--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity model-index: - name: SGPT-125M-weightedmean-nli-bitfit results: - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P metrics: - type: v_measure value: 0.28301902023313874 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 metrics: - type: cos_sim_pearson value: 0.76401935081936 - type: cos_sim_spearman value: 0.7723446219694267 - type: euclidean_pearson value: 0.7461017160439877 - type: euclidean_spearman value: 0.7585871531365609 - type: manhattan_pearson value: 0.7483034779539725 - type: manhattan_spearman value: 0.759594899358843 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P metrics: - type: v_measure value: 0.3474248247787077 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) metrics: - type: accuracy value: 0.35098 - type: f1 value: 0.34732656514357263 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) metrics: - type: accuracy value: 0.24516 - type: f1 value: 0.2421748200448397 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) metrics: - type: accuracy value: 0.29097999999999996 - type: f1 value: 0.28620040162757093 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) metrics: - type: accuracy value: 0.27396 - type: f1 value: 0.27146888644986283 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) metrics: - type: accuracy value: 0.21724000000000002 - type: f1 value: 0.2137230564276654 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) metrics: - type: accuracy value: 0.23975999999999997 - type: f1 value: 0.23741137981755484 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) metrics: - type: accuracy value: 0.010960334029227558 - type: f1 value: 0.01092553931802366 - type: precision value: 0.010908141962421711 - type: recall value: 0.010960334029227558 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) metrics: - type: accuracy value: 0.00022011886418666079 - type: f1 value: 0.00022011886418666079 - type: precision value: 0.00022011886418666079 - type: recall value: 0.00022011886418666079 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) metrics: - type: accuracy value: 0.0 - type: f1 value: 0.0 - type: precision value: 0.0 - type: recall value: 0.0 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) metrics: - type: accuracy value: 0.0 - type: f1 value: 0.0 - type: precision value: 0.0 - type: recall value: 0.0 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) metrics: - type: accuracy value: 0.8151846785225718 - type: f1 value: 0.81648869152345 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) metrics: - type: accuracy value: 0.6037475345167653 - type: f1 value: 0.5845264937551703 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) metrics: - type: accuracy value: 0.6736824549699799 - type: f1 value: 0.6535927434998515 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) metrics: - type: accuracy value: 0.6312871907297212 - type: f1 value: 0.6137620329272278 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) metrics: - type: accuracy value: 0.47045536034420943 - type: f1 value: 0.46203899126445613 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) metrics: - type: accuracy value: 0.5228209764918625 - type: f1 value: 0.5075489206473579 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval metrics: - type: map_at_1 value: 0.0808 - type: map_at_10 value: 0.11691 - type: map_at_100 value: 0.12312 - type: map_at_1000 value: 0.12439 - type: map_at_3 value: 0.10344 - type: map_at_5 value: 0.10996 - type: ndcg_at_1 value: 0.10697 - type: ndcg_at_10 value: 0.1448 - type: ndcg_at_100 value: 0.18161 - type: ndcg_at_1000 value: 0.21886 - type: ndcg_at_3 value: 0.11872 - type: ndcg_at_5 value: 0.12834 - type: precision_at_1 value: 0.10697 - type: precision_at_10 value: 0.02811 - type: precision_at_100 value: 0.00551 - type: precision_at_1000 value: 0.00102 - type: precision_at_3 value: 0.05804 - type: precision_at_5 value: 0.04154 - type: recall_at_1 value: 0.0808 - type: recall_at_10 value: 0.20235 - type: recall_at_100 value: 0.37526 - type: recall_at_1000 value: 0.65106 - type: recall_at_3 value: 0.12804 - type: recall_at_5 value: 0.15499 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) metrics: - type: accuracy value: 0.6588059701492537 - type: ap value: 0.28685493163579784 - type: f1 value: 0.5979951005816335 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) metrics: - type: accuracy value: 0.5907922912205568 - type: ap value: 0.7391887421019034 - type: f1 value: 0.566316368658711 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) metrics: - type: accuracy value: 0.6491754122938531 - type: ap value: 0.16360681214864226 - type: f1 value: 0.5312659206152377 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) metrics: - type: accuracy value: 0.56423982869379 - type: ap value: 0.12143003571907898 - type: f1 value: 0.45763637779874716 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval metrics: - type: map_at_1 value: 0.06496 - type: map_at_10 value: 0.09243 - type: map_at_100 value: 0.09841 - type: map_at_1000 value: 0.09946 - type: map_at_3 value: 0.08395 - type: map_at_5 value: 0.08872 - type: ndcg_at_1 value: 0.08224 - type: ndcg_at_10 value: 0.1124 - type: ndcg_at_100 value: 0.14525 - type: ndcg_at_1000 value: 0.17686 - type: ndcg_at_3 value: 0.09617 - type: ndcg_at_5 value: 0.1037 - type: precision_at_1 value: 0.08224 - type: precision_at_10 value: 0.02082 - type: precision_at_100 value: 0.00443 - type: precision_at_1000 value: 0.00085 - type: precision_at_3 value: 0.04623 - type: precision_at_5 value: 0.03331 - type: recall_at_1 value: 0.06496 - type: recall_at_10 value: 0.1531 - type: recall_at_100 value: 0.3068 - type: recall_at_1000 value: 0.54335 - type: recall_at_3 value: 0.10691 - type: recall_at_5 value: 0.12688 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking metrics: - type: map value: 0.2926934104146833 - type: mrr value: 0.3013214087687572 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus metrics: - type: map_at_1 value: 0.01227 - type: map_at_10 value: 0.03081 - type: map_at_100 value: 0.04104 - type: map_at_1000 value: 0.04989 - type: map_at_3 value: 0.02221 - type: map_at_5 value: 0.02535 - type: ndcg_at_1 value: 0.15015 - type: ndcg_at_10 value: 0.11805 - type: ndcg_at_100 value: 0.12452 - type: ndcg_at_1000 value: 0.22284 - type: ndcg_at_3 value: 0.13257 - type: ndcg_at_5 value: 0.12199 - type: precision_at_1 value: 0.16409 - type: precision_at_10 value: 0.09102 - type: precision_at_100 value: 0.03678 - type: precision_at_1000 value: 0.01609 - type: precision_at_3 value: 0.12797 - type: precision_at_5 value: 0.10464 - type: recall_at_1 value: 0.01227 - type: recall_at_10 value: 0.05838 - type: recall_at_100 value: 0.15716 - type: recall_at_1000 value: 0.48837 - type: recall_at_3 value: 0.02828 - type: recall_at_5 value: 0.03697 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO metrics: - type: map_at_1 value: 0.0288 - type: map_at_10 value: 0.04914 - type: map_at_100 value: 0.05459 - type: map_at_1000 value: 0.05538 - type: map_at_3 value: 0.04087 - type: map_at_5 value: 0.04518 - type: ndcg_at_1 value: 0.02937 - type: ndcg_at_10 value: 0.06273 - type: ndcg_at_100 value: 0.09426 - type: ndcg_at_1000 value: 0.12033 - type: ndcg_at_3 value: 0.04513 - type: ndcg_at_5 value: 0.05292 - type: precision_at_1 value: 0.02937 - type: precision_at_10 value: 0.01089 - type: precision_at_100 value: 0.00277 - type: precision_at_1000 value: 0.00051 - type: precision_at_3 value: 0.01929 - type: precision_at_5 value: 0.01547 - type: recall_at_1 value: 0.0288 - type: recall_at_10 value: 0.10578 - type: recall_at_100 value: 0.26267 - type: recall_at_1000 value: 0.4759 - type: recall_at_3 value: 0.05673 - type: recall_at_5 value: 0.07545 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval metrics: - type: map_at_1 value: 0.13843 - type: map_at_10 value: 0.17496 - type: map_at_100 value: 0.18304 - type: map_at_1000 value: 0.18426 - type: map_at_3 value: 0.16225 - type: map_at_5 value: 0.1683 - type: ndcg_at_1 value: 0.16698 - type: ndcg_at_10 value: 0.20301 - type: ndcg_at_100 value: 0.24523 - type: ndcg_at_1000 value: 0.27784 - type: ndcg_at_3 value: 0.17822 - type: ndcg_at_5 value: 0.18794 - type: precision_at_1 value: 0.16698 - type: precision_at_10 value: 0.03358 - type: precision_at_100 value: 0.00618 - type: precision_at_1000 value: 0.00101 - type: precision_at_3 value: 0.07898 - type: precision_at_5 value: 0.05429 - type: recall_at_1 value: 0.13843 - type: recall_at_10 value: 0.25888 - type: recall_at_100 value: 0.45028 - type: recall_at_1000 value: 0.68991 - type: recall_at_3 value: 0.18851 - type: recall_at_5 value: 0.21462 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 metrics: - type: cos_sim_pearson value: 0.8020938796088339 - type: cos_sim_spearman value: 0.6916914010333395 - type: euclidean_pearson value: 0.7933415250097545 - type: euclidean_spearman value: 0.7146707320292746 - type: manhattan_pearson value: 0.7973669837981976 - type: manhattan_spearman value: 0.7187919511134903 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering metrics: - type: v_measure value: 0.4459127540530939 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR metrics: - type: map value: 0.6835710819755543 - type: mrr value: 0.8805442832403617 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna metrics: - type: map_at_1 value: 0.13442 - type: map_at_10 value: 0.24275 - type: map_at_100 value: 0.25588 - type: map_at_1000 value: 0.25659 - type: map_at_3 value: 0.20092 - type: map_at_5 value: 0.2244 - type: ndcg_at_1 value: 0.13442 - type: ndcg_at_10 value: 0.3104 - type: ndcg_at_100 value: 0.37529 - type: ndcg_at_1000 value: 0.39348 - type: ndcg_at_3 value: 0.22342 - type: ndcg_at_5 value: 0.26596 - type: precision_at_1 value: 0.13442 - type: precision_at_10 value: 0.05299 - type: precision_at_100 value: 0.00836 - type: precision_at_1000 value: 0.00098 - type: precision_at_3 value: 0.09625 - type: precision_at_5 value: 0.07852 - type: recall_at_1 value: 0.13442 - type: recall_at_10 value: 0.52987 - type: recall_at_100 value: 0.83642 - type: recall_at_1000 value: 0.97795 - type: recall_at_3 value: 0.28876 - type: recall_at_5 value: 0.3926 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions metrics: - type: map value: 0.5263439984994702 - type: mrr value: 0.6575704612408213 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification metrics: - type: accuracy value: 0.5482173174872665 - type: f1 value: 0.5514729314789282 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S metrics: - type: v_measure value: 0.2467870651472156 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA metrics: - type: map_at_1 value: 0.09676 - type: map_at_10 value: 0.13351 - type: map_at_100 value: 0.13919 - type: map_at_1000 value: 0.1401 - type: map_at_3 value: 0.12223 - type: map_at_5 value: 0.12812 - type: ndcg_at_1 value: 0.19352 - type: ndcg_at_10 value: 0.17727 - type: ndcg_at_100 value: 0.20837 - type: ndcg_at_1000 value: 0.23412 - type: ndcg_at_3 value: 0.15317 - type: ndcg_at_5 value: 0.16436 - type: precision_at_1 value: 0.19352 - type: precision_at_10 value: 0.03993 - type: precision_at_100 value: 0.00651 - type: precision_at_1000 value: 0.001 - type: precision_at_3 value: 0.09669 - type: precision_at_5 value: 0.0669 - type: recall_at_1 value: 0.09676 - type: recall_at_10 value: 0.19966 - type: recall_at_100 value: 0.32573 - type: recall_at_1000 value: 0.49905 - type: recall_at_3 value: 0.14504 - type: recall_at_5 value: 0.16725 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 metrics: - type: map_at_1 value: 0.00645 - type: map_at_10 value: 0.04116 - type: map_at_100 value: 0.07527 - type: map_at_1000 value: 0.08678 - type: map_at_3 value: 0.01602 - type: map_at_5 value: 0.026 - type: ndcg_at_1 value: 0.10204 - type: ndcg_at_10 value: 0.1227 - type: ndcg_at_100 value: 0.22461 - type: ndcg_at_1000 value: 0.33543 - type: ndcg_at_3 value: 0.09982 - type: ndcg_at_5 value: 0.11498 - type: precision_at_1 value: 0.10204 - type: precision_at_10 value: 0.12245 - type: precision_at_100 value: 0.05286 - type: precision_at_1000 value: 0.01263 - type: precision_at_3 value: 0.10884 - type: precision_at_5 value: 0.13061 - type: recall_at_1 value: 0.00645 - type: recall_at_10 value: 0.08996 - type: recall_at_100 value: 0.33666 - type: recall_at_1000 value: 0.67704 - type: recall_at_3 value: 0.02504 - type: recall_at_5 value: 0.0495 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval metrics: - type: map_at_1 value: 0.18222 - type: map_at_10 value: 0.24506 - type: map_at_100 value: 0.25611 - type: map_at_1000 value: 0.25758 - type: map_at_3 value: 0.22265 - type: map_at_5 value: 0.23698 - type: ndcg_at_1 value: 0.23033 - type: ndcg_at_10 value: 0.28719 - type: ndcg_at_100 value: 0.33748 - type: ndcg_at_1000 value: 0.37056 - type: ndcg_at_3 value: 0.2524 - type: ndcg_at_5 value: 0.2712 - type: precision_at_1 value: 0.23033 - type: precision_at_10 value: 0.05408 - type: precision_at_100 value: 0.01004 - type: precision_at_1000 value: 0.00158 - type: precision_at_3 value: 0.11874 - type: precision_at_5 value: 0.08927 - type: recall_at_1 value: 0.18222 - type: recall_at_10 value: 0.36355 - type: recall_at_100 value: 0.58724 - type: recall_at_1000 value: 0.81335 - type: recall_at_3 value: 0.26334 - type: recall_at_5 value: 0.314 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval metrics: - type: cos_sim_pearson value: 0.3056303767714449 - type: cos_sim_spearman value: 0.30256847004390486 - type: dot_pearson value: 0.29453520030995006 - type: dot_spearman value: 0.2956173255092678 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification metrics: - type: accuracy value: 0.62896 - type: ap value: 0.5847769349850157 - type: f1 value: 0.6267885149592086 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 metrics: - type: cos_sim_pearson value: 0.7905293131911804 - type: cos_sim_spearman value: 0.7973794782598049 - type: euclidean_pearson value: 0.7817016171851057 - type: euclidean_spearman value: 0.7876038607583106 - type: manhattan_pearson value: 0.784994607532332 - type: manhattan_spearman value: 0.7913026720132872 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S metrics: - type: v_measure value: 0.24932123582259286 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER metrics: - type: map_at_1 value: 0.03714 - type: map_at_10 value: 0.06926 - type: map_at_100 value: 0.07879 - type: map_at_1000 value: 0.08032 - type: map_at_3 value: 0.05504 - type: map_at_5 value: 0.06357 - type: ndcg_at_1 value: 0.0886 - type: ndcg_at_10 value: 0.11007 - type: ndcg_at_100 value: 0.16154 - type: ndcg_at_1000 value: 0.19668 - type: ndcg_at_3 value: 0.08103 - type: ndcg_at_5 value: 0.09456 - type: precision_at_1 value: 0.0886 - type: precision_at_10 value: 0.0372 - type: precision_at_100 value: 0.00917 - type: precision_at_1000 value: 0.00156 - type: precision_at_3 value: 0.06254 - type: precision_at_5 value: 0.05381 - type: recall_at_1 value: 0.03714 - type: recall_at_10 value: 0.14382 - type: recall_at_100 value: 0.33166 - type: recall_at_1000 value: 0.53444 - type: recall_at_3 value: 0.07523 - type: recall_at_5 value: 0.1091 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 metrics: - type: cos_sim_pearson value: 0.7535551963935667 - type: cos_sim_spearman value: 0.7098892671568665 - type: euclidean_pearson value: 0.7324467338564629 - type: euclidean_spearman value: 0.7197533151639425 - type: manhattan_pearson value: 0.7327765593599381 - type: manhattan_spearman value: 0.722221421456084 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval metrics: - type: map_at_1 value: 0.12058 - type: map_at_10 value: 0.16051 - type: map_at_100 value: 0.16772 - type: map_at_1000 value: 0.16871 - type: map_at_3 value: 0.1478 - type: map_at_5 value: 0.155 - type: ndcg_at_1 value: 0.1535 - type: ndcg_at_10 value: 0.18804 - type: ndcg_at_100 value: 0.22346 - type: ndcg_at_1000 value: 0.25007 - type: ndcg_at_3 value: 0.16768 - type: ndcg_at_5 value: 0.17692 - type: precision_at_1 value: 0.1535 - type: precision_at_10 value: 0.0351 - type: precision_at_100 value: 0.00664 - type: precision_at_1000 value: 0.00111 - type: precision_at_3 value: 0.07983 - type: precision_at_5 value: 0.05656 - type: recall_at_1 value: 0.12058 - type: recall_at_10 value: 0.23644 - type: recall_at_100 value: 0.3976 - type: recall_at_1000 value: 0.5856 - type: recall_at_3 value: 0.17542 - type: recall_at_5 value: 0.20232 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval metrics: - type: map_at_1 value: 0.21183 - type: map_at_10 value: 0.289 - type: map_at_100 value: 0.29858 - type: map_at_1000 value: 0.29954 - type: map_at_3 value: 0.2658 - type: map_at_5 value: 0.27912 - type: ndcg_at_1 value: 0.24765 - type: ndcg_at_10 value: 0.3334 - type: ndcg_at_100 value: 0.37997 - type: ndcg_at_1000 value: 0.40416 - type: ndcg_at_3 value: 0.29045 - type: ndcg_at_5 value: 0.31121 - type: precision_at_1 value: 0.24765 - type: precision_at_10 value: 0.05599 - type: precision_at_100 value: 0.0087 - type: precision_at_1000 value: 0.00115 - type: precision_at_3 value: 0.13271 - type: precision_at_5 value: 0.09367 - type: recall_at_1 value: 0.21183 - type: recall_at_10 value: 0.43875 - type: recall_at_100 value: 0.65005 - type: recall_at_1000 value: 0.83017 - type: recall_at_3 value: 0.32232 - type: recall_at_5 value: 0.37308 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 metrics: - type: map_at_1 value: 0.03637 - type: map_at_10 value: 0.06084 - type: map_at_100 value: 0.06919 - type: map_at_1000 value: 0.07108 - type: map_at_3 value: 0.05071 - 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task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification metrics: - type: accuracy value: 0.627862 - type: ap value: 0.10958454618347832 - type: f1 value: 0.48372434170467626 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering metrics: - type: v_measure value: 0.2824295128553035 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 metrics: - type: cos_sim_accuracy value: 0.815640460153782 - type: cos_sim_accuracy_threshold value: 0.7118978500366211 - type: cos_sim_ap value: 0.5709409536692154 - type: cos_sim_f1 value: 0.5529607083563918 - type: cos_sim_f1_threshold value: 0.5981647968292236 - type: cos_sim_precision value: 0.47626310772163966 - type: cos_sim_recall value: 0.6591029023746702 - type: dot_accuracy value: 0.788162365142755 - type: dot_accuracy_threshold value: 1049.799072265625 - type: dot_ap value: 0.4742989400382077 - type: dot_f1 value: 0.5125944584382871 - type: dot_f1_threshold value: 723.3736572265625 - type: dot_precision value: 0.4255838271174625 - type: dot_recall value: 0.6443271767810026 - type: euclidean_accuracy value: 0.8029445073612684 - type: euclidean_accuracy_threshold value: 26.134265899658203 - type: euclidean_ap value: 0.5342012231336148 - type: euclidean_f1 value: 0.5186778356350464 - type: euclidean_f1_threshold value: 31.25627326965332 - type: euclidean_precision value: 0.454203013481364 - type: euclidean_recall value: 0.604485488126649 - type: manhattan_accuracy value: 0.802884901949097 - type: manhattan_accuracy_threshold value: 560.0760498046875 - type: manhattan_ap value: 0.5343205271323233 - type: manhattan_f1 value: 0.520141655599823 - type: manhattan_f1_threshold value: 658.3975830078125 - type: manhattan_precision value: 0.44796035074342355 - type: manhattan_recall value: 0.6200527704485488 - type: max_accuracy value: 0.815640460153782 - type: max_ap value: 0.5709409536692154 - type: max_f1 value: 0.5529607083563918 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) metrics: - type: accuracy value: 0.582421340629275 - type: f1 value: 0.40116960466226426 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) metrics: - type: accuracy value: 0.4506903353057199 - type: f1 value: 0.30468468273374966 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) metrics: - type: accuracy value: 0.4880920613742495 - type: f1 value: 0.3265985375400447 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) metrics: - type: accuracy value: 0.4433761352959599 - type: f1 value: 0.2930204743560644 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) metrics: - type: accuracy value: 0.34198637504481894 - type: f1 value: 0.2206370603224841 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) metrics: - type: accuracy value: 0.4311030741410488 - type: f1 value: 0.2692408933648504 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering metrics: - type: v_measure value: 0.3375741018380938 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval metrics: - type: map_at_1 value: 0.13909 - type: map_at_10 value: 0.19256 - type: map_at_100 value: 0.20286 - type: map_at_1000 value: 0.20429 - type: map_at_3 value: 0.17399 - type: map_at_5 value: 0.18399 - type: ndcg_at_1 value: 0.17421 - type: ndcg_at_10 value: 0.23106 - type: ndcg_at_100 value: 0.28129 - type: ndcg_at_1000 value: 0.31481 - type: ndcg_at_3 value: 0.19789 - type: ndcg_at_5 value: 0.21237 - type: precision_at_1 value: 0.17421 - type: precision_at_10 value: 0.04331 - type: precision_at_100 value: 0.00839 - type: precision_at_1000 value: 0.00131 - type: precision_at_3 value: 0.094 - type: precision_at_5 value: 0.06776 - type: recall_at_1 value: 0.13909 - type: recall_at_10 value: 0.31087 - type: recall_at_100 value: 0.52946 - type: recall_at_1000 value: 0.76546 - type: recall_at_3 value: 0.21351 - type: recall_at_5 value: 0.25265 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions metrics: - type: map value: 0.3996520488022785 - type: mrr value: 0.40189248047703935 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval metrics: - type: map_at_1 value: 0.12738416666666666 - type: map_at_10 value: 0.17235916666666667 - type: map_at_100 value: 0.1806333333333333 - type: map_at_1000 value: 0.18184333333333333 - type: map_at_3 value: 0.1574775 - type: map_at_5 value: 0.1657825 - type: ndcg_at_1 value: 0.15487416666666665 - type: ndcg_at_10 value: 0.20290166666666667 - type: ndcg_at_100 value: 0.24412916666666662 - type: ndcg_at_1000 value: 0.27586333333333335 - type: ndcg_at_3 value: 0.17622083333333333 - type: ndcg_at_5 value: 0.18859916666666668 - type: precision_at_1 value: 0.15487416666666665 - type: precision_at_10 value: 0.036226666666666664 - type: precision_at_100 value: 0.006820833333333333 - type: precision_at_1000 value: 0.0011216666666666666 - type: precision_at_3 value: 0.08163749999999999 - type: precision_at_5 value: 0.058654166666666674 - type: recall_at_1 value: 0.12738416666666666 - type: recall_at_10 value: 0.26599416666666664 - type: recall_at_100 value: 0.4541258333333334 - type: recall_at_1000 value: 0.687565 - type: recall_at_3 value: 0.19008166666666668 - type: recall_at_5 value: 0.2224991666666667 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions metrics: - type: cos_sim_accuracy value: 0.9949306930693069 - type: cos_sim_accuracy_threshold value: 0.7870972752571106 - type: cos_sim_ap value: 0.7773085502917281 - type: cos_sim_f1 value: 0.7178978681209718 - type: cos_sim_f1_threshold value: 0.7572916746139526 - type: cos_sim_precision value: 0.711897738446411 - type: cos_sim_recall value: 0.724 - type: dot_accuracy value: 0.9908118811881188 - type: dot_accuracy_threshold value: 1571.5850830078125 - type: dot_ap value: 0.30267748833368235 - type: dot_f1 value: 0.34335201222618444 - type: dot_f1_threshold value: 1329.530029296875 - type: dot_precision value: 0.34994807892004154 - type: dot_recall value: 0.337 - type: euclidean_accuracy value: 0.9951683168316832 - type: euclidean_accuracy_threshold value: 25.715721130371094 - type: euclidean_ap value: 0.7864498778235628 - type: euclidean_f1 value: 0.7309149972929074 - type: euclidean_f1_threshold value: 26.336116790771484 - type: euclidean_precision value: 0.7969303423848878 - type: euclidean_recall value: 0.675 - type: manhattan_accuracy value: 0.9953168316831683 - type: manhattan_accuracy_threshold value: 534.224609375 - type: manhattan_ap value: 0.7945274878693959 - type: manhattan_f1 value: 0.7419863373620599 - type: manhattan_f1_threshold value: 562.244140625 - type: manhattan_precision value: 0.7818383167220376 - type: manhattan_recall value: 0.706 - type: max_accuracy value: 0.9953168316831683 - type: max_ap value: 0.7945274878693959 - type: max_f1 value: 0.7419863373620599 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval metrics: - type: map_at_1 value: 0.09057 - type: map_at_10 value: 0.12721 - type: map_at_100 value: 0.1345 - type: map_at_1000 value: 0.13564 - type: map_at_3 value: 0.1134 - type: map_at_5 value: 0.12245 - type: ndcg_at_1 value: 0.09797 - type: ndcg_at_10 value: 0.15091 - type: ndcg_at_100 value: 0.18886 - type: ndcg_at_1000 value: 0.2229 - type: ndcg_at_3 value: 0.12365 - type: ndcg_at_5 value: 0.13931 - type: precision_at_1 value: 0.09797 - type: precision_at_10 value: 0.02477 - type: precision_at_100 value: 0.00466 - type: precision_at_1000 value: 0.00082 - type: precision_at_3 value: 0.05299 - type: precision_at_5 value: 0.04067 - type: recall_at_1 value: 0.09057 - type: recall_at_10 value: 0.21319 - type: recall_at_100 value: 0.38999 - type: recall_at_1000 value: 0.65374 - type: recall_at_3 value: 0.14331 - type: recall_at_5 value: 0.17917 --- # SGPT-125M-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 880, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "lr": 0.0002 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 881, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```