---
tags:
- mteb
- transformers
- sentence-transformers
model-index:
- name: cde-small-v1
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 87.02985074626866
- type: ap
value: 56.706190238632956
- type: ap_weighted
value: 56.706190238632956
- type: f1
value: 81.93161953007674
- type: f1_weighted
value: 87.7650174177188
- type: main_score
value: 87.02985074626866
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 94.664175
- type: ap
value: 91.68668057762052
- type: ap_weighted
value: 91.68668057762052
- type: f1
value: 94.65859470333152
- type: f1_weighted
value: 94.65859470333152
- type: main_score
value: 94.664175
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.762
- type: f1
value: 55.06427827477677
- type: f1_weighted
value: 55.06427827477677
- type: main_score
value: 55.762
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
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value: 71.99600000000001
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value: 49.004
- type: map_at_10
value: 64.741
- type: map_at_100
value: 65.045
- type: map_at_1000
value: 65.048
- type: map_at_20
value: 64.999
- type: map_at_3
value: 61.344
- type: map_at_5
value: 63.595
- type: mrr_at_1
value: 50.71123755334281
- type: mrr_at_10
value: 65.32688703741336
- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
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- type: mrr_at_5
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- type: precision_at_1
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- type: precision_at_3
value: 25.462
- type: precision_at_5
value: 17.255000000000003
- type: recall_at_1
value: 49.004
- type: recall_at_10
value: 94.523
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 98.08
- type: recall_at_3
value: 76.387
- type: recall_at_5
value: 86.273
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 48.629569816593516
- type: v_measure
value: 48.629569816593516
- type: v_measure_std
value: 14.01810149072028
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 40.52366904677561
- type: v_measure
value: 40.52366904677561
- type: v_measure_std
value: 14.375876773823757
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: main_score
value: 61.27347206107508
- type: map
value: 61.27347206107508
- type: mrr
value: 74.49105219188321
- type: nAUC_map_diff1
value: 13.442645655149457
- type: nAUC_map_max
value: 25.013363268430027
- type: nAUC_map_std
value: 17.60175231611674
- type: nAUC_mrr_diff1
value: 25.217675209249435
- type: nAUC_mrr_max
value: 32.37381560372622
- type: nAUC_mrr_std
value: 22.584922632508412
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 89.09452267906886
- type: cosine_spearman
value: 86.73450642504955
- type: euclidean_pearson
value: 87.1275130552617
- type: euclidean_spearman
value: 86.93812552248012
- type: main_score
value: 86.73450642504955
- type: manhattan_pearson
value: 86.79403606129864
- type: manhattan_spearman
value: 86.76824213349957
- type: pearson
value: 89.09452267906886
- type: spearman
value: 86.73450642504955
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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value: 88.58116883116884
- type: f1
value: 88.54536316207125
- type: f1_weighted
value: 88.54536316207125
- type: main_score
value: 88.58116883116884
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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value: 44.89554099528695
- type: v_measure
value: 44.89554099528695
- type: v_measure_std
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task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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value: 37.89775676199564
- type: v_measure
value: 37.89775676199564
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task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
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task:
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name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
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task:
type: Retrieval
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config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 96.0373917008664
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value: 95.77672920037678
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value: 96.06299804062722
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value: 96.0373917008664
task:
type: Classification
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config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 89.1655266757866
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value: 71.6595596649587
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value: 90.44597470884298
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value: 89.1655266757866
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 74.0382414657559
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value: 76.53055023019932
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task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
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value: 78.58195068346751
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value: 78.86975899493798
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value: 78.93073301950236
task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
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value: 37.66500681777215
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task:
type: Clustering
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config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 35.51021437644991
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task:
type: Clustering
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config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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task:
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config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
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config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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type: mteb/nq
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- type: nauc_recall_at_1_std
value: 4.523659534794796
- type: nauc_recall_at_20_diff1
value: 8.140101636033602
- type: nauc_recall_at_20_max
value: 29.59131501693498
- type: nauc_recall_at_20_std
value: 25.876120433055316
- type: nauc_recall_at_3_diff1
value: 13.725759049941843
- type: nauc_recall_at_3_max
value: 21.75055584058006
- type: nauc_recall_at_3_std
value: 8.965766944507815
- type: nauc_recall_at_5_diff1
value: 10.366069494614596
- type: nauc_recall_at_5_max
value: 23.031784865881054
- type: nauc_recall_at_5_std
value: 12.411188897743521
- type: ndcg_at_1
value: 25.2
- type: ndcg_at_10
value: 21.781
- type: ndcg_at_100
value: 30.273
- type: ndcg_at_1000
value: 35.768
- type: ndcg_at_20
value: 24.967
- type: ndcg_at_3
value: 20.580000000000002
- type: ndcg_at_5
value: 17.926000000000002
- type: precision_at_1
value: 25.2
- type: precision_at_10
value: 11.4
- type: precision_at_100
value: 2.359
- type: precision_at_1000
value: 0.368
- type: precision_at_20
value: 7.545
- type: precision_at_3
value: 19.3
- type: precision_at_5
value: 15.78
- type: recall_at_1
value: 5.103
- type: recall_at_10
value: 23.083000000000002
- type: recall_at_100
value: 47.882999999999996
- type: recall_at_1000
value: 74.783
- type: recall_at_20
value: 30.592000000000002
- type: recall_at_3
value: 11.753
- type: recall_at_5
value: 15.983
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 83.9841377195369
- type: cosine_spearman
value: 77.44919890597407
- type: euclidean_pearson
value: 81.21238548422511
- type: euclidean_spearman
value: 76.94405730272983
- type: main_score
value: 77.44919890597407
- type: manhattan_pearson
value: 81.16824677968528
- type: manhattan_spearman
value: 76.94296468591867
- type: pearson
value: 83.9841377195369
- type: spearman
value: 77.44919890597407
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 81.36071984442052
- type: cosine_spearman
value: 74.2212823495219
- type: euclidean_pearson
value: 78.31139429452078
- type: euclidean_spearman
value: 74.02790834412275
- type: main_score
value: 74.2212823495219
- type: manhattan_pearson
value: 78.26141328104697
- type: manhattan_spearman
value: 74.02545007676329
- type: pearson
value: 81.36071984442052
- type: spearman
value: 74.2212823495219
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 85.49925337918731
- type: cosine_spearman
value: 86.12368715292688
- type: euclidean_pearson
value: 85.71147581542367
- type: euclidean_spearman
value: 86.64112317821541
- type: main_score
value: 86.12368715292688
- type: manhattan_pearson
value: 85.58242941611371
- type: manhattan_spearman
value: 86.51041533466731
- type: pearson
value: 85.49925337918731
- type: spearman
value: 86.12368715292688
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 82.24735192639226
- type: cosine_spearman
value: 78.88155361224834
- type: euclidean_pearson
value: 80.52048132030517
- type: euclidean_spearman
value: 78.1335955670817
- type: main_score
value: 78.88155361224834
- type: manhattan_pearson
value: 80.48178866605353
- type: manhattan_spearman
value: 78.08994918255844
- type: pearson
value: 82.24735192639226
- type: spearman
value: 78.88155361224834
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 86.27381322229758
- type: cosine_spearman
value: 87.5038962579188
- type: euclidean_pearson
value: 86.7575259976948
- type: euclidean_spearman
value: 87.3358778981031
- type: main_score
value: 87.5038962579188
- type: manhattan_pearson
value: 86.72177109814491
- type: manhattan_spearman
value: 87.30593609243358
- type: pearson
value: 86.27381322229758
- type: spearman
value: 87.5038962579188
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 82.90364706517789
- type: cosine_spearman
value: 84.25854334490232
- type: euclidean_pearson
value: 83.30065780824273
- type: euclidean_spearman
value: 84.17467271748362
- type: main_score
value: 84.25854334490232
- type: manhattan_pearson
value: 83.21239264085494
- type: manhattan_spearman
value: 84.05456832118482
- type: pearson
value: 82.90364706517789
- type: spearman
value: 84.25854334490232
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 88.88258729094343
- type: cosine_spearman
value: 89.68436656381257
- type: euclidean_pearson
value: 88.23417725579127
- type: euclidean_spearman
value: 87.96688277361433
- type: main_score
value: 89.68436656381257
- type: manhattan_pearson
value: 88.07673471897155
- type: manhattan_spearman
value: 87.7976329721765
- type: pearson
value: 88.88258729094343
- type: spearman
value: 89.68436656381257
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 65.24627744968292
- type: cosine_spearman
value: 65.96283849168346
- type: euclidean_pearson
value: 66.2111925054528
- type: euclidean_spearman
value: 65.83563143944401
- type: main_score
value: 65.96283849168346
- type: manhattan_pearson
value: 66.25664281582083
- type: manhattan_spearman
value: 65.8830797513158
- type: pearson
value: 65.24627744968292
- type: spearman
value: 65.96283849168346
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 85.57515090752183
- type: cosine_spearman
value: 85.54441587714372
- type: euclidean_pearson
value: 85.53938106211463
- type: euclidean_spearman
value: 85.28473579067878
- type: main_score
value: 85.54441587714372
- type: manhattan_pearson
value: 85.51025100057596
- type: manhattan_spearman
value: 85.260887707662
- type: pearson
value: 85.57515090752183
- type: spearman
value: 85.54441587714372
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: main_score
value: 82.9058801876062
- type: map
value: 82.9058801876062
- type: mrr
value: 95.256220721907
- type: nAUC_map_diff1
value: 0.13078953297011875
- type: nAUC_map_max
value: 59.173980738758026
- type: nAUC_map_std
value: 73.35735418975649
- type: nAUC_mrr_diff1
value: 46.534353907114514
- type: nAUC_mrr_max
value: 89.56255914950661
- type: nAUC_mrr_std
value: 85.6716185155955
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: main_score
value: 71.844
- type: map_at_1
value: 57.278
- type: map_at_10
value: 67.109
- type: map_at_100
value: 67.66499999999999
- type: map_at_1000
value: 67.685
- type: map_at_20
value: 67.482
- type: map_at_3
value: 64.16199999999999
- type: map_at_5
value: 65.82900000000001
- type: mrr_at_1
value: 60.0
- type: mrr_at_10
value: 68.19960317460317
- type: mrr_at_100
value: 68.62748949394921
- type: mrr_at_1000
value: 68.64515905414915
- type: mrr_at_20
value: 68.472601010101
- type: mrr_at_3
value: 66.0
- type: mrr_at_5
value: 67.21666666666667
- type: nauc_map_at_1000_diff1
value: 70.04313292027558
- type: nauc_map_at_1000_max
value: 57.24529193476731
- type: nauc_map_at_1000_std
value: -4.8888921470785585
- type: nauc_map_at_100_diff1
value: 70.04624674117014
- type: nauc_map_at_100_max
value: 57.25302539508853
- type: nauc_map_at_100_std
value: -4.907703072069842
- type: nauc_map_at_10_diff1
value: 70.06943109940849
- type: nauc_map_at_10_max
value: 57.39452715929109
- type: nauc_map_at_10_std
value: -4.743417671263566
- type: nauc_map_at_1_diff1
value: 76.61111479875207
- type: nauc_map_at_1_max
value: 52.822124992902374
- type: nauc_map_at_1_std
value: -7.6071857283495445
- type: nauc_map_at_20_diff1
value: 69.95251393140202
- type: nauc_map_at_20_max
value: 57.328356768833146
- type: nauc_map_at_20_std
value: -4.871357691032887
- type: nauc_map_at_3_diff1
value: 69.71499509001714
- type: nauc_map_at_3_max
value: 53.645107897260026
- type: nauc_map_at_3_std
value: -7.908850295935557
- type: nauc_map_at_5_diff1
value: 69.7531280646943
- type: nauc_map_at_5_max
value: 55.71038914997073
- type: nauc_map_at_5_std
value: -6.7813041970848476
- type: nauc_mrr_at_1000_diff1
value: 69.61840192382927
- type: nauc_mrr_at_1000_max
value: 58.419734360225696
- type: nauc_mrr_at_1000_std
value: -1.8503761885586425
- type: nauc_mrr_at_100_diff1
value: 69.6153571701724
- type: nauc_mrr_at_100_max
value: 58.422378816414565
- type: nauc_mrr_at_100_std
value: -1.8731915889302972
- type: nauc_mrr_at_10_diff1
value: 69.5874772943516
- type: nauc_mrr_at_10_max
value: 58.78121978366665
- type: nauc_mrr_at_10_std
value: -1.2843146465927913
- type: nauc_mrr_at_1_diff1
value: 74.35688136934793
- type: nauc_mrr_at_1_max
value: 57.487384980706416
- type: nauc_mrr_at_1_std
value: -1.3005837538340144
- type: nauc_mrr_at_20_diff1
value: 69.53988639045606
- type: nauc_mrr_at_20_max
value: 58.49631860342686
- type: nauc_mrr_at_20_std
value: -1.7220227513588833
- type: nauc_mrr_at_3_diff1
value: 68.94320178615871
- type: nauc_mrr_at_3_max
value: 56.60856449749424
- type: nauc_mrr_at_3_std
value: -3.3432894595086866
- type: nauc_mrr_at_5_diff1
value: 68.94240340867633
- type: nauc_mrr_at_5_max
value: 58.27068018852665
- type: nauc_mrr_at_5_std
value: -2.320192066949136
- type: nauc_ndcg_at_1000_diff1
value: 69.15093538086137
- type: nauc_ndcg_at_1000_max
value: 58.6801221127507
- type: nauc_ndcg_at_1000_std
value: -3.002038837722594
- type: nauc_ndcg_at_100_diff1
value: 69.11507044508373
- type: nauc_ndcg_at_100_max
value: 58.843490113137605
- type: nauc_ndcg_at_100_std
value: -3.2810475322338566
- type: nauc_ndcg_at_10_diff1
value: 68.71920945656667
- type: nauc_ndcg_at_10_max
value: 60.13600198034469
- type: nauc_ndcg_at_10_std
value: -1.6190106644777749
- type: nauc_ndcg_at_1_diff1
value: 74.35688136934793
- type: nauc_ndcg_at_1_max
value: 57.487384980706416
- type: nauc_ndcg_at_1_std
value: -1.3005837538340144
- type: nauc_ndcg_at_20_diff1
value: 68.33714726670162
- type: nauc_ndcg_at_20_max
value: 59.45907982196103
- type: nauc_ndcg_at_20_std
value: -2.5953063304797754
- type: nauc_ndcg_at_3_diff1
value: 67.33605891922716
- type: nauc_ndcg_at_3_max
value: 55.01142849375101
- type: nauc_ndcg_at_3_std
value: -6.5632981093508205
- type: nauc_ndcg_at_5_diff1
value: 67.59450950578172
- type: nauc_ndcg_at_5_max
value: 57.50106057747294
- type: nauc_ndcg_at_5_std
value: -5.415038422866616
- type: nauc_precision_at_1000_diff1
value: -33.21156082089814
- type: nauc_precision_at_1000_max
value: 19.132732038554398
- type: nauc_precision_at_1000_std
value: 44.091281225705714
- type: nauc_precision_at_100_diff1
value: -20.015823755259245
- type: nauc_precision_at_100_max
value: 26.507243354636085
- type: nauc_precision_at_100_std
value: 37.87274756817076
- type: nauc_precision_at_10_diff1
value: 8.35057694800983
- type: nauc_precision_at_10_max
value: 49.60611953844157
- type: nauc_precision_at_10_std
value: 32.18410475820039
- type: nauc_precision_at_1_diff1
value: 74.35688136934793
- type: nauc_precision_at_1_max
value: 57.487384980706416
- type: nauc_precision_at_1_std
value: -1.3005837538340144
- type: nauc_precision_at_20_diff1
value: -3.0872665961524612
- type: nauc_precision_at_20_max
value: 40.5565038905005
- type: nauc_precision_at_20_std
value: 32.15291813716766
- type: nauc_precision_at_3_diff1
value: 34.627722605371545
- type: nauc_precision_at_3_max
value: 49.65219072739979
- type: nauc_precision_at_3_std
value: 7.7588985130719434
- type: nauc_precision_at_5_diff1
value: 22.06911561993657
- type: nauc_precision_at_5_max
value: 49.09578970278826
- type: nauc_precision_at_5_std
value: 16.038789872070705
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: 64.77257569694551
- type: nauc_recall_at_100_max
value: 65.07269574496497
- type: nauc_recall_at_100_std
value: -10.979947534569218
- type: nauc_recall_at_10_diff1
value: 62.14297161941494
- type: nauc_recall_at_10_max
value: 70.41353364022896
- type: nauc_recall_at_10_std
value: 9.172932719542075
- type: nauc_recall_at_1_diff1
value: 76.61111479875207
- type: nauc_recall_at_1_max
value: 52.822124992902374
- type: nauc_recall_at_1_std
value: -7.6071857283495445
- type: nauc_recall_at_20_diff1
value: 57.631464811333224
- type: nauc_recall_at_20_max
value: 67.83558221740536
- type: nauc_recall_at_20_std
value: 3.110691973832695
- type: nauc_recall_at_3_diff1
value: 60.39078444139112
- type: nauc_recall_at_3_max
value: 51.122425596651574
- type: nauc_recall_at_3_std
value: -10.307895490015559
- type: nauc_recall_at_5_diff1
value: 59.703727953513145
- type: nauc_recall_at_5_max
value: 59.81893786534298
- type: nauc_recall_at_5_std
value: -6.231017907901268
- type: ndcg_at_1
value: 60.0
- type: ndcg_at_10
value: 71.844
- type: ndcg_at_100
value: 74.278
- type: ndcg_at_1000
value: 74.74199999999999
- type: ndcg_at_20
value: 72.99
- type: ndcg_at_3
value: 66.721
- type: ndcg_at_5
value: 69.137
- type: precision_at_1
value: 60.0
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.067
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 17.267
- type: recall_at_1
value: 57.278
- type: recall_at_10
value: 85.344
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 89.589
- type: recall_at_3
value: 71.45
- type: recall_at_5
value: 77.361
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cosine_accuracy
value: 99.8019801980198
- type: cosine_accuracy_threshold
value: 74.77510571479797
- type: cosine_ap
value: 95.30006120252773
- type: cosine_f1
value: 89.75265017667844
- type: cosine_f1_threshold
value: 72.93492555618286
- type: cosine_precision
value: 90.62181447502549
- type: cosine_recall
value: 88.9
- type: dot_accuracy
value: 99.74554455445545
- type: dot_accuracy_threshold
value: 794.2790985107422
- type: dot_ap
value: 93.33073289508414
- type: dot_f1
value: 87.11779448621553
- type: dot_f1_threshold
value: 793.5191631317139
- type: dot_precision
value: 87.33668341708542
- type: dot_recall
value: 86.9
- type: euclidean_accuracy
value: 99.7960396039604
- type: euclidean_accuracy_threshold
value: 238.72876167297363
- type: euclidean_ap
value: 95.04815354196363
- type: euclidean_f1
value: 89.53252032520325
- type: euclidean_f1_threshold
value: 241.42813682556152
- type: euclidean_precision
value: 91.01239669421489
- type: euclidean_recall
value: 88.1
- type: main_score
value: 95.30006120252773
- type: manhattan_accuracy
value: 99.7960396039604
- type: manhattan_accuracy_threshold
value: 5224.44953918457
- type: manhattan_ap
value: 95.02798265540767
- type: manhattan_f1
value: 89.4552723638181
- type: manhattan_f1_threshold
value: 5434.450531005859
- type: manhattan_precision
value: 89.41058941058941
- type: manhattan_recall
value: 89.5
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 95.30006120252773
- type: max_f1
value: 89.75265017667844
- type: max_precision
value: 91.01239669421489
- type: max_recall
value: 89.5
- type: similarity_accuracy
value: 99.8019801980198
- type: similarity_accuracy_threshold
value: 74.77510571479797
- type: similarity_ap
value: 95.30006120252773
- type: similarity_f1
value: 89.75265017667844
- type: similarity_f1_threshold
value: 72.93492555618286
- type: similarity_precision
value: 90.62181447502549
- type: similarity_recall
value: 88.9
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
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value: 24.898
- type: recall_at_1
value: 2.355
- type: recall_at_10
value: 15.397
- type: recall_at_100
value: 43.647000000000006
- type: recall_at_1000
value: 77.089
- type: recall_at_20
value: 22.792
- type: recall_at_3
value: 6.847
- type: recall_at_5
value: 9.136
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 72.7734375
- type: ap
value: 15.655230461083708
- type: ap_weighted
value: 15.655230461083708
- type: f1
value: 56.31497978454638
- type: f1_weighted
value: 78.70509613747345
- type: main_score
value: 72.7734375
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 72.56366723259762
- type: f1
value: 72.90413275122202
- type: f1_weighted
value: 72.19948169084057
- type: main_score
value: 72.56366723259762
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score
value: 56.90970017457857
- type: v_measure
value: 56.90970017457857
- type: v_measure_std
value: 1.5885885070403738
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cosine_accuracy
value: 85.7006616200751
- type: cosine_accuracy_threshold
value: 75.78572630882263
- type: cosine_ap
value: 72.87577990245127
- type: cosine_f1
value: 67.36422521175885
- type: cosine_f1_threshold
value: 70.15678882598877
- type: cosine_precision
value: 63.80368098159509
- type: cosine_recall
value: 71.34564643799473
- type: dot_accuracy
value: 83.60851165285807
- type: dot_accuracy_threshold
value: 744.7918891906738
- type: dot_ap
value: 64.82619159813649
- type: dot_f1
value: 62.62379263968699
- type: dot_f1_threshold
value: 696.7735290527344
- type: dot_precision
value: 58.350421508316245
- type: dot_recall
value: 67.57255936675462
- type: euclidean_accuracy
value: 85.84371460928652
- type: euclidean_accuracy_threshold
value: 220.4747200012207
- type: euclidean_ap
value: 72.47837433257799
- type: euclidean_f1
value: 67.2811059907834
- type: euclidean_f1_threshold
value: 240.81902503967285
- type: euclidean_precision
value: 65.34062655395326
- type: euclidean_recall
value: 69.34036939313984
- type: main_score
value: 72.87577990245127
- type: manhattan_accuracy
value: 85.83179352685224
- type: manhattan_accuracy_threshold
value: 4910.404205322266
- type: manhattan_ap
value: 72.44111617709422
- type: manhattan_f1
value: 67.09989806320081
- type: manhattan_f1_threshold
value: 5333.793640136719
- type: manhattan_precision
value: 64.88417939871857
- type: manhattan_recall
value: 69.47229551451187
- type: max_accuracy
value: 85.84371460928652
- type: max_ap
value: 72.87577990245127
- type: max_f1
value: 67.36422521175885
- type: max_precision
value: 65.34062655395326
- type: max_recall
value: 71.34564643799473
- type: similarity_accuracy
value: 85.7006616200751
- type: similarity_accuracy_threshold
value: 75.78572630882263
- type: similarity_ap
value: 72.87577990245127
- type: similarity_f1
value: 67.36422521175885
- type: similarity_f1_threshold
value: 70.15678882598877
- type: similarity_precision
value: 63.80368098159509
- type: similarity_recall
value: 71.34564643799473
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cosine_accuracy
value: 88.88112702293631
- type: cosine_accuracy_threshold
value: 71.48405313491821
- type: cosine_ap
value: 85.88088882163336
- type: cosine_f1
value: 78.2251744598276
- type: cosine_f1_threshold
value: 70.09605169296265
- type: cosine_precision
value: 75.8997755087262
- type: cosine_recall
value: 80.69756698490914
- type: dot_accuracy
value: 88.04672643303451
- type: dot_accuracy_threshold
value: 700.6264686584473
- type: dot_ap
value: 83.52072844458456
- type: dot_f1
value: 76.24239256244634
- type: dot_f1_threshold
value: 664.9115562438965
- type: dot_precision
value: 74.0123233055455
- type: dot_recall
value: 78.61102556205728
- type: euclidean_accuracy
value: 88.72588970388482
- type: euclidean_accuracy_threshold
value: 226.53303146362305
- type: euclidean_ap
value: 85.51788295919707
- type: euclidean_f1
value: 77.73453426739316
- type: euclidean_f1_threshold
value: 238.7503147125244
- type: euclidean_precision
value: 74.94818097348296
- type: euclidean_recall
value: 80.73606405913151
- type: main_score
value: 85.88088882163336
- type: manhattan_accuracy
value: 88.68902084061008
- type: manhattan_accuracy_threshold
value: 5034.079742431641
- type: manhattan_ap
value: 85.49952903626239
- type: manhattan_f1
value: 77.74326743888625
- type: manhattan_f1_threshold
value: 5334.531021118164
- type: manhattan_precision
value: 73.98289171708741
- type: manhattan_recall
value: 81.90637511549123
- type: max_accuracy
value: 88.88112702293631
- type: max_ap
value: 85.88088882163336
- type: max_f1
value: 78.2251744598276
- type: max_precision
value: 75.8997755087262
- type: max_recall
value: 81.90637511549123
- type: similarity_accuracy
value: 88.88112702293631
- type: similarity_accuracy_threshold
value: 71.48405313491821
- type: similarity_ap
value: 85.88088882163336
- type: similarity_f1
value: 78.2251744598276
- type: similarity_f1_threshold
value: 70.09605169296265
- type: similarity_precision
value: 75.8997755087262
- type: similarity_recall
value: 80.69756698490914
task:
type: PairClassification
---
# Contextual Document Embeddings (CDE)
Our new model that naturally integrates "context tokens" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.
👉 Try on Colab
👉 Contextual Document Embeddings (ArXiv)
# How to use `cde-small-v1`
Our embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our "first-stage" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.
## With Sentence Transformers
Click to learn how to use cde-small-v1 with Sentence Transformers
### Loading the model
Our model can be loaded using `sentence-transformers` out-of-the-box with "trust remote code" enabled:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
```
#### Note on prefixes
*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can use `prompt_name="query"` and `prompt_name="document"` in the `encode` method of the model when embedding queries and documents, respectively.
### First stage
```python
minicorpus_size = model[0].config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
dataset_embeddings = model.encode(
minicorpus_docs,
prompt_name="document",
convert_to_tensor=True
)
```
### Running the second stage
Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prompt for documents:
```python
docs = [...]
queries = [...]
doc_embeddings = model.encode(
docs,
prompt_name="document",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
query_embeddings = model.encode(
queries,
prompt_name="query",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
```
these embeddings can be compared using cosine similarity via `model.similarity`:
```python
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
```
Click here for a full copy-paste ready example
```python
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
# 1. Load the Sentence Transformer model
model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
context_docs_size = model[0].config.transductive_corpus_size # 512
# 2. Load the dataset: context dataset, docs, and queries
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
dataset.shuffle(seed=42)
# 10 queries, 512 context docs, 500 docs
queries = dataset["query"][:10]
docs = dataset["answer"][:2000]
context_docs = dataset["answer"][-context_docs_size:] # Last 512 docs
# 3. First stage: embed the context docs
dataset_embeddings = model.encode(
context_docs,
prompt_name="document",
convert_to_tensor=True,
)
# 4. Second stage: embed the docs and queries
doc_embeddings = model.encode(
docs,
prompt_name="document",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
query_embeddings = model.encode(
queries,
prompt_name="query",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
# 5. Compute the similarity between the queries and docs
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
print(topk_values)
print(topk_indices)
"""
tensor([[0.5495, 0.5426, 0.5423, 0.5292, 0.5286],
[0.6357, 0.6334, 0.6177, 0.5862, 0.5794],
[0.7648, 0.5452, 0.5000, 0.4959, 0.4881],
[0.6802, 0.5225, 0.5178, 0.5160, 0.5075],
[0.6947, 0.5843, 0.5619, 0.5344, 0.5298],
[0.7742, 0.7742, 0.7742, 0.7231, 0.6224],
[0.8853, 0.6667, 0.5829, 0.5795, 0.5769],
[0.6911, 0.6127, 0.6003, 0.5986, 0.5936],
[0.6796, 0.6053, 0.6000, 0.5911, 0.5884],
[0.7624, 0.5589, 0.5428, 0.5278, 0.5275]], device='cuda:0')
tensor([[ 0, 296, 234, 1651, 1184],
[1542, 466, 438, 1207, 1911],
[ 2, 1562, 632, 1852, 382],
[ 3, 694, 932, 1765, 662],
[ 4, 35, 747, 26, 432],
[ 534, 175, 5, 1495, 575],
[ 6, 1802, 1875, 747, 21],
[ 7, 1913, 1936, 640, 6],
[ 8, 747, 167, 1318, 1743],
[ 9, 1583, 1145, 219, 357]], device='cuda:0')
"""
# As you can see, almost every query_i has document_i as the most similar document.
# 6. Print the top-k results
for query_idx, top_doc_idx in enumerate(topk_indices[:, 0]):
print(f"Query {query_idx}: {queries[query_idx]}")
print(f"Top Document: {docs[top_doc_idx]}")
print()
"""
Query 0: when did richmond last play in a preliminary final
Top Document: Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.
Query 1: who sang what in the world's come over you
Top Document: Life's What You Make It (Talk Talk song) "Life's What You Make It" is a song by the English band Talk Talk. It was released as a single in 1986, the first from the band's album The Colour of Spring. The single was a hit in the UK, peaking at No. 16, and charted in numerous other countries, often reaching the Top 20.
Query 2: who produces the most wool in the world
Top Document: Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.
Query 3: where does alaska the last frontier take place
Top Document: Alaska: The Last Frontier Alaska: The Last Frontier is an American reality cable television series on the Discovery Channel, currently in its 7th season of broadcast. The show documents the extended Kilcher family, descendants of Swiss immigrants and Alaskan pioneers, Yule and Ruth Kilcher, at their homestead 11 miles outside of Homer.[1] By living without plumbing or modern heating, the clan chooses to subsist by farming, hunting and preparing for the long winters.[2] The Kilcher family are relatives of the singer Jewel,[1][3] who has appeared on the show.[4]
Query 4: a day to remember all i want cameos
Top Document: All I Want (A Day to Remember song) The music video for the song, which was filmed in October 2010,[4] was released on January 6, 2011.[5] It features cameos of numerous popular bands and musicians. The cameos are: Tom Denney (A Day to Remember's former guitarist), Pete Wentz, Winston McCall of Parkway Drive, The Devil Wears Prada, Bring Me the Horizon, Sam Carter of Architects, Tim Lambesis of As I Lay Dying, Silverstein, Andrew WK, August Burns Red, Seventh Star, Matt Heafy of Trivium, Vic Fuentes of Pierce the Veil, Mike Herrera of MxPx, and Set Your Goals.[5] Rock Sound called the video "quite excellent".[5]
Query 5: what does the red stripes mean on the american flag
Top Document: Flag of the United States The flag of the United States of America, often referred to as the American flag, is the national flag of the United States. It consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton (referred to specifically as the "union") bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 states of the United States of America, and the 13 stripes represent the thirteen British colonies that declared independence from the Kingdom of Great Britain, and became the first states in the U.S.[1] Nicknames for the flag include The Stars and Stripes,[2] Old Glory,[3] and The Star-Spangled Banner.
Query 6: where did they film diary of a wimpy kid
Top Document: Diary of a Wimpy Kid (film) Filming of Diary of a Wimpy Kid was in Vancouver and wrapped up on October 16, 2009.
Query 7: where was beasts of the southern wild filmed
Top Document: Beasts of the Southern Wild The film's fictional setting, "Isle de Charles Doucet", known to its residents as the Bathtub, was inspired by several isolated and independent fishing communities threatened by erosion, hurricanes and rising sea levels in Louisiana's Terrebonne Parish, most notably the rapidly eroding Isle de Jean Charles. It was filmed in Terrebonne Parish town Montegut.[5]
Query 8: what part of the country are you likely to find the majority of the mollisols
Top Document: Mollisol Mollisols occur in savannahs and mountain valleys (such as Central Asia, or the North American Great Plains). These environments have historically been strongly influenced by fire and abundant pedoturbation from organisms such as ants and earthworms. It was estimated that in 2003, only 14 to 26 percent of grassland ecosystems still remained in a relatively natural state (that is, they were not used for agriculture due to the fertility of the A horizon). Globally, they represent ~7% of ice-free land area. As the world's most agriculturally productive soil order, the Mollisols represent one of the more economically important soil orders.
Query 9: when did fosters home for imaginary friends start
Top Document: Foster's Home for Imaginary Friends McCracken conceived the series after adopting two dogs from an animal shelter and applying the concept to imaginary friends. The show first premiered on Cartoon Network on August 13, 2004, as a 90-minute television film. On August 20, it began its normal run of twenty-to-thirty-minute episodes on Fridays, at 7 pm. The series finished its run on May 3, 2009, with a total of six seasons and seventy-nine episodes. McCracken left Cartoon Network shortly after the series ended. Reruns have aired on Boomerang from August 11, 2012 to November 3, 2013 and again from June 1, 2014 to April 3, 2017.
"""
```
## With Transformers
Click to learn how to use cde-small-v1 with Transformers
### Loading the model
Our model can be loaded using `transformers` out-of-the-box with "trust remote code" enabled. We use the default BERT uncased tokenizer:
```python
import transformers
model = transformers.AutoModel.from_pretrained("jxm/cde-small-v1", trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-uncased")
```
#### Note on prefixes
*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can prepend the following strings to queries & documents:
```python
query_prefix = "search_query: "
document_prefix = "search_document: "
```
### First stage
```python
minicorpus_size = model.config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
minicorpus_docs = tokenizer(
[document_prefix + doc for doc in minicorpus_docs],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
import torch
from tqdm.autonotebook import tqdm
batch_size = 32
dataset_embeddings = []
for i in tqdm(range(0, len(minicorpus_docs["input_ids"]), batch_size)):
minicorpus_docs_batch = {k: v[i:i+batch_size] for k,v in minicorpus_docs.items()}
with torch.no_grad():
dataset_embeddings.append(
model.first_stage_model(**minicorpus_docs_batch)
)
dataset_embeddings = torch.cat(dataset_embeddings)
```
### Running the second stage
Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prefix for documents:
```python
docs = tokenizer(
[document_prefix + doc for doc in docs],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
doc_embeddings = model.second_stage_model(
input_ids=docs["input_ids"],
attention_mask=docs["attention_mask"],
dataset_embeddings=dataset_embeddings,
)
doc_embeddings /= doc_embeddings.norm(p=2, dim=1, keepdim=True)
```
and the query prefix for queries:
```python
queries = queries.select(range(16))["text"]
queries = tokenizer(
[query_prefix + query for query in queries],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
query_embeddings = model.second_stage_model(
input_ids=queries["input_ids"],
attention_mask=queries["attention_mask"],
dataset_embeddings=dataset_embeddings,
)
query_embeddings /= query_embeddings.norm(p=2, dim=1, keepdim=True)
```
these embeddings can be compared using dot product, since they're normalized.
### What if I don't know what my corpus will be ahead of time?
If you can't obtain corpus information ahead of time, you still have to pass *something* as the dataset embeddings; our model will work fine in this case, but not quite as well; without corpus information, our model performance drops from 65.0 to 63.8 on MTEB. We provide [some random strings](https://huggingface.co/jxm/cde-small-v1/resolve/main/random_strings.txt) that worked well for us that can be used as a substitute for corpus sampling.
### Colab demo
We've set up a short demo in a Colab notebook showing how you might use our model:
[Try our model in Colab:](https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing)
### Acknowledgments
Early experiments on CDE were done with support from [Nomic](https://www.nomic.ai/) and [Hyperbolic](https://hyperbolic.xyz/). We're especially indebted to Nomic for [open-sourcing their efficient BERT implementation and contrastive pre-training data](https://www.nomic.ai/blog/posts/nomic-embed-text-v1), which proved vital in the development of CDE.
### Cite us
Used our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:
```
@misc{morris2024contextualdocumentembeddings,
title={Contextual Document Embeddings},
author={John X. Morris and Alexander M. Rush},
year={2024},
eprint={2410.02525},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.02525},
}
```