|
--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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datasets: |
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- jinaai/negation-dataset |
|
language: en |
|
inference: false |
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license: apache-2.0 |
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model-index: |
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- name: jina-embedding-s-en-v2 |
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results: |
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- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 71.35820895522387 |
|
- type: ap |
|
value: 33.99931933598115 |
|
- type: f1 |
|
value: 65.3853685535555 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
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: 82.90140000000001 |
|
- type: ap |
|
value: 78.01434597815617 |
|
- type: f1 |
|
value: 82.83357802722676 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 40.88999999999999 |
|
- type: f1 |
|
value: 39.209432767163456 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.257 |
|
- type: map_at_10 |
|
value: 37.946000000000005 |
|
- type: map_at_100 |
|
value: 39.17 |
|
- type: map_at_1000 |
|
value: 39.181 |
|
- type: map_at_3 |
|
value: 32.99 |
|
- type: map_at_5 |
|
value: 35.467999999999996 |
|
- type: mrr_at_1 |
|
value: 23.541999999999998 |
|
- type: mrr_at_10 |
|
value: 38.057 |
|
- type: mrr_at_100 |
|
value: 39.289 |
|
- type: mrr_at_1000 |
|
value: 39.299 |
|
- type: mrr_at_3 |
|
value: 33.096 |
|
- type: mrr_at_5 |
|
value: 35.628 |
|
- type: ndcg_at_1 |
|
value: 23.257 |
|
- type: ndcg_at_10 |
|
value: 46.729 |
|
- type: ndcg_at_100 |
|
value: 51.900999999999996 |
|
- type: ndcg_at_1000 |
|
value: 52.16 |
|
- type: ndcg_at_3 |
|
value: 36.323 |
|
- type: ndcg_at_5 |
|
value: 40.766999999999996 |
|
- type: precision_at_1 |
|
value: 23.257 |
|
- type: precision_at_10 |
|
value: 7.510999999999999 |
|
- type: precision_at_100 |
|
value: 0.976 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 15.339 |
|
- type: precision_at_5 |
|
value: 11.350999999999999 |
|
- type: recall_at_1 |
|
value: 23.257 |
|
- type: recall_at_10 |
|
value: 75.107 |
|
- type: recall_at_100 |
|
value: 97.58200000000001 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 46.017 |
|
- type: recall_at_5 |
|
value: 56.757000000000005 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 44.02420878391967 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 35.16136856000258 |
|
- 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 |
|
metrics: |
|
- type: map |
|
value: 59.61809790513646 |
|
- type: mrr |
|
value: 73.07215406938397 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
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split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.0167350090749 |
|
- type: cos_sim_spearman |
|
value: 80.51569002630401 |
|
- type: euclidean_pearson |
|
value: 81.46820525099726 |
|
- type: euclidean_spearman |
|
value: 80.51569002630401 |
|
- type: manhattan_pearson |
|
value: 81.35596555056757 |
|
- type: manhattan_spearman |
|
value: 80.12592210903303 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 78.25 |
|
- type: f1 |
|
value: 77.34950913540605 |
|
- 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: 35.57238596005698 |
|
- 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: 29.066444306196683 |
|
- 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 |
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metrics: |
|
- type: map_at_1 |
|
value: 31.891000000000002 |
|
- type: map_at_10 |
|
value: 42.772 |
|
- type: map_at_100 |
|
value: 44.108999999999995 |
|
- type: map_at_1000 |
|
value: 44.236 |
|
- type: map_at_3 |
|
value: 39.289 |
|
- type: map_at_5 |
|
value: 41.113 |
|
- type: mrr_at_1 |
|
value: 39.342 |
|
- type: mrr_at_10 |
|
value: 48.852000000000004 |
|
- type: mrr_at_100 |
|
value: 49.534 |
|
- type: mrr_at_1000 |
|
value: 49.582 |
|
- type: mrr_at_3 |
|
value: 46.089999999999996 |
|
- type: mrr_at_5 |
|
value: 47.685 |
|
- type: ndcg_at_1 |
|
value: 39.342 |
|
- type: ndcg_at_10 |
|
value: 48.988 |
|
- type: ndcg_at_100 |
|
value: 53.854 |
|
- type: ndcg_at_1000 |
|
value: 55.955 |
|
- type: ndcg_at_3 |
|
value: 43.877 |
|
- type: ndcg_at_5 |
|
value: 46.027 |
|
- type: precision_at_1 |
|
value: 39.342 |
|
- type: precision_at_10 |
|
value: 9.285 |
|
- type: precision_at_100 |
|
value: 1.488 |
|
- type: precision_at_1000 |
|
value: 0.194 |
|
- type: precision_at_3 |
|
value: 20.696 |
|
- type: precision_at_5 |
|
value: 14.878 |
|
- type: recall_at_1 |
|
value: 31.891000000000002 |
|
- type: recall_at_10 |
|
value: 60.608 |
|
- type: recall_at_100 |
|
value: 81.025 |
|
- type: recall_at_1000 |
|
value: 94.883 |
|
- type: recall_at_3 |
|
value: 45.694 |
|
- type: recall_at_5 |
|
value: 51.684 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.778 |
|
- type: map_at_10 |
|
value: 37.632 |
|
- type: map_at_100 |
|
value: 38.800000000000004 |
|
- type: map_at_1000 |
|
value: 38.934999999999995 |
|
- type: map_at_3 |
|
value: 35.293 |
|
- type: map_at_5 |
|
value: 36.547000000000004 |
|
- type: mrr_at_1 |
|
value: 35.35 |
|
- type: mrr_at_10 |
|
value: 42.936 |
|
- type: mrr_at_100 |
|
value: 43.69 |
|
- type: mrr_at_1000 |
|
value: 43.739 |
|
- type: mrr_at_3 |
|
value: 41.062 |
|
- type: mrr_at_5 |
|
value: 42.097 |
|
- type: ndcg_at_1 |
|
value: 35.35 |
|
- type: ndcg_at_10 |
|
value: 42.528 |
|
- type: ndcg_at_100 |
|
value: 46.983000000000004 |
|
- type: ndcg_at_1000 |
|
value: 49.187999999999995 |
|
- type: ndcg_at_3 |
|
value: 39.271 |
|
- type: ndcg_at_5 |
|
value: 40.654 |
|
- type: precision_at_1 |
|
value: 35.35 |
|
- type: precision_at_10 |
|
value: 7.828 |
|
- type: precision_at_100 |
|
value: 1.3010000000000002 |
|
- type: precision_at_1000 |
|
value: 0.17700000000000002 |
|
- type: precision_at_3 |
|
value: 18.96 |
|
- type: precision_at_5 |
|
value: 13.120999999999999 |
|
- type: recall_at_1 |
|
value: 28.778 |
|
- type: recall_at_10 |
|
value: 50.775000000000006 |
|
- type: recall_at_100 |
|
value: 69.66799999999999 |
|
- type: recall_at_1000 |
|
value: 83.638 |
|
- type: recall_at_3 |
|
value: 40.757 |
|
- type: recall_at_5 |
|
value: 44.86 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.584 |
|
- type: map_at_10 |
|
value: 49.69 |
|
- type: map_at_100 |
|
value: 50.639 |
|
- type: map_at_1000 |
|
value: 50.702999999999996 |
|
- type: map_at_3 |
|
value: 46.61 |
|
- type: map_at_5 |
|
value: 48.486000000000004 |
|
- type: mrr_at_1 |
|
value: 43.009 |
|
- type: mrr_at_10 |
|
value: 52.949999999999996 |
|
- type: mrr_at_100 |
|
value: 53.618 |
|
- type: mrr_at_1000 |
|
value: 53.65299999999999 |
|
- type: mrr_at_3 |
|
value: 50.605999999999995 |
|
- type: mrr_at_5 |
|
value: 52.095 |
|
- type: ndcg_at_1 |
|
value: 43.009 |
|
- type: ndcg_at_10 |
|
value: 55.278000000000006 |
|
- type: ndcg_at_100 |
|
value: 59.134 |
|
- type: ndcg_at_1000 |
|
value: 60.528999999999996 |
|
- type: ndcg_at_3 |
|
value: 50.184 |
|
- type: ndcg_at_5 |
|
value: 52.919000000000004 |
|
- type: precision_at_1 |
|
value: 43.009 |
|
- type: precision_at_10 |
|
value: 8.821 |
|
- type: precision_at_100 |
|
value: 1.161 |
|
- type: precision_at_1000 |
|
value: 0.133 |
|
- type: precision_at_3 |
|
value: 22.424 |
|
- type: precision_at_5 |
|
value: 15.436 |
|
- type: recall_at_1 |
|
value: 37.584 |
|
- type: recall_at_10 |
|
value: 68.514 |
|
- type: recall_at_100 |
|
value: 85.099 |
|
- type: recall_at_1000 |
|
value: 95.123 |
|
- type: recall_at_3 |
|
value: 55.007 |
|
- type: recall_at_5 |
|
value: 61.714999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.7 |
|
- type: map_at_10 |
|
value: 32.804 |
|
- type: map_at_100 |
|
value: 33.738 |
|
- type: map_at_1000 |
|
value: 33.825 |
|
- type: map_at_3 |
|
value: 30.639 |
|
- type: map_at_5 |
|
value: 31.781 |
|
- type: mrr_at_1 |
|
value: 26.328000000000003 |
|
- type: mrr_at_10 |
|
value: 34.679 |
|
- type: mrr_at_100 |
|
value: 35.510000000000005 |
|
- type: mrr_at_1000 |
|
value: 35.577999999999996 |
|
- type: mrr_at_3 |
|
value: 32.58 |
|
- type: mrr_at_5 |
|
value: 33.687 |
|
- type: ndcg_at_1 |
|
value: 26.328000000000003 |
|
- type: ndcg_at_10 |
|
value: 37.313 |
|
- type: ndcg_at_100 |
|
value: 42.004000000000005 |
|
- type: ndcg_at_1000 |
|
value: 44.232 |
|
- type: ndcg_at_3 |
|
value: 33.076 |
|
- type: ndcg_at_5 |
|
value: 34.966 |
|
- type: precision_at_1 |
|
value: 26.328000000000003 |
|
- type: precision_at_10 |
|
value: 5.627 |
|
- type: precision_at_100 |
|
value: 0.8410000000000001 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_3 |
|
value: 14.011000000000001 |
|
- type: precision_at_5 |
|
value: 9.582 |
|
- type: recall_at_1 |
|
value: 24.7 |
|
- type: recall_at_10 |
|
value: 49.324 |
|
- type: recall_at_100 |
|
value: 71.018 |
|
- type: recall_at_1000 |
|
value: 87.905 |
|
- type: recall_at_3 |
|
value: 37.7 |
|
- type: recall_at_5 |
|
value: 42.281 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.350999999999999 |
|
- type: map_at_10 |
|
value: 21.745 |
|
- type: map_at_100 |
|
value: 22.731 |
|
- type: map_at_1000 |
|
value: 22.852 |
|
- type: map_at_3 |
|
value: 19.245 |
|
- type: map_at_5 |
|
value: 20.788 |
|
- type: mrr_at_1 |
|
value: 18.159 |
|
- type: mrr_at_10 |
|
value: 25.833000000000002 |
|
- type: mrr_at_100 |
|
value: 26.728 |
|
- type: mrr_at_1000 |
|
value: 26.802 |
|
- type: mrr_at_3 |
|
value: 23.383000000000003 |
|
- type: mrr_at_5 |
|
value: 24.887999999999998 |
|
- type: ndcg_at_1 |
|
value: 18.159 |
|
- type: ndcg_at_10 |
|
value: 26.518000000000004 |
|
- type: ndcg_at_100 |
|
value: 31.473000000000003 |
|
- type: ndcg_at_1000 |
|
value: 34.576 |
|
- type: ndcg_at_3 |
|
value: 21.907 |
|
- type: ndcg_at_5 |
|
value: 24.39 |
|
- type: precision_at_1 |
|
value: 18.159 |
|
- type: precision_at_10 |
|
value: 4.938 |
|
- type: precision_at_100 |
|
value: 0.853 |
|
- type: precision_at_1000 |
|
value: 0.125 |
|
- type: precision_at_3 |
|
value: 10.655000000000001 |
|
- type: precision_at_5 |
|
value: 7.985 |
|
- type: recall_at_1 |
|
value: 14.350999999999999 |
|
- type: recall_at_10 |
|
value: 37.284 |
|
- type: recall_at_100 |
|
value: 59.11300000000001 |
|
- type: recall_at_1000 |
|
value: 81.634 |
|
- type: recall_at_3 |
|
value: 24.753 |
|
- type: recall_at_5 |
|
value: 30.979 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.978 |
|
- type: map_at_10 |
|
value: 36.276 |
|
- type: map_at_100 |
|
value: 37.547000000000004 |
|
- type: map_at_1000 |
|
value: 37.678 |
|
- type: map_at_3 |
|
value: 33.674 |
|
- type: map_at_5 |
|
value: 35.119 |
|
- type: mrr_at_1 |
|
value: 32.916000000000004 |
|
- type: mrr_at_10 |
|
value: 41.798 |
|
- type: mrr_at_100 |
|
value: 42.72 |
|
- type: mrr_at_1000 |
|
value: 42.778 |
|
- type: mrr_at_3 |
|
value: 39.493 |
|
- type: mrr_at_5 |
|
value: 40.927 |
|
- type: ndcg_at_1 |
|
value: 32.916000000000004 |
|
- type: ndcg_at_10 |
|
value: 41.81 |
|
- type: ndcg_at_100 |
|
value: 47.284 |
|
- type: ndcg_at_1000 |
|
value: 49.702 |
|
- type: ndcg_at_3 |
|
value: 37.486999999999995 |
|
- type: ndcg_at_5 |
|
value: 39.597 |
|
- type: precision_at_1 |
|
value: 32.916000000000004 |
|
- type: precision_at_10 |
|
value: 7.411 |
|
- type: precision_at_100 |
|
value: 1.189 |
|
- type: precision_at_1000 |
|
value: 0.158 |
|
- type: precision_at_3 |
|
value: 17.581 |
|
- type: precision_at_5 |
|
value: 12.397 |
|
- type: recall_at_1 |
|
value: 26.978 |
|
- type: recall_at_10 |
|
value: 52.869 |
|
- type: recall_at_100 |
|
value: 75.78399999999999 |
|
- type: recall_at_1000 |
|
value: 91.545 |
|
- type: recall_at_3 |
|
value: 40.717 |
|
- type: recall_at_5 |
|
value: 46.168 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.641 |
|
- type: map_at_10 |
|
value: 32.916000000000004 |
|
- type: map_at_100 |
|
value: 34.165 |
|
- type: map_at_1000 |
|
value: 34.286 |
|
- type: map_at_3 |
|
value: 30.335 |
|
- type: map_at_5 |
|
value: 31.569000000000003 |
|
- type: mrr_at_1 |
|
value: 30.593999999999998 |
|
- type: mrr_at_10 |
|
value: 38.448 |
|
- type: mrr_at_100 |
|
value: 39.299 |
|
- type: mrr_at_1000 |
|
value: 39.362 |
|
- type: mrr_at_3 |
|
value: 36.244 |
|
- type: mrr_at_5 |
|
value: 37.232 |
|
- type: ndcg_at_1 |
|
value: 30.593999999999998 |
|
- type: ndcg_at_10 |
|
value: 38.2 |
|
- type: ndcg_at_100 |
|
value: 43.742 |
|
- type: ndcg_at_1000 |
|
value: 46.217000000000006 |
|
- type: ndcg_at_3 |
|
value: 33.925 |
|
- type: ndcg_at_5 |
|
value: 35.394 |
|
- type: precision_at_1 |
|
value: 30.593999999999998 |
|
- type: precision_at_10 |
|
value: 6.895 |
|
- type: precision_at_100 |
|
value: 1.1320000000000001 |
|
- type: precision_at_1000 |
|
value: 0.153 |
|
- type: precision_at_3 |
|
value: 16.096 |
|
- type: precision_at_5 |
|
value: 11.05 |
|
- type: recall_at_1 |
|
value: 24.641 |
|
- type: recall_at_10 |
|
value: 48.588 |
|
- type: recall_at_100 |
|
value: 72.841 |
|
- type: recall_at_1000 |
|
value: 89.535 |
|
- type: recall_at_3 |
|
value: 36.087 |
|
- type: recall_at_5 |
|
value: 40.346 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.79425 |
|
- type: map_at_10 |
|
value: 33.12033333333333 |
|
- type: map_at_100 |
|
value: 34.221333333333334 |
|
- type: map_at_1000 |
|
value: 34.3435 |
|
- type: map_at_3 |
|
value: 30.636583333333338 |
|
- type: map_at_5 |
|
value: 31.974083333333326 |
|
- type: mrr_at_1 |
|
value: 29.242416666666664 |
|
- type: mrr_at_10 |
|
value: 37.11675 |
|
- type: mrr_at_100 |
|
value: 37.93783333333334 |
|
- type: mrr_at_1000 |
|
value: 38.003083333333336 |
|
- type: mrr_at_3 |
|
value: 34.904666666666664 |
|
- type: mrr_at_5 |
|
value: 36.12916666666667 |
|
- type: ndcg_at_1 |
|
value: 29.242416666666664 |
|
- type: ndcg_at_10 |
|
value: 38.03416666666667 |
|
- type: ndcg_at_100 |
|
value: 42.86674999999999 |
|
- type: ndcg_at_1000 |
|
value: 45.34550000000001 |
|
- type: ndcg_at_3 |
|
value: 33.76466666666666 |
|
- type: ndcg_at_5 |
|
value: 35.668666666666674 |
|
- type: precision_at_1 |
|
value: 29.242416666666664 |
|
- type: precision_at_10 |
|
value: 6.589833333333334 |
|
- type: precision_at_100 |
|
value: 1.0693333333333332 |
|
- type: precision_at_1000 |
|
value: 0.14641666666666667 |
|
- type: precision_at_3 |
|
value: 15.430749999999998 |
|
- type: precision_at_5 |
|
value: 10.833833333333333 |
|
- type: recall_at_1 |
|
value: 24.79425 |
|
- type: recall_at_10 |
|
value: 48.582916666666655 |
|
- type: recall_at_100 |
|
value: 69.88499999999999 |
|
- type: recall_at_1000 |
|
value: 87.211 |
|
- type: recall_at_3 |
|
value: 36.625499999999995 |
|
- type: recall_at_5 |
|
value: 41.553999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.767 |
|
- type: map_at_10 |
|
value: 28.450999999999997 |
|
- type: map_at_100 |
|
value: 29.332 |
|
- type: map_at_1000 |
|
value: 29.426000000000002 |
|
- type: map_at_3 |
|
value: 26.379 |
|
- type: map_at_5 |
|
value: 27.584999999999997 |
|
- type: mrr_at_1 |
|
value: 25.46 |
|
- type: mrr_at_10 |
|
value: 30.974 |
|
- type: mrr_at_100 |
|
value: 31.784000000000002 |
|
- type: mrr_at_1000 |
|
value: 31.857999999999997 |
|
- type: mrr_at_3 |
|
value: 28.962 |
|
- type: mrr_at_5 |
|
value: 30.066 |
|
- type: ndcg_at_1 |
|
value: 25.46 |
|
- type: ndcg_at_10 |
|
value: 32.041 |
|
- type: ndcg_at_100 |
|
value: 36.522 |
|
- type: ndcg_at_1000 |
|
value: 39.101 |
|
- type: ndcg_at_3 |
|
value: 28.152 |
|
- type: ndcg_at_5 |
|
value: 30.03 |
|
- type: precision_at_1 |
|
value: 25.46 |
|
- type: precision_at_10 |
|
value: 4.893 |
|
- type: precision_at_100 |
|
value: 0.77 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_3 |
|
value: 11.605 |
|
- type: precision_at_5 |
|
value: 8.19 |
|
- type: recall_at_1 |
|
value: 22.767 |
|
- type: recall_at_10 |
|
value: 40.71 |
|
- type: recall_at_100 |
|
value: 61.334999999999994 |
|
- type: recall_at_1000 |
|
value: 80.567 |
|
- type: recall_at_3 |
|
value: 30.198000000000004 |
|
- type: recall_at_5 |
|
value: 34.803 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.722 |
|
- type: map_at_10 |
|
value: 22.794 |
|
- type: map_at_100 |
|
value: 23.7 |
|
- type: map_at_1000 |
|
value: 23.822 |
|
- type: map_at_3 |
|
value: 20.781 |
|
- type: map_at_5 |
|
value: 22.024 |
|
- type: mrr_at_1 |
|
value: 20.061999999999998 |
|
- type: mrr_at_10 |
|
value: 26.346999999999998 |
|
- type: mrr_at_100 |
|
value: 27.153 |
|
- type: mrr_at_1000 |
|
value: 27.233 |
|
- type: mrr_at_3 |
|
value: 24.375 |
|
- type: mrr_at_5 |
|
value: 25.593 |
|
- type: ndcg_at_1 |
|
value: 20.061999999999998 |
|
- type: ndcg_at_10 |
|
value: 26.785999999999998 |
|
- type: ndcg_at_100 |
|
value: 31.319999999999997 |
|
- type: ndcg_at_1000 |
|
value: 34.346 |
|
- type: ndcg_at_3 |
|
value: 23.219 |
|
- type: ndcg_at_5 |
|
value: 25.107000000000003 |
|
- type: precision_at_1 |
|
value: 20.061999999999998 |
|
- type: precision_at_10 |
|
value: 4.78 |
|
- type: precision_at_100 |
|
value: 0.83 |
|
- type: precision_at_1000 |
|
value: 0.125 |
|
- type: precision_at_3 |
|
value: 10.874 |
|
- type: precision_at_5 |
|
value: 7.956 |
|
- type: recall_at_1 |
|
value: 16.722 |
|
- type: recall_at_10 |
|
value: 35.204 |
|
- type: recall_at_100 |
|
value: 55.797 |
|
- type: recall_at_1000 |
|
value: 77.689 |
|
- type: recall_at_3 |
|
value: 25.245 |
|
- type: recall_at_5 |
|
value: 30.115 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.842 |
|
- type: map_at_10 |
|
value: 32.917 |
|
- type: map_at_100 |
|
value: 33.961000000000006 |
|
- type: map_at_1000 |
|
value: 34.069 |
|
- type: map_at_3 |
|
value: 30.595 |
|
- type: map_at_5 |
|
value: 31.837 |
|
- type: mrr_at_1 |
|
value: 29.011 |
|
- type: mrr_at_10 |
|
value: 36.977 |
|
- type: mrr_at_100 |
|
value: 37.814 |
|
- type: mrr_at_1000 |
|
value: 37.885999999999996 |
|
- type: mrr_at_3 |
|
value: 34.966 |
|
- type: mrr_at_5 |
|
value: 36.043 |
|
- type: ndcg_at_1 |
|
value: 29.011 |
|
- type: ndcg_at_10 |
|
value: 37.735 |
|
- type: ndcg_at_100 |
|
value: 42.683 |
|
- type: ndcg_at_1000 |
|
value: 45.198 |
|
- type: ndcg_at_3 |
|
value: 33.650000000000006 |
|
- type: ndcg_at_5 |
|
value: 35.386 |
|
- type: precision_at_1 |
|
value: 29.011 |
|
- type: precision_at_10 |
|
value: 6.259 |
|
- type: precision_at_100 |
|
value: 0.984 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 15.329999999999998 |
|
- type: precision_at_5 |
|
value: 10.541 |
|
- type: recall_at_1 |
|
value: 24.842 |
|
- type: recall_at_10 |
|
value: 48.304 |
|
- type: recall_at_100 |
|
value: 70.04899999999999 |
|
- type: recall_at_1000 |
|
value: 87.82600000000001 |
|
- type: recall_at_3 |
|
value: 36.922 |
|
- type: recall_at_5 |
|
value: 41.449999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.252000000000002 |
|
- type: map_at_10 |
|
value: 32.293 |
|
- type: map_at_100 |
|
value: 33.816 |
|
- type: map_at_1000 |
|
value: 34.053 |
|
- type: map_at_3 |
|
value: 29.781999999999996 |
|
- type: map_at_5 |
|
value: 31.008000000000003 |
|
- type: mrr_at_1 |
|
value: 29.051 |
|
- type: mrr_at_10 |
|
value: 36.722 |
|
- type: mrr_at_100 |
|
value: 37.663000000000004 |
|
- type: mrr_at_1000 |
|
value: 37.734 |
|
- type: mrr_at_3 |
|
value: 34.354 |
|
- type: mrr_at_5 |
|
value: 35.609 |
|
- type: ndcg_at_1 |
|
value: 29.051 |
|
- type: ndcg_at_10 |
|
value: 37.775999999999996 |
|
- type: ndcg_at_100 |
|
value: 43.221 |
|
- type: ndcg_at_1000 |
|
value: 46.116 |
|
- type: ndcg_at_3 |
|
value: 33.403 |
|
- type: ndcg_at_5 |
|
value: 35.118 |
|
- type: precision_at_1 |
|
value: 29.051 |
|
- type: precision_at_10 |
|
value: 7.332 |
|
- type: precision_at_100 |
|
value: 1.49 |
|
- type: precision_at_1000 |
|
value: 0.23600000000000002 |
|
- type: precision_at_3 |
|
value: 15.415000000000001 |
|
- type: precision_at_5 |
|
value: 11.107 |
|
- type: recall_at_1 |
|
value: 24.252000000000002 |
|
- type: recall_at_10 |
|
value: 47.861 |
|
- type: recall_at_100 |
|
value: 72.21600000000001 |
|
- type: recall_at_1000 |
|
value: 90.886 |
|
- type: recall_at_3 |
|
value: 35.533 |
|
- type: recall_at_5 |
|
value: 39.959 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.025000000000002 |
|
- type: map_at_10 |
|
value: 27.154 |
|
- type: map_at_100 |
|
value: 28.118 |
|
- type: map_at_1000 |
|
value: 28.237000000000002 |
|
- type: map_at_3 |
|
value: 25.017 |
|
- type: map_at_5 |
|
value: 25.832 |
|
- type: mrr_at_1 |
|
value: 21.627 |
|
- type: mrr_at_10 |
|
value: 28.884999999999998 |
|
- type: mrr_at_100 |
|
value: 29.741 |
|
- type: mrr_at_1000 |
|
value: 29.831999999999997 |
|
- type: mrr_at_3 |
|
value: 26.741 |
|
- type: mrr_at_5 |
|
value: 27.628000000000004 |
|
- type: ndcg_at_1 |
|
value: 21.627 |
|
- type: ndcg_at_10 |
|
value: 31.436999999999998 |
|
- type: ndcg_at_100 |
|
value: 36.181000000000004 |
|
- type: ndcg_at_1000 |
|
value: 38.986 |
|
- type: ndcg_at_3 |
|
value: 27.025 |
|
- type: ndcg_at_5 |
|
value: 28.436 |
|
- type: precision_at_1 |
|
value: 21.627 |
|
- type: precision_at_10 |
|
value: 5.009 |
|
- type: precision_at_100 |
|
value: 0.7929999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 11.522 |
|
- type: precision_at_5 |
|
value: 7.763000000000001 |
|
- type: recall_at_1 |
|
value: 20.025000000000002 |
|
- type: recall_at_10 |
|
value: 42.954 |
|
- type: recall_at_100 |
|
value: 64.67500000000001 |
|
- type: recall_at_1000 |
|
value: 85.301 |
|
- type: recall_at_3 |
|
value: 30.892999999999997 |
|
- type: recall_at_5 |
|
value: 34.288000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.079 |
|
- type: map_at_10 |
|
value: 16.930999999999997 |
|
- type: map_at_100 |
|
value: 18.398999999999997 |
|
- type: map_at_1000 |
|
value: 18.561 |
|
- type: map_at_3 |
|
value: 14.294 |
|
- type: map_at_5 |
|
value: 15.579 |
|
- type: mrr_at_1 |
|
value: 22.606 |
|
- type: mrr_at_10 |
|
value: 32.513 |
|
- type: mrr_at_100 |
|
value: 33.463 |
|
- type: mrr_at_1000 |
|
value: 33.513999999999996 |
|
- type: mrr_at_3 |
|
value: 29.479 |
|
- type: mrr_at_5 |
|
value: 31.3 |
|
- type: ndcg_at_1 |
|
value: 22.606 |
|
- type: ndcg_at_10 |
|
value: 24.053 |
|
- type: ndcg_at_100 |
|
value: 30.258000000000003 |
|
- type: ndcg_at_1000 |
|
value: 33.516 |
|
- type: ndcg_at_3 |
|
value: 19.721 |
|
- type: ndcg_at_5 |
|
value: 21.144 |
|
- type: precision_at_1 |
|
value: 22.606 |
|
- type: precision_at_10 |
|
value: 7.55 |
|
- type: precision_at_100 |
|
value: 1.399 |
|
- type: precision_at_1000 |
|
value: 0.2 |
|
- type: precision_at_3 |
|
value: 14.701 |
|
- type: precision_at_5 |
|
value: 11.192 |
|
- type: recall_at_1 |
|
value: 10.079 |
|
- type: recall_at_10 |
|
value: 28.970000000000002 |
|
- type: recall_at_100 |
|
value: 50.805 |
|
- type: recall_at_1000 |
|
value: 69.378 |
|
- type: recall_at_3 |
|
value: 18.199 |
|
- type: recall_at_5 |
|
value: 22.442 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.794 |
|
- type: map_at_10 |
|
value: 15.165999999999999 |
|
- type: map_at_100 |
|
value: 20.508000000000003 |
|
- type: map_at_1000 |
|
value: 21.809 |
|
- type: map_at_3 |
|
value: 11.568000000000001 |
|
- type: map_at_5 |
|
value: 13.059000000000001 |
|
- type: mrr_at_1 |
|
value: 56.49999999999999 |
|
- type: mrr_at_10 |
|
value: 65.90899999999999 |
|
- type: mrr_at_100 |
|
value: 66.352 |
|
- type: mrr_at_1000 |
|
value: 66.369 |
|
- type: mrr_at_3 |
|
value: 64.0 |
|
- type: mrr_at_5 |
|
value: 65.10000000000001 |
|
- type: ndcg_at_1 |
|
value: 44.25 |
|
- type: ndcg_at_10 |
|
value: 32.649 |
|
- type: ndcg_at_100 |
|
value: 36.668 |
|
- type: ndcg_at_1000 |
|
value: 43.918 |
|
- type: ndcg_at_3 |
|
value: 37.096000000000004 |
|
- type: ndcg_at_5 |
|
value: 34.048 |
|
- type: precision_at_1 |
|
value: 56.49999999999999 |
|
- type: precision_at_10 |
|
value: 25.45 |
|
- type: precision_at_100 |
|
value: 8.055 |
|
- type: precision_at_1000 |
|
value: 1.7489999999999999 |
|
- type: precision_at_3 |
|
value: 41.0 |
|
- type: precision_at_5 |
|
value: 32.85 |
|
- type: recall_at_1 |
|
value: 7.794 |
|
- type: recall_at_10 |
|
value: 20.101 |
|
- type: recall_at_100 |
|
value: 42.448 |
|
- type: recall_at_1000 |
|
value: 65.88000000000001 |
|
- type: recall_at_3 |
|
value: 12.753 |
|
- type: recall_at_5 |
|
value: 15.307 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 44.01 |
|
- type: f1 |
|
value: 38.659680951114964 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 49.713 |
|
- type: map_at_10 |
|
value: 61.79 |
|
- type: map_at_100 |
|
value: 62.28 |
|
- type: map_at_1000 |
|
value: 62.297000000000004 |
|
- type: map_at_3 |
|
value: 59.361 |
|
- type: map_at_5 |
|
value: 60.92100000000001 |
|
- type: mrr_at_1 |
|
value: 53.405 |
|
- type: mrr_at_10 |
|
value: 65.79899999999999 |
|
- type: mrr_at_100 |
|
value: 66.219 |
|
- type: mrr_at_1000 |
|
value: 66.227 |
|
- type: mrr_at_3 |
|
value: 63.431000000000004 |
|
- type: mrr_at_5 |
|
value: 64.98 |
|
- type: ndcg_at_1 |
|
value: 53.405 |
|
- type: ndcg_at_10 |
|
value: 68.01899999999999 |
|
- type: ndcg_at_100 |
|
value: 70.197 |
|
- type: ndcg_at_1000 |
|
value: 70.571 |
|
- type: ndcg_at_3 |
|
value: 63.352 |
|
- type: ndcg_at_5 |
|
value: 66.018 |
|
- type: precision_at_1 |
|
value: 53.405 |
|
- type: precision_at_10 |
|
value: 9.119 |
|
- type: precision_at_100 |
|
value: 1.03 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_3 |
|
value: 25.602999999999998 |
|
- type: precision_at_5 |
|
value: 16.835 |
|
- type: recall_at_1 |
|
value: 49.713 |
|
- type: recall_at_10 |
|
value: 83.306 |
|
- type: recall_at_100 |
|
value: 92.92 |
|
- type: recall_at_1000 |
|
value: 95.577 |
|
- type: recall_at_3 |
|
value: 70.798 |
|
- type: recall_at_5 |
|
value: 77.254 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.310000000000002 |
|
- type: map_at_10 |
|
value: 26.204 |
|
- type: map_at_100 |
|
value: 27.932000000000002 |
|
- type: map_at_1000 |
|
value: 28.121000000000002 |
|
- type: map_at_3 |
|
value: 22.481 |
|
- type: map_at_5 |
|
value: 24.678 |
|
- type: mrr_at_1 |
|
value: 29.784 |
|
- type: mrr_at_10 |
|
value: 39.582 |
|
- type: mrr_at_100 |
|
value: 40.52 |
|
- type: mrr_at_1000 |
|
value: 40.568 |
|
- type: mrr_at_3 |
|
value: 37.114000000000004 |
|
- type: mrr_at_5 |
|
value: 38.596000000000004 |
|
- type: ndcg_at_1 |
|
value: 29.784 |
|
- type: ndcg_at_10 |
|
value: 33.432 |
|
- type: ndcg_at_100 |
|
value: 40.281 |
|
- type: ndcg_at_1000 |
|
value: 43.653999999999996 |
|
- type: ndcg_at_3 |
|
value: 29.612 |
|
- type: ndcg_at_5 |
|
value: 31.223 |
|
- type: precision_at_1 |
|
value: 29.784 |
|
- type: precision_at_10 |
|
value: 9.645 |
|
- type: precision_at_100 |
|
value: 1.645 |
|
- type: precision_at_1000 |
|
value: 0.22499999999999998 |
|
- type: precision_at_3 |
|
value: 20.165 |
|
- type: precision_at_5 |
|
value: 15.401000000000002 |
|
- type: recall_at_1 |
|
value: 15.310000000000002 |
|
- type: recall_at_10 |
|
value: 40.499 |
|
- type: recall_at_100 |
|
value: 66.643 |
|
- type: recall_at_1000 |
|
value: 87.059 |
|
- type: recall_at_3 |
|
value: 27.492 |
|
- type: recall_at_5 |
|
value: 33.748 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.599000000000004 |
|
- type: map_at_10 |
|
value: 47.347 |
|
- type: map_at_100 |
|
value: 48.191 |
|
- type: map_at_1000 |
|
value: 48.263 |
|
- type: map_at_3 |
|
value: 44.698 |
|
- type: map_at_5 |
|
value: 46.278999999999996 |
|
- type: mrr_at_1 |
|
value: 67.19800000000001 |
|
- type: mrr_at_10 |
|
value: 74.054 |
|
- type: mrr_at_100 |
|
value: 74.376 |
|
- type: mrr_at_1000 |
|
value: 74.392 |
|
- type: mrr_at_3 |
|
value: 72.849 |
|
- type: mrr_at_5 |
|
value: 73.643 |
|
- type: ndcg_at_1 |
|
value: 67.19800000000001 |
|
- type: ndcg_at_10 |
|
value: 56.482 |
|
- type: ndcg_at_100 |
|
value: 59.694 |
|
- type: ndcg_at_1000 |
|
value: 61.204 |
|
- type: ndcg_at_3 |
|
value: 52.43299999999999 |
|
- type: ndcg_at_5 |
|
value: 54.608000000000004 |
|
- type: precision_at_1 |
|
value: 67.19800000000001 |
|
- type: precision_at_10 |
|
value: 11.613999999999999 |
|
- type: precision_at_100 |
|
value: 1.415 |
|
- type: precision_at_1000 |
|
value: 0.16199999999999998 |
|
- type: precision_at_3 |
|
value: 32.726 |
|
- type: precision_at_5 |
|
value: 21.349999999999998 |
|
- type: recall_at_1 |
|
value: 33.599000000000004 |
|
- type: recall_at_10 |
|
value: 58.069 |
|
- type: recall_at_100 |
|
value: 70.736 |
|
- type: recall_at_1000 |
|
value: 80.804 |
|
- type: recall_at_3 |
|
value: 49.088 |
|
- type: recall_at_5 |
|
value: 53.376000000000005 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.64359999999999 |
|
- type: ap |
|
value: 67.54685976014599 |
|
- type: f1 |
|
value: 73.55148707559482 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.502 |
|
- type: map_at_10 |
|
value: 30.816 |
|
- type: map_at_100 |
|
value: 32.007999999999996 |
|
- type: map_at_1000 |
|
value: 32.067 |
|
- type: map_at_3 |
|
value: 27.215 |
|
- type: map_at_5 |
|
value: 29.304000000000002 |
|
- type: mrr_at_1 |
|
value: 20.072000000000003 |
|
- type: mrr_at_10 |
|
value: 31.406 |
|
- type: mrr_at_100 |
|
value: 32.549 |
|
- type: mrr_at_1000 |
|
value: 32.602 |
|
- type: mrr_at_3 |
|
value: 27.839000000000002 |
|
- type: mrr_at_5 |
|
value: 29.926000000000002 |
|
- type: ndcg_at_1 |
|
value: 20.086000000000002 |
|
- type: ndcg_at_10 |
|
value: 37.282 |
|
- type: ndcg_at_100 |
|
value: 43.206 |
|
- type: ndcg_at_1000 |
|
value: 44.690000000000005 |
|
- type: ndcg_at_3 |
|
value: 29.932 |
|
- type: ndcg_at_5 |
|
value: 33.668 |
|
- type: precision_at_1 |
|
value: 20.086000000000002 |
|
- type: precision_at_10 |
|
value: 5.961 |
|
- type: precision_at_100 |
|
value: 0.898 |
|
- type: precision_at_1000 |
|
value: 0.10200000000000001 |
|
- type: precision_at_3 |
|
value: 12.856000000000002 |
|
- type: precision_at_5 |
|
value: 9.596 |
|
- type: recall_at_1 |
|
value: 19.502 |
|
- type: recall_at_10 |
|
value: 57.182 |
|
- type: recall_at_100 |
|
value: 84.952 |
|
- type: recall_at_1000 |
|
value: 96.34700000000001 |
|
- type: recall_at_3 |
|
value: 37.193 |
|
- type: recall_at_5 |
|
value: 46.157 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.96488828089375 |
|
- type: f1 |
|
value: 93.32119260543482 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 72.4965800273598 |
|
- type: f1 |
|
value: 49.34896217536082 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.60928043039678 |
|
- type: f1 |
|
value: 64.34244712074538 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 69.75453934095493 |
|
- type: f1 |
|
value: 68.39224867489249 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.862573504920082 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 27.511123551196803 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.99145104942086 |
|
- type: mrr |
|
value: 32.03606480418627 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.015 |
|
- type: map_at_10 |
|
value: 11.054 |
|
- type: map_at_100 |
|
value: 13.773 |
|
- type: map_at_1000 |
|
value: 15.082999999999998 |
|
- type: map_at_3 |
|
value: 8.253 |
|
- type: map_at_5 |
|
value: 9.508999999999999 |
|
- type: mrr_at_1 |
|
value: 42.105 |
|
- type: mrr_at_10 |
|
value: 50.44499999999999 |
|
- type: mrr_at_100 |
|
value: 51.080000000000005 |
|
- type: mrr_at_1000 |
|
value: 51.129999999999995 |
|
- type: mrr_at_3 |
|
value: 48.555 |
|
- type: mrr_at_5 |
|
value: 49.84 |
|
- type: ndcg_at_1 |
|
value: 40.402 |
|
- type: ndcg_at_10 |
|
value: 30.403000000000002 |
|
- type: ndcg_at_100 |
|
value: 28.216 |
|
- type: ndcg_at_1000 |
|
value: 37.021 |
|
- type: ndcg_at_3 |
|
value: 35.53 |
|
- type: ndcg_at_5 |
|
value: 33.202999999999996 |
|
- type: precision_at_1 |
|
value: 42.105 |
|
- type: precision_at_10 |
|
value: 22.353 |
|
- type: precision_at_100 |
|
value: 7.266 |
|
- type: precision_at_1000 |
|
value: 2.011 |
|
- type: precision_at_3 |
|
value: 32.921 |
|
- type: precision_at_5 |
|
value: 28.297 |
|
- type: recall_at_1 |
|
value: 5.015 |
|
- type: recall_at_10 |
|
value: 14.393 |
|
- type: recall_at_100 |
|
value: 28.893 |
|
- type: recall_at_1000 |
|
value: 60.18 |
|
- type: recall_at_3 |
|
value: 9.184000000000001 |
|
- type: recall_at_5 |
|
value: 11.39 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.524 |
|
- type: map_at_10 |
|
value: 44.182 |
|
- type: map_at_100 |
|
value: 45.228 |
|
- type: map_at_1000 |
|
value: 45.265 |
|
- type: map_at_3 |
|
value: 39.978 |
|
- type: map_at_5 |
|
value: 42.482 |
|
- type: mrr_at_1 |
|
value: 33.256 |
|
- type: mrr_at_10 |
|
value: 46.661 |
|
- type: mrr_at_100 |
|
value: 47.47 |
|
- type: mrr_at_1000 |
|
value: 47.496 |
|
- type: mrr_at_3 |
|
value: 43.187999999999995 |
|
- type: mrr_at_5 |
|
value: 45.330999999999996 |
|
- type: ndcg_at_1 |
|
value: 33.227000000000004 |
|
- type: ndcg_at_10 |
|
value: 51.589 |
|
- type: ndcg_at_100 |
|
value: 56.043 |
|
- type: ndcg_at_1000 |
|
value: 56.937000000000005 |
|
- type: ndcg_at_3 |
|
value: 43.751 |
|
- type: ndcg_at_5 |
|
value: 47.937000000000005 |
|
- type: precision_at_1 |
|
value: 33.227000000000004 |
|
- type: precision_at_10 |
|
value: 8.556999999999999 |
|
- type: precision_at_100 |
|
value: 1.103 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 19.921 |
|
- type: precision_at_5 |
|
value: 14.396999999999998 |
|
- type: recall_at_1 |
|
value: 29.524 |
|
- type: recall_at_10 |
|
value: 71.615 |
|
- type: recall_at_100 |
|
value: 91.056 |
|
- type: recall_at_1000 |
|
value: 97.72800000000001 |
|
- type: recall_at_3 |
|
value: 51.451 |
|
- type: recall_at_5 |
|
value: 61.119 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 69.596 |
|
- type: map_at_10 |
|
value: 83.281 |
|
- type: map_at_100 |
|
value: 83.952 |
|
- type: map_at_1000 |
|
value: 83.97200000000001 |
|
- type: map_at_3 |
|
value: 80.315 |
|
- type: map_at_5 |
|
value: 82.223 |
|
- type: mrr_at_1 |
|
value: 80.17 |
|
- type: mrr_at_10 |
|
value: 86.522 |
|
- type: mrr_at_100 |
|
value: 86.644 |
|
- type: mrr_at_1000 |
|
value: 86.64500000000001 |
|
- type: mrr_at_3 |
|
value: 85.438 |
|
- type: mrr_at_5 |
|
value: 86.21799999999999 |
|
- type: ndcg_at_1 |
|
value: 80.19 |
|
- type: ndcg_at_10 |
|
value: 87.19 |
|
- type: ndcg_at_100 |
|
value: 88.567 |
|
- type: ndcg_at_1000 |
|
value: 88.70400000000001 |
|
- type: ndcg_at_3 |
|
value: 84.17999999999999 |
|
- type: ndcg_at_5 |
|
value: 85.931 |
|
- type: precision_at_1 |
|
value: 80.19 |
|
- type: precision_at_10 |
|
value: 13.209000000000001 |
|
- type: precision_at_100 |
|
value: 1.518 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 36.717 |
|
- type: precision_at_5 |
|
value: 24.248 |
|
- type: recall_at_1 |
|
value: 69.596 |
|
- type: recall_at_10 |
|
value: 94.533 |
|
- type: recall_at_100 |
|
value: 99.322 |
|
- type: recall_at_1000 |
|
value: 99.965 |
|
- type: recall_at_3 |
|
value: 85.911 |
|
- type: recall_at_5 |
|
value: 90.809 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 49.27650627571912 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 57.08550946534183 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.568 |
|
- type: map_at_10 |
|
value: 10.862 |
|
- type: map_at_100 |
|
value: 12.757 |
|
- type: map_at_1000 |
|
value: 13.031 |
|
- type: map_at_3 |
|
value: 7.960000000000001 |
|
- type: map_at_5 |
|
value: 9.337 |
|
- type: mrr_at_1 |
|
value: 22.5 |
|
- type: mrr_at_10 |
|
value: 32.6 |
|
- type: mrr_at_100 |
|
value: 33.603 |
|
- type: mrr_at_1000 |
|
value: 33.672000000000004 |
|
- type: mrr_at_3 |
|
value: 29.299999999999997 |
|
- type: mrr_at_5 |
|
value: 31.25 |
|
- type: ndcg_at_1 |
|
value: 22.5 |
|
- type: ndcg_at_10 |
|
value: 18.605 |
|
- type: ndcg_at_100 |
|
value: 26.029999999999998 |
|
- type: ndcg_at_1000 |
|
value: 31.256 |
|
- type: ndcg_at_3 |
|
value: 17.873 |
|
- type: ndcg_at_5 |
|
value: 15.511 |
|
- type: precision_at_1 |
|
value: 22.5 |
|
- type: precision_at_10 |
|
value: 9.58 |
|
- type: precision_at_100 |
|
value: 2.033 |
|
- type: precision_at_1000 |
|
value: 0.33 |
|
- type: precision_at_3 |
|
value: 16.633 |
|
- type: precision_at_5 |
|
value: 13.54 |
|
- type: recall_at_1 |
|
value: 4.568 |
|
- type: recall_at_10 |
|
value: 19.402 |
|
- type: recall_at_100 |
|
value: 41.277 |
|
- type: recall_at_1000 |
|
value: 66.963 |
|
- type: recall_at_3 |
|
value: 10.112 |
|
- type: recall_at_5 |
|
value: 13.712 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.31992291680787 |
|
- type: cos_sim_spearman |
|
value: 76.7212346922664 |
|
- type: euclidean_pearson |
|
value: 80.42189271706478 |
|
- type: euclidean_spearman |
|
value: 76.7212342532493 |
|
- type: manhattan_pearson |
|
value: 80.33171093031578 |
|
- type: manhattan_spearman |
|
value: 76.63192883074694 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.16654278886763 |
|
- type: cos_sim_spearman |
|
value: 73.66390263429565 |
|
- type: euclidean_pearson |
|
value: 79.7485360086639 |
|
- type: euclidean_spearman |
|
value: 73.66389870373436 |
|
- type: manhattan_pearson |
|
value: 79.73652237443706 |
|
- type: manhattan_spearman |
|
value: 73.65296117151647 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.40389689929246 |
|
- type: cos_sim_spearman |
|
value: 83.29727595993955 |
|
- type: euclidean_pearson |
|
value: 82.23970587854079 |
|
- type: euclidean_spearman |
|
value: 83.29727595993955 |
|
- type: manhattan_pearson |
|
value: 82.18823600831897 |
|
- type: manhattan_spearman |
|
value: 83.20746192209594 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.73505246913413 |
|
- type: cos_sim_spearman |
|
value: 79.1686548248754 |
|
- type: euclidean_pearson |
|
value: 80.48889135993412 |
|
- type: euclidean_spearman |
|
value: 79.16864112930354 |
|
- type: manhattan_pearson |
|
value: 80.40720651057302 |
|
- type: manhattan_spearman |
|
value: 79.0640155089286 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.3953512879065 |
|
- type: cos_sim_spearman |
|
value: 87.29947322714338 |
|
- type: euclidean_pearson |
|
value: 86.59759438529645 |
|
- type: euclidean_spearman |
|
value: 87.29947511092824 |
|
- type: manhattan_pearson |
|
value: 86.52097806169155 |
|
- type: manhattan_spearman |
|
value: 87.22987242146534 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.48565753792056 |
|
- type: cos_sim_spearman |
|
value: 83.6049720319893 |
|
- type: euclidean_pearson |
|
value: 82.56452023172913 |
|
- type: euclidean_spearman |
|
value: 83.60490168191697 |
|
- type: manhattan_pearson |
|
value: 82.58079941137872 |
|
- type: manhattan_spearman |
|
value: 83.60975807374051 |
|
- 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: 88.18239976618212 |
|
- type: cos_sim_spearman |
|
value: 88.23061724730616 |
|
- type: euclidean_pearson |
|
value: 87.78482472776658 |
|
- type: euclidean_spearman |
|
value: 88.23061724730616 |
|
- type: manhattan_pearson |
|
value: 87.75059641730239 |
|
- type: manhattan_spearman |
|
value: 88.22527413524622 |
|
- 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: 63.42816418706765 |
|
- type: cos_sim_spearman |
|
value: 63.4569864520124 |
|
- type: euclidean_pearson |
|
value: 64.35405409953853 |
|
- type: euclidean_spearman |
|
value: 63.4569864520124 |
|
- type: manhattan_pearson |
|
value: 63.96649236073056 |
|
- type: manhattan_spearman |
|
value: 63.01448583722708 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.41659638047614 |
|
- type: cos_sim_spearman |
|
value: 84.03893866106175 |
|
- type: euclidean_pearson |
|
value: 84.2251203953798 |
|
- type: euclidean_spearman |
|
value: 84.03893866106175 |
|
- type: manhattan_pearson |
|
value: 84.22733643205514 |
|
- type: manhattan_spearman |
|
value: 84.06504411263612 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 79.75608022582414 |
|
- type: mrr |
|
value: 94.0947732369301 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 50.161 |
|
- type: map_at_10 |
|
value: 59.458999999999996 |
|
- type: map_at_100 |
|
value: 60.156 |
|
- type: map_at_1000 |
|
value: 60.194 |
|
- type: map_at_3 |
|
value: 56.45400000000001 |
|
- type: map_at_5 |
|
value: 58.165 |
|
- type: mrr_at_1 |
|
value: 53.333 |
|
- type: mrr_at_10 |
|
value: 61.050000000000004 |
|
- type: mrr_at_100 |
|
value: 61.586 |
|
- type: mrr_at_1000 |
|
value: 61.624 |
|
- type: mrr_at_3 |
|
value: 58.889 |
|
- type: mrr_at_5 |
|
value: 60.122 |
|
- type: ndcg_at_1 |
|
value: 53.333 |
|
- type: ndcg_at_10 |
|
value: 63.888999999999996 |
|
- type: ndcg_at_100 |
|
value: 66.963 |
|
- type: ndcg_at_1000 |
|
value: 68.062 |
|
- type: ndcg_at_3 |
|
value: 59.01 |
|
- type: ndcg_at_5 |
|
value: 61.373999999999995 |
|
- type: precision_at_1 |
|
value: 53.333 |
|
- type: precision_at_10 |
|
value: 8.633000000000001 |
|
- type: precision_at_100 |
|
value: 1.027 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 23.111 |
|
- type: precision_at_5 |
|
value: 15.467 |
|
- type: recall_at_1 |
|
value: 50.161 |
|
- type: recall_at_10 |
|
value: 75.922 |
|
- type: recall_at_100 |
|
value: 90.0 |
|
- type: recall_at_1000 |
|
value: 98.667 |
|
- type: recall_at_3 |
|
value: 62.90599999999999 |
|
- type: recall_at_5 |
|
value: 68.828 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81188118811882 |
|
- type: cos_sim_ap |
|
value: 95.11619225962413 |
|
- type: cos_sim_f1 |
|
value: 90.35840484603736 |
|
- type: cos_sim_precision |
|
value: 91.23343527013252 |
|
- type: cos_sim_recall |
|
value: 89.5 |
|
- type: dot_accuracy |
|
value: 99.81188118811882 |
|
- type: dot_ap |
|
value: 95.11619225962413 |
|
- type: dot_f1 |
|
value: 90.35840484603736 |
|
- type: dot_precision |
|
value: 91.23343527013252 |
|
- type: dot_recall |
|
value: 89.5 |
|
- type: euclidean_accuracy |
|
value: 99.81188118811882 |
|
- type: euclidean_ap |
|
value: 95.11619225962413 |
|
- type: euclidean_f1 |
|
value: 90.35840484603736 |
|
- type: euclidean_precision |
|
value: 91.23343527013252 |
|
- type: euclidean_recall |
|
value: 89.5 |
|
- type: manhattan_accuracy |
|
value: 99.80891089108911 |
|
- type: manhattan_ap |
|
value: 95.07294266220966 |
|
- type: manhattan_f1 |
|
value: 90.21794221996959 |
|
- type: manhattan_precision |
|
value: 91.46968139773895 |
|
- type: manhattan_recall |
|
value: 89.0 |
|
- type: max_accuracy |
|
value: 99.81188118811882 |
|
- type: max_ap |
|
value: 95.11619225962413 |
|
- type: max_f1 |
|
value: 90.35840484603736 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 55.3481874105239 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 34.421291695525 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 49.98746633276634 |
|
- type: mrr |
|
value: 50.63143249724133 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.009961979844036 |
|
- type: cos_sim_spearman |
|
value: 30.558416108881044 |
|
- type: dot_pearson |
|
value: 31.009964941134253 |
|
- type: dot_spearman |
|
value: 30.545760761761393 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.207 |
|
- type: map_at_10 |
|
value: 1.6 |
|
- type: map_at_100 |
|
value: 8.594 |
|
- type: map_at_1000 |
|
value: 20.213 |
|
- type: map_at_3 |
|
value: 0.585 |
|
- type: map_at_5 |
|
value: 0.9039999999999999 |
|
- type: mrr_at_1 |
|
value: 78.0 |
|
- type: mrr_at_10 |
|
value: 87.4 |
|
- type: mrr_at_100 |
|
value: 87.4 |
|
- type: mrr_at_1000 |
|
value: 87.4 |
|
- type: mrr_at_3 |
|
value: 86.667 |
|
- type: mrr_at_5 |
|
value: 87.06700000000001 |
|
- type: ndcg_at_1 |
|
value: 73.0 |
|
- type: ndcg_at_10 |
|
value: 65.18 |
|
- type: ndcg_at_100 |
|
value: 49.631 |
|
- type: ndcg_at_1000 |
|
value: 43.498999999999995 |
|
- type: ndcg_at_3 |
|
value: 71.83800000000001 |
|
- type: ndcg_at_5 |
|
value: 69.271 |
|
- type: precision_at_1 |
|
value: 78.0 |
|
- type: precision_at_10 |
|
value: 69.19999999999999 |
|
- type: precision_at_100 |
|
value: 50.980000000000004 |
|
- type: precision_at_1000 |
|
value: 19.426 |
|
- type: precision_at_3 |
|
value: 77.333 |
|
- type: precision_at_5 |
|
value: 74.0 |
|
- type: recall_at_1 |
|
value: 0.207 |
|
- type: recall_at_10 |
|
value: 1.822 |
|
- type: recall_at_100 |
|
value: 11.849 |
|
- type: recall_at_1000 |
|
value: 40.492 |
|
- type: recall_at_3 |
|
value: 0.622 |
|
- type: recall_at_5 |
|
value: 0.9809999999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.001 |
|
- type: map_at_10 |
|
value: 10.376000000000001 |
|
- type: map_at_100 |
|
value: 16.936999999999998 |
|
- type: map_at_1000 |
|
value: 18.615000000000002 |
|
- type: map_at_3 |
|
value: 5.335999999999999 |
|
- type: map_at_5 |
|
value: 7.374 |
|
- type: mrr_at_1 |
|
value: 20.408 |
|
- type: mrr_at_10 |
|
value: 38.29 |
|
- type: mrr_at_100 |
|
value: 39.33 |
|
- type: mrr_at_1000 |
|
value: 39.347 |
|
- type: mrr_at_3 |
|
value: 32.993 |
|
- type: mrr_at_5 |
|
value: 36.973 |
|
- type: ndcg_at_1 |
|
value: 17.347 |
|
- type: ndcg_at_10 |
|
value: 23.515 |
|
- type: ndcg_at_100 |
|
value: 37.457 |
|
- type: ndcg_at_1000 |
|
value: 49.439 |
|
- type: ndcg_at_3 |
|
value: 22.762999999999998 |
|
- type: ndcg_at_5 |
|
value: 22.622 |
|
- type: precision_at_1 |
|
value: 20.408 |
|
- type: precision_at_10 |
|
value: 22.448999999999998 |
|
- type: precision_at_100 |
|
value: 8.184 |
|
- type: precision_at_1000 |
|
value: 1.608 |
|
- type: precision_at_3 |
|
value: 25.85 |
|
- type: precision_at_5 |
|
value: 25.306 |
|
- type: recall_at_1 |
|
value: 2.001 |
|
- type: recall_at_10 |
|
value: 17.422 |
|
- type: recall_at_100 |
|
value: 51.532999999999994 |
|
- type: recall_at_1000 |
|
value: 87.466 |
|
- type: recall_at_3 |
|
value: 6.861000000000001 |
|
- type: recall_at_5 |
|
value: 10.502 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.54419999999999 |
|
- type: ap |
|
value: 14.372170450843907 |
|
- type: f1 |
|
value: 54.94420257390529 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 59.402942840973395 |
|
- type: f1 |
|
value: 59.4166538875571 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 41.569064336457906 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.31322644096085 |
|
- type: cos_sim_ap |
|
value: 72.14518894837381 |
|
- type: cos_sim_f1 |
|
value: 66.67489813557229 |
|
- type: cos_sim_precision |
|
value: 62.65954977953121 |
|
- type: cos_sim_recall |
|
value: 71.2401055408971 |
|
- type: dot_accuracy |
|
value: 85.31322644096085 |
|
- type: dot_ap |
|
value: 72.14521480685293 |
|
- type: dot_f1 |
|
value: 66.67489813557229 |
|
- type: dot_precision |
|
value: 62.65954977953121 |
|
- type: dot_recall |
|
value: 71.2401055408971 |
|
- type: euclidean_accuracy |
|
value: 85.31322644096085 |
|
- type: euclidean_ap |
|
value: 72.14520820485349 |
|
- type: euclidean_f1 |
|
value: 66.67489813557229 |
|
- type: euclidean_precision |
|
value: 62.65954977953121 |
|
- type: euclidean_recall |
|
value: 71.2401055408971 |
|
- type: manhattan_accuracy |
|
value: 85.21785778148656 |
|
- type: manhattan_ap |
|
value: 72.01177147657364 |
|
- type: manhattan_f1 |
|
value: 66.62594673833374 |
|
- type: manhattan_precision |
|
value: 62.0336669699727 |
|
- type: manhattan_recall |
|
value: 71.95250659630607 |
|
- type: max_accuracy |
|
value: 85.31322644096085 |
|
- type: max_ap |
|
value: 72.14521480685293 |
|
- type: max_f1 |
|
value: 66.67489813557229 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 89.12756626693057 |
|
- type: cos_sim_ap |
|
value: 86.05430786440826 |
|
- type: cos_sim_f1 |
|
value: 78.27759692216631 |
|
- type: cos_sim_precision |
|
value: 75.33466248931929 |
|
- type: cos_sim_recall |
|
value: 81.45980905451185 |
|
- type: dot_accuracy |
|
value: 89.12950673341872 |
|
- type: dot_ap |
|
value: 86.05431161145492 |
|
- type: dot_f1 |
|
value: 78.27759692216631 |
|
- type: dot_precision |
|
value: 75.33466248931929 |
|
- type: dot_recall |
|
value: 81.45980905451185 |
|
- type: euclidean_accuracy |
|
value: 89.12756626693057 |
|
- type: euclidean_ap |
|
value: 86.05431303247397 |
|
- type: euclidean_f1 |
|
value: 78.27759692216631 |
|
- type: euclidean_precision |
|
value: 75.33466248931929 |
|
- type: euclidean_recall |
|
value: 81.45980905451185 |
|
- type: manhattan_accuracy |
|
value: 89.04994760740482 |
|
- type: manhattan_ap |
|
value: 86.00860610892074 |
|
- type: manhattan_f1 |
|
value: 78.1846776005392 |
|
- type: manhattan_precision |
|
value: 76.10438839480975 |
|
- type: manhattan_recall |
|
value: 80.3818909762858 |
|
- type: max_accuracy |
|
value: 89.12950673341872 |
|
- type: max_ap |
|
value: 86.05431303247397 |
|
- type: max_f1 |
|
value: 78.27759692216631 |
|
--- |
|
<!-- TODO: add evaluation results here --> |
|
<br><br> |
|
|
|
<p align="center"> |
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<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
|
</p> |
|
|
|
|
|
<p align="center"> |
|
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
|
</p> |
|
|
|
## Quick Start |
|
|
|
The easiest way to starting using `jina-embeddings-v2-small-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). |
|
|
|
|
|
## Intended Usage & Model Info |
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|
|
`jina-embeddings-v2-small-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. |
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It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length. |
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The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset. |
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The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. |
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These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. |
|
|
|
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. |
|
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc. |
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|
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This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance. |
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Additionally, we provide the following embedding models: |
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|
|
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**. |
|
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters. |
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- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings. |
|
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings. |
|
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon). |
|
|
|
## Data & Parameters |
|
|
|
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923) |
|
|
|
## Usage |
|
|
|
**<details><summary>Please apply mean pooling when integrating the model.</summary>** |
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<p> |
|
|
|
### Why mean pooling? |
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|
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`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. |
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It has been proved to be the most effective way to produce high-quality sentence embeddings. |
|
We offer an `encode` function to deal with this. |
|
|
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However, if you would like to do it without using the default `encode` function: |
|
|
|
```python |
|
import torch |
|
import torch.nn.functional as F |
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from transformers import AutoTokenizer, AutoModel |
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|
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] |
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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) |
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|
|
sentences = ['How is the weather today?', 'What is the current weather like today?'] |
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|
|
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en') |
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model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) |
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
with torch.no_grad(): |
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model_output = model(**encoded_input) |
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|
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embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
|
``` |
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|
|
</p> |
|
</details> |
|
|
|
You can use Jina Embedding models directly from transformers package. |
|
|
|
First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co/docs/hub/security-tokens): |
|
```bash |
|
huggingface-cli login |
|
``` |
|
Alternatively, you can provide the access token as an environment variable in the shell: |
|
```bash |
|
export HF_TOKEN="<your token here>" |
|
``` |
|
or in Python: |
|
```python |
|
import os |
|
|
|
os.environ['HF_TOKEN'] = "<your token here>" |
|
``` |
|
|
|
Then, you can use load and use the model via the `AutoModel` class: |
|
```python |
|
!pip install transformers |
|
from transformers import AutoModel |
|
from numpy.linalg import norm |
|
|
|
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) |
|
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method |
|
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?']) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function: |
|
|
|
```python |
|
embeddings = model.encode( |
|
['Very long ... document'], |
|
max_length=2048 |
|
) |
|
``` |
|
|
|
Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well): |
|
|
|
```python |
|
!pip install -U sentence-transformers |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
model = SentenceTransformer( |
|
"jinaai/jina-embeddings-v2-small-en", # switch to en/zh for English or Chinese |
|
trust_remote_code=True |
|
) |
|
|
|
# control your input sequence length up to 8192 |
|
model.max_seq_length = 1024 |
|
|
|
embeddings = model.encode([ |
|
'How is the weather today?', |
|
'What is the current weather like today?' |
|
]) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
## Alternatives to Using Transformers Package |
|
|
|
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). |
|
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy). |
|
|
|
## RAG Performance |
|
|
|
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83), |
|
|
|
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out. |
|
|
|
|
|
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px"> |
|
|
|
## Plans |
|
|
|
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese. |
|
2. Multimodal embedding models enable Multimodal RAG applications. |
|
3. High-performt rerankers. |
|
|
|
## Trouble Shooting |
|
|
|
**Loading of Model Code failed** |
|
|
|
If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized. |
|
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model: |
|
|
|
```bash |
|
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ... |
|
``` |
|
|
|
|
|
**User is not logged into Huggingface** |
|
|
|
The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated). |
|
This means you need to be logged into huggingface load load it. |
|
If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above: |
|
```bash |
|
OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' |
|
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`. |
|
``` |
|
|
|
## Contact |
|
|
|
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
|
|
|
## Citation |
|
|
|
If you find Jina Embeddings useful in your research, please cite the following paper: |
|
|
|
``` |
|
@misc{günther2023jina, |
|
title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, |
|
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao}, |
|
year={2023}, |
|
eprint={2310.19923}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
