|
--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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- transformers |
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- transformers.js |
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license: apache-2.0 |
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language: |
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- en |
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inference: false |
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model-index: |
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- name: epoch_0_model |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 76.98507462686568 |
|
- type: ap |
|
value: 39.47222193126652 |
|
- type: f1 |
|
value: 70.5923611893019 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 87.540175 |
|
- type: ap |
|
value: 83.16128207188409 |
|
- type: f1 |
|
value: 87.5231988227265 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 46.80799999999999 |
|
- type: f1 |
|
value: 46.2632547445265 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.583 |
|
- type: map_at_10 |
|
value: 46.17 |
|
- type: map_at_100 |
|
value: 47.115 |
|
- type: map_at_1000 |
|
value: 47.121 |
|
- type: map_at_3 |
|
value: 41.489 |
|
- type: map_at_5 |
|
value: 44.046 |
|
- type: mrr_at_1 |
|
value: 30.939 |
|
- type: mrr_at_10 |
|
value: 46.289 |
|
- type: mrr_at_100 |
|
value: 47.241 |
|
- type: mrr_at_1000 |
|
value: 47.247 |
|
- type: mrr_at_3 |
|
value: 41.596 |
|
- type: mrr_at_5 |
|
value: 44.149 |
|
- type: ndcg_at_1 |
|
value: 30.583 |
|
- type: ndcg_at_10 |
|
value: 54.812000000000005 |
|
- type: ndcg_at_100 |
|
value: 58.605 |
|
- type: ndcg_at_1000 |
|
value: 58.753 |
|
- type: ndcg_at_3 |
|
value: 45.095 |
|
- type: ndcg_at_5 |
|
value: 49.744 |
|
- type: precision_at_1 |
|
value: 30.583 |
|
- type: precision_at_10 |
|
value: 8.243 |
|
- type: precision_at_100 |
|
value: 0.984 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 18.516 |
|
- type: precision_at_5 |
|
value: 13.385 |
|
- type: recall_at_1 |
|
value: 30.583 |
|
- type: recall_at_10 |
|
value: 82.432 |
|
- type: recall_at_100 |
|
value: 98.43499999999999 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 55.547999999999995 |
|
- type: recall_at_5 |
|
value: 66.927 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.17830107652425 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
|
- type: v_measure |
|
value: 35.90561364087807 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 59.57222651819297 |
|
- type: mrr |
|
value: 73.19241085169062 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 89.55181686367382 |
|
- type: cos_sim_spearman |
|
value: 87.18933606575987 |
|
- type: euclidean_pearson |
|
value: 87.78077503434338 |
|
- type: euclidean_spearman |
|
value: 87.18933606575987 |
|
- type: manhattan_pearson |
|
value: 87.75124980168601 |
|
- type: manhattan_spearman |
|
value: 86.79113422137638 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 81.09415584415585 |
|
- type: f1 |
|
value: 80.60088693212091 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 36.57061229905462 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 32.05342946608653 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 34.376 |
|
- type: map_at_10 |
|
value: 45.214 |
|
- type: map_at_100 |
|
value: 46.635 |
|
- type: map_at_1000 |
|
value: 46.755 |
|
- type: map_at_3 |
|
value: 42.198 |
|
- type: map_at_5 |
|
value: 43.723 |
|
- type: mrr_at_1 |
|
value: 41.774 |
|
- type: mrr_at_10 |
|
value: 51.07000000000001 |
|
- type: mrr_at_100 |
|
value: 51.785000000000004 |
|
- type: mrr_at_1000 |
|
value: 51.824999999999996 |
|
- type: mrr_at_3 |
|
value: 48.808 |
|
- type: mrr_at_5 |
|
value: 50.11 |
|
- type: ndcg_at_1 |
|
value: 41.774 |
|
- type: ndcg_at_10 |
|
value: 51.105999999999995 |
|
- type: ndcg_at_100 |
|
value: 56.358 |
|
- type: ndcg_at_1000 |
|
value: 58.205 |
|
- type: ndcg_at_3 |
|
value: 46.965 |
|
- type: ndcg_at_5 |
|
value: 48.599 |
|
- type: precision_at_1 |
|
value: 41.774 |
|
- type: precision_at_10 |
|
value: 9.514 |
|
- type: precision_at_100 |
|
value: 1.508 |
|
- type: precision_at_1000 |
|
value: 0.196 |
|
- type: precision_at_3 |
|
value: 22.175 |
|
- type: precision_at_5 |
|
value: 15.508 |
|
- type: recall_at_1 |
|
value: 34.376 |
|
- type: recall_at_10 |
|
value: 61.748000000000005 |
|
- type: recall_at_100 |
|
value: 84.025 |
|
- type: recall_at_1000 |
|
value: 95.5 |
|
- type: recall_at_3 |
|
value: 49.378 |
|
- type: recall_at_5 |
|
value: 54.276 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.394 |
|
- type: map_at_10 |
|
value: 42.707 |
|
- type: map_at_100 |
|
value: 43.893 |
|
- type: map_at_1000 |
|
value: 44.019000000000005 |
|
- type: map_at_3 |
|
value: 39.51 |
|
- type: map_at_5 |
|
value: 41.381 |
|
- type: mrr_at_1 |
|
value: 41.019 |
|
- type: mrr_at_10 |
|
value: 49.042 |
|
- type: mrr_at_100 |
|
value: 49.669000000000004 |
|
- type: mrr_at_1000 |
|
value: 49.712 |
|
- type: mrr_at_3 |
|
value: 46.921 |
|
- type: mrr_at_5 |
|
value: 48.192 |
|
- type: ndcg_at_1 |
|
value: 41.019 |
|
- type: ndcg_at_10 |
|
value: 48.46 |
|
- type: ndcg_at_100 |
|
value: 52.537 |
|
- type: ndcg_at_1000 |
|
value: 54.491 |
|
- type: ndcg_at_3 |
|
value: 44.232 |
|
- type: ndcg_at_5 |
|
value: 46.305 |
|
- type: precision_at_1 |
|
value: 41.019 |
|
- type: precision_at_10 |
|
value: 9.134 |
|
- type: precision_at_100 |
|
value: 1.422 |
|
- type: precision_at_1000 |
|
value: 0.188 |
|
- type: precision_at_3 |
|
value: 21.38 |
|
- type: precision_at_5 |
|
value: 15.096000000000002 |
|
- type: recall_at_1 |
|
value: 32.394 |
|
- type: recall_at_10 |
|
value: 58.11500000000001 |
|
- type: recall_at_100 |
|
value: 75.509 |
|
- type: recall_at_1000 |
|
value: 87.812 |
|
- type: recall_at_3 |
|
value: 45.476 |
|
- type: recall_at_5 |
|
value: 51.549 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 43.47 |
|
- type: map_at_10 |
|
value: 55.871 |
|
- type: map_at_100 |
|
value: 56.745000000000005 |
|
- type: map_at_1000 |
|
value: 56.794 |
|
- type: map_at_3 |
|
value: 52.439 |
|
- type: map_at_5 |
|
value: 54.412000000000006 |
|
- type: mrr_at_1 |
|
value: 49.592000000000006 |
|
- type: mrr_at_10 |
|
value: 59.34199999999999 |
|
- type: mrr_at_100 |
|
value: 59.857000000000006 |
|
- type: mrr_at_1000 |
|
value: 59.88 |
|
- type: mrr_at_3 |
|
value: 56.897 |
|
- type: mrr_at_5 |
|
value: 58.339 |
|
- type: ndcg_at_1 |
|
value: 49.592000000000006 |
|
- type: ndcg_at_10 |
|
value: 61.67 |
|
- type: ndcg_at_100 |
|
value: 65.11099999999999 |
|
- type: ndcg_at_1000 |
|
value: 66.065 |
|
- type: ndcg_at_3 |
|
value: 56.071000000000005 |
|
- type: ndcg_at_5 |
|
value: 58.84700000000001 |
|
- type: precision_at_1 |
|
value: 49.592000000000006 |
|
- type: precision_at_10 |
|
value: 9.774 |
|
- type: precision_at_100 |
|
value: 1.2449999999999999 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 24.66 |
|
- type: precision_at_5 |
|
value: 16.878 |
|
- type: recall_at_1 |
|
value: 43.47 |
|
- type: recall_at_10 |
|
value: 75.387 |
|
- type: recall_at_100 |
|
value: 90.253 |
|
- type: recall_at_1000 |
|
value: 97.00800000000001 |
|
- type: recall_at_3 |
|
value: 60.616 |
|
- type: recall_at_5 |
|
value: 67.31899999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.633000000000003 |
|
- type: map_at_10 |
|
value: 35.497 |
|
- type: map_at_100 |
|
value: 36.504 |
|
- type: map_at_1000 |
|
value: 36.574 |
|
- type: map_at_3 |
|
value: 33.115 |
|
- type: map_at_5 |
|
value: 34.536 |
|
- type: mrr_at_1 |
|
value: 28.927000000000003 |
|
- type: mrr_at_10 |
|
value: 37.778 |
|
- type: mrr_at_100 |
|
value: 38.634 |
|
- type: mrr_at_1000 |
|
value: 38.690000000000005 |
|
- type: mrr_at_3 |
|
value: 35.518 |
|
- type: mrr_at_5 |
|
value: 36.908 |
|
- type: ndcg_at_1 |
|
value: 28.927000000000003 |
|
- type: ndcg_at_10 |
|
value: 40.327 |
|
- type: ndcg_at_100 |
|
value: 45.321 |
|
- type: ndcg_at_1000 |
|
value: 47.214 |
|
- type: ndcg_at_3 |
|
value: 35.762 |
|
- type: ndcg_at_5 |
|
value: 38.153999999999996 |
|
- type: precision_at_1 |
|
value: 28.927000000000003 |
|
- type: precision_at_10 |
|
value: 6.045 |
|
- type: precision_at_100 |
|
value: 0.901 |
|
- type: precision_at_1000 |
|
value: 0.11 |
|
- type: precision_at_3 |
|
value: 15.140999999999998 |
|
- type: precision_at_5 |
|
value: 10.485999999999999 |
|
- type: recall_at_1 |
|
value: 26.633000000000003 |
|
- type: recall_at_10 |
|
value: 52.99 |
|
- type: recall_at_100 |
|
value: 76.086 |
|
- type: recall_at_1000 |
|
value: 90.46300000000001 |
|
- type: recall_at_3 |
|
value: 40.738 |
|
- type: recall_at_5 |
|
value: 46.449 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.521 |
|
- type: map_at_10 |
|
value: 25.130000000000003 |
|
- type: map_at_100 |
|
value: 26.176 |
|
- type: map_at_1000 |
|
value: 26.289 |
|
- type: map_at_3 |
|
value: 22.829 |
|
- type: map_at_5 |
|
value: 24.082 |
|
- type: mrr_at_1 |
|
value: 21.766 |
|
- type: mrr_at_10 |
|
value: 29.801 |
|
- type: mrr_at_100 |
|
value: 30.682 |
|
- type: mrr_at_1000 |
|
value: 30.75 |
|
- type: mrr_at_3 |
|
value: 27.633000000000003 |
|
- type: mrr_at_5 |
|
value: 28.858 |
|
- type: ndcg_at_1 |
|
value: 21.766 |
|
- type: ndcg_at_10 |
|
value: 30.026000000000003 |
|
- type: ndcg_at_100 |
|
value: 35.429 |
|
- type: ndcg_at_1000 |
|
value: 38.236 |
|
- type: ndcg_at_3 |
|
value: 25.968000000000004 |
|
- type: ndcg_at_5 |
|
value: 27.785 |
|
- type: precision_at_1 |
|
value: 21.766 |
|
- type: precision_at_10 |
|
value: 5.498 |
|
- type: precision_at_100 |
|
value: 0.9450000000000001 |
|
- type: precision_at_1000 |
|
value: 0.133 |
|
- type: precision_at_3 |
|
value: 12.687000000000001 |
|
- type: precision_at_5 |
|
value: 9.005 |
|
- type: recall_at_1 |
|
value: 17.521 |
|
- type: recall_at_10 |
|
value: 40.454 |
|
- type: recall_at_100 |
|
value: 64.828 |
|
- type: recall_at_1000 |
|
value: 84.83800000000001 |
|
- type: recall_at_3 |
|
value: 28.758 |
|
- type: recall_at_5 |
|
value: 33.617000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.564999999999998 |
|
- type: map_at_10 |
|
value: 40.664 |
|
- type: map_at_100 |
|
value: 41.995 |
|
- type: map_at_1000 |
|
value: 42.104 |
|
- type: map_at_3 |
|
value: 37.578 |
|
- type: map_at_5 |
|
value: 39.247 |
|
- type: mrr_at_1 |
|
value: 37.44 |
|
- type: mrr_at_10 |
|
value: 46.533 |
|
- type: mrr_at_100 |
|
value: 47.363 |
|
- type: mrr_at_1000 |
|
value: 47.405 |
|
- type: mrr_at_3 |
|
value: 44.224999999999994 |
|
- type: mrr_at_5 |
|
value: 45.549 |
|
- type: ndcg_at_1 |
|
value: 37.44 |
|
- type: ndcg_at_10 |
|
value: 46.574 |
|
- type: ndcg_at_100 |
|
value: 52.024 |
|
- type: ndcg_at_1000 |
|
value: 53.93900000000001 |
|
- type: ndcg_at_3 |
|
value: 41.722 |
|
- type: ndcg_at_5 |
|
value: 43.973 |
|
- type: precision_at_1 |
|
value: 37.44 |
|
- type: precision_at_10 |
|
value: 8.344999999999999 |
|
- type: precision_at_100 |
|
value: 1.278 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 19.442 |
|
- type: precision_at_5 |
|
value: 13.802 |
|
- type: recall_at_1 |
|
value: 30.564999999999998 |
|
- type: recall_at_10 |
|
value: 58.207 |
|
- type: recall_at_100 |
|
value: 81.137 |
|
- type: recall_at_1000 |
|
value: 93.506 |
|
- type: recall_at_3 |
|
value: 44.606 |
|
- type: recall_at_5 |
|
value: 50.373000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.892 |
|
- type: map_at_10 |
|
value: 37.251 |
|
- type: map_at_100 |
|
value: 38.606 |
|
- type: map_at_1000 |
|
value: 38.716 |
|
- type: map_at_3 |
|
value: 34.312 |
|
- type: map_at_5 |
|
value: 35.791000000000004 |
|
- type: mrr_at_1 |
|
value: 34.247 |
|
- type: mrr_at_10 |
|
value: 42.696 |
|
- type: mrr_at_100 |
|
value: 43.659 |
|
- type: mrr_at_1000 |
|
value: 43.711 |
|
- type: mrr_at_3 |
|
value: 40.563 |
|
- type: mrr_at_5 |
|
value: 41.625 |
|
- type: ndcg_at_1 |
|
value: 34.247 |
|
- type: ndcg_at_10 |
|
value: 42.709 |
|
- type: ndcg_at_100 |
|
value: 48.422 |
|
- type: ndcg_at_1000 |
|
value: 50.544 |
|
- type: ndcg_at_3 |
|
value: 38.105 |
|
- type: ndcg_at_5 |
|
value: 39.846 |
|
- type: precision_at_1 |
|
value: 34.247 |
|
- type: precision_at_10 |
|
value: 7.66 |
|
- type: precision_at_100 |
|
value: 1.2109999999999999 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 17.884 |
|
- type: precision_at_5 |
|
value: 12.489 |
|
- type: recall_at_1 |
|
value: 27.892 |
|
- type: recall_at_10 |
|
value: 53.559 |
|
- type: recall_at_100 |
|
value: 78.018 |
|
- type: recall_at_1000 |
|
value: 92.07300000000001 |
|
- type: recall_at_3 |
|
value: 40.154 |
|
- type: recall_at_5 |
|
value: 45.078 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.29375 |
|
- type: map_at_10 |
|
value: 36.19533333333334 |
|
- type: map_at_100 |
|
value: 37.33183333333334 |
|
- type: map_at_1000 |
|
value: 37.44616666666667 |
|
- type: map_at_3 |
|
value: 33.49125 |
|
- type: map_at_5 |
|
value: 34.94166666666667 |
|
- type: mrr_at_1 |
|
value: 32.336666666666666 |
|
- type: mrr_at_10 |
|
value: 40.45983333333333 |
|
- type: mrr_at_100 |
|
value: 41.26533333333334 |
|
- type: mrr_at_1000 |
|
value: 41.321583333333336 |
|
- type: mrr_at_3 |
|
value: 38.23416666666667 |
|
- type: mrr_at_5 |
|
value: 39.48491666666666 |
|
- type: ndcg_at_1 |
|
value: 32.336666666666666 |
|
- type: ndcg_at_10 |
|
value: 41.39958333333333 |
|
- type: ndcg_at_100 |
|
value: 46.293 |
|
- type: ndcg_at_1000 |
|
value: 48.53425 |
|
- type: ndcg_at_3 |
|
value: 36.88833333333333 |
|
- type: ndcg_at_5 |
|
value: 38.90733333333333 |
|
- type: precision_at_1 |
|
value: 32.336666666666666 |
|
- type: precision_at_10 |
|
value: 7.175916666666667 |
|
- type: precision_at_100 |
|
value: 1.1311666666666669 |
|
- type: precision_at_1000 |
|
value: 0.15141666666666667 |
|
- type: precision_at_3 |
|
value: 16.841166666666666 |
|
- type: precision_at_5 |
|
value: 11.796583333333334 |
|
- type: recall_at_1 |
|
value: 27.29375 |
|
- type: recall_at_10 |
|
value: 52.514583333333334 |
|
- type: recall_at_100 |
|
value: 74.128 |
|
- type: recall_at_1000 |
|
value: 89.64125 |
|
- type: recall_at_3 |
|
value: 39.83258333333333 |
|
- type: recall_at_5 |
|
value: 45.126416666666664 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.62 |
|
- type: map_at_10 |
|
value: 31.517 |
|
- type: map_at_100 |
|
value: 32.322 |
|
- type: map_at_1000 |
|
value: 32.422000000000004 |
|
- type: map_at_3 |
|
value: 29.293999999999997 |
|
- type: map_at_5 |
|
value: 30.403999999999996 |
|
- type: mrr_at_1 |
|
value: 27.607 |
|
- type: mrr_at_10 |
|
value: 34.294999999999995 |
|
- type: mrr_at_100 |
|
value: 35.045 |
|
- type: mrr_at_1000 |
|
value: 35.114000000000004 |
|
- type: mrr_at_3 |
|
value: 32.311 |
|
- type: mrr_at_5 |
|
value: 33.369 |
|
- type: ndcg_at_1 |
|
value: 27.607 |
|
- type: ndcg_at_10 |
|
value: 35.853 |
|
- type: ndcg_at_100 |
|
value: 39.919 |
|
- type: ndcg_at_1000 |
|
value: 42.452 |
|
- type: ndcg_at_3 |
|
value: 31.702 |
|
- type: ndcg_at_5 |
|
value: 33.47 |
|
- type: precision_at_1 |
|
value: 27.607 |
|
- type: precision_at_10 |
|
value: 5.598 |
|
- type: precision_at_100 |
|
value: 0.83 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 13.700999999999999 |
|
- type: precision_at_5 |
|
value: 9.325 |
|
- type: recall_at_1 |
|
value: 24.62 |
|
- type: recall_at_10 |
|
value: 46.475 |
|
- type: recall_at_100 |
|
value: 64.891 |
|
- type: recall_at_1000 |
|
value: 83.524 |
|
- type: recall_at_3 |
|
value: 34.954 |
|
- type: recall_at_5 |
|
value: 39.471000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.858999999999998 |
|
- type: map_at_10 |
|
value: 23.746000000000002 |
|
- type: map_at_100 |
|
value: 24.731 |
|
- type: map_at_1000 |
|
value: 24.86 |
|
- type: map_at_3 |
|
value: 21.603 |
|
- type: map_at_5 |
|
value: 22.811999999999998 |
|
- type: mrr_at_1 |
|
value: 20.578 |
|
- type: mrr_at_10 |
|
value: 27.618 |
|
- type: mrr_at_100 |
|
value: 28.459 |
|
- type: mrr_at_1000 |
|
value: 28.543000000000003 |
|
- type: mrr_at_3 |
|
value: 25.533 |
|
- type: mrr_at_5 |
|
value: 26.730999999999998 |
|
- type: ndcg_at_1 |
|
value: 20.578 |
|
- type: ndcg_at_10 |
|
value: 28.147 |
|
- type: ndcg_at_100 |
|
value: 32.946999999999996 |
|
- type: ndcg_at_1000 |
|
value: 36.048 |
|
- type: ndcg_at_3 |
|
value: 24.32 |
|
- type: ndcg_at_5 |
|
value: 26.131999999999998 |
|
- type: precision_at_1 |
|
value: 20.578 |
|
- type: precision_at_10 |
|
value: 5.061999999999999 |
|
- type: precision_at_100 |
|
value: 0.8789999999999999 |
|
- type: precision_at_1000 |
|
value: 0.132 |
|
- type: precision_at_3 |
|
value: 11.448 |
|
- type: precision_at_5 |
|
value: 8.251999999999999 |
|
- type: recall_at_1 |
|
value: 16.858999999999998 |
|
- type: recall_at_10 |
|
value: 37.565 |
|
- type: recall_at_100 |
|
value: 59.239 |
|
- type: recall_at_1000 |
|
value: 81.496 |
|
- type: recall_at_3 |
|
value: 26.865 |
|
- type: recall_at_5 |
|
value: 31.581 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.11 |
|
- type: map_at_10 |
|
value: 34.214 |
|
- type: map_at_100 |
|
value: 35.291 |
|
- type: map_at_1000 |
|
value: 35.400999999999996 |
|
- type: map_at_3 |
|
value: 31.541000000000004 |
|
- type: map_at_5 |
|
value: 33.21 |
|
- type: mrr_at_1 |
|
value: 30.97 |
|
- type: mrr_at_10 |
|
value: 38.522 |
|
- type: mrr_at_100 |
|
value: 39.37 |
|
- type: mrr_at_1000 |
|
value: 39.437 |
|
- type: mrr_at_3 |
|
value: 36.193999999999996 |
|
- type: mrr_at_5 |
|
value: 37.691 |
|
- type: ndcg_at_1 |
|
value: 30.97 |
|
- type: ndcg_at_10 |
|
value: 39.2 |
|
- type: ndcg_at_100 |
|
value: 44.267 |
|
- type: ndcg_at_1000 |
|
value: 46.760000000000005 |
|
- type: ndcg_at_3 |
|
value: 34.474 |
|
- type: ndcg_at_5 |
|
value: 37.016 |
|
- type: precision_at_1 |
|
value: 30.97 |
|
- type: precision_at_10 |
|
value: 6.521000000000001 |
|
- type: precision_at_100 |
|
value: 1.011 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 15.392 |
|
- type: precision_at_5 |
|
value: 11.026 |
|
- type: recall_at_1 |
|
value: 26.11 |
|
- type: recall_at_10 |
|
value: 50.14999999999999 |
|
- type: recall_at_100 |
|
value: 72.398 |
|
- type: recall_at_1000 |
|
value: 89.764 |
|
- type: recall_at_3 |
|
value: 37.352999999999994 |
|
- type: recall_at_5 |
|
value: 43.736000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.514 |
|
- type: map_at_10 |
|
value: 34.278999999999996 |
|
- type: map_at_100 |
|
value: 35.847 |
|
- type: map_at_1000 |
|
value: 36.086 |
|
- type: map_at_3 |
|
value: 31.563999999999997 |
|
- type: map_at_5 |
|
value: 32.903999999999996 |
|
- type: mrr_at_1 |
|
value: 30.830000000000002 |
|
- type: mrr_at_10 |
|
value: 38.719 |
|
- type: mrr_at_100 |
|
value: 39.678999999999995 |
|
- type: mrr_at_1000 |
|
value: 39.741 |
|
- type: mrr_at_3 |
|
value: 36.265 |
|
- type: mrr_at_5 |
|
value: 37.599 |
|
- type: ndcg_at_1 |
|
value: 30.830000000000002 |
|
- type: ndcg_at_10 |
|
value: 39.997 |
|
- type: ndcg_at_100 |
|
value: 45.537 |
|
- type: ndcg_at_1000 |
|
value: 48.296 |
|
- type: ndcg_at_3 |
|
value: 35.429 |
|
- type: ndcg_at_5 |
|
value: 37.3 |
|
- type: precision_at_1 |
|
value: 30.830000000000002 |
|
- type: precision_at_10 |
|
value: 7.747 |
|
- type: precision_at_100 |
|
value: 1.516 |
|
- type: precision_at_1000 |
|
value: 0.24 |
|
- type: precision_at_3 |
|
value: 16.601 |
|
- type: precision_at_5 |
|
value: 11.818 |
|
- type: recall_at_1 |
|
value: 25.514 |
|
- type: recall_at_10 |
|
value: 50.71600000000001 |
|
- type: recall_at_100 |
|
value: 75.40299999999999 |
|
- type: recall_at_1000 |
|
value: 93.10300000000001 |
|
- type: recall_at_3 |
|
value: 37.466 |
|
- type: recall_at_5 |
|
value: 42.677 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.571 |
|
- type: map_at_10 |
|
value: 28.254 |
|
- type: map_at_100 |
|
value: 29.237000000000002 |
|
- type: map_at_1000 |
|
value: 29.334 |
|
- type: map_at_3 |
|
value: 25.912000000000003 |
|
- type: map_at_5 |
|
value: 26.798 |
|
- type: mrr_at_1 |
|
value: 23.29 |
|
- type: mrr_at_10 |
|
value: 30.102 |
|
- type: mrr_at_100 |
|
value: 30.982 |
|
- type: mrr_at_1000 |
|
value: 31.051000000000002 |
|
- type: mrr_at_3 |
|
value: 27.942 |
|
- type: mrr_at_5 |
|
value: 28.848000000000003 |
|
- type: ndcg_at_1 |
|
value: 23.29 |
|
- type: ndcg_at_10 |
|
value: 32.726 |
|
- type: ndcg_at_100 |
|
value: 37.644 |
|
- type: ndcg_at_1000 |
|
value: 40.161 |
|
- type: ndcg_at_3 |
|
value: 27.91 |
|
- type: ndcg_at_5 |
|
value: 29.461 |
|
- type: precision_at_1 |
|
value: 23.29 |
|
- type: precision_at_10 |
|
value: 5.213 |
|
- type: precision_at_100 |
|
value: 0.828 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 11.583 |
|
- type: precision_at_5 |
|
value: 7.8740000000000006 |
|
- type: recall_at_1 |
|
value: 21.571 |
|
- type: recall_at_10 |
|
value: 44.809 |
|
- type: recall_at_100 |
|
value: 67.74900000000001 |
|
- type: recall_at_1000 |
|
value: 86.60799999999999 |
|
- type: recall_at_3 |
|
value: 31.627 |
|
- type: recall_at_5 |
|
value: 35.391 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.953 |
|
- type: map_at_10 |
|
value: 17.183 |
|
- type: map_at_100 |
|
value: 18.926000000000002 |
|
- type: map_at_1000 |
|
value: 19.105 |
|
- type: map_at_3 |
|
value: 14.308000000000002 |
|
- type: map_at_5 |
|
value: 15.738 |
|
- type: mrr_at_1 |
|
value: 22.02 |
|
- type: mrr_at_10 |
|
value: 33.181 |
|
- type: mrr_at_100 |
|
value: 34.357 |
|
- type: mrr_at_1000 |
|
value: 34.398 |
|
- type: mrr_at_3 |
|
value: 29.793999999999997 |
|
- type: mrr_at_5 |
|
value: 31.817 |
|
- type: ndcg_at_1 |
|
value: 22.02 |
|
- type: ndcg_at_10 |
|
value: 24.712 |
|
- type: ndcg_at_100 |
|
value: 32.025 |
|
- type: ndcg_at_1000 |
|
value: 35.437000000000005 |
|
- type: ndcg_at_3 |
|
value: 19.852 |
|
- type: ndcg_at_5 |
|
value: 21.565 |
|
- type: precision_at_1 |
|
value: 22.02 |
|
- type: precision_at_10 |
|
value: 7.779 |
|
- type: precision_at_100 |
|
value: 1.554 |
|
- type: precision_at_1000 |
|
value: 0.219 |
|
- type: precision_at_3 |
|
value: 14.832 |
|
- type: precision_at_5 |
|
value: 11.453000000000001 |
|
- type: recall_at_1 |
|
value: 9.953 |
|
- type: recall_at_10 |
|
value: 30.375000000000004 |
|
- type: recall_at_100 |
|
value: 55.737 |
|
- type: recall_at_1000 |
|
value: 75.071 |
|
- type: recall_at_3 |
|
value: 18.529999999999998 |
|
- type: recall_at_5 |
|
value: 23.313 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.651 |
|
- type: map_at_10 |
|
value: 19.674 |
|
- type: map_at_100 |
|
value: 27.855999999999998 |
|
- type: map_at_1000 |
|
value: 29.348000000000003 |
|
- type: map_at_3 |
|
value: 14.247000000000002 |
|
- type: map_at_5 |
|
value: 16.453 |
|
- type: mrr_at_1 |
|
value: 61.75000000000001 |
|
- type: mrr_at_10 |
|
value: 71.329 |
|
- type: mrr_at_100 |
|
value: 71.69200000000001 |
|
- type: mrr_at_1000 |
|
value: 71.699 |
|
- type: mrr_at_3 |
|
value: 69.042 |
|
- type: mrr_at_5 |
|
value: 70.679 |
|
- type: ndcg_at_1 |
|
value: 50.125 |
|
- type: ndcg_at_10 |
|
value: 40.199 |
|
- type: ndcg_at_100 |
|
value: 45.378 |
|
- type: ndcg_at_1000 |
|
value: 52.376999999999995 |
|
- type: ndcg_at_3 |
|
value: 44.342 |
|
- type: ndcg_at_5 |
|
value: 41.730000000000004 |
|
- type: precision_at_1 |
|
value: 61.75000000000001 |
|
- type: precision_at_10 |
|
value: 32.2 |
|
- type: precision_at_100 |
|
value: 10.298 |
|
- type: precision_at_1000 |
|
value: 1.984 |
|
- type: precision_at_3 |
|
value: 48.667 |
|
- type: precision_at_5 |
|
value: 40.5 |
|
- type: recall_at_1 |
|
value: 8.651 |
|
- type: recall_at_10 |
|
value: 25.607000000000003 |
|
- type: recall_at_100 |
|
value: 53.062 |
|
- type: recall_at_1000 |
|
value: 74.717 |
|
- type: recall_at_3 |
|
value: 15.661 |
|
- type: recall_at_5 |
|
value: 19.409000000000002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 47.64500000000001 |
|
- type: f1 |
|
value: 43.71011316507787 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.613 |
|
- type: map_at_10 |
|
value: 68.02 |
|
- type: map_at_100 |
|
value: 68.366 |
|
- type: map_at_1000 |
|
value: 68.379 |
|
- type: map_at_3 |
|
value: 65.753 |
|
- type: map_at_5 |
|
value: 67.242 |
|
- type: mrr_at_1 |
|
value: 59.001000000000005 |
|
- type: mrr_at_10 |
|
value: 72.318 |
|
- type: mrr_at_100 |
|
value: 72.558 |
|
- type: mrr_at_1000 |
|
value: 72.56099999999999 |
|
- type: mrr_at_3 |
|
value: 70.22699999999999 |
|
- type: mrr_at_5 |
|
value: 71.655 |
|
- type: ndcg_at_1 |
|
value: 59.001000000000005 |
|
- type: ndcg_at_10 |
|
value: 74.386 |
|
- type: ndcg_at_100 |
|
value: 75.763 |
|
- type: ndcg_at_1000 |
|
value: 76.03 |
|
- type: ndcg_at_3 |
|
value: 70.216 |
|
- type: ndcg_at_5 |
|
value: 72.697 |
|
- type: precision_at_1 |
|
value: 59.001000000000005 |
|
- type: precision_at_10 |
|
value: 9.844 |
|
- type: precision_at_100 |
|
value: 1.068 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 28.523 |
|
- type: precision_at_5 |
|
value: 18.491 |
|
- type: recall_at_1 |
|
value: 54.613 |
|
- type: recall_at_10 |
|
value: 89.669 |
|
- type: recall_at_100 |
|
value: 95.387 |
|
- type: recall_at_1000 |
|
value: 97.129 |
|
- type: recall_at_3 |
|
value: 78.54100000000001 |
|
- type: recall_at_5 |
|
value: 84.637 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.348 |
|
- type: map_at_10 |
|
value: 32.464999999999996 |
|
- type: map_at_100 |
|
value: 34.235 |
|
- type: map_at_1000 |
|
value: 34.410000000000004 |
|
- type: map_at_3 |
|
value: 28.109 |
|
- type: map_at_5 |
|
value: 30.634 |
|
- type: mrr_at_1 |
|
value: 38.889 |
|
- type: mrr_at_10 |
|
value: 47.131 |
|
- type: mrr_at_100 |
|
value: 48.107 |
|
- type: mrr_at_1000 |
|
value: 48.138 |
|
- type: mrr_at_3 |
|
value: 44.599 |
|
- type: mrr_at_5 |
|
value: 46.181 |
|
- type: ndcg_at_1 |
|
value: 38.889 |
|
- type: ndcg_at_10 |
|
value: 39.86 |
|
- type: ndcg_at_100 |
|
value: 46.619 |
|
- type: ndcg_at_1000 |
|
value: 49.525999999999996 |
|
- type: ndcg_at_3 |
|
value: 35.768 |
|
- type: ndcg_at_5 |
|
value: 37.4 |
|
- type: precision_at_1 |
|
value: 38.889 |
|
- type: precision_at_10 |
|
value: 11.003 |
|
- type: precision_at_100 |
|
value: 1.796 |
|
- type: precision_at_1000 |
|
value: 0.233 |
|
- type: precision_at_3 |
|
value: 23.714 |
|
- type: precision_at_5 |
|
value: 17.901 |
|
- type: recall_at_1 |
|
value: 20.348 |
|
- type: recall_at_10 |
|
value: 46.781 |
|
- type: recall_at_100 |
|
value: 71.937 |
|
- type: recall_at_1000 |
|
value: 89.18599999999999 |
|
- type: recall_at_3 |
|
value: 32.16 |
|
- type: recall_at_5 |
|
value: 38.81 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.198 |
|
- type: map_at_10 |
|
value: 54.065 |
|
- type: map_at_100 |
|
value: 54.984 |
|
- type: map_at_1000 |
|
value: 55.05 |
|
- type: map_at_3 |
|
value: 50.758 |
|
- type: map_at_5 |
|
value: 52.758 |
|
- type: mrr_at_1 |
|
value: 74.396 |
|
- type: mrr_at_10 |
|
value: 81.352 |
|
- type: mrr_at_100 |
|
value: 81.562 |
|
- type: mrr_at_1000 |
|
value: 81.57 |
|
- type: mrr_at_3 |
|
value: 80.30199999999999 |
|
- type: mrr_at_5 |
|
value: 80.963 |
|
- type: ndcg_at_1 |
|
value: 74.396 |
|
- type: ndcg_at_10 |
|
value: 63.70099999999999 |
|
- type: ndcg_at_100 |
|
value: 66.874 |
|
- type: ndcg_at_1000 |
|
value: 68.171 |
|
- type: ndcg_at_3 |
|
value: 58.916999999999994 |
|
- type: ndcg_at_5 |
|
value: 61.495999999999995 |
|
- type: precision_at_1 |
|
value: 74.396 |
|
- type: precision_at_10 |
|
value: 13.228000000000002 |
|
- type: precision_at_100 |
|
value: 1.569 |
|
- type: precision_at_1000 |
|
value: 0.174 |
|
- type: precision_at_3 |
|
value: 37.007 |
|
- type: precision_at_5 |
|
value: 24.248 |
|
- type: recall_at_1 |
|
value: 37.198 |
|
- type: recall_at_10 |
|
value: 66.13799999999999 |
|
- type: recall_at_100 |
|
value: 78.45400000000001 |
|
- type: recall_at_1000 |
|
value: 87.04899999999999 |
|
- type: recall_at_3 |
|
value: 55.510000000000005 |
|
- type: recall_at_5 |
|
value: 60.621 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 86.32240000000002 |
|
- type: ap |
|
value: 81.37708984744188 |
|
- type: f1 |
|
value: 86.29645005523952 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.402 |
|
- type: map_at_10 |
|
value: 28.097 |
|
- type: map_at_100 |
|
value: 29.421999999999997 |
|
- type: map_at_1000 |
|
value: 29.476999999999997 |
|
- type: map_at_3 |
|
value: 24.015 |
|
- type: map_at_5 |
|
value: 26.316 |
|
- type: mrr_at_1 |
|
value: 16.905 |
|
- type: mrr_at_10 |
|
value: 28.573999999999998 |
|
- type: mrr_at_100 |
|
value: 29.862 |
|
- type: mrr_at_1000 |
|
value: 29.912 |
|
- type: mrr_at_3 |
|
value: 24.589 |
|
- type: mrr_at_5 |
|
value: 26.851000000000003 |
|
- type: ndcg_at_1 |
|
value: 16.905 |
|
- type: ndcg_at_10 |
|
value: 34.99 |
|
- type: ndcg_at_100 |
|
value: 41.419 |
|
- type: ndcg_at_1000 |
|
value: 42.815999999999995 |
|
- type: ndcg_at_3 |
|
value: 26.695 |
|
- type: ndcg_at_5 |
|
value: 30.789 |
|
- type: precision_at_1 |
|
value: 16.905 |
|
- type: precision_at_10 |
|
value: 5.891 |
|
- type: precision_at_100 |
|
value: 0.91 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 11.724 |
|
- type: precision_at_5 |
|
value: 9.097 |
|
- type: recall_at_1 |
|
value: 16.402 |
|
- type: recall_at_10 |
|
value: 56.462999999999994 |
|
- type: recall_at_100 |
|
value: 86.246 |
|
- type: recall_at_1000 |
|
value: 96.926 |
|
- type: recall_at_3 |
|
value: 33.897 |
|
- type: recall_at_5 |
|
value: 43.718 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.35978112175103 |
|
- type: f1 |
|
value: 92.04704651024416 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 65.20063839489283 |
|
- type: f1 |
|
value: 45.34047546059121 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.74714189643578 |
|
- type: f1 |
|
value: 65.36156843270334 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 74.03160726294554 |
|
- type: f1 |
|
value: 73.42899064973165 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.347360980344476 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 29.56022733162805 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.60132765358296 |
|
- type: mrr |
|
value: 31.710892632824468 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.827999999999999 |
|
- type: map_at_10 |
|
value: 13.547 |
|
- type: map_at_100 |
|
value: 16.869 |
|
- type: map_at_1000 |
|
value: 18.242 |
|
- type: map_at_3 |
|
value: 9.917 |
|
- type: map_at_5 |
|
value: 11.648 |
|
- type: mrr_at_1 |
|
value: 46.44 |
|
- type: mrr_at_10 |
|
value: 55.062 |
|
- type: mrr_at_100 |
|
value: 55.513999999999996 |
|
- type: mrr_at_1000 |
|
value: 55.564 |
|
- type: mrr_at_3 |
|
value: 52.735 |
|
- type: mrr_at_5 |
|
value: 54.391 |
|
- type: ndcg_at_1 |
|
value: 44.582 |
|
- type: ndcg_at_10 |
|
value: 35.684 |
|
- type: ndcg_at_100 |
|
value: 31.913999999999998 |
|
- type: ndcg_at_1000 |
|
value: 40.701 |
|
- type: ndcg_at_3 |
|
value: 40.819 |
|
- type: ndcg_at_5 |
|
value: 39.117000000000004 |
|
- type: precision_at_1 |
|
value: 46.129999999999995 |
|
- type: precision_at_10 |
|
value: 26.687 |
|
- type: precision_at_100 |
|
value: 8.062 |
|
- type: precision_at_1000 |
|
value: 2.073 |
|
- type: precision_at_3 |
|
value: 38.493 |
|
- type: precision_at_5 |
|
value: 34.241 |
|
- type: recall_at_1 |
|
value: 5.827999999999999 |
|
- type: recall_at_10 |
|
value: 17.391000000000002 |
|
- type: recall_at_100 |
|
value: 31.228 |
|
- type: recall_at_1000 |
|
value: 63.943000000000005 |
|
- type: recall_at_3 |
|
value: 10.81 |
|
- type: recall_at_5 |
|
value: 13.618 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.02 |
|
- type: map_at_10 |
|
value: 40.054 |
|
- type: map_at_100 |
|
value: 41.318 |
|
- type: map_at_1000 |
|
value: 41.343999999999994 |
|
- type: map_at_3 |
|
value: 35.221999999999994 |
|
- type: map_at_5 |
|
value: 38.057 |
|
- type: mrr_at_1 |
|
value: 27.230999999999998 |
|
- type: mrr_at_10 |
|
value: 42.315999999999995 |
|
- type: mrr_at_100 |
|
value: 43.254 |
|
- type: mrr_at_1000 |
|
value: 43.272 |
|
- type: mrr_at_3 |
|
value: 38.176 |
|
- type: mrr_at_5 |
|
value: 40.64 |
|
- type: ndcg_at_1 |
|
value: 27.230999999999998 |
|
- type: ndcg_at_10 |
|
value: 48.551 |
|
- type: ndcg_at_100 |
|
value: 53.737 |
|
- type: ndcg_at_1000 |
|
value: 54.313 |
|
- type: ndcg_at_3 |
|
value: 39.367999999999995 |
|
- type: ndcg_at_5 |
|
value: 44.128 |
|
- type: precision_at_1 |
|
value: 27.230999999999998 |
|
- type: precision_at_10 |
|
value: 8.578 |
|
- type: precision_at_100 |
|
value: 1.145 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 18.704 |
|
- type: precision_at_5 |
|
value: 13.927999999999999 |
|
- type: recall_at_1 |
|
value: 24.02 |
|
- type: recall_at_10 |
|
value: 72.258 |
|
- type: recall_at_100 |
|
value: 94.489 |
|
- type: recall_at_1000 |
|
value: 98.721 |
|
- type: recall_at_3 |
|
value: 48.373 |
|
- type: recall_at_5 |
|
value: 59.388 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.476 |
|
- type: map_at_10 |
|
value: 84.41300000000001 |
|
- type: map_at_100 |
|
value: 85.036 |
|
- type: map_at_1000 |
|
value: 85.055 |
|
- type: map_at_3 |
|
value: 81.45599999999999 |
|
- type: map_at_5 |
|
value: 83.351 |
|
- type: mrr_at_1 |
|
value: 81.07 |
|
- type: mrr_at_10 |
|
value: 87.408 |
|
- type: mrr_at_100 |
|
value: 87.509 |
|
- type: mrr_at_1000 |
|
value: 87.51 |
|
- type: mrr_at_3 |
|
value: 86.432 |
|
- type: mrr_at_5 |
|
value: 87.128 |
|
- type: ndcg_at_1 |
|
value: 81.13 |
|
- type: ndcg_at_10 |
|
value: 88.18599999999999 |
|
- type: ndcg_at_100 |
|
value: 89.401 |
|
- type: ndcg_at_1000 |
|
value: 89.515 |
|
- type: ndcg_at_3 |
|
value: 85.332 |
|
- type: ndcg_at_5 |
|
value: 86.97 |
|
- type: precision_at_1 |
|
value: 81.13 |
|
- type: precision_at_10 |
|
value: 13.361 |
|
- type: precision_at_100 |
|
value: 1.5230000000000001 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 37.31 |
|
- type: precision_at_5 |
|
value: 24.548000000000002 |
|
- type: recall_at_1 |
|
value: 70.476 |
|
- type: recall_at_10 |
|
value: 95.3 |
|
- type: recall_at_100 |
|
value: 99.46000000000001 |
|
- type: recall_at_1000 |
|
value: 99.96000000000001 |
|
- type: recall_at_3 |
|
value: 87.057 |
|
- type: recall_at_5 |
|
value: 91.739 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 55.36775089400664 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 60.05041008018361 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.743 |
|
- type: map_at_10 |
|
value: 12.171 |
|
- type: map_at_100 |
|
value: 14.174999999999999 |
|
- type: map_at_1000 |
|
value: 14.446 |
|
- type: map_at_3 |
|
value: 8.698 |
|
- type: map_at_5 |
|
value: 10.444 |
|
- type: mrr_at_1 |
|
value: 23.400000000000002 |
|
- type: mrr_at_10 |
|
value: 34.284 |
|
- type: mrr_at_100 |
|
value: 35.400999999999996 |
|
- type: mrr_at_1000 |
|
value: 35.451 |
|
- type: mrr_at_3 |
|
value: 31.167 |
|
- type: mrr_at_5 |
|
value: 32.946999999999996 |
|
- type: ndcg_at_1 |
|
value: 23.400000000000002 |
|
- type: ndcg_at_10 |
|
value: 20.169999999999998 |
|
- type: ndcg_at_100 |
|
value: 27.967 |
|
- type: ndcg_at_1000 |
|
value: 32.982 |
|
- type: ndcg_at_3 |
|
value: 19.308 |
|
- type: ndcg_at_5 |
|
value: 16.837 |
|
- type: precision_at_1 |
|
value: 23.400000000000002 |
|
- type: precision_at_10 |
|
value: 10.41 |
|
- type: precision_at_100 |
|
value: 2.162 |
|
- type: precision_at_1000 |
|
value: 0.338 |
|
- type: precision_at_3 |
|
value: 18.067 |
|
- type: precision_at_5 |
|
value: 14.78 |
|
- type: recall_at_1 |
|
value: 4.743 |
|
- type: recall_at_10 |
|
value: 21.098 |
|
- type: recall_at_100 |
|
value: 43.85 |
|
- type: recall_at_1000 |
|
value: 68.60000000000001 |
|
- type: recall_at_3 |
|
value: 10.993 |
|
- type: recall_at_5 |
|
value: 14.998000000000001 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.129376905658 |
|
- type: cos_sim_spearman |
|
value: 74.18938626206575 |
|
- type: euclidean_pearson |
|
value: 77.95192851803141 |
|
- type: euclidean_spearman |
|
value: 74.18938626206575 |
|
- type: manhattan_pearson |
|
value: 77.97718819383338 |
|
- type: manhattan_spearman |
|
value: 74.20580317409417 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.36913772828827 |
|
- type: cos_sim_spearman |
|
value: 73.22311186990363 |
|
- type: euclidean_pearson |
|
value: 74.45263405031004 |
|
- type: euclidean_spearman |
|
value: 73.22311186990363 |
|
- type: manhattan_pearson |
|
value: 74.56201270071791 |
|
- type: manhattan_spearman |
|
value: 73.26490493774821 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.79920796384403 |
|
- type: cos_sim_spearman |
|
value: 84.77145185366201 |
|
- type: euclidean_pearson |
|
value: 83.90638366191354 |
|
- type: euclidean_spearman |
|
value: 84.77145185366201 |
|
- type: manhattan_pearson |
|
value: 83.83788216629048 |
|
- type: manhattan_spearman |
|
value: 84.70515987131665 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.18883765092875 |
|
- type: cos_sim_spearman |
|
value: 79.9948128016449 |
|
- type: euclidean_pearson |
|
value: 81.57436738666773 |
|
- type: euclidean_spearman |
|
value: 79.9948128016449 |
|
- type: manhattan_pearson |
|
value: 81.55274202648187 |
|
- type: manhattan_spearman |
|
value: 79.99854975019382 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.89669110871021 |
|
- type: cos_sim_spearman |
|
value: 87.26758456901442 |
|
- type: euclidean_pearson |
|
value: 86.62614163641416 |
|
- type: euclidean_spearman |
|
value: 87.26758456901442 |
|
- type: manhattan_pearson |
|
value: 86.58584490012353 |
|
- type: manhattan_spearman |
|
value: 87.20340001562076 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.983023415916 |
|
- type: cos_sim_spearman |
|
value: 82.31169002657151 |
|
- type: euclidean_pearson |
|
value: 81.52305092886222 |
|
- type: euclidean_spearman |
|
value: 82.31169002657151 |
|
- type: manhattan_pearson |
|
value: 81.63024996600281 |
|
- type: manhattan_spearman |
|
value: 82.44579116264026 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 89.27779520541694 |
|
- type: cos_sim_spearman |
|
value: 89.54137104681308 |
|
- type: euclidean_pearson |
|
value: 88.99136079955996 |
|
- type: euclidean_spearman |
|
value: 89.54137104681308 |
|
- type: manhattan_pearson |
|
value: 88.95980417618277 |
|
- type: manhattan_spearman |
|
value: 89.55178819334718 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.50806758829178 |
|
- type: cos_sim_spearman |
|
value: 65.92675365587571 |
|
- type: euclidean_pearson |
|
value: 67.09216876696559 |
|
- type: euclidean_spearman |
|
value: 65.92675365587571 |
|
- type: manhattan_pearson |
|
value: 67.37398716891478 |
|
- type: manhattan_spearman |
|
value: 66.34811143508206 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.557575753862 |
|
- type: cos_sim_spearman |
|
value: 83.95859527071087 |
|
- type: euclidean_pearson |
|
value: 83.77287626715369 |
|
- type: euclidean_spearman |
|
value: 83.95859527071087 |
|
- type: manhattan_pearson |
|
value: 83.7898033034244 |
|
- type: manhattan_spearman |
|
value: 83.94860981294184 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 79.90679624144718 |
|
- type: mrr |
|
value: 94.33150183150182 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 56.81699999999999 |
|
- type: map_at_10 |
|
value: 67.301 |
|
- type: map_at_100 |
|
value: 67.73599999999999 |
|
- type: map_at_1000 |
|
value: 67.757 |
|
- type: map_at_3 |
|
value: 64.865 |
|
- type: map_at_5 |
|
value: 66.193 |
|
- type: mrr_at_1 |
|
value: 59.667 |
|
- type: mrr_at_10 |
|
value: 68.324 |
|
- type: mrr_at_100 |
|
value: 68.66 |
|
- type: mrr_at_1000 |
|
value: 68.676 |
|
- type: mrr_at_3 |
|
value: 66.556 |
|
- type: mrr_at_5 |
|
value: 67.472 |
|
- type: ndcg_at_1 |
|
value: 59.667 |
|
- type: ndcg_at_10 |
|
value: 71.982 |
|
- type: ndcg_at_100 |
|
value: 74.149 |
|
- type: ndcg_at_1000 |
|
value: 74.60799999999999 |
|
- type: ndcg_at_3 |
|
value: 67.796 |
|
- type: ndcg_at_5 |
|
value: 69.64099999999999 |
|
- type: precision_at_1 |
|
value: 59.667 |
|
- type: precision_at_10 |
|
value: 9.633 |
|
- type: precision_at_100 |
|
value: 1.08 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 26.889000000000003 |
|
- type: precision_at_5 |
|
value: 17.467 |
|
- type: recall_at_1 |
|
value: 56.81699999999999 |
|
- type: recall_at_10 |
|
value: 85.18900000000001 |
|
- type: recall_at_100 |
|
value: 95.6 |
|
- type: recall_at_1000 |
|
value: 99.0 |
|
- type: recall_at_3 |
|
value: 73.617 |
|
- type: recall_at_5 |
|
value: 78.444 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.83465346534653 |
|
- type: cos_sim_ap |
|
value: 95.93387984443646 |
|
- type: cos_sim_f1 |
|
value: 91.49261334691798 |
|
- type: cos_sim_precision |
|
value: 93.25025960539979 |
|
- type: cos_sim_recall |
|
value: 89.8 |
|
- type: dot_accuracy |
|
value: 99.83465346534653 |
|
- type: dot_ap |
|
value: 95.93389375761485 |
|
- type: dot_f1 |
|
value: 91.49261334691798 |
|
- type: dot_precision |
|
value: 93.25025960539979 |
|
- type: dot_recall |
|
value: 89.8 |
|
- type: euclidean_accuracy |
|
value: 99.83465346534653 |
|
- type: euclidean_ap |
|
value: 95.93389375761487 |
|
- type: euclidean_f1 |
|
value: 91.49261334691798 |
|
- type: euclidean_precision |
|
value: 93.25025960539979 |
|
- type: euclidean_recall |
|
value: 89.8 |
|
- type: manhattan_accuracy |
|
value: 99.83564356435643 |
|
- type: manhattan_ap |
|
value: 95.89877504534601 |
|
- type: manhattan_f1 |
|
value: 91.53061224489795 |
|
- type: manhattan_precision |
|
value: 93.4375 |
|
- type: manhattan_recall |
|
value: 89.7 |
|
- type: max_accuracy |
|
value: 99.83564356435643 |
|
- type: max_ap |
|
value: 95.93389375761487 |
|
- type: max_f1 |
|
value: 91.53061224489795 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 62.2780055191805 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.94461701798904 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 49.865789666749535 |
|
- type: mrr |
|
value: 50.61783804430863 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 29.97703436199298 |
|
- type: cos_sim_spearman |
|
value: 30.71880290978946 |
|
- type: dot_pearson |
|
value: 29.977036284086818 |
|
- type: dot_spearman |
|
value: 30.71880290978946 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.22799999999999998 |
|
- type: map_at_10 |
|
value: 1.559 |
|
- type: map_at_100 |
|
value: 8.866 |
|
- type: map_at_1000 |
|
value: 23.071 |
|
- type: map_at_3 |
|
value: 0.592 |
|
- type: map_at_5 |
|
value: 0.906 |
|
- type: mrr_at_1 |
|
value: 84.0 |
|
- type: mrr_at_10 |
|
value: 88.567 |
|
- type: mrr_at_100 |
|
value: 88.748 |
|
- type: mrr_at_1000 |
|
value: 88.748 |
|
- type: mrr_at_3 |
|
value: 87.667 |
|
- type: mrr_at_5 |
|
value: 88.067 |
|
- type: ndcg_at_1 |
|
value: 73.0 |
|
- type: ndcg_at_10 |
|
value: 62.202999999999996 |
|
- type: ndcg_at_100 |
|
value: 49.66 |
|
- type: ndcg_at_1000 |
|
value: 48.760999999999996 |
|
- type: ndcg_at_3 |
|
value: 67.52 |
|
- type: ndcg_at_5 |
|
value: 64.80799999999999 |
|
- type: precision_at_1 |
|
value: 84.0 |
|
- type: precision_at_10 |
|
value: 65.4 |
|
- type: precision_at_100 |
|
value: 51.72 |
|
- type: precision_at_1000 |
|
value: 22.014 |
|
- type: precision_at_3 |
|
value: 74.0 |
|
- type: precision_at_5 |
|
value: 69.19999999999999 |
|
- type: recall_at_1 |
|
value: 0.22799999999999998 |
|
- type: recall_at_10 |
|
value: 1.7680000000000002 |
|
- type: recall_at_100 |
|
value: 12.581999999999999 |
|
- type: recall_at_1000 |
|
value: 46.883 |
|
- type: recall_at_3 |
|
value: 0.618 |
|
- type: recall_at_5 |
|
value: 0.9690000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.295 |
|
- type: map_at_10 |
|
value: 7.481 |
|
- type: map_at_100 |
|
value: 13.120999999999999 |
|
- type: map_at_1000 |
|
value: 14.863999999999999 |
|
- type: map_at_3 |
|
value: 3.266 |
|
- type: map_at_5 |
|
value: 4.662 |
|
- type: mrr_at_1 |
|
value: 14.285999999999998 |
|
- type: mrr_at_10 |
|
value: 31.995 |
|
- type: mrr_at_100 |
|
value: 33.415 |
|
- type: mrr_at_1000 |
|
value: 33.432 |
|
- type: mrr_at_3 |
|
value: 27.551 |
|
- type: mrr_at_5 |
|
value: 30.306 |
|
- type: ndcg_at_1 |
|
value: 11.224 |
|
- type: ndcg_at_10 |
|
value: 19.166 |
|
- type: ndcg_at_100 |
|
value: 31.86 |
|
- type: ndcg_at_1000 |
|
value: 44.668 |
|
- type: ndcg_at_3 |
|
value: 17.371 |
|
- type: ndcg_at_5 |
|
value: 18.567 |
|
- type: precision_at_1 |
|
value: 14.285999999999998 |
|
- type: precision_at_10 |
|
value: 18.98 |
|
- type: precision_at_100 |
|
value: 7.041 |
|
- type: precision_at_1000 |
|
value: 1.555 |
|
- type: precision_at_3 |
|
value: 19.728 |
|
- type: precision_at_5 |
|
value: 20.816000000000003 |
|
- type: recall_at_1 |
|
value: 1.295 |
|
- type: recall_at_10 |
|
value: 14.482000000000001 |
|
- type: recall_at_100 |
|
value: 45.149 |
|
- type: recall_at_1000 |
|
value: 84.317 |
|
- type: recall_at_3 |
|
value: 4.484 |
|
- type: recall_at_5 |
|
value: 7.7170000000000005 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 72.96340000000001 |
|
- type: ap |
|
value: 15.62835559397026 |
|
- type: f1 |
|
value: 56.42561616707867 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 55.280135823429546 |
|
- type: f1 |
|
value: 55.61428067547153 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 45.426677723253555 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.57411933003517 |
|
- type: cos_sim_ap |
|
value: 69.68254951354992 |
|
- type: cos_sim_f1 |
|
value: 65.05232416646386 |
|
- type: cos_sim_precision |
|
value: 60.36585365853659 |
|
- type: cos_sim_recall |
|
value: 70.52770448548813 |
|
- type: dot_accuracy |
|
value: 84.57411933003517 |
|
- type: dot_ap |
|
value: 69.68256519978905 |
|
- type: dot_f1 |
|
value: 65.05232416646386 |
|
- type: dot_precision |
|
value: 60.36585365853659 |
|
- type: dot_recall |
|
value: 70.52770448548813 |
|
- type: euclidean_accuracy |
|
value: 84.57411933003517 |
|
- type: euclidean_ap |
|
value: 69.6825655240522 |
|
- type: euclidean_f1 |
|
value: 65.05232416646386 |
|
- type: euclidean_precision |
|
value: 60.36585365853659 |
|
- type: euclidean_recall |
|
value: 70.52770448548813 |
|
- type: manhattan_accuracy |
|
value: 84.5502771651666 |
|
- type: manhattan_ap |
|
value: 69.61700491283233 |
|
- type: manhattan_f1 |
|
value: 64.83962148211872 |
|
- type: manhattan_precision |
|
value: 60.68553025074765 |
|
- type: manhattan_recall |
|
value: 69.6042216358839 |
|
- type: max_accuracy |
|
value: 84.57411933003517 |
|
- type: max_ap |
|
value: 69.6825655240522 |
|
- type: max_f1 |
|
value: 65.05232416646386 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.80350836341057 |
|
- type: cos_sim_ap |
|
value: 85.41051415803449 |
|
- type: cos_sim_f1 |
|
value: 77.99305633329602 |
|
- type: cos_sim_precision |
|
value: 75.70113776360607 |
|
- type: cos_sim_recall |
|
value: 80.42808746535263 |
|
- type: dot_accuracy |
|
value: 88.80350836341057 |
|
- type: dot_ap |
|
value: 85.41051488820463 |
|
- type: dot_f1 |
|
value: 77.99305633329602 |
|
- type: dot_precision |
|
value: 75.70113776360607 |
|
- type: dot_recall |
|
value: 80.42808746535263 |
|
- type: euclidean_accuracy |
|
value: 88.80350836341057 |
|
- type: euclidean_ap |
|
value: 85.41051374760137 |
|
- type: euclidean_f1 |
|
value: 77.99305633329602 |
|
- type: euclidean_precision |
|
value: 75.70113776360607 |
|
- type: euclidean_recall |
|
value: 80.42808746535263 |
|
- type: manhattan_accuracy |
|
value: 88.74529436876625 |
|
- type: manhattan_ap |
|
value: 85.38380242074525 |
|
- type: manhattan_f1 |
|
value: 78.02957839746892 |
|
- type: manhattan_precision |
|
value: 74.71466816964914 |
|
- type: manhattan_recall |
|
value: 81.65229442562365 |
|
- type: max_accuracy |
|
value: 88.80350836341057 |
|
- type: max_ap |
|
value: 85.41051488820463 |
|
- type: max_f1 |
|
value: 78.02957839746892 |
|
--- |
|
|
|
|
|
# nomic-embed-text-v1-unsupervised: A Reproducible Long Context (8192) Text Embedder |
|
|
|
`nomic-embed-text-v1-unsupervised` is 8192 context length text encoder. This is a checkpoint after contrastive pretraining from multi-stage contrastive training of the |
|
[final model](https://huggingface.co/nomic-ai/nomic-embed-text-v1). If you want to extract embeddings, we suggest using [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) |
|
. |
|
|
|
|
|
| Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data | |
|
| :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- | |
|
| nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ | |
|
| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ | |
|
| text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ | |
|
| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ | |
|
|
|
|
|
If you would like to finetune a model on more data, you can use this model as an initialization |
|
|
|
## Hosted Inference API |
|
|
|
The easiest way to get started with Nomic Embed is through the Nomic Embedding API. |
|
|
|
Generating embeddings with the `nomic` Python client is as easy as |
|
|
|
```python |
|
from nomic import embed |
|
|
|
output = embed.text( |
|
texts=['Nomic Embedding API', '#keepAIOpen'], |
|
model='nomic-embed-text-v1', |
|
task_type='search_document' |
|
) |
|
|
|
print(output) |
|
``` |
|
|
|
For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text) |
|
|
|
## Data Visualization |
|
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data! |
|
|
|
|
|
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample) |
|
|
|
|
|
## Training Details |
|
|
|
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048), |
|
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles. |
|
|
|
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. |
|
|
|
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). |
|
|
|
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) |
|
|
|
## Usage |
|
|
|
### Sentence Transformers |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-unsupervised", trust_remote_code=True) |
|
sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?'] |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
### Transformers |
|
```python |
|
import torch |
|
import torch.nn.functional as F |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
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) |
|
|
|
sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?'] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
|
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True) |
|
model.eval() |
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
print(embeddings) |
|
``` |
|
|
|
The model natively supports scaling of the sequence length past 2048 tokens. To do so, |
|
|
|
```diff |
|
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
|
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192) |
|
|
|
|
|
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True) |
|
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True, rotary_scaling_factor=2) |
|
``` |
|
|
|
# Join the Nomic Community |
|
|
|
- Nomic: [https://nomic.ai](https://nomic.ai) |
|
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8) |
|
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) |
|
|
