README.md

metadata

model-index:
  - name: stella_en_1.5B_v5
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          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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          type: mteb/amazon_counterfactual
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          - type: main_score
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        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
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          - type: ap_weighted
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          - type: f1_weighted
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          - type: main_score
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        task:
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      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
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          - type: f1_weighted
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          - type: main_score
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        task:
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          name: MTEB ArguAna
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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          type: mteb/arguana
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        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
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          - type: v_measure
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          - type: v_measure_std
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        task:
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      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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          type: mteb/arxiv-clustering-s2s
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        task:
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      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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          type: mteb/askubuntudupquestions-reranking
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        task:
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      - dataset:
          config: default
          name: MTEB BIOSSES
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
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          name: MTEB Banking77Classification
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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        task:
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          name: MTEB BiorxivClusteringP2P
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        task:
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        task:
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        task:
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          type: mteb/sts14-sts
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          type: mteb/sts15-sts
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          type: mteb/sts16-sts
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          type: mteb/sts17-crosslingual-sts
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          type: mteb/stackexchange-clustering-p2p
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          name: MTEB StackOverflowDupQuestions
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          - type: ndcg_at_3
            value: 89.98899999999999
          - type: ndcg_at_5
            value: 87.905
          - type: precision_at_1
            value: 96
          - type: precision_at_10
            value: 89.4
          - type: precision_at_100
            value: 71.54
          - type: precision_at_1000
            value: 27.594
          - type: precision_at_20
            value: 87.2
          - type: precision_at_3
            value: 92.667
          - type: precision_at_5
            value: 90.8
          - type: recall_at_1
            value: 0.247
          - type: recall_at_10
            value: 2.315
          - type: recall_at_100
            value: 17.574
          - type: recall_at_1000
            value: 59.336999999999996
          - type: recall_at_20
            value: 4.491
          - type: recall_at_3
            value: 0.7250000000000001
          - type: recall_at_5
            value: 1.1820000000000002
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Touche2020
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
          split: test
          type: mteb/touche2020
        metrics:
          - type: main_score
            value: 29.944
          - type: map_at_1
            value: 3.064
          - type: map_at_10
            value: 11.501999999999999
          - type: map_at_100
            value: 18.736
          - type: map_at_1000
            value: 20.333000000000002
          - type: map_at_20
            value: 14.057
          - type: map_at_3
            value: 6.300999999999999
          - type: map_at_5
            value: 8.463
          - type: mrr_at_1
            value: 44.89795918367347
          - type: mrr_at_10
            value: 58.41188856494979
          - type: mrr_at_100
            value: 58.93964266413245
          - type: mrr_at_1000
            value: 58.93964266413245
          - type: mrr_at_20
            value: 58.767485349118
          - type: mrr_at_3
            value: 54.42176870748299
          - type: mrr_at_5
            value: 56.666666666666664
          - type: nauc_map_at_1000_diff1
            value: 11.478593385608479
          - type: nauc_map_at_1000_max
            value: 10.309889845044324
          - type: nauc_map_at_1000_std
            value: 21.16721939940238
          - type: nauc_map_at_100_diff1
            value: 11.570438543562418
          - type: nauc_map_at_100_max
            value: 8.426183648064834
          - type: nauc_map_at_100_std
            value: 18.56231985033613
          - type: nauc_map_at_10_diff1
            value: 22.37735506247481
          - type: nauc_map_at_10_max
            value: 5.455946239060806
          - type: nauc_map_at_10_std
            value: -4.2848826518388154
          - type: nauc_map_at_1_diff1
            value: 27.853645380676824
          - type: nauc_map_at_1_max
            value: 7.30739948053113
          - type: nauc_map_at_1_std
            value: -0.2773663157814586
          - type: nauc_map_at_20_diff1
            value: 14.724669779924648
          - type: nauc_map_at_20_max
            value: 10.12882779173533
          - type: nauc_map_at_20_std
            value: 4.4803777672120875
          - type: nauc_map_at_3_diff1
            value: 31.891173385921263
          - type: nauc_map_at_3_max
            value: 4.889652271827218
          - type: nauc_map_at_3_std
            value: -9.477460238651643
          - type: nauc_map_at_5_diff1
            value: 31.489012040465003
          - type: nauc_map_at_5_max
            value: 1.7330092417337482
          - type: nauc_map_at_5_std
            value: -8.137018608469637
          - type: nauc_mrr_at_1000_diff1
            value: 24.411522237082416
          - type: nauc_mrr_at_1000_max
            value: 11.286971076556688
          - type: nauc_mrr_at_1000_std
            value: 23.443174210894043
          - type: nauc_mrr_at_100_diff1
            value: 24.411522237082416
          - type: nauc_mrr_at_100_max
            value: 11.286971076556688
          - type: nauc_mrr_at_100_std
            value: 23.443174210894043
          - type: nauc_mrr_at_10_diff1
            value: 23.948152308265186
          - type: nauc_mrr_at_10_max
            value: 12.22420979621155
          - type: nauc_mrr_at_10_std
            value: 23.557939024705544
          - type: nauc_mrr_at_1_diff1
            value: 17.902334894536107
          - type: nauc_mrr_at_1_max
            value: 17.36969662861018
          - type: nauc_mrr_at_1_std
            value: 19.425714969048734
          - type: nauc_mrr_at_20_diff1
            value: 24.635893795899797
          - type: nauc_mrr_at_20_max
            value: 11.330541067194913
          - type: nauc_mrr_at_20_std
            value: 23.74518583400233
          - type: nauc_mrr_at_3_diff1
            value: 25.045536328282587
          - type: nauc_mrr_at_3_max
            value: 7.497967004732733
          - type: nauc_mrr_at_3_std
            value: 24.167153007320078
          - type: nauc_mrr_at_5_diff1
            value: 24.328479930592454
          - type: nauc_mrr_at_5_max
            value: 10.037126854938336
          - type: nauc_mrr_at_5_std
            value: 25.236208055346136
          - type: nauc_ndcg_at_1000_diff1
            value: 15.555347444667389
          - type: nauc_ndcg_at_1000_max
            value: 13.356591700655718
          - type: nauc_ndcg_at_1000_std
            value: 42.42395845935052
          - type: nauc_ndcg_at_100_diff1
            value: 13.110526060413708
          - type: nauc_ndcg_at_100_max
            value: 3.140006440162515
          - type: nauc_ndcg_at_100_std
            value: 39.02733288398033
          - type: nauc_ndcg_at_10_diff1
            value: 20.68853369009725
          - type: nauc_ndcg_at_10_max
            value: 2.435389817058852
          - type: nauc_ndcg_at_10_std
            value: 10.038202768784316
          - type: nauc_ndcg_at_1_diff1
            value: 20.17287594582385
          - type: nauc_ndcg_at_1_max
            value: 12.487205168273196
          - type: nauc_ndcg_at_1_std
            value: 20.639827614373075
          - type: nauc_ndcg_at_20_diff1
            value: 16.987577348502985
          - type: nauc_ndcg_at_20_max
            value: 2.9978717644469266
          - type: nauc_ndcg_at_20_std
            value: 13.015690866750354
          - type: nauc_ndcg_at_3_diff1
            value: 32.392223079245575
          - type: nauc_ndcg_at_3_max
            value: 1.587587110582544
          - type: nauc_ndcg_at_3_std
            value: 12.850592473446609
          - type: nauc_ndcg_at_5_diff1
            value: 32.80244517369626
          - type: nauc_ndcg_at_5_max
            value: 5.8939933777508084
          - type: nauc_ndcg_at_5_std
            value: 15.779687411463414
          - type: nauc_precision_at_1000_diff1
            value: -14.314031720452537
          - type: nauc_precision_at_1000_max
            value: 32.87886666567266
          - type: nauc_precision_at_1000_std
            value: 21.49347046886851
          - type: nauc_precision_at_100_diff1
            value: -9.4034008613839
          - type: nauc_precision_at_100_max
            value: 16.784075123309645
          - type: nauc_precision_at_100_std
            value: 73.14688535393604
          - type: nauc_precision_at_10_diff1
            value: 6.855101404043058
          - type: nauc_precision_at_10_max
            value: 6.52491228645612
          - type: nauc_precision_at_10_std
            value: 16.104602266016744
          - type: nauc_precision_at_1_diff1
            value: 17.902334894536107
          - type: nauc_precision_at_1_max
            value: 17.36969662861018
          - type: nauc_precision_at_1_std
            value: 19.425714969048734
          - type: nauc_precision_at_20_diff1
            value: -5.337534613602212
          - type: nauc_precision_at_20_max
            value: 17.722925454767218
          - type: nauc_precision_at_20_std
            value: 34.26680462132849
          - type: nauc_precision_at_3_diff1
            value: 31.054623397809255
          - type: nauc_precision_at_3_max
            value: -0.92038600946826
          - type: nauc_precision_at_3_std
            value: 8.326997076862916
          - type: nauc_precision_at_5_diff1
            value: 29.784942296920462
          - type: nauc_precision_at_5_max
            value: 6.337469263434779
          - type: nauc_precision_at_5_std
            value: 12.789597196020974
          - type: nauc_recall_at_1000_diff1
            value: -3.8177981862041364
          - type: nauc_recall_at_1000_max
            value: 14.206064332229163
          - type: nauc_recall_at_1000_std
            value: 74.18853420771269
          - type: nauc_recall_at_100_diff1
            value: 0.7677996771461106
          - type: nauc_recall_at_100_max
            value: -4.139924106878441
          - type: nauc_recall_at_100_std
            value: 48.319930706362896
          - type: nauc_recall_at_10_diff1
            value: 12.038835537494322
          - type: nauc_recall_at_10_max
            value: -2.0498983557854418
          - type: nauc_recall_at_10_std
            value: -2.0339180690854493
          - type: nauc_recall_at_1_diff1
            value: 27.853645380676824
          - type: nauc_recall_at_1_max
            value: 7.30739948053113
          - type: nauc_recall_at_1_std
            value: -0.2773663157814586
          - type: nauc_recall_at_20_diff1
            value: 0.7907893667756708
          - type: nauc_recall_at_20_max
            value: 0.8795499810558195
          - type: nauc_recall_at_20_std
            value: 11.512483291688282
          - type: nauc_recall_at_3_diff1
            value: 33.19440392639576
          - type: nauc_recall_at_3_max
            value: -1.5494237697432613
          - type: nauc_recall_at_3_std
            value: -8.560408808376984
          - type: nauc_recall_at_5_diff1
            value: 27.42193873870941
          - type: nauc_recall_at_5_max
            value: -4.74350293281128
          - type: nauc_recall_at_5_std
            value: -7.618060131179654
          - type: ndcg_at_1
            value: 42.857
          - type: ndcg_at_10
            value: 29.944
          - type: ndcg_at_100
            value: 42.624
          - type: ndcg_at_1000
            value: 53.384
          - type: ndcg_at_20
            value: 30.135
          - type: ndcg_at_3
            value: 34.847
          - type: ndcg_at_5
            value: 32.573
          - type: precision_at_1
            value: 44.897999999999996
          - type: precision_at_10
            value: 25.306
          - type: precision_at_100
            value: 8.694
          - type: precision_at_1000
            value: 1.616
          - type: precision_at_20
            value: 19.082
          - type: precision_at_3
            value: 34.014
          - type: precision_at_5
            value: 31.019999999999996
          - type: recall_at_1
            value: 3.064
          - type: recall_at_10
            value: 17.849999999999998
          - type: recall_at_100
            value: 53.217999999999996
          - type: recall_at_1000
            value: 87.095
          - type: recall_at_20
            value: 26.111
          - type: recall_at_3
            value: 7.383000000000001
          - type: recall_at_5
            value: 11.434
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 88.759765625
          - type: ap
            value: 36.49152357863017
          - type: ap_weighted
            value: 36.49152357863017
          - type: f1
            value: 74.4692714448641
          - type: f1_weighted
            value: 90.54372649306606
          - type: main_score
            value: 88.759765625
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 74.8443689869836
          - type: f1
            value: 75.1139662898148
          - type: f1_weighted
            value: 74.7369003946243
          - type: main_score
            value: 74.8443689869836
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: main_score
            value: 61.42918790942448
          - type: v_measure
            value: 61.42918790942448
          - type: v_measure_std
            value: 1.0156550098843082
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 88.22197055492639
          - type: cosine_accuracy_threshold
            value: 83.30042362213135
          - type: cosine_ap
            value: 80.57754959194938
          - type: cosine_f1
            value: 73.70579190158894
          - type: cosine_f1_threshold
            value: 81.04978799819946
          - type: cosine_precision
            value: 71.64922770303936
          - type: cosine_recall
            value: 75.8839050131926
          - type: dot_accuracy
            value: 88.23985217857782
          - type: dot_accuracy_threshold
            value: 83.31039547920227
          - type: dot_ap
            value: 80.57533213448181
          - type: dot_f1
            value: 73.61309601143302
          - type: dot_f1_threshold
            value: 81.33968114852905
          - type: dot_precision
            value: 72.51087791144101
          - type: dot_recall
            value: 74.74934036939314
          - type: euclidean_accuracy
            value: 88.22197055492639
          - type: euclidean_accuracy_threshold
            value: 58.290231227874756
          - type: euclidean_ap
            value: 80.57982723880139
          - type: euclidean_f1
            value: 73.63426519620417
          - type: euclidean_f1_threshold
            value: 61.55576705932617
          - type: euclidean_precision
            value: 71.63173652694611
          - type: euclidean_recall
            value: 75.75197889182058
          - type: main_score
            value: 80.57982723880139
          - type: manhattan_accuracy
            value: 88.14448351910353
          - type: manhattan_accuracy_threshold
            value: 3907.2471618652344
          - type: manhattan_ap
            value: 80.3538079655539
          - type: manhattan_f1
            value: 73.40466675261054
          - type: manhattan_f1_threshold
            value: 4103.794097900391
          - type: manhattan_precision
            value: 71.76707839677337
          - type: manhattan_recall
            value: 75.11873350923483
          - type: max_ap
            value: 80.57982723880139
          - type: max_f1
            value: 73.70579190158894
          - type: max_precision
            value: 72.51087791144101
          - type: max_recall
            value: 75.8839050131926
          - type: similarity_accuracy
            value: 88.22197055492639
          - type: similarity_accuracy_threshold
            value: 83.30042362213135
          - type: similarity_ap
            value: 80.57754959194938
          - type: similarity_f1
            value: 73.70579190158894
          - type: similarity_f1_threshold
            value: 81.04978799819946
          - type: similarity_precision
            value: 71.64922770303936
          - type: similarity_recall
            value: 75.8839050131926
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 89.88628866379477
          - type: cosine_accuracy_threshold
            value: 80.8050274848938
          - type: cosine_ap
            value: 87.57594591596816
          - type: cosine_f1
            value: 80.0812257707218
          - type: cosine_f1_threshold
            value: 77.990061044693
          - type: cosine_precision
            value: 76.93126197063205
          - type: cosine_recall
            value: 83.50015398829689
          - type: dot_accuracy
            value: 89.87852679784221
          - type: dot_accuracy_threshold
            value: 80.84419965744019
          - type: dot_ap
            value: 87.56136742222151
          - type: dot_f1
            value: 80.05898617511521
          - type: dot_f1_threshold
            value: 77.92385816574097
          - type: dot_precision
            value: 76.80554573106035
          - type: dot_recall
            value: 83.60024638127503
          - type: euclidean_accuracy
            value: 89.86882446540149
          - type: euclidean_accuracy_threshold
            value: 62.08193898200989
          - type: euclidean_ap
            value: 87.57517549192228
          - type: euclidean_f1
            value: 80.05286925872892
          - type: euclidean_f1_threshold
            value: 66.65036082267761
          - type: euclidean_precision
            value: 76.51063232507545
          - type: euclidean_recall
            value: 83.93902063443178
          - type: main_score
            value: 87.64162614197194
          - type: manhattan_accuracy
            value: 89.8959909962355
          - type: manhattan_accuracy_threshold
            value: 4176.108169555664
          - type: manhattan_ap
            value: 87.64162614197194
          - type: manhattan_f1
            value: 80.17116279069768
          - type: manhattan_f1_threshold
            value: 4433.153533935547
          - type: manhattan_precision
            value: 77.57615035644848
          - type: manhattan_recall
            value: 82.94579611949491
          - type: max_ap
            value: 87.64162614197194
          - type: max_f1
            value: 80.17116279069768
          - type: max_precision
            value: 77.57615035644848
          - type: max_recall
            value: 83.93902063443178
          - type: similarity_accuracy
            value: 89.88628866379477
          - type: similarity_accuracy_threshold
            value: 80.8050274848938
          - type: similarity_ap
            value: 87.57594591596816
          - type: similarity_f1
            value: 80.0812257707218
          - type: similarity_f1_threshold
            value: 77.990061044693
          - type: similarity_precision
            value: 76.93126197063205
          - type: similarity_recall
            value: 83.50015398829689
        task:
          type: PairClassification
tags:
  - mteb
  - sentence-transformers
  - transformers
  - sentence-similarity
license: mit

Updates

Hi, everyone, thanks for using stella models. After six months of work, I trained the jasper model on top of the stella model, which is a multimodal model, and it can be ranked 2 in mteb (submitted the results on 2024-12-11, which may need official review https://github.com/embeddings-benchmark/results/pull/68).

Model link: https://huggingface.co./infgrad/jasper_en_vision_language_v1

I'll focus on the technical report, training data and related code, hopefully the tricks I've used will be of some help to you guys!

This work was accomplished during my free time, it's a personal hobby. One person's time and energy is limited, and you are welcome to make any contributions!

You can also find these models on my homepage.

Introduction

The models are trained based on Alibaba-NLP/gte-large-en-v1.5 and Alibaba-NLP/gte-Qwen2-1.5B-instruct. Thanks for their contributions!

We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.

Prompt of s2p task(e.g. retrieve task):

Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}

Prompt of s2s task(e.g. semantic textual similarity task):

Instruct: Retrieve semantically similar text.\nQuery: {query}

The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.

The higher the dimension, the better the performance. Generally speaking, 1024d is good enough. The MTEB score of 1024d is only 0.001 lower than 8192d.

Model directory structure

The model directory structure is very simple, it is a standard SentenceTransformer directory with a series of 2_Dense_{dims} folders, where dims represents the final vector dimension.

For example, the 2_Dense_256 folder stores Linear weights that convert vector dimensions to 256 dimensions. Please refer to the following chapters for specific instructions on how to use them.

Usage

You can use SentenceTransformers or transformers library to encode text.

Sentence Transformers

from sentence_transformers import SentenceTransformer

# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`
query_prompt_name = "s2p_query"
queries = [
    "What are some ways to reduce stress?",
    "What are the benefits of drinking green tea?",
]
# docs do not need any prompts
docs = [
    "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
    "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]

# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
model = SentenceTransformer("dunzhang/stella_en_1.5B_v5", trust_remote_code=True).cuda()
query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
doc_embeddings = model.encode(docs)
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 1024) (2, 1024)

similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.8179, 0.2958],
#         [0.3194, 0.7854]])

Transformers

import os
import torch
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize

query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
    "What are some ways to reduce stress?",
    "What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
    "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
    "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]

# The path of your model after cloning it
model_dir = "{Your MODEL_PATH}"

vector_dim = 1024
vector_linear_directory = f"2_Dense_{vector_dim}"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim)
vector_linear_dict = {
    k.replace("linear.", ""): v for k, v in
    torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items()
}
vector_linear.load_state_dict(vector_linear_dict)
vector_linear.cuda()

# Embed the queries
with torch.no_grad():
    input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
    input_data = {k: v.cuda() for k, v in input_data.items()}
    attention_mask = input_data["attention_mask"]
    last_hidden_state = model(**input_data)[0]
    last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
    query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
    query_vectors = normalize(vector_linear(query_vectors).cpu().numpy())

# Embed the documents
with torch.no_grad():
    input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
    input_data = {k: v.cuda() for k, v in input_data.items()}
    attention_mask = input_data["attention_mask"]
    last_hidden_state = model(**input_data)[0]
    last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
    docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
    docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy())

print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)

similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8178789  0.2958377 ]
#  [0.31938642 0.7853526 ]]

FAQ

Q: The details of training?

A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper)

Q: How to choose a suitable prompt for my own task?

A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training data.

Q: How to reproduce MTEB results?

A: Please use evaluation scripts in Alibaba-NLP/gte-Qwen2-1.5B-instruct or intfloat/e5-mistral-7b-instruct

Q: Why each dimension has a linear weight?

A: MRL has multiple training methods, we choose this method which has the best performance.

Q: What is the sequence length of models?

A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the model is trained on datasets of 512 length. This may be an optimization term.

If you have any questions, please start a discussion on community.