--- pipeline_tag: text-classification tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: KE Sieve_model results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 42.7075359884682 - type: f1 value: 72.99649470402085 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 70.193 - type: ap value: 64.37171698026376 - type: f1 value: 69.99260638185035 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 34.288000000000004 - type: f1 value: 34.00390576721439 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 70.37283775714532 - type: cos_sim_spearman value: 65.28702977793742 - type: euclidean_pearson value: 68.81678452970543 - type: euclidean_spearman value: 66.10212250382912 - type: manhattan_pearson value: 70.06439132928513 - type: manhattan_spearman value: 66.10212250382912 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.88961038961038 - type: f1 value: 75.71295362599926 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 40.26 - type: f1 value: 35.91571484611428 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 61.1396 - type: ap value: 57.0336104684589 - type: f1 value: 60.711055351249385 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.21842225262198 - type: f1 value: 86.60570158294514 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 69.44824441404468 - type: f1 value: 51.1702284173121 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.60188298587761 - type: f1 value: 64.57658770410065 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.36987222595829 - type: f1 value: 70.34853403058946 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 78.1402991982508 - type: cos_sim_spearman value: 76.01438891892613 - type: euclidean_pearson value: 76.07791972310307 - type: euclidean_spearman value: 76.4750927224088 - type: manhattan_pearson value: 78.7022742184064 - type: manhattan_spearman value: 76.4750927224088 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 77.41946856528065 - type: cos_sim_spearman value: 71.2452368975646 - type: euclidean_pearson value: 68.76119955717198 - type: euclidean_spearman value: 70.40762520824568 - type: manhattan_pearson value: 76.1638570991111 - type: manhattan_spearman value: 70.40762520824568 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.86983630535461 - type: cos_sim_spearman value: 78.39885607110992 - type: euclidean_pearson value: 75.81565277674996 - type: euclidean_spearman value: 78.70053430302474 - type: manhattan_pearson value: 78.14484348028292 - type: manhattan_spearman value: 78.70053430302474 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 76.52542250553228 - type: cos_sim_spearman value: 74.23425444398934 - type: euclidean_pearson value: 73.63790688920109 - type: euclidean_spearman value: 74.14127580980806 - type: manhattan_pearson value: 76.76724842158396 - type: manhattan_spearman value: 74.14127580980806 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 80.9319282262523 - type: cos_sim_spearman value: 81.40861373830771 - type: euclidean_pearson value: 79.61339072348075 - type: euclidean_spearman value: 82.1601716091385 - type: manhattan_pearson value: 81.76770515821788 - type: manhattan_spearman value: 82.1601716091385 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 78.83953330477087 - type: cos_sim_spearman value: 79.1312883671738 - type: euclidean_pearson value: 77.02068269010785 - type: euclidean_spearman value: 78.85332564873545 - type: manhattan_pearson value: 78.66151014252961 - type: manhattan_spearman value: 78.85332564873545 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ko-ko) config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 77.06164373590121 - type: cos_sim_spearman value: 76.99890844656588 - type: euclidean_pearson value: 73.39118839457844 - type: euclidean_spearman value: 77.11144988540109 - type: manhattan_pearson value: 77.20681515013695 - type: manhattan_spearman value: 77.11144988540109 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ar-ar) config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 77.60555084043324 - type: cos_sim_spearman value: 76.04445852887906 - type: euclidean_pearson value: 72.71133101639413 - type: euclidean_spearman value: 75.91338695530828 - type: manhattan_pearson value: 77.35612564470868 - type: manhattan_spearman value: 75.91338695530828 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-ar) config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 78.41618617815928 - type: cos_sim_spearman value: 77.60195378076503 - type: euclidean_pearson value: 78.16168735305624 - type: euclidean_spearman value: 77.67819163961478 - type: manhattan_pearson value: 78.40140865643386 - type: manhattan_spearman value: 77.67819163961478 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 71.44561691901534 - type: cos_sim_spearman value: 70.39834592402187 - type: euclidean_pearson value: 71.5559771884868 - type: euclidean_spearman value: 70.11301222833383 - type: manhattan_pearson value: 71.51922693185502 - type: manhattan_spearman value: 70.11301222833383 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.7214978664316 - 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type: cos_sim_precision value: 71.88118811881188 - type: cos_sim_recall value: 72.6 - type: dot_accuracy value: 99.409900990099 - type: dot_ap value: 68.42835773716114 - type: dot_f1 value: 65.83783783783784 - type: dot_precision value: 71.6470588235294 - type: dot_recall value: 60.9 - type: euclidean_accuracy value: 99.48019801980197 - type: euclidean_ap value: 76.69004973047716 - type: euclidean_f1 value: 72.51638930912759 - type: euclidean_precision value: 73.14343845371313 - type: euclidean_recall value: 71.89999999999999 - type: manhattan_accuracy value: 99.48019801980197 - type: manhattan_ap value: 76.69004973047716 - type: manhattan_f1 value: 72.51638930912759 - type: manhattan_precision value: 73.14343845371313 - type: manhattan_recall value: 71.89999999999999 - type: max_accuracy value: 99.48019801980197 - type: max_ap value: 77.36042895972905 - type: max_f1 value: 72.51638930912759 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.2614 - type: ap value: 13.421228681716107 - type: f1 value: 53.71534671651974 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 54.48783248443689 - type: f1 value: 54.7405015752634 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.22703701496096 - type: cos_sim_ap value: 63.58031791834936 - type: cos_sim_f1 value: 59.3132854578097 - type: cos_sim_precision value: 51.60093713393206 - type: cos_sim_recall value: 69.73614775725594 - type: dot_accuracy value: 81.96936281814389 - type: dot_ap value: 59.07547966241098 - type: dot_f1 value: 56.032535020334386 - type: dot_precision value: 48.99249308573686 - type: dot_recall value: 65.4353562005277 - type: euclidean_accuracy value: 83.26280026226381 - type: euclidean_ap value: 63.64817520735364 - type: euclidean_f1 value: 59.91221653255303 - type: euclidean_precision value: 55.68902991840435 - type: euclidean_recall value: 64.82849604221636 - type: manhattan_accuracy value: 83.26280026226381 - type: manhattan_ap value: 63.64817520735364 - type: manhattan_f1 value: 59.91221653255303 - type: manhattan_precision value: 55.68902991840435 - type: manhattan_recall value: 64.82849604221636 - type: max_accuracy value: 83.26280026226381 - type: max_ap value: 63.64817520735364 - type: max_f1 value: 59.91221653255303 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.49563395040167 - type: cos_sim_ap value: 82.6398035947217 - type: cos_sim_f1 value: 74.74134990715125 - type: cos_sim_precision value: 73.59504440629898 - type: cos_sim_recall value: 75.92392978133662 - type: dot_accuracy value: 85.70264291535685 - type: dot_ap value: 76.35175453791561 - type: dot_f1 value: 70.42039872869113 - type: dot_precision value: 66.31972789115646 - type: dot_recall value: 75.06159531875576 - type: euclidean_accuracy value: 87.51503861528312 - type: euclidean_ap value: 82.74416973508781 - type: euclidean_f1 value: 75.0812647754137 - type: euclidean_precision value: 72.15989775631922 - type: euclidean_recall value: 78.2491530643671 - type: manhattan_accuracy value: 87.51503861528312 - type: manhattan_ap value: 82.74416973508781 - type: manhattan_f1 value: 75.0812647754137 - type: manhattan_precision value: 72.15989775631922 - type: manhattan_recall value: 78.2491530643671 - type: max_accuracy value: 87.51503861528312 - type: max_ap value: 82.74416973508781 - type: max_f1 value: 75.0812647754137 --- # paraphrase-multilingual-mpnet-base-v2-KE_Sieve This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]