--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - bge - mteb - mergekit - merge base_model: - Mihaiii/Wartortle - TaylorAI/bge-micro-v2 model-index: - name: Giratina results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.56716417910448 - type: ap value: 31.399435128856624 - type: f1 value: 63.139089415537256 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 74.73525000000001 - type: ap value: 69.2327764533514 - type: f1 value: 74.61617659775962 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.356 - type: f1 value: 35.165109893437204 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 17.141000000000002 - type: map_at_10 value: 28.292 - type: map_at_100 value: 29.532000000000004 - type: map_at_1000 value: 29.580000000000002 - type: map_at_20 value: 29.048000000000002 - type: map_at_3 value: 24.277 - type: map_at_5 value: 26.339000000000002 - type: mrr_at_1 value: 17.781 - type: mrr_at_10 value: 28.534 - type: mrr_at_100 value: 29.779 - type: mrr_at_1000 value: 29.826999999999998 - type: mrr_at_20 value: 29.293000000000003 - type: mrr_at_3 value: 24.490000000000002 - type: mrr_at_5 value: 26.564 - type: ndcg_at_1 value: 17.141000000000002 - type: ndcg_at_10 value: 35.004000000000005 - type: ndcg_at_100 value: 41.056 - type: ndcg_at_1000 value: 42.388 - type: ndcg_at_20 value: 37.721 - type: ndcg_at_3 value: 26.592 - type: ndcg_at_5 value: 30.294999999999998 - type: precision_at_1 value: 17.141000000000002 - type: precision_at_10 value: 5.676 - type: precision_at_100 value: 0.851 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 3.3709999999999996 - type: precision_at_3 value: 11.094999999999999 - type: precision_at_5 value: 8.450000000000001 - type: recall_at_1 value: 17.141000000000002 - type: recall_at_10 value: 56.757000000000005 - type: recall_at_100 value: 85.064 - type: recall_at_1000 value: 95.661 - type: recall_at_20 value: 67.425 - type: recall_at_3 value: 33.286 - type: recall_at_5 value: 42.248000000000005 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 37.86211319797047 - type: v_measures 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0.24157240034932226] - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.156 - type: map_at_10 value: 26.989 - type: map_at_100 value: 28.165000000000003 - type: map_at_1000 value: 28.302 - type: map_at_20 value: 27.505000000000003 - type: map_at_3 value: 24.631 - type: map_at_5 value: 25.886 - type: mrr_at_1 value: 25.607999999999997 - type: mrr_at_10 value: 31.972 - type: mrr_at_100 value: 32.993 - type: mrr_at_1000 value: 33.061 - type: mrr_at_20 value: 32.471 - type: mrr_at_3 value: 30.019000000000002 - type: mrr_at_5 value: 31.041999999999998 - type: ndcg_at_1 value: 25.607999999999997 - type: ndcg_at_10 value: 31.438 - type: ndcg_at_100 value: 37.347 - type: ndcg_at_1000 value: 40.075 - type: ndcg_at_20 value: 33.068 - type: ndcg_at_3 value: 27.846 - type: ndcg_at_5 value: 29.304999999999996 - type: precision_at_1 value: 25.607999999999997 - type: precision_at_10 value: 5.923 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.161 - type: precision_at_20 value: 3.5340000000000003 - type: precision_at_3 value: 13.305 - type: precision_at_5 value: 9.585 - type: recall_at_1 value: 20.156 - type: recall_at_10 value: 39.741 - type: recall_at_100 value: 66.428 - type: recall_at_1000 value: 84.694 - type: recall_at_20 value: 45.688 - type: recall_at_3 value: 28.876 - type: recall_at_5 value: 33.284000000000006 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 14.568 - type: map_at_10 value: 19.356 - type: map_at_100 value: 20.044 - type: map_at_1000 value: 20.146 - type: map_at_20 value: 19.717000000000002 - type: map_at_3 value: 17.82 - type: map_at_5 value: 18.724 - type: mrr_at_1 value: 18.025 - type: mrr_at_10 value: 22.933 - type: mrr_at_100 value: 23.599 - type: mrr_at_1000 value: 23.669999999999998 - type: mrr_at_20 value: 23.283 - type: mrr_at_3 value: 21.295 - type: mrr_at_5 value: 22.314 - type: ndcg_at_1 value: 18.025 - type: ndcg_at_10 value: 22.559 - type: ndcg_at_100 value: 26.045 - type: ndcg_at_1000 value: 28.785 - type: ndcg_at_20 value: 23.727999999999998 - type: ndcg_at_3 value: 19.914 - type: ndcg_at_5 value: 21.241 - type: precision_at_1 value: 18.025 - type: precision_at_10 value: 4.102 - type: precision_at_100 value: 0.715 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_20 value: 2.452 - type: precision_at_3 value: 9.447999999999999 - type: precision_at_5 value: 6.827999999999999 - type: recall_at_1 value: 14.568 - type: recall_at_10 value: 28.677999999999997 - type: recall_at_100 value: 44.362 - type: recall_at_1000 value: 63.705999999999996 - type: recall_at_20 value: 32.932 - type: recall_at_3 value: 21.029999999999998 - type: recall_at_5 value: 24.573 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.104 - type: map_at_10 value: 33.857 - type: map_at_100 value: 34.808 - type: map_at_1000 value: 34.904 - type: map_at_20 value: 34.404 - type: map_at_3 value: 31.176 - type: map_at_5 value: 32.626 - type: mrr_at_1 value: 28.84 - type: mrr_at_10 value: 36.817 - type: mrr_at_100 value: 37.633 - type: mrr_at_1000 value: 37.698 - type: mrr_at_20 value: 37.312 - type: mrr_at_3 value: 34.451 - type: mrr_at_5 value: 35.748999999999995 - type: ndcg_at_1 value: 28.84 - type: ndcg_at_10 value: 38.745000000000005 - type: ndcg_at_100 value: 43.183 - type: ndcg_at_1000 value: 45.419 - type: ndcg_at_20 value: 40.571 - type: ndcg_at_3 value: 33.751 - type: ndcg_at_5 value: 36.042 - type: precision_at_1 value: 28.84 - type: precision_at_10 value: 6.389 - type: 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value: 14.753 - type: mrr_at_100 value: 15.501000000000001 - type: mrr_at_1000 value: 15.592 - type: mrr_at_20 value: 15.148 - type: mrr_at_3 value: 13.425999999999998 - type: mrr_at_5 value: 14.059 - type: ndcg_at_1 value: 10.771 - type: ndcg_at_10 value: 14.788 - type: ndcg_at_100 value: 18.769 - type: ndcg_at_1000 value: 21.939 - type: ndcg_at_20 value: 16.113 - type: ndcg_at_3 value: 12.356 - type: ndcg_at_5 value: 13.316 - type: precision_at_1 value: 10.771 - type: precision_at_10 value: 2.842 - type: precision_at_100 value: 0.58 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 1.807 - type: precision_at_3 value: 5.976 - type: precision_at_5 value: 4.322 - type: recall_at_1 value: 8.448 - type: recall_at_10 value: 20.666 - type: recall_at_100 value: 39.111000000000004 - type: recall_at_1000 value: 62.673 - type: recall_at_20 value: 25.686999999999998 - type: recall_at_3 value: 13.572999999999999 - type: recall_at_5 value: 16.239 - task: type: Retrieval 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0.29801751933798737, 0.30045501200974795, 0.2750357568275408, 0.28736739490829033, 0.26884372823491953, 0.2883920927532625] - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.23895809739524 - type: cos_sim_ap value: 63.43837390346798 - type: cos_sim_f1 value: 60.3425871234495 - type: cos_sim_precision value: 54.63101604278074 - type: cos_sim_recall value: 67.38786279683377 - type: dot_accuracy value: 79.70435715562974 - type: dot_ap value: 50.219858779642024 - type: dot_f1 value: 52.03935006079363 - type: dot_precision value: 44.778390717139054 - type: dot_recall value: 62.11081794195251 - type: euclidean_accuracy value: 83.3581689217381 - type: euclidean_ap value: 63.866502871821886 - type: euclidean_f1 value: 60.66180862501495 - type: euclidean_precision value: 55.42457978607291 - type: euclidean_recall value: 66.99208443271768 - type: manhattan_accuracy value: 83.32836621565238 - type: manhattan_ap value: 63.58246341419401 - type: manhattan_f1 value: 60.405654578979714 - type: manhattan_precision value: 56.54775604142692 - type: manhattan_recall value: 64.82849604221636 - type: max_accuracy value: 83.3581689217381 - type: max_ap value: 63.866502871821886 - type: max_f1 value: 60.66180862501495 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.77894205767066 - type: cos_sim_ap value: 83.5297230824822 - type: cos_sim_f1 value: 75.65036420395423 - type: cos_sim_precision value: 73.11781609195403 - type: cos_sim_recall value: 78.3646442870342 - type: dot_accuracy value: 86.03058175185313 - type: dot_ap value: 78.95144253575621 - type: dot_f1 value: 72.20582032897512 - type: dot_precision value: 66.42524573202276 - type: dot_recall value: 79.08838928241454 - type: euclidean_accuracy value: 87.7265494624908 - type: euclidean_ap value: 83.29997302389856 - type: euclidean_f1 value: 75.38237163905613 - type: euclidean_precision value: 73.28582854649895 - type: euclidean_recall value: 77.60240221743148 - type: manhattan_accuracy value: 87.65475220242946 - type: manhattan_ap value: 83.1779453049763 - type: manhattan_f1 value: 75.17620001483792 - type: manhattan_precision value: 72.53400143163923 - type: manhattan_recall value: 78.01817061903296 - type: max_accuracy value: 87.77894205767066 - type: max_ap value: 83.5297230824822 - type: max_f1 value: 75.65036420395423 --- # Giratina This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Mihaiii/Wartortle](https://huggingface.co/Mihaiii/Wartortle) * [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Mihaiii/Wartortle - model: TaylorAI/bge-micro-v2 merge_method: slerp base_model: TaylorAI/bge-micro-v2 parameters: t: - value: 0.5 dtype: float32 ```