diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,26 +1,8986 @@ --- -language: en -tags: -- medembed -- medical-embedding -- clinical-embedding -- information-retrieval -- sentence-transformers -license: apache-2.0 +base_model: +- BAAI/bge-small-en-v1.5 datasets: - MedicalQARetrieval - NFCorpus - PublicHealthQA - TRECCOVID - ArguAna +language: en +license: apache-2.0 metrics: - nDCG - MAP - Recall - Precision - MRR -base_model: -- BAAI/bge-small-en-v1.5 +model-index: +- name: abhinand/MedEmbed-small-v0.1 + results: + - dataset: + config: en-ext + name: MTEB AmazonCounterfactualClassification (en-ext) + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + split: test + type: mteb/amazon_counterfactual + metrics: + - type: accuracy + value: 72.17391304347827 + - type: ap + value: 21.757637881353535 + - type: ap_weighted + value: 21.757637881353535 + - type: f1 + value: 59.80304692298741 + - type: f1_weighted + value: 77.3761270422597 + - type: main_score + value: 72.17391304347827 + task: + type: Classification + - 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type: accuracy + value: 61.349745331069606 + - type: f1 + value: 61.502480965412744 + - type: f1_weighted + value: 60.39561856225271 + - type: main_score + value: 61.349745331069606 + task: + type: Classification + - dataset: + config: default + name: MTEB TwentyNewsgroupsClustering (default) + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + split: test + type: mteb/twentynewsgroups-clustering + metrics: + - type: main_score + value: 47.338360343180106 + - type: v_measure + value: 47.338360343180106 + - type: v_measure_std + value: 2.01314014968057 + task: + type: Clustering + - dataset: + config: default + name: MTEB TwitterSemEval2015 (default) + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + split: test + type: mteb/twittersemeval2015-pairclassification + metrics: + - type: cosine_accuracy + value: 85.49204267747511 + - type: cosine_accuracy_threshold + value: 86.85047030448914 + - type: cosine_ap + value: 72.98051397210814 + - type: cosine_f1 + value: 66.8282725184996 + - type: cosine_f1_threshold + value: 84.59665775299072 + - type: cosine_precision + value: 64.69861660079052 + - type: cosine_recall + value: 69.10290237467018 + - type: dot_accuracy + value: 85.49204267747511 + - type: dot_accuracy_threshold + value: 86.85047030448914 + - type: dot_ap + value: 72.98051075246349 + - type: dot_f1 + value: 66.8282725184996 + - type: dot_f1_threshold + value: 84.59665775299072 + - type: dot_precision + value: 64.69861660079052 + - type: dot_recall + value: 69.10290237467018 + - type: euclidean_accuracy + value: 85.49204267747511 + - type: euclidean_accuracy_threshold + value: 51.28260850906372 + - type: euclidean_ap + value: 72.98052075606988 + - type: euclidean_f1 + value: 66.8282725184996 + - type: euclidean_f1_threshold + value: 55.50377368927002 + - type: euclidean_precision + value: 64.69861660079052 + - type: euclidean_recall + value: 69.10290237467018 + - type: main_score + value: 72.98052075606988 + - type: manhattan_accuracy + value: 85.43839780652083 + - type: manhattan_accuracy_threshold + value: 796.9008445739746 + - type: manhattan_ap + value: 72.80895903518599 + - type: manhattan_f1 + value: 66.64168852254278 + - type: manhattan_f1_threshold + value: 871.8400955200195 + - type: manhattan_precision + value: 63.267725871472614 + - type: manhattan_recall + value: 70.3957783641161 + - type: max_accuracy + value: 85.49204267747511 + - type: max_ap + value: 72.98052075606988 + - type: max_f1 + value: 66.8282725184996 + - type: max_precision + value: 64.69861660079052 + - type: max_recall + value: 70.3957783641161 + - type: similarity_accuracy + value: 85.49204267747511 + - type: similarity_accuracy_threshold + value: 86.85047030448914 + - type: similarity_ap + value: 72.98051397210814 + - type: similarity_f1 + value: 66.8282725184996 + - type: similarity_f1_threshold + value: 84.59665775299072 + - type: similarity_precision + value: 64.69861660079052 + - type: similarity_recall + value: 69.10290237467018 + task: + type: PairClassification + - dataset: + config: default + name: MTEB TwitterURLCorpus (default) + revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf + split: test + type: mteb/twitterurlcorpus-pairclassification + metrics: + - type: cosine_accuracy + value: 88.4192959987581 + - type: cosine_accuracy_threshold + value: 82.84748792648315 + - type: cosine_ap + value: 84.98986658033178 + - type: cosine_f1 + value: 77.24466264970617 + - type: cosine_f1_threshold + value: 81.41384720802307 + - type: cosine_precision + value: 74.71578644409269 + - type: cosine_recall + value: 79.95072374499537 + - type: dot_accuracy + value: 88.4192959987581 + - type: dot_accuracy_threshold + value: 82.84748792648315 + - type: dot_ap + value: 84.98983415174361 + - type: dot_f1 + value: 77.24466264970617 + - type: dot_f1_threshold + value: 81.41384720802307 + - type: dot_precision + value: 74.71578644409269 + - type: dot_recall + value: 79.95072374499537 + - type: euclidean_accuracy + value: 88.4192959987581 + - type: euclidean_accuracy_threshold + value: 58.57049226760864 + - type: euclidean_ap + value: 84.9898314712826 + - type: euclidean_f1 + value: 77.24466264970617 + - type: euclidean_f1_threshold + value: 60.96909046173096 + - type: euclidean_precision + value: 74.71578644409269 + - type: euclidean_recall + value: 79.95072374499537 + - type: main_score + value: 84.98986658033178 + - type: manhattan_accuracy + value: 88.4192959987581 + - type: manhattan_accuracy_threshold + value: 907.758617401123 + - type: manhattan_ap + value: 84.92522577660164 + - type: manhattan_f1 + value: 76.9788698516079 + - type: manhattan_f1_threshold + value: 952.1110534667969 + - type: manhattan_precision + value: 74.10758817242608 + - type: manhattan_recall + value: 80.08161379735141 + - type: max_accuracy + value: 88.4192959987581 + - type: max_ap + value: 84.98986658033178 + - type: max_f1 + value: 77.24466264970617 + - type: max_precision + value: 74.71578644409269 + - type: max_recall + value: 80.08161379735141 + - type: similarity_accuracy + value: 88.4192959987581 + - type: similarity_accuracy_threshold + value: 82.84748792648315 + - type: similarity_ap + value: 84.98986658033178 + - type: similarity_f1 + value: 77.24466264970617 + - type: similarity_f1_threshold + value: 81.41384720802307 + - type: similarity_precision + value: 74.71578644409269 + - type: similarity_recall + value: 79.95072374499537 + task: + type: PairClassification +tags: +- medembed +- medical-embedding +- clinical-embedding +- information-retrieval +- sentence-transformers +- mteb --- # MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval