|
--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- finetuner |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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datasets: |
|
- jinaai/negation-dataset |
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language: en |
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license: apache-2.0 |
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model-index: |
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- name: jina-embedding-s-en-v1 |
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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: 64.58208955223881 |
|
- type: ap |
|
value: 27.24359671025387 |
|
- type: f1 |
|
value: 58.201387941715495 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
|
config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 61.926550000000006 |
|
- type: ap |
|
value: 58.40954250092862 |
|
- type: f1 |
|
value: 59.921771639047904 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 28.499999999999996 |
|
- type: f1 |
|
value: 27.160929516206465 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.262 |
|
- type: map_at_10 |
|
value: 36.677 |
|
- type: map_at_100 |
|
value: 37.839 |
|
- type: map_at_1000 |
|
value: 37.857 |
|
- type: map_at_3 |
|
value: 31.685999999999996 |
|
- type: map_at_5 |
|
value: 34.544999999999995 |
|
- type: mrr_at_1 |
|
value: 22.404 |
|
- type: mrr_at_10 |
|
value: 36.713 |
|
- type: mrr_at_100 |
|
value: 37.881 |
|
- type: mrr_at_1000 |
|
value: 37.899 |
|
- type: mrr_at_3 |
|
value: 31.709 |
|
- type: mrr_at_5 |
|
value: 34.629 |
|
- type: ndcg_at_1 |
|
value: 22.262 |
|
- type: ndcg_at_10 |
|
value: 45.18 |
|
- type: ndcg_at_100 |
|
value: 50.4 |
|
- type: ndcg_at_1000 |
|
value: 50.841 |
|
- type: ndcg_at_3 |
|
value: 34.882000000000005 |
|
- type: ndcg_at_5 |
|
value: 40.036 |
|
- type: precision_at_1 |
|
value: 22.262 |
|
- type: precision_at_10 |
|
value: 7.255000000000001 |
|
- type: precision_at_100 |
|
value: 0.959 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 14.723 |
|
- type: precision_at_5 |
|
value: 11.337 |
|
- type: recall_at_1 |
|
value: 22.262 |
|
- type: recall_at_10 |
|
value: 72.54599999999999 |
|
- type: recall_at_100 |
|
value: 95.946 |
|
- type: recall_at_1000 |
|
value: 99.36 |
|
- type: recall_at_3 |
|
value: 44.168 |
|
- type: recall_at_5 |
|
value: 56.686 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 34.97570470844357 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 24.372872291698265 |
|
- 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: 60.58753030525579 |
|
- type: mrr |
|
value: 75.03484588664644 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.21378425036666 |
|
- type: cos_sim_spearman |
|
value: 80.45665253651644 |
|
- type: euclidean_pearson |
|
value: 46.71436482437946 |
|
- type: euclidean_spearman |
|
value: 45.13476336596072 |
|
- type: manhattan_pearson |
|
value: 47.06449770246884 |
|
- type: manhattan_spearman |
|
value: 45.498627078529 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
|
- type: accuracy |
|
value: 74.48701298701299 |
|
- type: f1 |
|
value: 73.30813366682357 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 29.66289767477026 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 22.324367934720776 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
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name: MTEB ClimateFEVER |
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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: 6.524000000000001 |
|
- type: map_at_10 |
|
value: 11.187 |
|
- type: map_at_100 |
|
value: 12.389999999999999 |
|
- type: map_at_1000 |
|
value: 12.559000000000001 |
|
- type: map_at_3 |
|
value: 9.386 |
|
- type: map_at_5 |
|
value: 10.295 |
|
- type: mrr_at_1 |
|
value: 13.941 |
|
- type: mrr_at_10 |
|
value: 22.742 |
|
- type: mrr_at_100 |
|
value: 23.896 |
|
- type: mrr_at_1000 |
|
value: 23.965 |
|
- type: mrr_at_3 |
|
value: 19.881 |
|
- type: mrr_at_5 |
|
value: 21.555 |
|
- type: ndcg_at_1 |
|
value: 13.941 |
|
- type: ndcg_at_10 |
|
value: 16.619999999999997 |
|
- type: ndcg_at_100 |
|
value: 22.415 |
|
- type: ndcg_at_1000 |
|
value: 26.05 |
|
- type: ndcg_at_3 |
|
value: 13.148000000000001 |
|
- type: ndcg_at_5 |
|
value: 14.433000000000002 |
|
- type: precision_at_1 |
|
value: 13.941 |
|
- type: precision_at_10 |
|
value: 5.153 |
|
- type: precision_at_100 |
|
value: 1.124 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_3 |
|
value: 9.685 |
|
- type: precision_at_5 |
|
value: 7.582999999999999 |
|
- type: recall_at_1 |
|
value: 6.524000000000001 |
|
- type: recall_at_10 |
|
value: 21.041999999999998 |
|
- type: recall_at_100 |
|
value: 41.515 |
|
- type: recall_at_1000 |
|
value: 62.507999999999996 |
|
- type: recall_at_3 |
|
value: 12.549 |
|
- type: recall_at_5 |
|
value: 15.939999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.483 |
|
- type: map_at_10 |
|
value: 11.955 |
|
- type: map_at_100 |
|
value: 15.470999999999998 |
|
- type: map_at_1000 |
|
value: 16.308 |
|
- type: map_at_3 |
|
value: 9.292 |
|
- type: map_at_5 |
|
value: 10.459 |
|
- type: mrr_at_1 |
|
value: 50.74999999999999 |
|
- type: mrr_at_10 |
|
value: 58.743 |
|
- type: mrr_at_100 |
|
value: 59.41499999999999 |
|
- type: mrr_at_1000 |
|
value: 59.431999999999995 |
|
- type: mrr_at_3 |
|
value: 56.708000000000006 |
|
- type: mrr_at_5 |
|
value: 57.80800000000001 |
|
- type: ndcg_at_1 |
|
value: 39.0 |
|
- type: ndcg_at_10 |
|
value: 26.721 |
|
- type: ndcg_at_100 |
|
value: 29.366999999999997 |
|
- type: ndcg_at_1000 |
|
value: 35.618 |
|
- type: ndcg_at_3 |
|
value: 31.244 |
|
- type: ndcg_at_5 |
|
value: 28.614 |
|
- type: precision_at_1 |
|
value: 50.74999999999999 |
|
- type: precision_at_10 |
|
value: 20.45 |
|
- type: precision_at_100 |
|
value: 6.0600000000000005 |
|
- type: precision_at_1000 |
|
value: 1.346 |
|
- type: precision_at_3 |
|
value: 33.917 |
|
- type: precision_at_5 |
|
value: 26.950000000000003 |
|
- type: recall_at_1 |
|
value: 6.483 |
|
- type: recall_at_10 |
|
value: 16.215 |
|
- type: recall_at_100 |
|
value: 33.382 |
|
- type: recall_at_1000 |
|
value: 54.445 |
|
- type: recall_at_3 |
|
value: 10.6 |
|
- type: recall_at_5 |
|
value: 12.889999999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 34.39 |
|
- type: f1 |
|
value: 31.334865751249474 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 44.698 |
|
- type: map_at_10 |
|
value: 55.30500000000001 |
|
- type: map_at_100 |
|
value: 55.838 |
|
- type: map_at_1000 |
|
value: 55.87 |
|
- type: map_at_3 |
|
value: 52.884 |
|
- type: map_at_5 |
|
value: 54.352000000000004 |
|
- type: mrr_at_1 |
|
value: 48.32 |
|
- type: mrr_at_10 |
|
value: 59.39 |
|
- type: mrr_at_100 |
|
value: 59.89 |
|
- type: mrr_at_1000 |
|
value: 59.913000000000004 |
|
- type: mrr_at_3 |
|
value: 56.977999999999994 |
|
- type: mrr_at_5 |
|
value: 58.44200000000001 |
|
- type: ndcg_at_1 |
|
value: 48.32 |
|
- type: ndcg_at_10 |
|
value: 61.23800000000001 |
|
- type: ndcg_at_100 |
|
value: 63.79 |
|
- type: ndcg_at_1000 |
|
value: 64.575 |
|
- type: ndcg_at_3 |
|
value: 56.489999999999995 |
|
- type: ndcg_at_5 |
|
value: 59.016999999999996 |
|
- type: precision_at_1 |
|
value: 48.32 |
|
- type: precision_at_10 |
|
value: 8.288 |
|
- type: precision_at_100 |
|
value: 0.964 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 22.867 |
|
- type: precision_at_5 |
|
value: 15.098 |
|
- type: recall_at_1 |
|
value: 44.698 |
|
- type: recall_at_10 |
|
value: 75.752 |
|
- type: recall_at_100 |
|
value: 87.402 |
|
- type: recall_at_1000 |
|
value: 93.316 |
|
- type: recall_at_3 |
|
value: 62.82600000000001 |
|
- type: recall_at_5 |
|
value: 69.01899999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.119 |
|
- type: map_at_10 |
|
value: 20.299 |
|
- type: map_at_100 |
|
value: 21.863 |
|
- type: map_at_1000 |
|
value: 22.064 |
|
- type: map_at_3 |
|
value: 17.485999999999997 |
|
- type: map_at_5 |
|
value: 19.148 |
|
- type: mrr_at_1 |
|
value: 24.383 |
|
- type: mrr_at_10 |
|
value: 33.074 |
|
- type: mrr_at_100 |
|
value: 34.03 |
|
- type: mrr_at_1000 |
|
value: 34.102 |
|
- type: mrr_at_3 |
|
value: 30.736 |
|
- type: mrr_at_5 |
|
value: 32.202 |
|
- type: ndcg_at_1 |
|
value: 24.383 |
|
- type: ndcg_at_10 |
|
value: 26.645999999999997 |
|
- type: ndcg_at_100 |
|
value: 33.348 |
|
- type: ndcg_at_1000 |
|
value: 37.294 |
|
- type: ndcg_at_3 |
|
value: 23.677 |
|
- type: ndcg_at_5 |
|
value: 24.935 |
|
- type: precision_at_1 |
|
value: 24.383 |
|
- type: precision_at_10 |
|
value: 7.654 |
|
- type: precision_at_100 |
|
value: 1.461 |
|
- type: precision_at_1000 |
|
value: 0.214 |
|
- type: precision_at_3 |
|
value: 16.101 |
|
- type: precision_at_5 |
|
value: 12.222 |
|
- type: recall_at_1 |
|
value: 12.119 |
|
- type: recall_at_10 |
|
value: 32.531 |
|
- type: recall_at_100 |
|
value: 58.028999999999996 |
|
- type: recall_at_1000 |
|
value: 82.513 |
|
- type: recall_at_3 |
|
value: 21.787 |
|
- type: recall_at_5 |
|
value: 27.229999999999997 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.057000000000002 |
|
- type: map_at_10 |
|
value: 34.892 |
|
- type: map_at_100 |
|
value: 35.687000000000005 |
|
- type: map_at_1000 |
|
value: 35.763 |
|
- type: map_at_3 |
|
value: 32.879000000000005 |
|
- type: map_at_5 |
|
value: 34.105000000000004 |
|
- type: mrr_at_1 |
|
value: 52.113 |
|
- type: mrr_at_10 |
|
value: 58.940000000000005 |
|
- type: mrr_at_100 |
|
value: 59.438 |
|
- type: mrr_at_1000 |
|
value: 59.473 |
|
- type: mrr_at_3 |
|
value: 57.299 |
|
- type: mrr_at_5 |
|
value: 58.353 |
|
- type: ndcg_at_1 |
|
value: 52.113 |
|
- type: ndcg_at_10 |
|
value: 43.105 |
|
- type: ndcg_at_100 |
|
value: 46.44 |
|
- type: ndcg_at_1000 |
|
value: 48.241 |
|
- type: ndcg_at_3 |
|
value: 39.566 |
|
- type: ndcg_at_5 |
|
value: 41.508 |
|
- type: precision_at_1 |
|
value: 52.113 |
|
- type: precision_at_10 |
|
value: 8.892999999999999 |
|
- type: precision_at_100 |
|
value: 1.1520000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 24.398 |
|
- type: precision_at_5 |
|
value: 16.181 |
|
- type: recall_at_1 |
|
value: 26.057000000000002 |
|
- type: recall_at_10 |
|
value: 44.463 |
|
- type: recall_at_100 |
|
value: 57.616 |
|
- type: recall_at_1000 |
|
value: 69.65599999999999 |
|
- type: recall_at_3 |
|
value: 36.597 |
|
- type: recall_at_5 |
|
value: 40.452 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 58.268399999999986 |
|
- type: ap |
|
value: 55.03852332714837 |
|
- type: f1 |
|
value: 57.23656436062262 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.273 |
|
- type: map_at_10 |
|
value: 23.953 |
|
- type: map_at_100 |
|
value: 25.207 |
|
- type: map_at_1000 |
|
value: 25.285999999999998 |
|
- type: map_at_3 |
|
value: 20.727 |
|
- type: map_at_5 |
|
value: 22.492 |
|
- type: mrr_at_1 |
|
value: 14.685 |
|
- type: mrr_at_10 |
|
value: 24.423000000000002 |
|
- type: mrr_at_100 |
|
value: 25.64 |
|
- type: mrr_at_1000 |
|
value: 25.713 |
|
- type: mrr_at_3 |
|
value: 21.213 |
|
- type: mrr_at_5 |
|
value: 22.979 |
|
- type: ndcg_at_1 |
|
value: 14.685 |
|
- type: ndcg_at_10 |
|
value: 29.698 |
|
- type: ndcg_at_100 |
|
value: 36.010999999999996 |
|
- type: ndcg_at_1000 |
|
value: 38.102999999999994 |
|
- type: ndcg_at_3 |
|
value: 23.0 |
|
- type: ndcg_at_5 |
|
value: 26.186 |
|
- type: precision_at_1 |
|
value: 14.685 |
|
- type: precision_at_10 |
|
value: 4.954 |
|
- type: precision_at_100 |
|
value: 0.815 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 10.038 |
|
- type: precision_at_5 |
|
value: 7.636 |
|
- type: recall_at_1 |
|
value: 14.273 |
|
- type: recall_at_10 |
|
value: 47.559000000000005 |
|
- type: recall_at_100 |
|
value: 77.375 |
|
- type: recall_at_1000 |
|
value: 93.616 |
|
- type: recall_at_3 |
|
value: 29.110999999999997 |
|
- type: recall_at_5 |
|
value: 36.825 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 89.85636114911081 |
|
- type: f1 |
|
value: 89.65403786390279 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 59.03784769721842 |
|
- type: f1 |
|
value: 42.57604111096128 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 65.00336247478144 |
|
- type: f1 |
|
value: 63.12578076844032 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 72.14862138533962 |
|
- type: f1 |
|
value: 71.91174720216141 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 28.259326082067094 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 23.874256261395775 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 29.251614283788385 |
|
- type: mrr |
|
value: 29.9695581475798 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.9309999999999996 |
|
- type: map_at_10 |
|
value: 8.472 |
|
- type: map_at_100 |
|
value: 10.461 |
|
- type: map_at_1000 |
|
value: 11.588 |
|
- type: map_at_3 |
|
value: 6.343999999999999 |
|
- type: map_at_5 |
|
value: 7.379 |
|
- type: mrr_at_1 |
|
value: 35.913000000000004 |
|
- type: mrr_at_10 |
|
value: 43.91 |
|
- type: mrr_at_100 |
|
value: 44.519999999999996 |
|
- type: mrr_at_1000 |
|
value: 44.59 |
|
- type: mrr_at_3 |
|
value: 41.589 |
|
- type: mrr_at_5 |
|
value: 42.626 |
|
- type: ndcg_at_1 |
|
value: 34.52 |
|
- type: ndcg_at_10 |
|
value: 25.128 |
|
- type: ndcg_at_100 |
|
value: 22.917 |
|
- type: ndcg_at_1000 |
|
value: 31.64 |
|
- type: ndcg_at_3 |
|
value: 29.866999999999997 |
|
- type: ndcg_at_5 |
|
value: 27.494000000000003 |
|
- type: precision_at_1 |
|
value: 35.913000000000004 |
|
- type: precision_at_10 |
|
value: 18.607000000000003 |
|
- type: precision_at_100 |
|
value: 6.006 |
|
- type: precision_at_1000 |
|
value: 1.814 |
|
- type: precision_at_3 |
|
value: 28.277 |
|
- type: precision_at_5 |
|
value: 23.777 |
|
- type: recall_at_1 |
|
value: 3.9309999999999996 |
|
- type: recall_at_10 |
|
value: 11.684 |
|
- type: recall_at_100 |
|
value: 24.212 |
|
- type: recall_at_1000 |
|
value: 55.36 |
|
- type: recall_at_3 |
|
value: 7.329 |
|
- type: recall_at_5 |
|
value: 9.059000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.03 |
|
- type: map_at_10 |
|
value: 30.990000000000002 |
|
- type: map_at_100 |
|
value: 32.211 |
|
- type: map_at_1000 |
|
value: 32.267 |
|
- type: map_at_3 |
|
value: 26.833000000000002 |
|
- type: map_at_5 |
|
value: 29.128 |
|
- type: mrr_at_1 |
|
value: 21.523999999999997 |
|
- type: mrr_at_10 |
|
value: 33.085 |
|
- type: mrr_at_100 |
|
value: 34.096 |
|
- type: mrr_at_1000 |
|
value: 34.139 |
|
- type: mrr_at_3 |
|
value: 29.354999999999997 |
|
- type: mrr_at_5 |
|
value: 31.441999999999997 |
|
- type: ndcg_at_1 |
|
value: 21.495 |
|
- type: ndcg_at_10 |
|
value: 37.971 |
|
- type: ndcg_at_100 |
|
value: 43.492999999999995 |
|
- type: ndcg_at_1000 |
|
value: 44.925 |
|
- type: ndcg_at_3 |
|
value: 29.808 |
|
- type: ndcg_at_5 |
|
value: 33.748 |
|
- type: precision_at_1 |
|
value: 21.495 |
|
- type: precision_at_10 |
|
value: 6.819 |
|
- type: precision_at_100 |
|
value: 0.991 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 13.886000000000001 |
|
- type: precision_at_5 |
|
value: 10.574 |
|
- type: recall_at_1 |
|
value: 19.03 |
|
- type: recall_at_10 |
|
value: 57.493 |
|
- type: recall_at_100 |
|
value: 82.03200000000001 |
|
- type: recall_at_1000 |
|
value: 92.879 |
|
- type: recall_at_3 |
|
value: 35.899 |
|
- type: recall_at_5 |
|
value: 45.092 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 67.97 |
|
- type: map_at_10 |
|
value: 81.478 |
|
- type: map_at_100 |
|
value: 82.147 |
|
- type: map_at_1000 |
|
value: 82.172 |
|
- type: map_at_3 |
|
value: 78.456 |
|
- type: map_at_5 |
|
value: 80.337 |
|
- type: mrr_at_1 |
|
value: 78.24 |
|
- type: mrr_at_10 |
|
value: 84.941 |
|
- type: mrr_at_100 |
|
value: 85.08099999999999 |
|
- type: mrr_at_1000 |
|
value: 85.083 |
|
- type: mrr_at_3 |
|
value: 83.743 |
|
- type: mrr_at_5 |
|
value: 84.553 |
|
- type: ndcg_at_1 |
|
value: 78.24 |
|
- type: ndcg_at_10 |
|
value: 85.61999999999999 |
|
- type: ndcg_at_100 |
|
value: 87.113 |
|
- type: ndcg_at_1000 |
|
value: 87.318 |
|
- type: ndcg_at_3 |
|
value: 82.403 |
|
- type: ndcg_at_5 |
|
value: 84.15700000000001 |
|
- type: precision_at_1 |
|
value: 78.24 |
|
- type: precision_at_10 |
|
value: 12.979 |
|
- type: precision_at_100 |
|
value: 1.503 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 35.9 |
|
- type: precision_at_5 |
|
value: 23.704 |
|
- type: recall_at_1 |
|
value: 67.97 |
|
- type: recall_at_10 |
|
value: 93.563 |
|
- type: recall_at_100 |
|
value: 98.834 |
|
- type: recall_at_1000 |
|
value: 99.901 |
|
- type: recall_at_3 |
|
value: 84.319 |
|
- type: recall_at_5 |
|
value: 89.227 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 35.853649010160694 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 47.270443152349415 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.803 |
|
- type: map_at_10 |
|
value: 8.790000000000001 |
|
- type: map_at_100 |
|
value: 10.313 |
|
- type: map_at_1000 |
|
value: 10.562000000000001 |
|
- type: map_at_3 |
|
value: 6.483 |
|
- type: map_at_5 |
|
value: 7.591 |
|
- type: mrr_at_1 |
|
value: 18.7 |
|
- type: mrr_at_10 |
|
value: 27.349 |
|
- type: mrr_at_100 |
|
value: 28.474 |
|
- type: mrr_at_1000 |
|
value: 28.544999999999998 |
|
- type: mrr_at_3 |
|
value: 24.567 |
|
- type: mrr_at_5 |
|
value: 26.172 |
|
- type: ndcg_at_1 |
|
value: 18.7 |
|
- type: ndcg_at_10 |
|
value: 15.155 |
|
- type: ndcg_at_100 |
|
value: 21.63 |
|
- type: ndcg_at_1000 |
|
value: 26.595999999999997 |
|
- type: ndcg_at_3 |
|
value: 14.706 |
|
- type: ndcg_at_5 |
|
value: 12.681999999999999 |
|
- type: precision_at_1 |
|
value: 18.7 |
|
- type: precision_at_10 |
|
value: 7.6899999999999995 |
|
- type: precision_at_100 |
|
value: 1.7080000000000002 |
|
- type: precision_at_1000 |
|
value: 0.291 |
|
- type: precision_at_3 |
|
value: 13.567000000000002 |
|
- type: precision_at_5 |
|
value: 10.9 |
|
- type: recall_at_1 |
|
value: 3.803 |
|
- type: recall_at_10 |
|
value: 15.607 |
|
- type: recall_at_100 |
|
value: 34.717999999999996 |
|
- type: recall_at_1000 |
|
value: 59.150000000000006 |
|
- type: recall_at_3 |
|
value: 8.258000000000001 |
|
- type: recall_at_5 |
|
value: 11.063 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.05755556071047 |
|
- type: cos_sim_spearman |
|
value: 72.44408263672771 |
|
- type: euclidean_pearson |
|
value: 71.65314814604668 |
|
- type: euclidean_spearman |
|
value: 65.1833695751109 |
|
- type: manhattan_pearson |
|
value: 71.81874115177355 |
|
- type: manhattan_spearman |
|
value: 65.45940792270201 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.75836272926722 |
|
- type: cos_sim_spearman |
|
value: 73.63905703662927 |
|
- type: euclidean_pearson |
|
value: 67.58539517215293 |
|
- type: euclidean_spearman |
|
value: 58.88440181413321 |
|
- type: manhattan_pearson |
|
value: 66.56872028174024 |
|
- type: manhattan_spearman |
|
value: 58.48195528793699 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 76.58680032464127 |
|
- type: cos_sim_spearman |
|
value: 78.03760988363273 |
|
- type: euclidean_pearson |
|
value: 68.23192805876019 |
|
- type: euclidean_spearman |
|
value: 69.21753515532978 |
|
- type: manhattan_pearson |
|
value: 68.07876685109447 |
|
- type: manhattan_spearman |
|
value: 69.08026107263751 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.72357139489792 |
|
- type: cos_sim_spearman |
|
value: 74.53681843472086 |
|
- type: euclidean_pearson |
|
value: 66.73161230236408 |
|
- type: euclidean_spearman |
|
value: 63.81392957525887 |
|
- type: manhattan_pearson |
|
value: 66.33322201893088 |
|
- type: manhattan_spearman |
|
value: 63.55218357111819 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.62456549757793 |
|
- type: cos_sim_spearman |
|
value: 83.89301877076606 |
|
- type: euclidean_pearson |
|
value: 58.128415035981554 |
|
- type: euclidean_spearman |
|
value: 58.47993973876889 |
|
- type: manhattan_pearson |
|
value: 58.37634990795807 |
|
- type: manhattan_spearman |
|
value: 58.89541748905865 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 76.79731685895317 |
|
- type: cos_sim_spearman |
|
value: 79.04240201103201 |
|
- type: euclidean_pearson |
|
value: 64.26869512572189 |
|
- type: euclidean_spearman |
|
value: 65.09728500847595 |
|
- type: manhattan_pearson |
|
value: 64.2772185991136 |
|
- type: manhattan_spearman |
|
value: 65.18852760227209 |
|
- 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: 86.30962737077412 |
|
- type: cos_sim_spearman |
|
value: 86.77386963770132 |
|
- type: euclidean_pearson |
|
value: 70.0534100015362 |
|
- type: euclidean_spearman |
|
value: 68.17903243639661 |
|
- type: manhattan_pearson |
|
value: 70.03048392176451 |
|
- type: manhattan_spearman |
|
value: 68.19594588464386 |
|
- 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: 64.77791754851359 |
|
- type: cos_sim_spearman |
|
value: 64.28210927783513 |
|
- type: euclidean_pearson |
|
value: 36.337603238543956 |
|
- type: euclidean_spearman |
|
value: 52.70617012481411 |
|
- type: manhattan_pearson |
|
value: 35.49141141164909 |
|
- type: manhattan_spearman |
|
value: 52.084744319382835 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.741579322503 |
|
- type: cos_sim_spearman |
|
value: 78.83687709048151 |
|
- type: euclidean_pearson |
|
value: 66.59151974274772 |
|
- type: euclidean_spearman |
|
value: 63.76907648545863 |
|
- type: manhattan_pearson |
|
value: 66.91555116739791 |
|
- type: manhattan_spearman |
|
value: 64.2024945118848 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 74.31125049985503 |
|
- type: mrr |
|
value: 91.5911222038673 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.983000000000004 |
|
- type: map_at_10 |
|
value: 48.79 |
|
- type: map_at_100 |
|
value: 49.419999999999995 |
|
- type: map_at_1000 |
|
value: 49.495 |
|
- type: map_at_3 |
|
value: 46.394000000000005 |
|
- type: map_at_5 |
|
value: 47.772999999999996 |
|
- type: mrr_at_1 |
|
value: 42.667 |
|
- type: mrr_at_10 |
|
value: 51.088 |
|
- type: mrr_at_100 |
|
value: 51.498999999999995 |
|
- type: mrr_at_1000 |
|
value: 51.564 |
|
- type: mrr_at_3 |
|
value: 49.111 |
|
- type: mrr_at_5 |
|
value: 50.278 |
|
- type: ndcg_at_1 |
|
value: 42.667 |
|
- type: ndcg_at_10 |
|
value: 53.586999999999996 |
|
- type: ndcg_at_100 |
|
value: 56.519 |
|
- type: ndcg_at_1000 |
|
value: 58.479000000000006 |
|
- type: ndcg_at_3 |
|
value: 49.053000000000004 |
|
- type: ndcg_at_5 |
|
value: 51.209 |
|
- type: precision_at_1 |
|
value: 42.667 |
|
- type: precision_at_10 |
|
value: 7.3999999999999995 |
|
- type: precision_at_100 |
|
value: 0.9129999999999999 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 19.444 |
|
- type: precision_at_5 |
|
value: 13.067 |
|
- type: recall_at_1 |
|
value: 39.983000000000004 |
|
- type: recall_at_10 |
|
value: 66.333 |
|
- type: recall_at_100 |
|
value: 80.256 |
|
- type: recall_at_1000 |
|
value: 95.667 |
|
- type: recall_at_3 |
|
value: 53.449999999999996 |
|
- type: recall_at_5 |
|
value: 58.989000000000004 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.6930693069307 |
|
- type: cos_sim_ap |
|
value: 90.94265768188356 |
|
- type: cos_sim_f1 |
|
value: 84.15792103948026 |
|
- type: cos_sim_precision |
|
value: 84.11588411588411 |
|
- type: cos_sim_recall |
|
value: 84.2 |
|
- type: dot_accuracy |
|
value: 99.12178217821783 |
|
- type: dot_ap |
|
value: 42.77306613711772 |
|
- type: dot_f1 |
|
value: 44.23963133640553 |
|
- type: dot_precision |
|
value: 38.0677721701514 |
|
- type: dot_recall |
|
value: 52.800000000000004 |
|
- type: euclidean_accuracy |
|
value: 99.55049504950495 |
|
- type: euclidean_ap |
|
value: 78.83886818298362 |
|
- type: euclidean_f1 |
|
value: 74.54645409565696 |
|
- type: euclidean_precision |
|
value: 82.78388278388277 |
|
- type: euclidean_recall |
|
value: 67.80000000000001 |
|
- type: manhattan_accuracy |
|
value: 99.54257425742574 |
|
- type: manhattan_ap |
|
value: 77.98046807031727 |
|
- type: manhattan_f1 |
|
value: 74.18822234452395 |
|
- type: manhattan_precision |
|
value: 82.4969400244798 |
|
- type: manhattan_recall |
|
value: 67.4 |
|
- type: max_accuracy |
|
value: 99.6930693069307 |
|
- type: max_ap |
|
value: 90.94265768188356 |
|
- type: max_f1 |
|
value: 84.15792103948026 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 47.81120799399627 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 29.82642033698617 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 47.861728758923675 |
|
- type: mrr |
|
value: 48.53185213479331 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.09237795780992 |
|
- type: cos_sim_spearman |
|
value: 28.95547545518808 |
|
- type: dot_pearson |
|
value: 19.99986205111785 |
|
- type: dot_spearman |
|
value: 21.34033389331779 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.169 |
|
- type: map_at_10 |
|
value: 1.077 |
|
- type: map_at_100 |
|
value: 4.9750000000000005 |
|
- type: map_at_1000 |
|
value: 11.802 |
|
- type: map_at_3 |
|
value: 0.48700000000000004 |
|
- type: map_at_5 |
|
value: 0.679 |
|
- type: mrr_at_1 |
|
value: 62.0 |
|
- type: mrr_at_10 |
|
value: 76.25 |
|
- type: mrr_at_100 |
|
value: 76.337 |
|
- type: mrr_at_1000 |
|
value: 76.337 |
|
- type: mrr_at_3 |
|
value: 74.333 |
|
- type: mrr_at_5 |
|
value: 75.333 |
|
- type: ndcg_at_1 |
|
value: 56.00000000000001 |
|
- type: ndcg_at_10 |
|
value: 50.631 |
|
- type: ndcg_at_100 |
|
value: 36.39 |
|
- type: ndcg_at_1000 |
|
value: 32.879000000000005 |
|
- type: ndcg_at_3 |
|
value: 59.961 |
|
- type: ndcg_at_5 |
|
value: 55.913999999999994 |
|
- type: precision_at_1 |
|
value: 62.0 |
|
- type: precision_at_10 |
|
value: 53.0 |
|
- type: precision_at_100 |
|
value: 37.2 |
|
- type: precision_at_1000 |
|
value: 14.804 |
|
- type: precision_at_3 |
|
value: 67.333 |
|
- type: precision_at_5 |
|
value: 60.4 |
|
- type: recall_at_1 |
|
value: 0.169 |
|
- type: recall_at_10 |
|
value: 1.324 |
|
- type: recall_at_100 |
|
value: 8.352 |
|
- type: recall_at_1000 |
|
value: 31.041999999999998 |
|
- type: recall_at_3 |
|
value: 0.532 |
|
- type: recall_at_5 |
|
value: 0.777 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.018 |
|
- type: map_at_10 |
|
value: 8.036 |
|
- type: map_at_100 |
|
value: 12.814 |
|
- type: map_at_1000 |
|
value: 14.204 |
|
- type: map_at_3 |
|
value: 3.9759999999999995 |
|
- type: map_at_5 |
|
value: 5.585 |
|
- type: mrr_at_1 |
|
value: 24.490000000000002 |
|
- type: mrr_at_10 |
|
value: 38.903 |
|
- type: mrr_at_100 |
|
value: 39.893 |
|
- type: mrr_at_1000 |
|
value: 39.895 |
|
- type: mrr_at_3 |
|
value: 35.034 |
|
- type: mrr_at_5 |
|
value: 37.789 |
|
- type: ndcg_at_1 |
|
value: 21.429000000000002 |
|
- type: ndcg_at_10 |
|
value: 20.082 |
|
- type: ndcg_at_100 |
|
value: 30.299 |
|
- type: ndcg_at_1000 |
|
value: 42.323 |
|
- type: ndcg_at_3 |
|
value: 19.826 |
|
- type: ndcg_at_5 |
|
value: 19.861 |
|
- type: precision_at_1 |
|
value: 24.490000000000002 |
|
- type: precision_at_10 |
|
value: 18.776 |
|
- type: precision_at_100 |
|
value: 6.551 |
|
- type: precision_at_1000 |
|
value: 1.455 |
|
- type: precision_at_3 |
|
value: 21.088 |
|
- type: precision_at_5 |
|
value: 21.633 |
|
- type: recall_at_1 |
|
value: 2.018 |
|
- type: recall_at_10 |
|
value: 14.094999999999999 |
|
- type: recall_at_100 |
|
value: 40.482 |
|
- type: recall_at_1000 |
|
value: 78.214 |
|
- type: recall_at_3 |
|
value: 4.884 |
|
- type: recall_at_5 |
|
value: 8.203000000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 59.69140000000001 |
|
- type: ap |
|
value: 10.299275820958274 |
|
- type: f1 |
|
value: 45.697311005218154 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 53.542727787209955 |
|
- type: f1 |
|
value: 53.59495510018717 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 32.405659957745534 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.34487691482386 |
|
- type: cos_sim_ap |
|
value: 61.4880638625752 |
|
- type: cos_sim_f1 |
|
value: 59.350775193798455 |
|
- type: cos_sim_precision |
|
value: 54.858934169278996 |
|
- type: cos_sim_recall |
|
value: 64.64379947229551 |
|
- type: dot_accuracy |
|
value: 77.68373368301842 |
|
- type: dot_ap |
|
value: 36.846940578266626 |
|
- type: dot_f1 |
|
value: 42.67407473787974 |
|
- type: dot_precision |
|
value: 32.311032704573215 |
|
- type: dot_recall |
|
value: 62.82321899736147 |
|
- type: euclidean_accuracy |
|
value: 80.40770101925256 |
|
- type: euclidean_ap |
|
value: 53.51906185864526 |
|
- type: euclidean_f1 |
|
value: 53.24030024315466 |
|
- type: euclidean_precision |
|
value: 44.41700476274475 |
|
- type: euclidean_recall |
|
value: 66.43799472295514 |
|
- type: manhattan_accuracy |
|
value: 80.31829290099542 |
|
- type: manhattan_ap |
|
value: 53.67183195163967 |
|
- type: manhattan_f1 |
|
value: 53.28358208955224 |
|
- type: manhattan_precision |
|
value: 44.70483005366726 |
|
- type: manhattan_recall |
|
value: 65.93667546174143 |
|
- type: max_accuracy |
|
value: 82.34487691482386 |
|
- type: max_ap |
|
value: 61.4880638625752 |
|
- type: max_f1 |
|
value: 59.350775193798455 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.71684713005007 |
|
- type: cos_sim_ap |
|
value: 82.85441942604702 |
|
- type: cos_sim_f1 |
|
value: 75.69942543843179 |
|
- type: cos_sim_precision |
|
value: 73.88754490140019 |
|
- type: cos_sim_recall |
|
value: 77.60240221743148 |
|
- type: dot_accuracy |
|
value: 82.23696976753212 |
|
- type: dot_ap |
|
value: 68.47562727147806 |
|
- type: dot_f1 |
|
value: 64.99698249849123 |
|
- type: dot_precision |
|
value: 57.566219265946074 |
|
- type: dot_recall |
|
value: 74.63042808746535 |
|
- type: euclidean_accuracy |
|
value: 81.52481856638336 |
|
- type: euclidean_ap |
|
value: 65.96678666430529 |
|
- type: euclidean_f1 |
|
value: 59.14671467146715 |
|
- type: euclidean_precision |
|
value: 55.54879285859201 |
|
- type: euclidean_recall |
|
value: 63.24299353249153 |
|
- type: manhattan_accuracy |
|
value: 81.56750882912253 |
|
- type: manhattan_ap |
|
value: 66.07646774834106 |
|
- type: manhattan_f1 |
|
value: 59.161485036907756 |
|
- type: manhattan_precision |
|
value: 56.05319368841728 |
|
- type: manhattan_recall |
|
value: 62.634739759778256 |
|
- type: max_accuracy |
|
value: 87.71684713005007 |
|
- type: max_ap |
|
value: 82.85441942604702 |
|
- type: max_f1 |
|
value: 75.69942543843179 |
|
--- |
|
--- |
|
|
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" 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 suite trained by Jina AI, Finetuner team.</b> |
|
</p> |
|
|
|
|
|
## Intented Usage & Model Info |
|
|
|
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset. |
|
This dataset consists of 380 million pairs of sentences, which include both query-document pairs. |
|
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. |
|
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs. |
|
|
|
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more. |
|
|
|
With a compact size of just 35 million parameters, |
|
the model enables lightning-fast inference while still delivering impressive performance. |
|
Additionally, we provide the following options: |
|
|
|
- `jina-embedding-s-en-v1`: 35 million parameters **(you are here)**. |
|
- `jina-embedding-b-en-v1`: 110 million parameters. |
|
- `jina-embedding-l-en-v1`: 330 million parameters. |
|
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10* bert-base size (soon). |
|
- `jina-embedding-6b-en-v1`: 6 billion parameters 30* bert-base size(soon). |
|
|
|
## Data & Parameters |
|
|
|
More info will be released together with the technique report. |
|
|
|
## Metrics |
|
|
|
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI: |
|
|
|
|Name|param |context| |
|
|------------------------------|-----|------| |
|
|all-minilm-l6-v2|33m |128| |
|
|all-mpnet-base-v2 |110m |128| |
|
|ada-embedding-002|Unknown/OpenAI API |8192| |
|
|jina-embedding-s-en-v1|35m |512| |
|
|jina-embedding-b-en-v1|110m |512| |
|
|jina-embedding-l-en-v1|330m |512| |
|
|
|
|
|
|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact| |
|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----| |
|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 | |
|
|all-mpnet-base-v2|0.726|0.835|**0.78** |0.857|0.8 |**0.906**|0.513 |0.875|0.656 | |
|
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** | |
|
|jina-embedding-s-en-v1|0.742|0.786|0.738|0.837|0.80|0.875|0.543 |0.857|0.608 | |
|
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.89|0.601 |0.876|0.645 | |
|
|jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.829|0.896|0.526 |**0.882**|0.652 | |
|
|
|
*update: we have updated the checkpoints for small/base model, re-evaluation of large model and BEIR is running in progress.* |
|
|
|
## Usage |
|
|
|
Use with Jina AI Finetuner |
|
|
|
```python |
|
!pip install finetuner |
|
import finetuner |
|
|
|
model = finetuner.build_model('jinaai/jina-embedding-s-en-v1') |
|
embeddings = finetuner.encode( |
|
model=model, |
|
data=['how is the weather today', 'What is the current weather like today?'] |
|
) |
|
print(finetuner.cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
Use directly with Huggingface Transformers: |
|
|
|
```python |
|
import torch |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
|
|
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 we want sentence embeddings for |
|
sentences = ['how is the weather today', 'What is the current weather like today?'] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embedding-s-en-v1') |
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model = AutoModel.from_pretrained('jinaai/jina-embedding-s-en-v1') |
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|
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with torch.inference_mode(): |
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encoded_input = tokenizer( |
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sentences, padding=True, truncation=True, return_tensors='pt' |
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) |
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model_output = model.encoder(**encoded_input) |
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embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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``` |
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## Fine-tuning |
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|
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Please consider [Finetuner](https://github.com/jina-ai/finetuner). |
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## Plans |
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1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length. |
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2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`. |
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## Contact |
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Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
