diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,2996 +1,142 @@ --- +base_model: nomic-ai/modernbert-embed +library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers -- feature-extraction - sentence-similarity -- mteb -model-index: -- name: binarize_False - results: - - task: - type: Classification - dataset: - type: None - name: MTEB AmazonCounterfactualClassification (en) - config: en - split: test - revision: e8379541af4e31359cca9fbcf4b00f2671dba205 - metrics: - - type: accuracy - value: 78.13432835820896 - - type: ap - value: 42.190424731303246 - - type: f1 - value: 72.34446401534811 - - task: - type: Classification - dataset: - type: None - name: MTEB AmazonPolarityClassification - config: default - split: test - revision: e2d317d38cd51312af73b3d32a06d1a08b442046 - metrics: - - type: accuracy - value: 93.093825 - - type: ap - value: 90.03727505544286 - - type: f1 - value: 93.0874055138833 - - task: - type: Classification - dataset: - type: None - name: MTEB AmazonReviewsClassification (en) - config: en - split: test - revision: 1399c76144fd37290681b995c656ef9b2e06e26d - metrics: - - type: accuracy - value: 48.428000000000004 - - type: f1 - value: 47.74311520203536 - - task: - type: Retrieval - dataset: - type: None - name: MTEB ArguAna - config: default - split: test - revision: c22ab2a51041ffd869aaddef7af8d8215647e41a - metrics: - - type: map_at_1 - value: 23.898 - - type: map_at_10 - value: 39.775 - - type: map_at_100 - value: 40.827000000000005 - - type: map_at_1000 - value: 40.837 - - type: map_at_20 - value: 40.604 - - type: map_at_3 - value: 34.519 - - type: map_at_5 - value: 37.307 - - type: mrr_at_1 - value: 24.395 - - type: mrr_at_10 - value: 39.963 - - type: mrr_at_100 - value: 41.014 - - type: mrr_at_1000 - value: 41.024 - - type: mrr_at_20 - value: 40.791 - - type: mrr_at_3 - value: 34.732 - - type: mrr_at_5 - value: 37.480999999999995 - - type: ndcg_at_1 - value: 23.898 - - type: ndcg_at_10 - value: 48.962 - - type: ndcg_at_100 - value: 53.386 - - type: ndcg_at_1000 - value: 53.634 - - type: ndcg_at_20 - value: 51.898999999999994 - - type: ndcg_at_3 - value: 38.034 - - type: ndcg_at_5 - value: 43.036 - - type: precision_at_1 - value: 23.898 - - type: precision_at_10 - value: 7.852 - - type: precision_at_100 - value: 0.9769999999999999 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_20 - value: 4.4990000000000006 - - type: precision_at_3 - value: 16.073999999999998 - - type: precision_at_5 - value: 12.063 - - type: recall_at_1 - value: 23.898 - - type: recall_at_10 - value: 78.521 - - type: recall_at_100 - value: 97.724 - - type: recall_at_1000 - value: 99.644 - - type: recall_at_20 - value: 89.972 - - type: recall_at_3 - value: 48.222 - - type: recall_at_5 - value: 60.313 - - task: - type: Clustering - dataset: - type: None - name: MTEB ArxivClusteringP2P - config: default - split: test - revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d 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47.235 - - type: ndcg_at_3 - value: 39.505 - - type: ndcg_at_5 - value: 42.016 - - type: precision_at_1 - value: 35.515 - - type: precision_at_10 - value: 8.152 - - type: precision_at_100 - value: 1.262 - - type: precision_at_1000 - value: 0.16 - - type: precision_at_20 - value: 4.851 - - type: precision_at_3 - value: 18.447 - - type: precision_at_5 - value: 13.321 - - type: recall_at_1 - value: 29.098000000000003 - - type: recall_at_10 - value: 57.115 - - type: recall_at_100 - value: 79.467 - - type: recall_at_1000 - value: 92.162 - - type: recall_at_20 - value: 65.161 - - type: recall_at_3 - value: 42.254000000000005 - - type: recall_at_5 - value: 48.415 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackProgrammersRetrieval - config: default - split: test - revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 - metrics: - - type: map_at_1 - value: 27.372000000000003 - - type: map_at_10 - value: 37.781 - - type: map_at_100 - value: 39.128 - - type: map_at_1000 - value: 39.238 - - type: map_at_20 - value: 38.592 - - type: map_at_3 - value: 34.782999999999994 - - type: map_at_5 - value: 36.466 - - type: mrr_at_1 - value: 33.904 - - type: mrr_at_10 - value: 43.15 - - type: mrr_at_100 - value: 44.049 - - type: mrr_at_1000 - value: 44.107 - - type: mrr_at_20 - value: 43.721 - - type: mrr_at_3 - value: 40.677 - - type: mrr_at_5 - value: 42.19 - - type: ndcg_at_1 - value: 33.904 - - type: ndcg_at_10 - value: 43.527 - - type: ndcg_at_100 - value: 49.004999999999995 - - type: ndcg_at_1000 - value: 51.276999999999994 - - type: ndcg_at_20 - value: 45.988 - - type: ndcg_at_3 - value: 38.824999999999996 - - type: ndcg_at_5 - value: 41.04 - - type: precision_at_1 - value: 33.904 - - type: precision_at_10 - value: 7.854 - - type: precision_at_100 - value: 1.2309999999999999 - - type: precision_at_1000 - value: 0.16 - - type: precision_at_20 - value: 4.692 - - type: precision_at_3 - value: 18.531 - - type: precision_at_5 - value: 13.150999999999998 - - type: recall_at_1 - value: 27.372000000000003 - - type: recall_at_10 - value: 55.245999999999995 - - type: recall_at_100 - value: 78.278 - - type: recall_at_1000 - value: 93.718 - - type: recall_at_20 - value: 64.095 - - type: recall_at_3 - value: 41.665 - - type: recall_at_5 - value: 47.632000000000005 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackRetrieval - config: default - split: test - revision: f46a197baaae43b4f621051089b82a364682dfeb - metrics: - - type: map_at_1 - value: 27.734166666666667 - - type: map_at_10 - value: 36.858 - - type: map_at_100 - value: 38.043833333333325 - - type: map_at_1000 - value: 38.15541666666667 - - type: map_at_20 - value: 37.521249999999995 - - type: map_at_3 - value: 34.07658333333333 - - type: map_at_5 - value: 35.62683333333333 - - type: mrr_at_1 - value: 32.676249999999996 - - type: mrr_at_10 - value: 40.999 - - type: mrr_at_100 - value: 41.835 - - type: mrr_at_1000 - value: 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43.112 - - type: ndcg_at_20 - value: 37.852999999999994 - - type: ndcg_at_3 - value: 32.562000000000005 - - type: ndcg_at_5 - value: 33.983000000000004 - - type: precision_at_1 - value: 28.988000000000003 - - type: precision_at_10 - value: 5.475 - - type: precision_at_100 - value: 0.8500000000000001 - - type: precision_at_1000 - value: 0.11199999999999999 - - type: precision_at_20 - value: 3.229 - - type: precision_at_3 - value: 13.905999999999999 - - type: precision_at_5 - value: 9.386999999999999 - - type: recall_at_1 - value: 25.653 - - type: recall_at_10 - value: 44.962 - - type: recall_at_100 - value: 66.405 - - type: recall_at_1000 - value: 83.88799999999999 - - type: recall_at_20 - value: 51.79899999999999 - - type: recall_at_3 - value: 35.144999999999996 - - type: recall_at_5 - value: 38.814 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackTexRetrieval - config: default - split: test - revision: 46989137a86843e03a6195de44b09deda022eec7 - metrics: - - type: map_at_1 - value: 17.825 - - type: map_at_10 - value: 25.592 - - type: map_at_100 - value: 26.613999999999997 - - type: map_at_1000 - value: 26.734 - - type: map_at_20 - value: 26.115 - - type: map_at_3 - value: 23.119 - - type: map_at_5 - value: 24.54 - - type: mrr_at_1 - value: 21.335 - - type: mrr_at_10 - value: 29.165000000000003 - - type: mrr_at_100 - value: 30.049 - - type: mrr_at_1000 - value: 30.121 - - type: mrr_at_20 - value: 29.639 - - type: mrr_at_3 - value: 26.863999999999997 - - type: mrr_at_5 - value: 28.185 - - type: ndcg_at_1 - value: 21.335 - - type: ndcg_at_10 - value: 30.357 - - type: ndcg_at_100 - value: 35.410000000000004 - - type: ndcg_at_1000 - value: 38.24 - - type: ndcg_at_20 - value: 32.08 - - type: ndcg_at_3 - value: 25.95 - - type: ndcg_at_5 - value: 28.081 - - type: precision_at_1 - value: 21.335 - - type: precision_at_10 - value: 5.506 - - type: precision_at_100 - value: 0.928 - - type: precision_at_1000 - value: 0.135 - - type: precision_at_20 - value: 3.2550000000000003 - - type: precision_at_3 - value: 12.239 - - type: precision_at_5 - value: 8.885 - - type: recall_at_1 - value: 17.825 - - type: recall_at_10 - value: 41.105999999999995 - - type: recall_at_100 - value: 64.17 - - type: recall_at_1000 - value: 84.19200000000001 - - type: recall_at_20 - value: 47.497 - - type: recall_at_3 - value: 28.862 - - type: recall_at_5 - value: 34.348 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackUnixRetrieval - config: default - split: test - revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 - metrics: - - type: map_at_1 - value: 29.435 - - type: map_at_10 - value: 38.261 - - type: map_at_100 - value: 39.242 - - type: map_at_1000 - value: 39.347 - - type: map_at_20 - value: 38.742 - - type: map_at_3 - value: 35.457 - - type: map_at_5 - value: 37.043 - - type: mrr_at_1 - value: 34.235 - - type: mrr_at_10 - value: 42.24 - - type: mrr_at_100 - value: 42.988 - - type: mrr_at_1000 - value: 43.043 - - type: mrr_at_20 - value: 42.613 - - type: mrr_at_3 - value: 39.832 - - type: mrr_at_5 - value: 41.227000000000004 - - type: ndcg_at_1 - value: 34.235 - - type: ndcg_at_10 - value: 43.384 - - type: ndcg_at_100 - value: 48.14 - - type: ndcg_at_1000 - value: 50.414 - - type: ndcg_at_20 - value: 44.913 - - type: ndcg_at_3 - value: 38.454 - - type: ndcg_at_5 - value: 40.776 - - type: precision_at_1 - value: 34.235 - - type: precision_at_10 - value: 7.164 - - type: precision_at_100 - value: 1.065 - - type: precision_at_1000 - value: 0.13699999999999998 - - type: precision_at_20 - value: 4.021 - - type: precision_at_3 - value: 17.226 - - type: precision_at_5 - value: 12.071 - - type: recall_at_1 - value: 29.435 - - type: recall_at_10 - value: 54.93900000000001 - - type: recall_at_100 - value: 76.176 - - type: recall_at_1000 - value: 91.989 - - type: recall_at_20 - value: 60.451 - - type: recall_at_3 - value: 41.332 - - type: recall_at_5 - value: 47.316 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackWebmastersRetrieval - config: default - split: test - revision: 160c094312a0e1facb97e55eeddb698c0abe3571 - metrics: - - type: map_at_1 - value: 25.605 - - type: map_at_10 - value: 34.162 - - type: map_at_100 - value: 35.827999999999996 - - type: map_at_1000 - value: 36.04 - - type: map_at_20 - value: 35.016000000000005 - - type: map_at_3 - value: 30.984 - - type: map_at_5 - value: 32.717 - - type: mrr_at_1 - value: 30.435000000000002 - - type: mrr_at_10 - value: 38.681 - - type: mrr_at_100 - value: 39.656000000000006 - - type: mrr_at_1000 - value: 39.71 - - type: mrr_at_20 - value: 39.208999999999996 - - type: mrr_at_3 - value: 35.903 - - type: mrr_at_5 - value: 37.454 - - type: ndcg_at_1 - value: 30.435000000000002 - - type: ndcg_at_10 - value: 39.916000000000004 - - type: ndcg_at_100 - value: 45.958 - - type: ndcg_at_1000 - value: 48.449999999999996 - - type: ndcg_at_20 - value: 42.085 - - type: ndcg_at_3 - value: 34.696 - - type: ndcg_at_5 - value: 37.147000000000006 - - type: precision_at_1 - value: 30.435000000000002 - - type: precision_at_10 - value: 7.767 - - type: precision_at_100 - value: 1.547 - - type: precision_at_1000 - value: 0.23800000000000002 - - type: precision_at_20 - value: 4.941 - - type: precision_at_3 - value: 16.073999999999998 - - type: precision_at_5 - value: 11.937000000000001 - - type: recall_at_1 - value: 25.605 - - type: recall_at_10 - value: 50.654999999999994 - - type: recall_at_100 - value: 77.609 - - type: recall_at_1000 - value: 93.518 - - type: recall_at_20 - value: 58.845000000000006 - - type: recall_at_3 - value: 36.272 - - type: recall_at_5 - value: 42.596000000000004 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackWordpressRetrieval - config: default - split: test - revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 - metrics: - - type: map_at_1 - value: 23.666 - - type: map_at_10 - value: 30.980999999999998 - - type: map_at_100 - value: 32.0 - - type: map_at_1000 - value: 32.098 - - type: map_at_20 - value: 31.621 - - type: map_at_3 - value: 28.449999999999996 - - type: map_at_5 - value: 29.731999999999996 - - type: mrr_at_1 - value: 25.692999999999998 - - type: mrr_at_10 - value: 32.788000000000004 - - type: mrr_at_100 - value: 33.783 - - type: mrr_at_1000 - value: 33.849000000000004 - - type: mrr_at_20 - value: 33.408 - - type: mrr_at_3 - value: 30.561 - - type: mrr_at_5 - value: 31.716 - - type: ndcg_at_1 - value: 25.692999999999998 - - type: ndcg_at_10 - value: 35.428 - - type: ndcg_at_100 - value: 40.375 - - type: ndcg_at_1000 - value: 42.802 - - type: ndcg_at_20 - value: 37.621 - - type: ndcg_at_3 - value: 30.476999999999997 - - type: ndcg_at_5 - value: 32.621 - - type: precision_at_1 - value: 25.692999999999998 - - type: precision_at_10 - value: 5.508 - - type: precision_at_100 - value: 0.848 - - type: precision_at_1000 - value: 0.116 - - type: precision_at_20 - value: 3.272 - - type: precision_at_3 - value: 12.631 - - type: precision_at_5 - value: 8.872 - - type: recall_at_1 - value: 23.666 - - type: recall_at_10 - value: 47.532000000000004 - - type: recall_at_100 - value: 69.73700000000001 - - type: recall_at_1000 - value: 87.83800000000001 - - type: recall_at_20 - value: 55.61000000000001 - - type: recall_at_3 - value: 34.06 - - type: recall_at_5 - value: 39.254 - - task: - type: Retrieval - dataset: - type: None - name: MTEB ClimateFEVER - config: default - split: test - revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 - metrics: - - type: map_at_1 - value: 16.337 - - type: map_at_10 - value: 26.488 - - type: map_at_100 - value: 28.415000000000003 - - type: map_at_1000 - value: 28.584 - - type: map_at_20 - value: 27.557 - - type: map_at_3 - value: 22.665 - - type: map_at_5 - value: 24.542 - - type: mrr_at_1 - value: 36.417 - - type: mrr_at_10 - value: 48.001 - - type: mrr_at_100 - value: 48.784 - - type: mrr_at_1000 - value: 48.809000000000005 - - type: mrr_at_20 - value: 48.507 - - type: mrr_at_3 - value: 45.103 - - type: mrr_at_5 - value: 46.843 - - type: ndcg_at_1 - value: 36.417 - - type: ndcg_at_10 - value: 35.67 - - type: ndcg_at_100 - value: 42.716 - - type: ndcg_at_1000 - value: 45.639 - - type: ndcg_at_20 - value: 38.471 - - type: ndcg_at_3 - value: 30.444 - - type: ndcg_at_5 - value: 32.004 - - type: precision_at_1 - value: 36.417 - - type: precision_at_10 - value: 10.73 - - type: precision_at_100 - value: 1.833 - - type: precision_at_1000 - value: 0.23800000000000002 - - type: precision_at_20 - value: 6.596 - - type: precision_at_3 - value: 22.302 - - type: precision_at_5 - value: 16.521 - - type: recall_at_1 - value: 16.337 - - type: recall_at_10 - value: 40.671 - - type: recall_at_100 - value: 64.55300000000001 - - type: recall_at_1000 - value: 80.934 - - type: recall_at_20 - value: 48.381 - - type: recall_at_3 - value: 27.279999999999998 - - type: recall_at_5 - value: 32.621 - - task: - type: Retrieval - dataset: - type: None - name: MTEB DBPedia - config: default - split: test - revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 - metrics: - - type: map_at_1 - value: 9.056000000000001 - - type: map_at_10 - value: 19.419 - - type: map_at_100 - value: 27.069 - - type: map_at_1000 - value: 28.666000000000004 - - type: map_at_20 - value: 22.434 - - type: map_at_3 - value: 13.895 - - type: map_at_5 - value: 16.121 - - type: mrr_at_1 - value: 69.0 - - type: mrr_at_10 - value: 75.804 - - type: mrr_at_100 - value: 76.117 - - type: mrr_at_1000 - value: 76.125 - - type: mrr_at_20 - value: 76.009 - - type: mrr_at_3 - value: 74.375 - - type: mrr_at_5 - value: 75.4 - - type: ndcg_at_1 - value: 57.49999999999999 - - type: ndcg_at_10 - value: 41.495 - - type: ndcg_at_100 - value: 45.208 - - type: ndcg_at_1000 - value: 52.221 - - type: ndcg_at_20 - value: 40.617999999999995 - - type: ndcg_at_3 - value: 46.592 - - type: ndcg_at_5 - value: 43.559 - - type: precision_at_1 - value: 69.0 - - type: precision_at_10 - value: 32.574999999999996 - - type: precision_at_100 - value: 10.205 - - type: precision_at_1000 - value: 2.036 - - type: precision_at_20 - value: 24.687 - - type: precision_at_3 - value: 49.75 - - type: precision_at_5 - value: 42.0 - - type: recall_at_1 - value: 9.056000000000001 - - type: recall_at_10 - value: 24.866 - - type: recall_at_100 - value: 50.097 - - type: recall_at_1000 - value: 72.038 - - type: recall_at_20 - value: 31.858999999999998 - - type: recall_at_3 - value: 15.096000000000002 - - type: recall_at_5 - value: 18.548000000000002 - - task: - type: Classification - dataset: - type: None - name: MTEB EmotionClassification - config: default - split: test - revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 - metrics: - - type: accuracy - value: 48.259999999999984 - - type: f1 - value: 43.1498589523159 - - task: - type: Retrieval - dataset: - type: None - name: MTEB FEVER - config: default - split: test - revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 - metrics: - - type: 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85.69380834788831 - - type: manhattan_spearman - value: 86.10784739081191 - - task: - type: STS - dataset: - type: None - name: MTEB STS14 - config: default - split: test - revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 - metrics: - - type: cos_sim_pearson - value: 83.43368314724589 - - type: cos_sim_spearman - value: 81.26767916144169 - - type: euclidean_pearson - value: 83.23234690932492 - - type: euclidean_spearman - value: 81.2671726214706 - - type: manhattan_pearson - value: 83.2381239261109 - - type: manhattan_spearman - value: 81.27674961470714 - - task: - type: STS - dataset: - type: None - name: MTEB STS15 - config: default - split: test - revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 - metrics: - - type: cos_sim_pearson - value: 86.8637546411748 - - type: cos_sim_spearman - value: 88.25330888676139 - - type: euclidean_pearson - value: 87.81194589390417 - - type: euclidean_spearman - value: 88.25258669625579 - - type: manhattan_pearson - value: 87.8131866998459 - - 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-language: -- en -base_model: -- answerdotai/ModernBERT-base -- nomic-ai/modernbert-embed-unsupervised +- feature-extraction --- -# ModernBERT Embed +# SentenceTransformer based on nomic-ai/modernbert-embed -ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), brining the new advances of ModernBERT to embeddings! +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed](https://huggingface.co/nomic-ai/modernbert-embed). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. -## Performance +## Model Details -| Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Overall/Summ (1) | -|-------|------------|--------------|--------------------:|-----------------|------------------------|---------------|----------------|-----------|-----------------| -| nomic-embed-text-v1 | 768 | 62.4 | 74.1 | 43.9 | 85.2 | 55.7 | 52.8 | 82.1 | 30.1 | -| nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01 | 81.94 | 30.4 | -| ModernBERT | 768 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | 31.39 | -| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34| -| ModernBERT | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 | +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [nomic-ai/modernbert-embed](https://huggingface.co/nomic-ai/modernbert-embed) +- **Maximum Sequence Length:** 8192 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity + + + -## Usage +### Model Sources -You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing transformers from main: +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture -```bash -pip install git+https://github.com/huggingface/transformers.git +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): Normalize() +) ``` -Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes). +## Usage -Most use cases, adding `search_query` to the query and `search_document` to the documents will be sufficient. +### Direct Usage (Sentence Transformers) -### Transformers +First install the Sentence Transformers library: +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. ```python -import torch -import torch.nn.functional as F -from transformers import AutoTokenizer, AutoModel +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("nomic-ai/modernbert-embed") +# Run inference +sentences = [ + 'The weather is lovely today.', + "It's so sunny outside!", + 'He drove to the stadium.', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` -def mean_pooling(model_output, attention_mask): - token_embeddings = model_output[0] - input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) + -encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') + -embeddings = mean_pooling(model_output, encoded_input['attention_mask']) -embeddings = embeddings[:, :matryoshka_dim] -embeddings = F.normalize(embeddings, p=2, dim=1) -print(embeddings) -``` + -## Training + -[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample) + -In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage. +## Training Details -For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1). +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 3.3.1 +- Transformers: 4.48.0.dev0 +- PyTorch: 2.4.1+cu121 +- Accelerate: 1.0.0 +- Datasets: 2.18.0 +- Tokenizers: 0.21.0 -Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) +## Citation +### BibTeX -## Join the Nomic Community + -## Citation + -```bibtex -@misc{nussbaum2024nomic, - title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, - author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar}, - year={2024}, - eprint={2402.01613}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` + \ No newline at end of file