|
--- |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- mteb |
|
- transformers |
|
- transformers.js |
|
language: |
|
- de |
|
- en |
|
inference: false |
|
license: apache-2.0 |
|
model-index: |
|
- name: jina-embeddings-v2-base-de |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 73.76119402985076 |
|
- type: ap |
|
value: 35.99577188521176 |
|
- type: f1 |
|
value: 67.50397431543269 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (de) |
|
config: de |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 68.9186295503212 |
|
- type: ap |
|
value: 79.73307115840507 |
|
- type: f1 |
|
value: 66.66245744831339 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 77.52215 |
|
- type: ap |
|
value: 71.85051037177416 |
|
- type: f1 |
|
value: 77.4171096157774 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 38.498 |
|
- type: f1 |
|
value: 38.058193386555956 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (de) |
|
config: de |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 37.717999999999996 |
|
- type: f1 |
|
value: 37.22674371574757 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.319999999999997 |
|
- type: map_at_10 |
|
value: 40.351 |
|
- type: map_at_100 |
|
value: 41.435 |
|
- type: map_at_1000 |
|
value: 41.443000000000005 |
|
- type: map_at_3 |
|
value: 35.266 |
|
- type: map_at_5 |
|
value: 37.99 |
|
- type: mrr_at_1 |
|
value: 25.746999999999996 |
|
- type: mrr_at_10 |
|
value: 40.515 |
|
- type: mrr_at_100 |
|
value: 41.606 |
|
- type: mrr_at_1000 |
|
value: 41.614000000000004 |
|
- type: mrr_at_3 |
|
value: 35.42 |
|
- type: mrr_at_5 |
|
value: 38.112 |
|
- type: ndcg_at_1 |
|
value: 25.319999999999997 |
|
- type: ndcg_at_10 |
|
value: 49.332 |
|
- type: ndcg_at_100 |
|
value: 53.909 |
|
- type: ndcg_at_1000 |
|
value: 54.089 |
|
- type: ndcg_at_3 |
|
value: 38.705 |
|
- type: ndcg_at_5 |
|
value: 43.606 |
|
- type: precision_at_1 |
|
value: 25.319999999999997 |
|
- type: precision_at_10 |
|
value: 7.831 |
|
- type: precision_at_100 |
|
value: 0.9820000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.24 |
|
- type: precision_at_5 |
|
value: 12.119 |
|
- type: recall_at_1 |
|
value: 25.319999999999997 |
|
- type: recall_at_10 |
|
value: 78.307 |
|
- type: recall_at_100 |
|
value: 98.222 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 48.72 |
|
- type: recall_at_5 |
|
value: 60.597 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 41.43100588255654 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 32.08988904593667 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 60.55514765595906 |
|
- type: mrr |
|
value: 73.51393835465858 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.6723823121172 |
|
- type: cos_sim_spearman |
|
value: 76.90596922214986 |
|
- type: euclidean_pearson |
|
value: 77.87910737957918 |
|
- type: euclidean_spearman |
|
value: 76.66319260598262 |
|
- type: manhattan_pearson |
|
value: 77.37039493457965 |
|
- type: manhattan_spearman |
|
value: 76.09872191280964 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
type: mteb/bucc-bitext-mining |
|
name: MTEB BUCC (de-en) |
|
config: de-en |
|
split: test |
|
revision: d51519689f32196a32af33b075a01d0e7c51e252 |
|
metrics: |
|
- type: accuracy |
|
value: 98.97703549060543 |
|
- type: f1 |
|
value: 98.86569241475296 |
|
- type: precision |
|
value: 98.81002087682673 |
|
- type: recall |
|
value: 98.97703549060543 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 83.93506493506493 |
|
- type: f1 |
|
value: 83.91014949949302 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 34.970675877585144 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 28.779230269190954 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: slvnwhrl/blurbs-clustering-p2p |
|
name: MTEB BlurbsClusteringP2P |
|
config: default |
|
split: test |
|
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496 |
|
metrics: |
|
- type: v_measure |
|
value: 35.490175601567216 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: slvnwhrl/blurbs-clustering-s2s |
|
name: MTEB BlurbsClusteringS2S |
|
config: default |
|
split: test |
|
revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d |
|
metrics: |
|
- type: v_measure |
|
value: 16.16638280560168 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.830999999999996 |
|
- type: map_at_10 |
|
value: 41.355 |
|
- type: map_at_100 |
|
value: 42.791000000000004 |
|
- type: map_at_1000 |
|
value: 42.918 |
|
- type: map_at_3 |
|
value: 38.237 |
|
- type: map_at_5 |
|
value: 40.066 |
|
- type: mrr_at_1 |
|
value: 38.484 |
|
- type: mrr_at_10 |
|
value: 47.593 |
|
- type: mrr_at_100 |
|
value: 48.388 |
|
- type: mrr_at_1000 |
|
value: 48.439 |
|
- type: mrr_at_3 |
|
value: 45.279 |
|
- type: mrr_at_5 |
|
value: 46.724 |
|
- type: ndcg_at_1 |
|
value: 38.484 |
|
- type: ndcg_at_10 |
|
value: 47.27 |
|
- type: ndcg_at_100 |
|
value: 52.568000000000005 |
|
- type: ndcg_at_1000 |
|
value: 54.729000000000006 |
|
- type: ndcg_at_3 |
|
value: 43.061 |
|
- type: ndcg_at_5 |
|
value: 45.083 |
|
- type: precision_at_1 |
|
value: 38.484 |
|
- type: precision_at_10 |
|
value: 8.927 |
|
- type: precision_at_100 |
|
value: 1.425 |
|
- type: precision_at_1000 |
|
value: 0.19 |
|
- type: precision_at_3 |
|
value: 20.791999999999998 |
|
- type: precision_at_5 |
|
value: 14.85 |
|
- type: recall_at_1 |
|
value: 30.830999999999996 |
|
- type: recall_at_10 |
|
value: 57.87799999999999 |
|
- type: recall_at_100 |
|
value: 80.124 |
|
- type: recall_at_1000 |
|
value: 94.208 |
|
- type: recall_at_3 |
|
value: 45.083 |
|
- type: recall_at_5 |
|
value: 51.154999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.782 |
|
- type: map_at_10 |
|
value: 34.492 |
|
- type: map_at_100 |
|
value: 35.521 |
|
- type: map_at_1000 |
|
value: 35.638 |
|
- type: map_at_3 |
|
value: 31.735999999999997 |
|
- type: map_at_5 |
|
value: 33.339 |
|
- type: mrr_at_1 |
|
value: 32.357 |
|
- type: mrr_at_10 |
|
value: 39.965 |
|
- type: mrr_at_100 |
|
value: 40.644000000000005 |
|
- type: mrr_at_1000 |
|
value: 40.695 |
|
- type: mrr_at_3 |
|
value: 37.739 |
|
- type: mrr_at_5 |
|
value: 39.061 |
|
- type: ndcg_at_1 |
|
value: 32.357 |
|
- type: ndcg_at_10 |
|
value: 39.644 |
|
- type: ndcg_at_100 |
|
value: 43.851 |
|
- type: ndcg_at_1000 |
|
value: 46.211999999999996 |
|
- type: ndcg_at_3 |
|
value: 35.675000000000004 |
|
- type: ndcg_at_5 |
|
value: 37.564 |
|
- type: precision_at_1 |
|
value: 32.357 |
|
- type: precision_at_10 |
|
value: 7.344 |
|
- type: precision_at_100 |
|
value: 1.201 |
|
- type: precision_at_1000 |
|
value: 0.168 |
|
- type: precision_at_3 |
|
value: 17.155 |
|
- type: precision_at_5 |
|
value: 12.166 |
|
- type: recall_at_1 |
|
value: 25.782 |
|
- type: recall_at_10 |
|
value: 49.132999999999996 |
|
- type: recall_at_100 |
|
value: 67.24 |
|
- type: recall_at_1000 |
|
value: 83.045 |
|
- type: recall_at_3 |
|
value: 37.021 |
|
- type: recall_at_5 |
|
value: 42.548 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 35.778999999999996 |
|
- type: map_at_10 |
|
value: 47.038000000000004 |
|
- type: map_at_100 |
|
value: 48.064 |
|
- type: map_at_1000 |
|
value: 48.128 |
|
- type: map_at_3 |
|
value: 44.186 |
|
- type: map_at_5 |
|
value: 45.788000000000004 |
|
- type: mrr_at_1 |
|
value: 41.254000000000005 |
|
- type: mrr_at_10 |
|
value: 50.556999999999995 |
|
- type: mrr_at_100 |
|
value: 51.296 |
|
- type: mrr_at_1000 |
|
value: 51.331 |
|
- type: mrr_at_3 |
|
value: 48.318 |
|
- type: mrr_at_5 |
|
value: 49.619 |
|
- type: ndcg_at_1 |
|
value: 41.254000000000005 |
|
- type: ndcg_at_10 |
|
value: 52.454 |
|
- type: ndcg_at_100 |
|
value: 56.776 |
|
- type: ndcg_at_1000 |
|
value: 58.181000000000004 |
|
- type: ndcg_at_3 |
|
value: 47.713 |
|
- type: ndcg_at_5 |
|
value: 49.997 |
|
- type: precision_at_1 |
|
value: 41.254000000000005 |
|
- type: precision_at_10 |
|
value: 8.464 |
|
- type: precision_at_100 |
|
value: 1.157 |
|
- type: precision_at_1000 |
|
value: 0.133 |
|
- type: precision_at_3 |
|
value: 21.526 |
|
- type: precision_at_5 |
|
value: 14.696000000000002 |
|
- type: recall_at_1 |
|
value: 35.778999999999996 |
|
- type: recall_at_10 |
|
value: 64.85300000000001 |
|
- type: recall_at_100 |
|
value: 83.98400000000001 |
|
- type: recall_at_1000 |
|
value: 94.18299999999999 |
|
- type: recall_at_3 |
|
value: 51.929 |
|
- type: recall_at_5 |
|
value: 57.666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.719 |
|
- type: map_at_10 |
|
value: 29.326999999999998 |
|
- type: map_at_100 |
|
value: 30.314000000000004 |
|
- type: map_at_1000 |
|
value: 30.397000000000002 |
|
- type: map_at_3 |
|
value: 27.101 |
|
- type: map_at_5 |
|
value: 28.141 |
|
- type: mrr_at_1 |
|
value: 23.503 |
|
- type: mrr_at_10 |
|
value: 31.225 |
|
- type: mrr_at_100 |
|
value: 32.096000000000004 |
|
- type: mrr_at_1000 |
|
value: 32.159 |
|
- type: mrr_at_3 |
|
value: 29.076999999999998 |
|
- type: mrr_at_5 |
|
value: 30.083 |
|
- type: ndcg_at_1 |
|
value: 23.503 |
|
- type: ndcg_at_10 |
|
value: 33.842 |
|
- type: ndcg_at_100 |
|
value: 39.038000000000004 |
|
- type: ndcg_at_1000 |
|
value: 41.214 |
|
- type: ndcg_at_3 |
|
value: 29.347 |
|
- type: ndcg_at_5 |
|
value: 31.121 |
|
- type: precision_at_1 |
|
value: 23.503 |
|
- type: precision_at_10 |
|
value: 5.266 |
|
- type: precision_at_100 |
|
value: 0.831 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_3 |
|
value: 12.504999999999999 |
|
- type: precision_at_5 |
|
value: 8.565000000000001 |
|
- type: recall_at_1 |
|
value: 21.719 |
|
- type: recall_at_10 |
|
value: 46.024 |
|
- type: recall_at_100 |
|
value: 70.78999999999999 |
|
- type: recall_at_1000 |
|
value: 87.022 |
|
- type: recall_at_3 |
|
value: 33.64 |
|
- type: recall_at_5 |
|
value: 37.992 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.601 |
|
- type: map_at_10 |
|
value: 22.054000000000002 |
|
- type: map_at_100 |
|
value: 23.177 |
|
- type: map_at_1000 |
|
value: 23.308 |
|
- type: map_at_3 |
|
value: 19.772000000000002 |
|
- type: map_at_5 |
|
value: 21.055 |
|
- type: mrr_at_1 |
|
value: 19.403000000000002 |
|
- type: mrr_at_10 |
|
value: 26.409 |
|
- type: mrr_at_100 |
|
value: 27.356 |
|
- type: mrr_at_1000 |
|
value: 27.441 |
|
- type: mrr_at_3 |
|
value: 24.108999999999998 |
|
- type: mrr_at_5 |
|
value: 25.427 |
|
- type: ndcg_at_1 |
|
value: 19.403000000000002 |
|
- type: ndcg_at_10 |
|
value: 26.474999999999998 |
|
- type: ndcg_at_100 |
|
value: 32.086 |
|
- type: ndcg_at_1000 |
|
value: 35.231 |
|
- type: ndcg_at_3 |
|
value: 22.289 |
|
- type: ndcg_at_5 |
|
value: 24.271 |
|
- type: precision_at_1 |
|
value: 19.403000000000002 |
|
- type: precision_at_10 |
|
value: 4.813 |
|
- type: precision_at_100 |
|
value: 0.8869999999999999 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 10.531 |
|
- type: precision_at_5 |
|
value: 7.710999999999999 |
|
- type: recall_at_1 |
|
value: 15.601 |
|
- type: recall_at_10 |
|
value: 35.916 |
|
- type: recall_at_100 |
|
value: 60.8 |
|
- type: recall_at_1000 |
|
value: 83.245 |
|
- type: recall_at_3 |
|
value: 24.321 |
|
- type: recall_at_5 |
|
value: 29.372999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.522 |
|
- type: map_at_10 |
|
value: 34.854 |
|
- type: map_at_100 |
|
value: 36.269 |
|
- type: map_at_1000 |
|
value: 36.387 |
|
- type: map_at_3 |
|
value: 32.187 |
|
- type: map_at_5 |
|
value: 33.692 |
|
- type: mrr_at_1 |
|
value: 31.375999999999998 |
|
- type: mrr_at_10 |
|
value: 40.471000000000004 |
|
- type: mrr_at_100 |
|
value: 41.481 |
|
- type: mrr_at_1000 |
|
value: 41.533 |
|
- type: mrr_at_3 |
|
value: 38.274 |
|
- type: mrr_at_5 |
|
value: 39.612 |
|
- type: ndcg_at_1 |
|
value: 31.375999999999998 |
|
- type: ndcg_at_10 |
|
value: 40.298 |
|
- type: ndcg_at_100 |
|
value: 46.255 |
|
- type: ndcg_at_1000 |
|
value: 48.522 |
|
- type: ndcg_at_3 |
|
value: 36.049 |
|
- type: ndcg_at_5 |
|
value: 38.095 |
|
- type: precision_at_1 |
|
value: 31.375999999999998 |
|
- type: precision_at_10 |
|
value: 7.305000000000001 |
|
- type: precision_at_100 |
|
value: 1.201 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 17.132 |
|
- type: precision_at_5 |
|
value: 12.107999999999999 |
|
- type: recall_at_1 |
|
value: 25.522 |
|
- type: recall_at_10 |
|
value: 50.988 |
|
- type: recall_at_100 |
|
value: 76.005 |
|
- type: recall_at_1000 |
|
value: 91.11200000000001 |
|
- type: recall_at_3 |
|
value: 38.808 |
|
- type: recall_at_5 |
|
value: 44.279 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.615000000000002 |
|
- type: map_at_10 |
|
value: 32.843 |
|
- type: map_at_100 |
|
value: 34.172999999999995 |
|
- type: map_at_1000 |
|
value: 34.286 |
|
- type: map_at_3 |
|
value: 30.125 |
|
- type: map_at_5 |
|
value: 31.495 |
|
- type: mrr_at_1 |
|
value: 30.023 |
|
- type: mrr_at_10 |
|
value: 38.106 |
|
- type: mrr_at_100 |
|
value: 39.01 |
|
- type: mrr_at_1000 |
|
value: 39.071 |
|
- type: mrr_at_3 |
|
value: 35.674 |
|
- type: mrr_at_5 |
|
value: 36.924 |
|
- type: ndcg_at_1 |
|
value: 30.023 |
|
- type: ndcg_at_10 |
|
value: 38.091 |
|
- type: ndcg_at_100 |
|
value: 43.771 |
|
- type: ndcg_at_1000 |
|
value: 46.315 |
|
- type: ndcg_at_3 |
|
value: 33.507 |
|
- type: ndcg_at_5 |
|
value: 35.304 |
|
- type: precision_at_1 |
|
value: 30.023 |
|
- type: precision_at_10 |
|
value: 6.837999999999999 |
|
- type: precision_at_100 |
|
value: 1.124 |
|
- type: precision_at_1000 |
|
value: 0.152 |
|
- type: precision_at_3 |
|
value: 15.562999999999999 |
|
- type: precision_at_5 |
|
value: 10.936 |
|
- type: recall_at_1 |
|
value: 24.615000000000002 |
|
- type: recall_at_10 |
|
value: 48.691 |
|
- type: recall_at_100 |
|
value: 72.884 |
|
- type: recall_at_1000 |
|
value: 90.387 |
|
- type: recall_at_3 |
|
value: 35.659 |
|
- type: recall_at_5 |
|
value: 40.602 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.223666666666666 |
|
- type: map_at_10 |
|
value: 31.338166666666673 |
|
- type: map_at_100 |
|
value: 32.47358333333333 |
|
- type: map_at_1000 |
|
value: 32.5955 |
|
- type: map_at_3 |
|
value: 28.84133333333333 |
|
- type: map_at_5 |
|
value: 30.20808333333333 |
|
- type: mrr_at_1 |
|
value: 27.62483333333333 |
|
- type: mrr_at_10 |
|
value: 35.385916666666674 |
|
- type: mrr_at_100 |
|
value: 36.23325 |
|
- type: mrr_at_1000 |
|
value: 36.29966666666667 |
|
- type: mrr_at_3 |
|
value: 33.16583333333333 |
|
- type: mrr_at_5 |
|
value: 34.41983333333334 |
|
- type: ndcg_at_1 |
|
value: 27.62483333333333 |
|
- type: ndcg_at_10 |
|
value: 36.222 |
|
- type: ndcg_at_100 |
|
value: 41.29491666666666 |
|
- type: ndcg_at_1000 |
|
value: 43.85508333333333 |
|
- type: ndcg_at_3 |
|
value: 31.95116666666667 |
|
- type: ndcg_at_5 |
|
value: 33.88541666666667 |
|
- type: precision_at_1 |
|
value: 27.62483333333333 |
|
- type: precision_at_10 |
|
value: 6.339916666666667 |
|
- type: precision_at_100 |
|
value: 1.0483333333333333 |
|
- type: precision_at_1000 |
|
value: 0.14608333333333334 |
|
- type: precision_at_3 |
|
value: 14.726500000000003 |
|
- type: precision_at_5 |
|
value: 10.395 |
|
- type: recall_at_1 |
|
value: 23.223666666666666 |
|
- type: recall_at_10 |
|
value: 46.778999999999996 |
|
- type: recall_at_100 |
|
value: 69.27141666666667 |
|
- type: recall_at_1000 |
|
value: 87.27383333333334 |
|
- type: recall_at_3 |
|
value: 34.678749999999994 |
|
- type: recall_at_5 |
|
value: 39.79900000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.677 |
|
- type: map_at_10 |
|
value: 27.828000000000003 |
|
- type: map_at_100 |
|
value: 28.538999999999998 |
|
- type: map_at_1000 |
|
value: 28.64 |
|
- type: map_at_3 |
|
value: 26.105 |
|
- type: map_at_5 |
|
value: 27.009 |
|
- type: mrr_at_1 |
|
value: 24.387 |
|
- type: mrr_at_10 |
|
value: 30.209999999999997 |
|
- type: mrr_at_100 |
|
value: 30.953000000000003 |
|
- type: mrr_at_1000 |
|
value: 31.029 |
|
- type: mrr_at_3 |
|
value: 28.707 |
|
- type: mrr_at_5 |
|
value: 29.610999999999997 |
|
- type: ndcg_at_1 |
|
value: 24.387 |
|
- type: ndcg_at_10 |
|
value: 31.378 |
|
- type: ndcg_at_100 |
|
value: 35.249 |
|
- type: ndcg_at_1000 |
|
value: 37.923 |
|
- type: ndcg_at_3 |
|
value: 28.213 |
|
- type: ndcg_at_5 |
|
value: 29.658 |
|
- type: precision_at_1 |
|
value: 24.387 |
|
- type: precision_at_10 |
|
value: 4.8309999999999995 |
|
- type: precision_at_100 |
|
value: 0.73 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 12.168 |
|
- type: precision_at_5 |
|
value: 8.251999999999999 |
|
- type: recall_at_1 |
|
value: 21.677 |
|
- type: recall_at_10 |
|
value: 40.069 |
|
- type: recall_at_100 |
|
value: 58.077 |
|
- type: recall_at_1000 |
|
value: 77.97 |
|
- type: recall_at_3 |
|
value: 31.03 |
|
- type: recall_at_5 |
|
value: 34.838 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.484 |
|
- type: map_at_10 |
|
value: 20.355 |
|
- type: map_at_100 |
|
value: 21.382 |
|
- type: map_at_1000 |
|
value: 21.511 |
|
- type: map_at_3 |
|
value: 18.448 |
|
- type: map_at_5 |
|
value: 19.451999999999998 |
|
- type: mrr_at_1 |
|
value: 17.584 |
|
- type: mrr_at_10 |
|
value: 23.825 |
|
- type: mrr_at_100 |
|
value: 24.704 |
|
- type: mrr_at_1000 |
|
value: 24.793000000000003 |
|
- type: mrr_at_3 |
|
value: 21.92 |
|
- type: mrr_at_5 |
|
value: 22.97 |
|
- type: ndcg_at_1 |
|
value: 17.584 |
|
- type: ndcg_at_10 |
|
value: 24.315 |
|
- type: ndcg_at_100 |
|
value: 29.354999999999997 |
|
- type: ndcg_at_1000 |
|
value: 32.641999999999996 |
|
- type: ndcg_at_3 |
|
value: 20.802 |
|
- type: ndcg_at_5 |
|
value: 22.335 |
|
- type: precision_at_1 |
|
value: 17.584 |
|
- type: precision_at_10 |
|
value: 4.443 |
|
- type: precision_at_100 |
|
value: 0.8160000000000001 |
|
- type: precision_at_1000 |
|
value: 0.128 |
|
- type: precision_at_3 |
|
value: 9.807 |
|
- type: precision_at_5 |
|
value: 7.0889999999999995 |
|
- type: recall_at_1 |
|
value: 14.484 |
|
- type: recall_at_10 |
|
value: 32.804 |
|
- type: recall_at_100 |
|
value: 55.679 |
|
- type: recall_at_1000 |
|
value: 79.63 |
|
- type: recall_at_3 |
|
value: 22.976 |
|
- type: recall_at_5 |
|
value: 26.939 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.983999999999998 |
|
- type: map_at_10 |
|
value: 30.812 |
|
- type: map_at_100 |
|
value: 31.938 |
|
- type: map_at_1000 |
|
value: 32.056000000000004 |
|
- type: map_at_3 |
|
value: 28.449999999999996 |
|
- type: map_at_5 |
|
value: 29.542 |
|
- type: mrr_at_1 |
|
value: 27.145999999999997 |
|
- type: mrr_at_10 |
|
value: 34.782999999999994 |
|
- type: mrr_at_100 |
|
value: 35.699 |
|
- type: mrr_at_1000 |
|
value: 35.768 |
|
- type: mrr_at_3 |
|
value: 32.572 |
|
- type: mrr_at_5 |
|
value: 33.607 |
|
- type: ndcg_at_1 |
|
value: 27.145999999999997 |
|
- type: ndcg_at_10 |
|
value: 35.722 |
|
- type: ndcg_at_100 |
|
value: 40.964 |
|
- type: ndcg_at_1000 |
|
value: 43.598 |
|
- type: ndcg_at_3 |
|
value: 31.379 |
|
- type: ndcg_at_5 |
|
value: 32.924 |
|
- type: precision_at_1 |
|
value: 27.145999999999997 |
|
- type: precision_at_10 |
|
value: 6.063000000000001 |
|
- type: precision_at_100 |
|
value: 0.9730000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 14.366000000000001 |
|
- type: precision_at_5 |
|
value: 9.776 |
|
- type: recall_at_1 |
|
value: 22.983999999999998 |
|
- type: recall_at_10 |
|
value: 46.876 |
|
- type: recall_at_100 |
|
value: 69.646 |
|
- type: recall_at_1000 |
|
value: 88.305 |
|
- type: recall_at_3 |
|
value: 34.471000000000004 |
|
- type: recall_at_5 |
|
value: 38.76 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.017000000000003 |
|
- type: map_at_10 |
|
value: 31.049 |
|
- type: map_at_100 |
|
value: 32.582 |
|
- type: map_at_1000 |
|
value: 32.817 |
|
- type: map_at_3 |
|
value: 28.303 |
|
- type: map_at_5 |
|
value: 29.854000000000003 |
|
- type: mrr_at_1 |
|
value: 27.866000000000003 |
|
- type: mrr_at_10 |
|
value: 35.56 |
|
- type: mrr_at_100 |
|
value: 36.453 |
|
- type: mrr_at_1000 |
|
value: 36.519 |
|
- type: mrr_at_3 |
|
value: 32.938 |
|
- type: mrr_at_5 |
|
value: 34.391 |
|
- type: ndcg_at_1 |
|
value: 27.866000000000003 |
|
- type: ndcg_at_10 |
|
value: 36.506 |
|
- type: ndcg_at_100 |
|
value: 42.344 |
|
- type: ndcg_at_1000 |
|
value: 45.213 |
|
- type: ndcg_at_3 |
|
value: 31.805 |
|
- type: ndcg_at_5 |
|
value: 33.933 |
|
- type: precision_at_1 |
|
value: 27.866000000000003 |
|
- type: precision_at_10 |
|
value: 7.016 |
|
- type: precision_at_100 |
|
value: 1.468 |
|
- type: precision_at_1000 |
|
value: 0.23900000000000002 |
|
- type: precision_at_3 |
|
value: 14.822 |
|
- type: precision_at_5 |
|
value: 10.791 |
|
- type: recall_at_1 |
|
value: 23.017000000000003 |
|
- type: recall_at_10 |
|
value: 47.053 |
|
- type: recall_at_100 |
|
value: 73.177 |
|
- type: recall_at_1000 |
|
value: 91.47800000000001 |
|
- type: recall_at_3 |
|
value: 33.675 |
|
- type: recall_at_5 |
|
value: 39.36 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.673 |
|
- type: map_at_10 |
|
value: 24.051000000000002 |
|
- type: map_at_100 |
|
value: 24.933 |
|
- type: map_at_1000 |
|
value: 25.06 |
|
- type: map_at_3 |
|
value: 21.446 |
|
- type: map_at_5 |
|
value: 23.064 |
|
- type: mrr_at_1 |
|
value: 18.115000000000002 |
|
- type: mrr_at_10 |
|
value: 25.927 |
|
- type: mrr_at_100 |
|
value: 26.718999999999998 |
|
- type: mrr_at_1000 |
|
value: 26.817999999999998 |
|
- type: mrr_at_3 |
|
value: 23.383000000000003 |
|
- type: mrr_at_5 |
|
value: 25.008999999999997 |
|
- type: ndcg_at_1 |
|
value: 18.115000000000002 |
|
- type: ndcg_at_10 |
|
value: 28.669 |
|
- type: ndcg_at_100 |
|
value: 33.282000000000004 |
|
- type: ndcg_at_1000 |
|
value: 36.481 |
|
- type: ndcg_at_3 |
|
value: 23.574 |
|
- type: ndcg_at_5 |
|
value: 26.340000000000003 |
|
- type: precision_at_1 |
|
value: 18.115000000000002 |
|
- type: precision_at_10 |
|
value: 4.769 |
|
- type: precision_at_100 |
|
value: 0.767 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 10.351 |
|
- type: precision_at_5 |
|
value: 7.8 |
|
- type: recall_at_1 |
|
value: 16.673 |
|
- type: recall_at_10 |
|
value: 41.063 |
|
- type: recall_at_100 |
|
value: 62.851 |
|
- type: recall_at_1000 |
|
value: 86.701 |
|
- type: recall_at_3 |
|
value: 27.532 |
|
- type: recall_at_5 |
|
value: 34.076 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.752 |
|
- type: map_at_10 |
|
value: 15.120000000000001 |
|
- type: map_at_100 |
|
value: 16.678 |
|
- type: map_at_1000 |
|
value: 16.854 |
|
- type: map_at_3 |
|
value: 12.603 |
|
- type: map_at_5 |
|
value: 13.918 |
|
- type: mrr_at_1 |
|
value: 19.283 |
|
- type: mrr_at_10 |
|
value: 29.145 |
|
- type: mrr_at_100 |
|
value: 30.281000000000002 |
|
- type: mrr_at_1000 |
|
value: 30.339 |
|
- type: mrr_at_3 |
|
value: 26.069 |
|
- type: mrr_at_5 |
|
value: 27.864 |
|
- type: ndcg_at_1 |
|
value: 19.283 |
|
- type: ndcg_at_10 |
|
value: 21.804000000000002 |
|
- type: ndcg_at_100 |
|
value: 28.576 |
|
- type: ndcg_at_1000 |
|
value: 32.063 |
|
- type: ndcg_at_3 |
|
value: 17.511 |
|
- type: ndcg_at_5 |
|
value: 19.112000000000002 |
|
- type: precision_at_1 |
|
value: 19.283 |
|
- type: precision_at_10 |
|
value: 6.873 |
|
- type: precision_at_100 |
|
value: 1.405 |
|
- type: precision_at_1000 |
|
value: 0.20500000000000002 |
|
- type: precision_at_3 |
|
value: 13.16 |
|
- type: precision_at_5 |
|
value: 10.189 |
|
- type: recall_at_1 |
|
value: 8.752 |
|
- type: recall_at_10 |
|
value: 27.004 |
|
- type: recall_at_100 |
|
value: 50.648 |
|
- type: recall_at_1000 |
|
value: 70.458 |
|
- type: recall_at_3 |
|
value: 16.461000000000002 |
|
- type: recall_at_5 |
|
value: 20.973 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.81 |
|
- type: map_at_10 |
|
value: 14.056 |
|
- type: map_at_100 |
|
value: 18.961 |
|
- type: map_at_1000 |
|
value: 20.169 |
|
- type: map_at_3 |
|
value: 10.496 |
|
- type: map_at_5 |
|
value: 11.952 |
|
- type: mrr_at_1 |
|
value: 53.5 |
|
- type: mrr_at_10 |
|
value: 63.479 |
|
- type: mrr_at_100 |
|
value: 63.971999999999994 |
|
- type: mrr_at_1000 |
|
value: 63.993 |
|
- type: mrr_at_3 |
|
value: 61.541999999999994 |
|
- type: mrr_at_5 |
|
value: 62.778999999999996 |
|
- type: ndcg_at_1 |
|
value: 42.25 |
|
- type: ndcg_at_10 |
|
value: 31.471 |
|
- type: ndcg_at_100 |
|
value: 35.115 |
|
- type: ndcg_at_1000 |
|
value: 42.408 |
|
- type: ndcg_at_3 |
|
value: 35.458 |
|
- type: ndcg_at_5 |
|
value: 32.973 |
|
- type: precision_at_1 |
|
value: 53.5 |
|
- type: precision_at_10 |
|
value: 24.85 |
|
- type: precision_at_100 |
|
value: 7.79 |
|
- type: precision_at_1000 |
|
value: 1.599 |
|
- type: precision_at_3 |
|
value: 38.667 |
|
- type: precision_at_5 |
|
value: 31.55 |
|
- type: recall_at_1 |
|
value: 6.81 |
|
- type: recall_at_10 |
|
value: 19.344 |
|
- type: recall_at_100 |
|
value: 40.837 |
|
- type: recall_at_1000 |
|
value: 64.661 |
|
- type: recall_at_3 |
|
value: 11.942 |
|
- type: recall_at_5 |
|
value: 14.646 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 44.64499999999999 |
|
- type: f1 |
|
value: 39.39106911352714 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 48.196 |
|
- type: map_at_10 |
|
value: 61.404 |
|
- type: map_at_100 |
|
value: 61.846000000000004 |
|
- type: map_at_1000 |
|
value: 61.866 |
|
- type: map_at_3 |
|
value: 58.975 |
|
- type: map_at_5 |
|
value: 60.525 |
|
- type: mrr_at_1 |
|
value: 52.025 |
|
- type: mrr_at_10 |
|
value: 65.43299999999999 |
|
- type: mrr_at_100 |
|
value: 65.80799999999999 |
|
- type: mrr_at_1000 |
|
value: 65.818 |
|
- type: mrr_at_3 |
|
value: 63.146 |
|
- type: mrr_at_5 |
|
value: 64.64 |
|
- type: ndcg_at_1 |
|
value: 52.025 |
|
- type: ndcg_at_10 |
|
value: 67.889 |
|
- type: ndcg_at_100 |
|
value: 69.864 |
|
- type: ndcg_at_1000 |
|
value: 70.337 |
|
- type: ndcg_at_3 |
|
value: 63.315 |
|
- type: ndcg_at_5 |
|
value: 65.91799999999999 |
|
- type: precision_at_1 |
|
value: 52.025 |
|
- type: precision_at_10 |
|
value: 9.182 |
|
- type: precision_at_100 |
|
value: 1.027 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 25.968000000000004 |
|
- type: precision_at_5 |
|
value: 17.006 |
|
- type: recall_at_1 |
|
value: 48.196 |
|
- type: recall_at_10 |
|
value: 83.885 |
|
- type: recall_at_100 |
|
value: 92.671 |
|
- type: recall_at_1000 |
|
value: 96.018 |
|
- type: recall_at_3 |
|
value: 71.59 |
|
- type: recall_at_5 |
|
value: 77.946 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.193000000000001 |
|
- type: map_at_10 |
|
value: 25.168000000000003 |
|
- type: map_at_100 |
|
value: 27.017000000000003 |
|
- type: map_at_1000 |
|
value: 27.205000000000002 |
|
- type: map_at_3 |
|
value: 21.746 |
|
- type: map_at_5 |
|
value: 23.579 |
|
- type: mrr_at_1 |
|
value: 31.635999999999996 |
|
- type: mrr_at_10 |
|
value: 40.077 |
|
- type: mrr_at_100 |
|
value: 41.112 |
|
- type: mrr_at_1000 |
|
value: 41.160999999999994 |
|
- type: mrr_at_3 |
|
value: 37.937 |
|
- type: mrr_at_5 |
|
value: 39.18 |
|
- type: ndcg_at_1 |
|
value: 31.635999999999996 |
|
- type: ndcg_at_10 |
|
value: 32.298 |
|
- type: ndcg_at_100 |
|
value: 39.546 |
|
- type: ndcg_at_1000 |
|
value: 42.88 |
|
- type: ndcg_at_3 |
|
value: 29.221999999999998 |
|
- type: ndcg_at_5 |
|
value: 30.069000000000003 |
|
- type: precision_at_1 |
|
value: 31.635999999999996 |
|
- type: precision_at_10 |
|
value: 9.367 |
|
- type: precision_at_100 |
|
value: 1.645 |
|
- type: precision_at_1000 |
|
value: 0.22399999999999998 |
|
- type: precision_at_3 |
|
value: 20.01 |
|
- type: precision_at_5 |
|
value: 14.753 |
|
- type: recall_at_1 |
|
value: 15.193000000000001 |
|
- type: recall_at_10 |
|
value: 38.214999999999996 |
|
- type: recall_at_100 |
|
value: 65.95 |
|
- type: recall_at_1000 |
|
value: 85.85300000000001 |
|
- type: recall_at_3 |
|
value: 26.357000000000003 |
|
- type: recall_at_5 |
|
value: 31.319999999999997 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: jinaai/ger_da_lir |
|
name: MTEB GerDaLIR |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.363 |
|
- type: map_at_10 |
|
value: 16.222 |
|
- type: map_at_100 |
|
value: 17.28 |
|
- type: map_at_1000 |
|
value: 17.380000000000003 |
|
- type: map_at_3 |
|
value: 14.054 |
|
- type: map_at_5 |
|
value: 15.203 |
|
- type: mrr_at_1 |
|
value: 11.644 |
|
- type: mrr_at_10 |
|
value: 17.625 |
|
- type: mrr_at_100 |
|
value: 18.608 |
|
- type: mrr_at_1000 |
|
value: 18.695999999999998 |
|
- type: mrr_at_3 |
|
value: 15.481 |
|
- type: mrr_at_5 |
|
value: 16.659 |
|
- type: ndcg_at_1 |
|
value: 11.628 |
|
- type: ndcg_at_10 |
|
value: 20.028000000000002 |
|
- type: ndcg_at_100 |
|
value: 25.505 |
|
- type: ndcg_at_1000 |
|
value: 28.288000000000004 |
|
- type: ndcg_at_3 |
|
value: 15.603 |
|
- type: ndcg_at_5 |
|
value: 17.642 |
|
- type: precision_at_1 |
|
value: 11.628 |
|
- type: precision_at_10 |
|
value: 3.5589999999999997 |
|
- type: precision_at_100 |
|
value: 0.664 |
|
- type: precision_at_1000 |
|
value: 0.092 |
|
- type: precision_at_3 |
|
value: 7.109999999999999 |
|
- type: precision_at_5 |
|
value: 5.401 |
|
- type: recall_at_1 |
|
value: 10.363 |
|
- type: recall_at_10 |
|
value: 30.586000000000002 |
|
- type: recall_at_100 |
|
value: 56.43 |
|
- type: recall_at_1000 |
|
value: 78.142 |
|
- type: recall_at_3 |
|
value: 18.651 |
|
- type: recall_at_5 |
|
value: 23.493 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: deepset/germandpr |
|
name: MTEB GermanDPR |
|
config: default |
|
split: test |
|
revision: 5129d02422a66be600ac89cd3e8531b4f97d347d |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.78 |
|
- type: map_at_10 |
|
value: 73.91499999999999 |
|
- type: map_at_100 |
|
value: 74.089 |
|
- type: map_at_1000 |
|
value: 74.09400000000001 |
|
- type: map_at_3 |
|
value: 71.87 |
|
- type: map_at_5 |
|
value: 73.37700000000001 |
|
- type: mrr_at_1 |
|
value: 60.78 |
|
- type: mrr_at_10 |
|
value: 73.91499999999999 |
|
- type: mrr_at_100 |
|
value: 74.089 |
|
- type: mrr_at_1000 |
|
value: 74.09400000000001 |
|
- type: mrr_at_3 |
|
value: 71.87 |
|
- type: mrr_at_5 |
|
value: 73.37700000000001 |
|
- type: ndcg_at_1 |
|
value: 60.78 |
|
- type: ndcg_at_10 |
|
value: 79.35600000000001 |
|
- type: ndcg_at_100 |
|
value: 80.077 |
|
- type: ndcg_at_1000 |
|
value: 80.203 |
|
- type: ndcg_at_3 |
|
value: 75.393 |
|
- type: ndcg_at_5 |
|
value: 78.077 |
|
- type: precision_at_1 |
|
value: 60.78 |
|
- type: precision_at_10 |
|
value: 9.59 |
|
- type: precision_at_100 |
|
value: 0.9900000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 28.52 |
|
- type: precision_at_5 |
|
value: 18.4 |
|
- type: recall_at_1 |
|
value: 60.78 |
|
- type: recall_at_10 |
|
value: 95.902 |
|
- type: recall_at_100 |
|
value: 99.024 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 85.56099999999999 |
|
- type: recall_at_5 |
|
value: 92.0 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: jinaai/german-STSbenchmark |
|
name: MTEB GermanSTSBenchmark |
|
config: default |
|
split: test |
|
revision: 49d9b423b996fea62b483f9ee6dfb5ec233515ca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.49524420894356 |
|
- type: cos_sim_spearman |
|
value: 88.32407839427714 |
|
- type: euclidean_pearson |
|
value: 87.25098779877104 |
|
- type: euclidean_spearman |
|
value: 88.22738098593608 |
|
- type: manhattan_pearson |
|
value: 87.23872691839607 |
|
- type: manhattan_spearman |
|
value: 88.2002968380165 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.81 |
|
- type: map_at_10 |
|
value: 46.238 |
|
- type: map_at_100 |
|
value: 47.141 |
|
- type: map_at_1000 |
|
value: 47.213 |
|
- type: map_at_3 |
|
value: 43.248999999999995 |
|
- type: map_at_5 |
|
value: 45.078 |
|
- type: mrr_at_1 |
|
value: 63.619 |
|
- type: mrr_at_10 |
|
value: 71.279 |
|
- type: mrr_at_100 |
|
value: 71.648 |
|
- type: mrr_at_1000 |
|
value: 71.665 |
|
- type: mrr_at_3 |
|
value: 69.76599999999999 |
|
- type: mrr_at_5 |
|
value: 70.743 |
|
- type: ndcg_at_1 |
|
value: 63.619 |
|
- type: ndcg_at_10 |
|
value: 55.38999999999999 |
|
- type: ndcg_at_100 |
|
value: 58.80800000000001 |
|
- type: ndcg_at_1000 |
|
value: 60.331999999999994 |
|
- type: ndcg_at_3 |
|
value: 50.727 |
|
- type: ndcg_at_5 |
|
value: 53.284 |
|
- type: precision_at_1 |
|
value: 63.619 |
|
- type: precision_at_10 |
|
value: 11.668000000000001 |
|
- type: precision_at_100 |
|
value: 1.434 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 32.001000000000005 |
|
- type: precision_at_5 |
|
value: 21.223 |
|
- type: recall_at_1 |
|
value: 31.81 |
|
- type: recall_at_10 |
|
value: 58.339 |
|
- type: recall_at_100 |
|
value: 71.708 |
|
- type: recall_at_1000 |
|
value: 81.85 |
|
- type: recall_at_3 |
|
value: 48.001 |
|
- type: recall_at_5 |
|
value: 53.059 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 68.60640000000001 |
|
- type: ap |
|
value: 62.84296904042086 |
|
- type: f1 |
|
value: 68.50643633327537 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: jinaai/miracl |
|
name: MTEB MIRACL |
|
config: default |
|
split: test |
|
revision: 8741c3b61cd36ed9ca1b3d4203543a41793239e2 |
|
metrics: |
|
- type: map |
|
value: 64.29704335389768 |
|
- type: mrr |
|
value: 72.11962197159565 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 89.3844049247606 |
|
- type: f1 |
|
value: 89.2124328528015 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (de) |
|
config: de |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 88.36855452240067 |
|
- type: f1 |
|
value: 87.35458822097442 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 66.48654810761514 |
|
- type: f1 |
|
value: 50.07229882504409 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (de) |
|
config: de |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 63.832065370526905 |
|
- type: f1 |
|
value: 46.283579383385806 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (de) |
|
config: de |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 63.89038332212509 |
|
- type: f1 |
|
value: 61.86279849685129 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 69.11230665770006 |
|
- type: f1 |
|
value: 67.44780095350535 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (de) |
|
config: de |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 71.25084061869536 |
|
- type: f1 |
|
value: 71.43965023016408 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.73907195696032 |
|
- type: f1 |
|
value: 73.69920814839061 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.32577306498249 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.759349326367783 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.401342674703425 |
|
- type: mrr |
|
value: 31.384379585660987 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.855 |
|
- type: map_at_10 |
|
value: 10.01 |
|
- type: map_at_100 |
|
value: 12.461 |
|
- type: map_at_1000 |
|
value: 13.776 |
|
- type: map_at_3 |
|
value: 7.252 |
|
- type: map_at_5 |
|
value: 8.679 |
|
- type: mrr_at_1 |
|
value: 41.176 |
|
- type: mrr_at_10 |
|
value: 49.323 |
|
- type: mrr_at_100 |
|
value: 49.954 |
|
- type: mrr_at_1000 |
|
value: 49.997 |
|
- type: mrr_at_3 |
|
value: 46.904 |
|
- type: mrr_at_5 |
|
value: 48.375 |
|
- type: ndcg_at_1 |
|
value: 39.318999999999996 |
|
- type: ndcg_at_10 |
|
value: 28.607 |
|
- type: ndcg_at_100 |
|
value: 26.554 |
|
- type: ndcg_at_1000 |
|
value: 35.731 |
|
- type: ndcg_at_3 |
|
value: 32.897999999999996 |
|
- type: ndcg_at_5 |
|
value: 31.53 |
|
- type: precision_at_1 |
|
value: 41.176 |
|
- type: precision_at_10 |
|
value: 20.867 |
|
- type: precision_at_100 |
|
value: 6.796 |
|
- type: precision_at_1000 |
|
value: 1.983 |
|
- type: precision_at_3 |
|
value: 30.547 |
|
- type: precision_at_5 |
|
value: 27.245 |
|
- type: recall_at_1 |
|
value: 4.855 |
|
- type: recall_at_10 |
|
value: 14.08 |
|
- type: recall_at_100 |
|
value: 28.188000000000002 |
|
- type: recall_at_1000 |
|
value: 60.07900000000001 |
|
- type: recall_at_3 |
|
value: 7.947 |
|
- type: recall_at_5 |
|
value: 10.786 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.906999999999996 |
|
- type: map_at_10 |
|
value: 41.147 |
|
- type: map_at_100 |
|
value: 42.269 |
|
- type: map_at_1000 |
|
value: 42.308 |
|
- type: map_at_3 |
|
value: 36.638999999999996 |
|
- type: map_at_5 |
|
value: 39.285 |
|
- type: mrr_at_1 |
|
value: 30.359 |
|
- type: mrr_at_10 |
|
value: 43.607 |
|
- type: mrr_at_100 |
|
value: 44.454 |
|
- type: mrr_at_1000 |
|
value: 44.481 |
|
- type: mrr_at_3 |
|
value: 39.644 |
|
- type: mrr_at_5 |
|
value: 42.061 |
|
- type: ndcg_at_1 |
|
value: 30.330000000000002 |
|
- type: ndcg_at_10 |
|
value: 48.899 |
|
- type: ndcg_at_100 |
|
value: 53.612 |
|
- type: ndcg_at_1000 |
|
value: 54.51200000000001 |
|
- type: ndcg_at_3 |
|
value: 40.262 |
|
- type: ndcg_at_5 |
|
value: 44.787 |
|
- type: precision_at_1 |
|
value: 30.330000000000002 |
|
- type: precision_at_10 |
|
value: 8.323 |
|
- type: precision_at_100 |
|
value: 1.0959999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 18.395 |
|
- type: precision_at_5 |
|
value: 13.627 |
|
- type: recall_at_1 |
|
value: 26.906999999999996 |
|
- type: recall_at_10 |
|
value: 70.215 |
|
- type: recall_at_100 |
|
value: 90.61200000000001 |
|
- type: recall_at_1000 |
|
value: 97.294 |
|
- type: recall_at_3 |
|
value: 47.784 |
|
- type: recall_at_5 |
|
value: 58.251 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: paws-x |
|
name: MTEB PawsX |
|
config: default |
|
split: test |
|
revision: 8a04d940a42cd40658986fdd8e3da561533a3646 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 60.5 |
|
- type: cos_sim_ap |
|
value: 57.606096528877494 |
|
- type: cos_sim_f1 |
|
value: 62.24240307369892 |
|
- type: cos_sim_precision |
|
value: 45.27439024390244 |
|
- type: cos_sim_recall |
|
value: 99.55307262569832 |
|
- type: dot_accuracy |
|
value: 57.699999999999996 |
|
- type: dot_ap |
|
value: 51.289351057160616 |
|
- type: dot_f1 |
|
value: 62.25953130465197 |
|
- type: dot_precision |
|
value: 45.31568228105906 |
|
- type: dot_recall |
|
value: 99.4413407821229 |
|
- type: euclidean_accuracy |
|
value: 60.45 |
|
- type: euclidean_ap |
|
value: 57.616461421424034 |
|
- type: euclidean_f1 |
|
value: 62.313697657913416 |
|
- type: euclidean_precision |
|
value: 45.657826313052524 |
|
- type: euclidean_recall |
|
value: 98.10055865921787 |
|
- type: manhattan_accuracy |
|
value: 60.3 |
|
- type: manhattan_ap |
|
value: 57.580565271667325 |
|
- type: manhattan_f1 |
|
value: 62.24240307369892 |
|
- type: manhattan_precision |
|
value: 45.27439024390244 |
|
- type: manhattan_recall |
|
value: 99.55307262569832 |
|
- type: max_accuracy |
|
value: 60.5 |
|
- type: max_ap |
|
value: 57.616461421424034 |
|
- type: max_f1 |
|
value: 62.313697657913416 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.21300000000001 |
|
- type: map_at_10 |
|
value: 84.136 |
|
- type: map_at_100 |
|
value: 84.796 |
|
- type: map_at_1000 |
|
value: 84.812 |
|
- type: map_at_3 |
|
value: 81.182 |
|
- type: map_at_5 |
|
value: 83.027 |
|
- type: mrr_at_1 |
|
value: 80.91000000000001 |
|
- type: mrr_at_10 |
|
value: 87.155 |
|
- type: mrr_at_100 |
|
value: 87.27000000000001 |
|
- type: mrr_at_1000 |
|
value: 87.271 |
|
- type: mrr_at_3 |
|
value: 86.158 |
|
- type: mrr_at_5 |
|
value: 86.828 |
|
- type: ndcg_at_1 |
|
value: 80.88 |
|
- type: ndcg_at_10 |
|
value: 87.926 |
|
- type: ndcg_at_100 |
|
value: 89.223 |
|
- type: ndcg_at_1000 |
|
value: 89.321 |
|
- type: ndcg_at_3 |
|
value: 85.036 |
|
- type: ndcg_at_5 |
|
value: 86.614 |
|
- type: precision_at_1 |
|
value: 80.88 |
|
- type: precision_at_10 |
|
value: 13.350000000000001 |
|
- type: precision_at_100 |
|
value: 1.5310000000000001 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.173 |
|
- type: precision_at_5 |
|
value: 24.476 |
|
- type: recall_at_1 |
|
value: 70.21300000000001 |
|
- type: recall_at_10 |
|
value: 95.12 |
|
- type: recall_at_100 |
|
value: 99.535 |
|
- type: recall_at_1000 |
|
value: 99.977 |
|
- type: recall_at_3 |
|
value: 86.833 |
|
- type: recall_at_5 |
|
value: 91.26100000000001 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 47.754688783184875 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 54.875736374329364 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.773 |
|
- type: map_at_10 |
|
value: 9.447 |
|
- type: map_at_100 |
|
value: 11.1 |
|
- type: map_at_1000 |
|
value: 11.37 |
|
- type: map_at_3 |
|
value: 6.787 |
|
- type: map_at_5 |
|
value: 8.077 |
|
- type: mrr_at_1 |
|
value: 18.5 |
|
- type: mrr_at_10 |
|
value: 28.227000000000004 |
|
- type: mrr_at_100 |
|
value: 29.445 |
|
- type: mrr_at_1000 |
|
value: 29.515 |
|
- type: mrr_at_3 |
|
value: 25.2 |
|
- type: mrr_at_5 |
|
value: 27.055 |
|
- type: ndcg_at_1 |
|
value: 18.5 |
|
- type: ndcg_at_10 |
|
value: 16.29 |
|
- type: ndcg_at_100 |
|
value: 23.250999999999998 |
|
- type: ndcg_at_1000 |
|
value: 28.445999999999998 |
|
- type: ndcg_at_3 |
|
value: 15.376000000000001 |
|
- type: ndcg_at_5 |
|
value: 13.528 |
|
- type: precision_at_1 |
|
value: 18.5 |
|
- type: precision_at_10 |
|
value: 8.51 |
|
- type: precision_at_100 |
|
value: 1.855 |
|
- type: precision_at_1000 |
|
value: 0.311 |
|
- type: precision_at_3 |
|
value: 14.533 |
|
- type: precision_at_5 |
|
value: 12.0 |
|
- type: recall_at_1 |
|
value: 3.773 |
|
- type: recall_at_10 |
|
value: 17.282 |
|
- type: recall_at_100 |
|
value: 37.645 |
|
- type: recall_at_1000 |
|
value: 63.138000000000005 |
|
- type: recall_at_3 |
|
value: 8.853 |
|
- type: recall_at_5 |
|
value: 12.168 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.32789517976525 |
|
- type: cos_sim_spearman |
|
value: 80.32750384145629 |
|
- type: euclidean_pearson |
|
value: 81.5025131452508 |
|
- type: euclidean_spearman |
|
value: 80.24797115147175 |
|
- type: manhattan_pearson |
|
value: 81.51634463412002 |
|
- type: manhattan_spearman |
|
value: 80.24614721495055 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.47050448992432 |
|
- type: cos_sim_spearman |
|
value: 80.58919997743621 |
|
- type: euclidean_pearson |
|
value: 85.83258918113664 |
|
- type: euclidean_spearman |
|
value: 80.97441389240902 |
|
- type: manhattan_pearson |
|
value: 85.7798262013878 |
|
- type: manhattan_spearman |
|
value: 80.97208703064196 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.95341439711532 |
|
- type: cos_sim_spearman |
|
value: 86.59127484634989 |
|
- type: euclidean_pearson |
|
value: 85.57850603454227 |
|
- type: euclidean_spearman |
|
value: 86.47130477363419 |
|
- type: manhattan_pearson |
|
value: 85.59387925447652 |
|
- type: manhattan_spearman |
|
value: 86.50665427391583 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.39810909161844 |
|
- type: cos_sim_spearman |
|
value: 82.98595295546008 |
|
- type: euclidean_pearson |
|
value: 84.04681129969951 |
|
- type: euclidean_spearman |
|
value: 82.98197460689866 |
|
- type: manhattan_pearson |
|
value: 83.9918798171185 |
|
- type: manhattan_spearman |
|
value: 82.91148131768082 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.02072712147692 |
|
- type: cos_sim_spearman |
|
value: 88.78821332623012 |
|
- type: euclidean_pearson |
|
value: 88.12132045572747 |
|
- type: euclidean_spearman |
|
value: 88.74273451067364 |
|
- type: manhattan_pearson |
|
value: 88.05431550059166 |
|
- type: manhattan_spearman |
|
value: 88.67610233020723 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.96134704624787 |
|
- type: cos_sim_spearman |
|
value: 84.44062976314666 |
|
- type: euclidean_pearson |
|
value: 84.03642536310323 |
|
- type: euclidean_spearman |
|
value: 84.4535014579785 |
|
- type: manhattan_pearson |
|
value: 83.92874228901483 |
|
- type: manhattan_spearman |
|
value: 84.33634314951631 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-de) |
|
config: en-de |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.3154168064887 |
|
- type: cos_sim_spearman |
|
value: 86.72393652571682 |
|
- type: euclidean_pearson |
|
value: 86.04193246174164 |
|
- type: euclidean_spearman |
|
value: 86.30482896608093 |
|
- type: manhattan_pearson |
|
value: 85.95524084651859 |
|
- type: manhattan_spearman |
|
value: 86.06031431994282 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 89.91079682750804 |
|
- type: cos_sim_spearman |
|
value: 89.30961836617064 |
|
- type: euclidean_pearson |
|
value: 88.86249564158628 |
|
- type: euclidean_spearman |
|
value: 89.04772899592396 |
|
- type: manhattan_pearson |
|
value: 88.85579791315043 |
|
- type: manhattan_spearman |
|
value: 88.94190462541333 |
|
- 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: 67.00558145551088 |
|
- type: cos_sim_spearman |
|
value: 67.96601170393878 |
|
- type: euclidean_pearson |
|
value: 67.87627043214336 |
|
- type: euclidean_spearman |
|
value: 66.76402572303859 |
|
- type: manhattan_pearson |
|
value: 67.88306560555452 |
|
- type: manhattan_spearman |
|
value: 66.6273862035506 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (de) |
|
config: de |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 50.83759332748726 |
|
- type: cos_sim_spearman |
|
value: 59.066344562858006 |
|
- type: euclidean_pearson |
|
value: 50.08955848154131 |
|
- type: euclidean_spearman |
|
value: 58.36517305855221 |
|
- type: manhattan_pearson |
|
value: 50.05257267223111 |
|
- type: manhattan_spearman |
|
value: 58.37570252804986 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (de-en) |
|
config: de-en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 59.22749007956492 |
|
- type: cos_sim_spearman |
|
value: 55.97282077657827 |
|
- type: euclidean_pearson |
|
value: 62.10661533695752 |
|
- type: euclidean_spearman |
|
value: 53.62780854854067 |
|
- type: manhattan_pearson |
|
value: 62.37138085709719 |
|
- type: manhattan_spearman |
|
value: 54.17556356828155 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.91145397065878 |
|
- type: cos_sim_spearman |
|
value: 88.13960018389005 |
|
- type: euclidean_pearson |
|
value: 87.67618876224006 |
|
- type: euclidean_spearman |
|
value: 87.99119480810556 |
|
- type: manhattan_pearson |
|
value: 87.67920297334753 |
|
- type: manhattan_spearman |
|
value: 87.99113250064492 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 78.09133563707582 |
|
- type: mrr |
|
value: 93.2415288052543 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 47.760999999999996 |
|
- type: map_at_10 |
|
value: 56.424 |
|
- type: map_at_100 |
|
value: 57.24399999999999 |
|
- type: map_at_1000 |
|
value: 57.278 |
|
- type: map_at_3 |
|
value: 53.68000000000001 |
|
- type: map_at_5 |
|
value: 55.442 |
|
- type: mrr_at_1 |
|
value: 50.666999999999994 |
|
- type: mrr_at_10 |
|
value: 58.012 |
|
- type: mrr_at_100 |
|
value: 58.736 |
|
- type: mrr_at_1000 |
|
value: 58.769000000000005 |
|
- type: mrr_at_3 |
|
value: 56.056 |
|
- type: mrr_at_5 |
|
value: 57.321999999999996 |
|
- type: ndcg_at_1 |
|
value: 50.666999999999994 |
|
- type: ndcg_at_10 |
|
value: 60.67700000000001 |
|
- type: ndcg_at_100 |
|
value: 64.513 |
|
- type: ndcg_at_1000 |
|
value: 65.62400000000001 |
|
- type: ndcg_at_3 |
|
value: 56.186 |
|
- type: ndcg_at_5 |
|
value: 58.692 |
|
- type: precision_at_1 |
|
value: 50.666999999999994 |
|
- type: precision_at_10 |
|
value: 8.200000000000001 |
|
- type: precision_at_100 |
|
value: 1.023 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 21.889 |
|
- type: precision_at_5 |
|
value: 14.866999999999999 |
|
- type: recall_at_1 |
|
value: 47.760999999999996 |
|
- type: recall_at_10 |
|
value: 72.006 |
|
- type: recall_at_100 |
|
value: 89.767 |
|
- type: recall_at_1000 |
|
value: 98.833 |
|
- type: recall_at_3 |
|
value: 60.211000000000006 |
|
- type: recall_at_5 |
|
value: 66.3 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.79009900990098 |
|
- type: cos_sim_ap |
|
value: 94.86690691995835 |
|
- type: cos_sim_f1 |
|
value: 89.37875751503007 |
|
- type: cos_sim_precision |
|
value: 89.5582329317269 |
|
- type: cos_sim_recall |
|
value: 89.2 |
|
- type: dot_accuracy |
|
value: 99.76336633663367 |
|
- type: dot_ap |
|
value: 94.26453740761586 |
|
- type: dot_f1 |
|
value: 88.00783162016641 |
|
- type: dot_precision |
|
value: 86.19367209971237 |
|
- type: dot_recall |
|
value: 89.9 |
|
- type: euclidean_accuracy |
|
value: 99.7940594059406 |
|
- type: euclidean_ap |
|
value: 94.85459757524379 |
|
- type: euclidean_f1 |
|
value: 89.62779156327544 |
|
- type: euclidean_precision |
|
value: 88.96551724137932 |
|
- type: euclidean_recall |
|
value: 90.3 |
|
- type: manhattan_accuracy |
|
value: 99.79009900990098 |
|
- type: manhattan_ap |
|
value: 94.76971336654465 |
|
- type: manhattan_f1 |
|
value: 89.35323383084577 |
|
- type: manhattan_precision |
|
value: 88.91089108910892 |
|
- type: manhattan_recall |
|
value: 89.8 |
|
- type: max_accuracy |
|
value: 99.7940594059406 |
|
- type: max_ap |
|
value: 94.86690691995835 |
|
- type: max_f1 |
|
value: 89.62779156327544 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 55.38197670064987 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.08330158937971 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 49.50367079063226 |
|
- type: mrr |
|
value: 50.30444943128768 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.37739520909561 |
|
- type: cos_sim_spearman |
|
value: 31.548500943973913 |
|
- type: dot_pearson |
|
value: 29.983610104303 |
|
- type: dot_spearman |
|
value: 29.90185869098618 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.198 |
|
- type: map_at_10 |
|
value: 1.5810000000000002 |
|
- type: map_at_100 |
|
value: 9.064 |
|
- type: map_at_1000 |
|
value: 22.161 |
|
- type: map_at_3 |
|
value: 0.536 |
|
- type: map_at_5 |
|
value: 0.8370000000000001 |
|
- type: mrr_at_1 |
|
value: 80.0 |
|
- type: mrr_at_10 |
|
value: 86.75 |
|
- type: mrr_at_100 |
|
value: 86.799 |
|
- type: mrr_at_1000 |
|
value: 86.799 |
|
- type: mrr_at_3 |
|
value: 85.0 |
|
- type: mrr_at_5 |
|
value: 86.5 |
|
- type: ndcg_at_1 |
|
value: 73.0 |
|
- type: ndcg_at_10 |
|
value: 65.122 |
|
- type: ndcg_at_100 |
|
value: 51.853 |
|
- type: ndcg_at_1000 |
|
value: 47.275 |
|
- type: ndcg_at_3 |
|
value: 66.274 |
|
- type: ndcg_at_5 |
|
value: 64.826 |
|
- type: precision_at_1 |
|
value: 80.0 |
|
- type: precision_at_10 |
|
value: 70.19999999999999 |
|
- type: precision_at_100 |
|
value: 53.480000000000004 |
|
- type: precision_at_1000 |
|
value: 20.946 |
|
- type: precision_at_3 |
|
value: 71.333 |
|
- type: precision_at_5 |
|
value: 70.0 |
|
- type: recall_at_1 |
|
value: 0.198 |
|
- type: recall_at_10 |
|
value: 1.884 |
|
- type: recall_at_100 |
|
value: 12.57 |
|
- type: recall_at_1000 |
|
value: 44.208999999999996 |
|
- type: recall_at_3 |
|
value: 0.5890000000000001 |
|
- type: recall_at_5 |
|
value: 0.95 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: slvnwhrl/tenkgnad-clustering-p2p |
|
name: MTEB TenKGnadClusteringP2P |
|
config: default |
|
split: test |
|
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558 |
|
metrics: |
|
- type: v_measure |
|
value: 42.84199261133083 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: slvnwhrl/tenkgnad-clustering-s2s |
|
name: MTEB TenKGnadClusteringS2S |
|
config: default |
|
split: test |
|
revision: 6cddbe003f12b9b140aec477b583ac4191f01786 |
|
metrics: |
|
- type: v_measure |
|
value: 23.689557114798838 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.941 |
|
- type: map_at_10 |
|
value: 8.222 |
|
- type: map_at_100 |
|
value: 14.277999999999999 |
|
- type: map_at_1000 |
|
value: 15.790000000000001 |
|
- type: map_at_3 |
|
value: 4.4670000000000005 |
|
- type: map_at_5 |
|
value: 5.762 |
|
- type: mrr_at_1 |
|
value: 24.490000000000002 |
|
- type: mrr_at_10 |
|
value: 38.784 |
|
- type: mrr_at_100 |
|
value: 39.724 |
|
- type: mrr_at_1000 |
|
value: 39.724 |
|
- type: mrr_at_3 |
|
value: 33.333 |
|
- type: mrr_at_5 |
|
value: 37.415 |
|
- type: ndcg_at_1 |
|
value: 22.448999999999998 |
|
- type: ndcg_at_10 |
|
value: 21.026 |
|
- type: ndcg_at_100 |
|
value: 33.721000000000004 |
|
- type: ndcg_at_1000 |
|
value: 45.045 |
|
- type: ndcg_at_3 |
|
value: 20.053 |
|
- type: ndcg_at_5 |
|
value: 20.09 |
|
- type: precision_at_1 |
|
value: 24.490000000000002 |
|
- type: precision_at_10 |
|
value: 19.796 |
|
- type: precision_at_100 |
|
value: 7.469 |
|
- type: precision_at_1000 |
|
value: 1.48 |
|
- type: precision_at_3 |
|
value: 21.769 |
|
- type: precision_at_5 |
|
value: 21.224 |
|
- type: recall_at_1 |
|
value: 1.941 |
|
- type: recall_at_10 |
|
value: 14.915999999999999 |
|
- type: recall_at_100 |
|
value: 46.155 |
|
- type: recall_at_1000 |
|
value: 80.664 |
|
- type: recall_at_3 |
|
value: 5.629 |
|
- type: recall_at_5 |
|
value: 8.437 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 69.64800000000001 |
|
- type: ap |
|
value: 12.914826731261094 |
|
- type: f1 |
|
value: 53.05213503422915 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 60.427277872099594 |
|
- type: f1 |
|
value: 60.78292007556828 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 40.48134168406559 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.79465935506944 |
|
- type: cos_sim_ap |
|
value: 70.24589055290592 |
|
- type: cos_sim_f1 |
|
value: 65.0994575045208 |
|
- type: cos_sim_precision |
|
value: 63.76518218623482 |
|
- type: cos_sim_recall |
|
value: 66.49076517150397 |
|
- type: dot_accuracy |
|
value: 84.63968528342374 |
|
- type: dot_ap |
|
value: 69.84683095084355 |
|
- type: dot_f1 |
|
value: 64.50606169727523 |
|
- type: dot_precision |
|
value: 59.1719885487778 |
|
- type: dot_recall |
|
value: 70.89709762532982 |
|
- type: euclidean_accuracy |
|
value: 84.76485664898374 |
|
- type: euclidean_ap |
|
value: 70.20556438685551 |
|
- type: euclidean_f1 |
|
value: 65.06796614516543 |
|
- type: euclidean_precision |
|
value: 63.29840319361277 |
|
- type: euclidean_recall |
|
value: 66.93931398416886 |
|
- type: manhattan_accuracy |
|
value: 84.72313286046374 |
|
- type: manhattan_ap |
|
value: 70.17151475534308 |
|
- type: manhattan_f1 |
|
value: 65.31379180759113 |
|
- type: manhattan_precision |
|
value: 62.17505366086334 |
|
- type: manhattan_recall |
|
value: 68.7862796833773 |
|
- type: max_accuracy |
|
value: 84.79465935506944 |
|
- type: max_ap |
|
value: 70.24589055290592 |
|
- type: max_f1 |
|
value: 65.31379180759113 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.95874568246207 |
|
- type: cos_sim_ap |
|
value: 85.82517548264127 |
|
- type: cos_sim_f1 |
|
value: 78.22288041466125 |
|
- type: cos_sim_precision |
|
value: 75.33875338753387 |
|
- type: cos_sim_recall |
|
value: 81.33661841700031 |
|
- type: dot_accuracy |
|
value: 88.836496293709 |
|
- type: dot_ap |
|
value: 85.53430720252186 |
|
- type: dot_f1 |
|
value: 78.10616085869725 |
|
- type: dot_precision |
|
value: 74.73269555430501 |
|
- type: dot_recall |
|
value: 81.79858330766862 |
|
- type: euclidean_accuracy |
|
value: 88.92769821865176 |
|
- type: euclidean_ap |
|
value: 85.65904346964223 |
|
- type: euclidean_f1 |
|
value: 77.98774074208407 |
|
- type: euclidean_precision |
|
value: 73.72282795035315 |
|
- type: euclidean_recall |
|
value: 82.77640899291654 |
|
- type: manhattan_accuracy |
|
value: 88.86366282454303 |
|
- type: manhattan_ap |
|
value: 85.61599642231819 |
|
- type: manhattan_f1 |
|
value: 78.01480509061737 |
|
- type: manhattan_precision |
|
value: 74.10460685833044 |
|
- type: manhattan_recall |
|
value: 82.36064059131506 |
|
- type: max_accuracy |
|
value: 88.95874568246207 |
|
- type: max_ap |
|
value: 85.82517548264127 |
|
- type: max_f1 |
|
value: 78.22288041466125 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: None |
|
name: MTEB WikiCLIR |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.9539999999999997 |
|
- type: map_at_10 |
|
value: 7.407 |
|
- type: map_at_100 |
|
value: 8.677999999999999 |
|
- type: map_at_1000 |
|
value: 9.077 |
|
- type: map_at_3 |
|
value: 5.987 |
|
- type: map_at_5 |
|
value: 6.6979999999999995 |
|
- type: mrr_at_1 |
|
value: 35.65 |
|
- type: mrr_at_10 |
|
value: 45.097 |
|
- type: mrr_at_100 |
|
value: 45.83 |
|
- type: mrr_at_1000 |
|
value: 45.871 |
|
- type: mrr_at_3 |
|
value: 42.63 |
|
- type: mrr_at_5 |
|
value: 44.104 |
|
- type: ndcg_at_1 |
|
value: 29.215000000000003 |
|
- type: ndcg_at_10 |
|
value: 22.694 |
|
- type: ndcg_at_100 |
|
value: 22.242 |
|
- type: ndcg_at_1000 |
|
value: 27.069 |
|
- type: ndcg_at_3 |
|
value: 27.641 |
|
- type: ndcg_at_5 |
|
value: 25.503999999999998 |
|
- type: precision_at_1 |
|
value: 35.65 |
|
- type: precision_at_10 |
|
value: 12.795000000000002 |
|
- type: precision_at_100 |
|
value: 3.354 |
|
- type: precision_at_1000 |
|
value: 0.743 |
|
- type: precision_at_3 |
|
value: 23.403 |
|
- type: precision_at_5 |
|
value: 18.474 |
|
- type: recall_at_1 |
|
value: 3.9539999999999997 |
|
- type: recall_at_10 |
|
value: 11.301 |
|
- type: recall_at_100 |
|
value: 22.919999999999998 |
|
- type: recall_at_1000 |
|
value: 40.146 |
|
- type: recall_at_3 |
|
value: 7.146 |
|
- type: recall_at_5 |
|
value: 8.844000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: jinaai/xmarket_de |
|
name: MTEB XMarket |
|
config: default |
|
split: test |
|
revision: 2336818db4c06570fcdf263e1bcb9993b786f67a |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.872 |
|
- type: map_at_10 |
|
value: 10.658 |
|
- type: map_at_100 |
|
value: 13.422999999999998 |
|
- type: map_at_1000 |
|
value: 14.245 |
|
- type: map_at_3 |
|
value: 7.857 |
|
- type: map_at_5 |
|
value: 9.142999999999999 |
|
- type: mrr_at_1 |
|
value: 16.744999999999997 |
|
- type: mrr_at_10 |
|
value: 24.416 |
|
- type: mrr_at_100 |
|
value: 25.432 |
|
- type: mrr_at_1000 |
|
value: 25.502999999999997 |
|
- type: mrr_at_3 |
|
value: 22.096 |
|
- type: mrr_at_5 |
|
value: 23.421 |
|
- type: ndcg_at_1 |
|
value: 16.695999999999998 |
|
- type: ndcg_at_10 |
|
value: 18.66 |
|
- type: ndcg_at_100 |
|
value: 24.314 |
|
- type: ndcg_at_1000 |
|
value: 29.846 |
|
- type: ndcg_at_3 |
|
value: 17.041999999999998 |
|
- type: ndcg_at_5 |
|
value: 17.585 |
|
- type: precision_at_1 |
|
value: 16.695999999999998 |
|
- type: precision_at_10 |
|
value: 10.374 |
|
- type: precision_at_100 |
|
value: 3.988 |
|
- type: precision_at_1000 |
|
value: 1.1860000000000002 |
|
- type: precision_at_3 |
|
value: 14.21 |
|
- type: precision_at_5 |
|
value: 12.623000000000001 |
|
- type: recall_at_1 |
|
value: 4.872 |
|
- type: recall_at_10 |
|
value: 18.624 |
|
- type: recall_at_100 |
|
value: 40.988 |
|
- type: recall_at_1000 |
|
value: 65.33 |
|
- type: recall_at_3 |
|
value: 10.162 |
|
- type: recall_at_5 |
|
value: 13.517999999999999 |
|
--- |
|
<!-- TODO: add evaluation results here --> |
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Jina AI logo: Jina AI is your Portal to Multimodal AI" 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-base-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). |
|
|
|
## Intended Usage & Model Info |
|
|
|
`jina-embeddings-v2-base-de` is a German/English bilingual text **embedding model** supporting **8192 sequence length**. |
|
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. |
|
We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. |
|
Additionally, we provide the following embedding models: |
|
|
|
`jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch, |
|
welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**. |
|
Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, |
|
welche mithilfe einer symmetrische Variante von [ALiBi](https://arxiv.org/abs/2108.12409) längere Eingabetexte erlaubt. |
|
Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, |
|
gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. |
|
Des Weiteren stellen wir folgende Embedding-Modelle bereit: |
|
|
|
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters. |
|
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters. |
|
- [`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 **(you are here)**. |
|
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon). |
|
- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings. |
|
|
|
## Data & Parameters |
|
|
|
The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016). |
|
|
|
## Usage |
|
|
|
**<details><summary>Please apply mean pooling when integrating the model.</summary>** |
|
<p> |
|
|
|
### Why mean pooling? |
|
|
|
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. |
|
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. |
|
|
|
However, if you would like to do it without using the default `encode` function: |
|
|
|
```python |
|
import torch |
|
import torch.nn.functional as F |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] |
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
sentences = ['How is the weather today?', 'What is the current weather like today?'] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-de') |
|
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True) |
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
``` |
|
|
|
</p> |
|
</details> |
|
|
|
You can use Jina Embedding models directly from transformers package. |
|
|
|
```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-base-de', trust_remote_code=True) # trust_remote_code is needed to use the encode method |
|
embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?']) |
|
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-base-de", # 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?', |
|
'Wie ist das Wetter heute?' |
|
]) |
|
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). |
|
|
|
## Benchmark Results |
|
|
|
We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks: |
|
|
|
<img src="de_evaluation_results.png" width="780px"> |
|
|
|
## Use Jina Embeddings for RAG |
|
|
|
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"> |
|
|
|
## 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: |
|
|
|
``` |
|
@article{mohr2024multi, |
|
title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings}, |
|
author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others}, |
|
journal={arXiv preprint arXiv:2402.17016}, |
|
year={2024} |
|
} |
|
``` |
|
|
