 value: 0.54 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.54 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.51 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6642484604451891 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6294126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.6162769242153361 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.4666666666666666 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7133333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7666666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4666666666666666 name: Dot Precision@1 - type: dot_precision@3 value: 0.2755555555555555 name: Dot Precision@3 - type: dot_precision@5 value: 0.21333333333333335 name: Dot Precision@5 - type: dot_precision@10 value: 0.1413333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.3317079824798988 name: Dot Recall@1 - type: dot_recall@3 value: 0.46027749545078783 name: Dot Recall@3 - type: dot_recall@5 value: 0.5297984177125533 name: Dot Recall@5 - type: dot_recall@10 value: 0.5919758437819499 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5409587648376507 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.570153439153439 name: Dot Mrr@10 - type: dot_map@100 value: 0.4532857697703016 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.18666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.10799999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.12166666666666665 name: Dot Recall@1 - type: dot_recall@3 value: 0.23233333333333334 name: Dot Recall@3 - type: dot_recall@5 value: 0.348 name: Dot Recall@5 - type: dot_recall@10 value: 0.42633333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33235923006734097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43644444444444447 name: Dot Mrr@10 - type: dot_map@100 value: 0.24903211945618525 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 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: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.6466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.56 name: Dot Precision@5 - type: dot_precision@10 value: 0.474 name: Dot Precision@10 - type: dot_recall@1 value: 0.09128542236179474 name: Dot Recall@1 - type: dot_recall@3 value: 0.17409405829521904 name: Dot Recall@3 - type: dot_recall@5 value: 0.22516141018064886 name: Dot Recall@5 - type: dot_recall@10 value: 0.321390285824061 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.600179050204524 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8425 name: Dot Mrr@10 - type: dot_map@100 value: 0.45264984932006563 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.32 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7866666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8866666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9266666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8816129048397259 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.89 name: Dot Mrr@10 - type: dot_map@100 value: 0.8589881484317317 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 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: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.3066666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.2592460317460317 name: Dot Recall@1 - type: dot_recall@3 value: 0.39734920634920634 name: Dot Recall@3 - type: dot_recall@5 value: 0.4497857142857143 name: Dot Recall@5 - type: dot_recall@10 value: 0.5795634920634921 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.48812055653800884 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5517460317460319 name: Dot Mrr@10 - type: dot_map@100 value: 0.42554170336694114 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.5133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.16999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.77 name: Dot Recall@3 - type: dot_recall@5 value: 0.82 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8106522538764799 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8966666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.7565706035126855 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6329477813439243 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5677777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.5762304873870092 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.34800000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.258 name: Dot Precision@10 - type: dot_recall@1 value: 0.04486258380333274 name: Dot Recall@1 - type: dot_recall@3 value: 0.08768477299713343 name: Dot Recall@3 - type: dot_recall@5 value: 0.10844641112515632 name: Dot Recall@5 - type: dot_recall@10 value: 0.135531563356284 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3285187113745097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5009999999999999 name: Dot Mrr@10 - type: dot_map@100 value: 0.16174125549238802 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.58 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.58 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.55 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.677342414343143 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6521666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.6420660106369513 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 1.0 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.4133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.27199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7773333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.9620000000000001 name: Dot Recall@3 - type: dot_recall@5 value: 0.9933333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.9966666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9509657098958008 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9466666666666665 name: Dot Mrr@10 - type: dot_map@100 value: 0.9297051282051282 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.35333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.3 name: Dot Precision@5 - type: dot_precision@10 value: 0.20800000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.09066666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.22166666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.3096666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.42566666666666664 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4022717287490821 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5887222222222221 name: Dot Mrr@10 - type: dot_map@100 value: 0.32075091248131626 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.7 name: Dot Recall@3 - type: dot_recall@5 value: 0.8 name: Dot Recall@5 - type: dot_recall@10 value: 0.92 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6550827948648061 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5706349206349206 name: Dot Mrr@10 - type: dot_map@100 value: 0.5760927960927961 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.62 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.62 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.17199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.595 name: Dot Recall@1 - type: dot_recall@3 value: 0.705 name: Dot Recall@3 - type: dot_recall@5 value: 0.755 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7193800580696723 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6823888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.6850911930363545 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.4897959183673469 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4897959183673469 name: Dot Precision@1 - type: dot_precision@3 value: 0.5170068027210885 name: Dot Precision@3 - type: dot_precision@5 value: 0.5346938775510204 name: Dot Precision@5 - type: dot_precision@10 value: 0.4346938775510204 name: Dot Precision@10 - type: dot_recall@1 value: 0.03422245985964837 name: Dot Recall@1 - type: dot_recall@3 value: 0.10897367065265 name: Dot Recall@3 - type: dot_recall@5 value: 0.18115391425134045 name: Dot Recall@5 - type: dot_recall@10 value: 0.2884686031356881 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.47678328743473813 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6784580498866212 name: Dot Mrr@10 - type: dot_map@100 value: 0.3590479959667369 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.576138147566719 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7505180533751962 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.821475667189953 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8722762951334379 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.576138147566719 name: Dot Precision@1 - type: dot_precision@3 value: 0.3525902668759811 name: Dot Precision@3 - type: dot_precision@5 value: 0.27559183673469384 name: Dot Precision@5 - type: dot_precision@10 value: 0.18374568288854 name: Dot Precision@10 - type: dot_recall@1 value: 0.35314998700801087 name: Dot Recall@1 - type: dot_recall@3 value: 0.5019821826892981 name: Dot Recall@3 - type: dot_recall@5 value: 0.5697857012699635 name: Dot Recall@5 - type: dot_recall@10 value: 0.6415605598240661 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6120166524309044 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6773209488923774 name: Dot Mrr@10 - type: dot_map@100 value: 0.5379621694912531 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) 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 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** CSR Sparse Encoder - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions) - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](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): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```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/csr-mxbai-embed-large-v1-nq-updated-reconstruction-2") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[118.6570, 32.2072, 21.3971]]) ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` | Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 | |:----------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.38 | 0.44 | 0.48 | | dot_accuracy@3 | 0.66 | 0.54 | 0.66 | | dot_accuracy@5 | 0.72 | 0.64 | 0.7 | | dot_accuracy@10 | 0.82 | 0.68 | 0.84 | | dot_precision@1 | 0.38 | 0.44 | 0.48 | | dot_precision@3 | 0.22 | 0.3133 | 0.2267 | | dot_precision@5 | 0.144 | 0.28 | 0.148 | | dot_precision@10 | 0.082 | 0.246 | 0.09 | | dot_recall@1 | 0.38 | 0.0451 | 0.45 | | dot_recall@3 | 0.66 | 0.0675 | 0.62 | | dot_recall@5 | 0.72 | 0.0877 | 0.67 | | dot_recall@10 | 0.82 | 0.1204 | 0.81 | | **dot_ndcg@10** | **0.6075** | **0.3038** | **0.6338** | | dot_mrr@10 | 0.5393 | 0.5082 | 0.5933 | | dot_map@100 | 0.5478 | 0.1387 | 0.5762 | | query_active_dims | 128.0 | 128.0 | 128.0 | | query_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 | | corpus_active_dims | 128.0 | 128.0 | 128.0 | | corpus_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * 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" ], "max_active_dims": 128 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.4333 | | dot_accuracy@3 | 0.62 | | dot_accuracy@5 | 0.6867 | | dot_accuracy@10 | 0.78 | | dot_precision@1 | 0.4333 | | dot_precision@3 | 0.2533 | | dot_precision@5 | 0.1907 | | dot_precision@10 | 0.1393 | | dot_recall@1 | 0.2917 | | dot_recall@3 | 0.4492 | | dot_recall@5 | 0.4926 | | dot_recall@10 | 0.5835 | | **dot_ndcg@10** | **0.515** | | dot_mrr@10 | 0.5469 | | dot_map@100 | 0.4209 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 0.9688 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` | Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 | |:----------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.44 | 0.42 | 0.54 | | dot_accuracy@3 | 0.64 | 0.58 | 0.7 | | dot_accuracy@5 | 0.74 | 0.6 | 0.8 | | dot_accuracy@10 | 0.84 | 0.62 | 0.84 | | dot_precision@1 | 0.44 | 0.42 | 0.54 | | dot_precision@3 | 0.2133 | 0.3733 | 0.24 | | dot_precision@5 | 0.148 | 0.324 | 0.168 | | dot_precision@10 | 0.084 | 0.248 | 0.092 | | dot_recall@1 | 0.44 | 0.0451 | 0.51 | | dot_recall@3 | 0.64 | 0.0808 | 0.66 | | dot_recall@5 | 0.74 | 0.0994 | 0.75 | | dot_recall@10 | 0.84 | 0.1259 | 0.81 | | **dot_ndcg@10** | **0.6405** | **0.3181** | **0.6642** | | dot_mrr@10 | 0.5769 | 0.5042 | 0.6294 | | dot_map@100 | 0.5851 | 0.1585 | 0.6163 | | query_active_dims | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * 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" ], "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.4667 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.7133 | | dot_accuracy@10 | 0.7667 | | dot_precision@1 | 0.4667 | | dot_precision@3 | 0.2756 | | dot_precision@5 | 0.2133 | | dot_precision@10 | 0.1413 | | dot_recall@1 | 0.3317 | | dot_recall@3 | 0.4603 | | dot_recall@5 | 0.5298 | | dot_recall@10 | 0.592 | | **dot_ndcg@10** | **0.541** | | dot_mrr@10 | 0.5702 | | dot_map@100 | 0.4533 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:----------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.28 | 0.8 | 0.84 | 0.48 | 0.84 | 0.44 | 0.42 | 0.58 | 0.9 | 0.42 | 0.38 | 0.62 | 0.4898 | | dot_accuracy@3 | 0.52 | 0.9 | 0.92 | 0.6 | 0.96 | 0.62 | 0.56 | 0.7 | 1.0 | 0.72 | 0.7 | 0.72 | 0.8367 | | dot_accuracy@5 | 0.7 | 0.9 | 0.96 | 0.64 | 0.96 | 0.74 | 0.64 | 0.8 | 1.0 | 0.82 | 0.8 | 0.76 | 0.9592 | | dot_accuracy@10 | 0.8 | 0.92 | 0.96 | 0.74 | 0.98 | 0.84 | 0.66 | 0.82 | 1.0 | 0.88 | 0.92 | 0.84 | 0.9796 | | dot_precision@1 | 0.28 | 0.8 | 0.84 | 0.48 | 0.84 | 0.44 | 0.42 | 0.58 | 0.9 | 0.42 | 0.38 | 0.62 | 0.4898 | | dot_precision@3 | 0.1867 | 0.6467 | 0.32 | 0.3067 | 0.5133 | 0.2067 | 0.38 | 0.24 | 0.4133 | 0.3533 | 0.2333 | 0.2667 | 0.517 | | dot_precision@5 | 0.168 | 0.56 | 0.2 | 0.224 | 0.328 | 0.148 | 0.348 | 0.168 | 0.272 | 0.3 | 0.16 | 0.172 | 0.5347 | | dot_precision@10 | 0.108 | 0.474 | 0.1 | 0.136 | 0.17 | 0.084 | 0.258 | 0.09 | 0.138 | 0.208 | 0.092 | 0.096 | 0.4347 | | dot_recall@1 | 0.1217 | 0.0913 | 0.7867 | 0.2592 | 0.42 | 0.44 | 0.0449 | 0.55 | 0.7773 | 0.0907 | 0.38 | 0.595 | 0.0342 | | dot_recall@3 | 0.2323 | 0.1741 | 0.8867 | 0.3973 | 0.77 | 0.62 | 0.0877 | 0.66 | 0.962 | 0.2217 | 0.7 | 0.705 | 0.109 | | dot_recall@5 | 0.348 | 0.2252 | 0.9267 | 0.4498 | 0.82 | 0.74 | 0.1084 | 0.75 | 0.9933 | 0.3097 | 0.8 | 0.755 | 0.1812 | | dot_recall@10 | 0.4263 | 0.3214 | 0.9267 | 0.5796 | 0.85 | 0.84 | 0.1355 | 0.79 | 0.9967 | 0.4257 | 0.92 | 0.84 | 0.2885 | | **dot_ndcg@10** | **0.3324** | **0.6002** | **0.8816** | **0.4881** | **0.8107** | **0.6329** | **0.3285** | **0.6773** | **0.951** | **0.4023** | **0.6551** | **0.7194** | **0.4768** | | dot_mrr@10 | 0.4364 | 0.8425 | 0.89 | 0.5517 | 0.8967 | 0.5678 | 0.501 | 0.6522 | 0.9467 | 0.5887 | 0.5706 | 0.6824 | 0.6785 | | dot_map@100 | 0.249 | 0.4526 | 0.859 | 0.4255 | 0.7566 | 0.5762 | 0.1617 | 0.6421 | 0.9297 | 0.3208 | 0.5761 | 0.6851 | 0.359 | | query_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](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 | 0.5761 | | dot_accuracy@3 | 0.7505 | | dot_accuracy@5 | 0.8215 | | dot_accuracy@10 | 0.8723 | | dot_precision@1 | 0.5761 | | dot_precision@3 | 0.3526 | | dot_precision@5 | 0.2756 | | dot_precision@10 | 0.1837 | | dot_recall@1 | 0.3531 | | dot_recall@3 | 0.502 | | dot_recall@5 | 0.5698 | | dot_recall@10 | 0.6416 | | **dot_ndcg@10** | **0.612** | | dot_mrr@10 | 0.6773 | | dot_map@100 | 0.538 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](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: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### 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: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:| | 0.0646 | 100 | 0.3565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.3568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 300 | 0.3545 | 0.3458 | 0.6322 | 0.2796 | 0.5893 | 0.5004 | 0.6232 | 0.3253 | 0.6548 | 0.5345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 400 | 0.3393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.3484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.3567 | 0.3452 | 0.6245 | 0.3038 | 0.5719 | 0.5000 | 0.6385 | 0.3375 | 0.6496 | 0.5419 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.3471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.3582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.3758 | 0.3417 | 0.5849 | 0.3074 | 0.5866 | 0.4929 | 0.6147 | 0.3310 | 0.6729 | 0.5395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.3515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.3287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.7757** | **1200** | **0.3486** | **0.3314** | **0.5937** | **0.2998** | **0.6317** | **0.5084** | **0.6309** | **0.3303** | **0.6773** | **0.5462** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.8403 | 1300 | 0.3527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.3161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.3279 | 0.3244 | 0.6075 | 0.3038 | 0.6338 | 0.5150 | 0.6405 | 0.3181 | 0.6642 | 0.5410 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3324 | 0.6002 | 0.8816 | 0.4881 | 0.8107 | 0.6329 | 0.3285 | 0.6773 | 0.9510 | 0.4023 | 0.6551 | 0.7194 | 0.4768 | 0.6120 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.136 kWh - **Carbon Emitted**: 0.053 kg of CO2 - **Hours Used**: 0.41 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```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", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```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} } ```