|
--- |
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datasets: |
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- allenai/c4 |
|
library_name: transformers |
|
tags: |
|
- sentence-transformers |
|
- gte |
|
- mteb |
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- transformers.js |
|
- sentence-similarity |
|
license: apache-2.0 |
|
language: |
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- en |
|
model-index: |
|
- name: gte-large-en-v1.5 |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 73.01492537313432 |
|
- type: ap |
|
value: 35.05341696659522 |
|
- type: f1 |
|
value: 66.71270310883853 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 93.97189999999999 |
|
- type: ap |
|
value: 90.5952493948908 |
|
- type: f1 |
|
value: 93.95848137716877 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 54.196 |
|
- type: f1 |
|
value: 53.80122334012787 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
|
metrics: |
|
- type: map_at_1 |
|
value: 47.297 |
|
- type: map_at_10 |
|
value: 64.303 |
|
- type: map_at_100 |
|
value: 64.541 |
|
- type: map_at_1000 |
|
value: 64.541 |
|
- type: map_at_3 |
|
value: 60.728 |
|
- type: map_at_5 |
|
value: 63.114000000000004 |
|
- type: mrr_at_1 |
|
value: 48.435 |
|
- type: mrr_at_10 |
|
value: 64.657 |
|
- type: mrr_at_100 |
|
value: 64.901 |
|
- type: mrr_at_1000 |
|
value: 64.901 |
|
- type: mrr_at_3 |
|
value: 61.06 |
|
- type: mrr_at_5 |
|
value: 63.514 |
|
- type: ndcg_at_1 |
|
value: 47.297 |
|
- type: ndcg_at_10 |
|
value: 72.107 |
|
- type: ndcg_at_100 |
|
value: 72.963 |
|
- type: ndcg_at_1000 |
|
value: 72.963 |
|
- type: ndcg_at_3 |
|
value: 65.063 |
|
- type: ndcg_at_5 |
|
value: 69.352 |
|
- type: precision_at_1 |
|
value: 47.297 |
|
- type: precision_at_10 |
|
value: 9.623 |
|
- type: precision_at_100 |
|
value: 0.996 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 25.865 |
|
- type: precision_at_5 |
|
value: 17.596 |
|
- type: recall_at_1 |
|
value: 47.297 |
|
- type: recall_at_10 |
|
value: 96.23 |
|
- type: recall_at_100 |
|
value: 99.644 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 77.596 |
|
- type: recall_at_5 |
|
value: 87.98 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 48.467787861077475 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 43.39198391914257 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 63.12794820591384 |
|
- type: mrr |
|
value: 75.9331442641692 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.85062993863319 |
|
- type: cos_sim_spearman |
|
value: 85.39049989733459 |
|
- type: euclidean_pearson |
|
value: 86.00222680278333 |
|
- type: euclidean_spearman |
|
value: 85.45556162077396 |
|
- type: manhattan_pearson |
|
value: 85.88769871785621 |
|
- type: manhattan_spearman |
|
value: 85.11760211290839 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 87.32792207792208 |
|
- type: f1 |
|
value: 87.29132945999555 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 40.5779328301945 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 37.94425623865118 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-android |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: f46a197baaae43b4f621051089b82a364682dfeb |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.978 |
|
- type: map_at_10 |
|
value: 44.45 |
|
- type: map_at_100 |
|
value: 46.19 |
|
- type: map_at_1000 |
|
value: 46.303 |
|
- type: map_at_3 |
|
value: 40.849000000000004 |
|
- type: map_at_5 |
|
value: 42.55 |
|
- type: mrr_at_1 |
|
value: 40.629 |
|
- type: mrr_at_10 |
|
value: 50.848000000000006 |
|
- type: mrr_at_100 |
|
value: 51.669 |
|
- type: mrr_at_1000 |
|
value: 51.705 |
|
- type: mrr_at_3 |
|
value: 47.997 |
|
- type: mrr_at_5 |
|
value: 49.506 |
|
- type: ndcg_at_1 |
|
value: 40.629 |
|
- type: ndcg_at_10 |
|
value: 51.102000000000004 |
|
- type: ndcg_at_100 |
|
value: 57.159000000000006 |
|
- type: ndcg_at_1000 |
|
value: 58.669000000000004 |
|
- type: ndcg_at_3 |
|
value: 45.738 |
|
- type: ndcg_at_5 |
|
value: 47.632999999999996 |
|
- type: precision_at_1 |
|
value: 40.629 |
|
- type: precision_at_10 |
|
value: 9.700000000000001 |
|
- type: precision_at_100 |
|
value: 1.5970000000000002 |
|
- type: precision_at_1000 |
|
value: 0.202 |
|
- type: precision_at_3 |
|
value: 21.698 |
|
- type: precision_at_5 |
|
value: 15.393 |
|
- type: recall_at_1 |
|
value: 32.978 |
|
- type: recall_at_10 |
|
value: 63.711 |
|
- type: recall_at_100 |
|
value: 88.39399999999999 |
|
- type: recall_at_1000 |
|
value: 97.513 |
|
- type: recall_at_3 |
|
value: 48.025 |
|
- type: recall_at_5 |
|
value: 53.52 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-english |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.767 |
|
- type: map_at_10 |
|
value: 42.195 |
|
- type: map_at_100 |
|
value: 43.541999999999994 |
|
- type: map_at_1000 |
|
value: 43.673 |
|
- type: map_at_3 |
|
value: 38.561 |
|
- type: map_at_5 |
|
value: 40.532000000000004 |
|
- type: mrr_at_1 |
|
value: 38.79 |
|
- type: mrr_at_10 |
|
value: 48.021 |
|
- type: mrr_at_100 |
|
value: 48.735 |
|
- type: mrr_at_1000 |
|
value: 48.776 |
|
- type: mrr_at_3 |
|
value: 45.594 |
|
- type: mrr_at_5 |
|
value: 46.986 |
|
- type: ndcg_at_1 |
|
value: 38.79 |
|
- type: ndcg_at_10 |
|
value: 48.468 |
|
- type: ndcg_at_100 |
|
value: 53.037 |
|
- type: ndcg_at_1000 |
|
value: 55.001999999999995 |
|
- type: ndcg_at_3 |
|
value: 43.409 |
|
- type: ndcg_at_5 |
|
value: 45.654 |
|
- type: precision_at_1 |
|
value: 38.79 |
|
- type: precision_at_10 |
|
value: 9.452 |
|
- type: precision_at_100 |
|
value: 1.518 |
|
- type: precision_at_1000 |
|
value: 0.201 |
|
- type: precision_at_3 |
|
value: 21.21 |
|
- type: precision_at_5 |
|
value: 15.171999999999999 |
|
- type: recall_at_1 |
|
value: 30.767 |
|
- type: recall_at_10 |
|
value: 60.118 |
|
- type: recall_at_100 |
|
value: 79.271 |
|
- type: recall_at_1000 |
|
value: 91.43299999999999 |
|
- type: recall_at_3 |
|
value: 45.36 |
|
- type: recall_at_5 |
|
value: 51.705 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gaming |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.007 |
|
- type: map_at_10 |
|
value: 53.529 |
|
- type: map_at_100 |
|
value: 54.602 |
|
- type: map_at_1000 |
|
value: 54.647 |
|
- type: map_at_3 |
|
value: 49.951 |
|
- type: map_at_5 |
|
value: 52.066 |
|
- type: mrr_at_1 |
|
value: 45.705 |
|
- type: mrr_at_10 |
|
value: 56.745000000000005 |
|
- type: mrr_at_100 |
|
value: 57.43899999999999 |
|
- type: mrr_at_1000 |
|
value: 57.462999999999994 |
|
- type: mrr_at_3 |
|
value: 54.25299999999999 |
|
- type: mrr_at_5 |
|
value: 55.842000000000006 |
|
- type: ndcg_at_1 |
|
value: 45.705 |
|
- type: ndcg_at_10 |
|
value: 59.809 |
|
- type: ndcg_at_100 |
|
value: 63.837999999999994 |
|
- type: ndcg_at_1000 |
|
value: 64.729 |
|
- type: ndcg_at_3 |
|
value: 53.994 |
|
- type: ndcg_at_5 |
|
value: 57.028 |
|
- type: precision_at_1 |
|
value: 45.705 |
|
- type: precision_at_10 |
|
value: 9.762 |
|
- type: precision_at_100 |
|
value: 1.275 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 24.368000000000002 |
|
- type: precision_at_5 |
|
value: 16.84 |
|
- type: recall_at_1 |
|
value: 40.007 |
|
- type: recall_at_10 |
|
value: 75.017 |
|
- type: recall_at_100 |
|
value: 91.99000000000001 |
|
- type: recall_at_1000 |
|
value: 98.265 |
|
- type: recall_at_3 |
|
value: 59.704 |
|
- type: recall_at_5 |
|
value: 67.109 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gis |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: 5003b3064772da1887988e05400cf3806fe491f2 |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.639000000000003 |
|
- type: map_at_10 |
|
value: 35.926 |
|
- type: map_at_100 |
|
value: 37.126999999999995 |
|
- type: map_at_1000 |
|
value: 37.202 |
|
- type: map_at_3 |
|
value: 32.989000000000004 |
|
- type: map_at_5 |
|
value: 34.465 |
|
- type: mrr_at_1 |
|
value: 28.475 |
|
- type: mrr_at_10 |
|
value: 37.7 |
|
- type: mrr_at_100 |
|
value: 38.753 |
|
- type: mrr_at_1000 |
|
value: 38.807 |
|
- type: mrr_at_3 |
|
value: 35.066 |
|
- type: mrr_at_5 |
|
value: 36.512 |
|
- type: ndcg_at_1 |
|
value: 28.475 |
|
- type: ndcg_at_10 |
|
value: 41.245 |
|
- type: ndcg_at_100 |
|
value: 46.814 |
|
- type: ndcg_at_1000 |
|
value: 48.571 |
|
- type: ndcg_at_3 |
|
value: 35.528999999999996 |
|
- type: ndcg_at_5 |
|
value: 38.066 |
|
- type: precision_at_1 |
|
value: 28.475 |
|
- type: precision_at_10 |
|
value: 6.497 |
|
- type: precision_at_100 |
|
value: 0.9650000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 15.065999999999999 |
|
- type: precision_at_5 |
|
value: 10.599 |
|
- type: recall_at_1 |
|
value: 26.639000000000003 |
|
- type: recall_at_10 |
|
value: 55.759 |
|
- type: recall_at_100 |
|
value: 80.913 |
|
- type: recall_at_1000 |
|
value: 93.929 |
|
- type: recall_at_3 |
|
value: 40.454 |
|
- type: recall_at_5 |
|
value: 46.439 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-mathematica |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.767999999999999 |
|
- type: map_at_10 |
|
value: 24.811 |
|
- type: map_at_100 |
|
value: 26.064999999999998 |
|
- type: map_at_1000 |
|
value: 26.186999999999998 |
|
- type: map_at_3 |
|
value: 21.736 |
|
- type: map_at_5 |
|
value: 23.283 |
|
- type: mrr_at_1 |
|
value: 19.527 |
|
- type: mrr_at_10 |
|
value: 29.179 |
|
- type: mrr_at_100 |
|
value: 30.153999999999996 |
|
- type: mrr_at_1000 |
|
value: 30.215999999999998 |
|
- type: mrr_at_3 |
|
value: 26.223000000000003 |
|
- type: mrr_at_5 |
|
value: 27.733999999999998 |
|
- type: ndcg_at_1 |
|
value: 19.527 |
|
- type: ndcg_at_10 |
|
value: 30.786 |
|
- type: ndcg_at_100 |
|
value: 36.644 |
|
- type: ndcg_at_1000 |
|
value: 39.440999999999995 |
|
- type: ndcg_at_3 |
|
value: 24.958 |
|
- type: ndcg_at_5 |
|
value: 27.392 |
|
- type: precision_at_1 |
|
value: 19.527 |
|
- type: precision_at_10 |
|
value: 5.995 |
|
- type: precision_at_100 |
|
value: 1.03 |
|
- type: precision_at_1000 |
|
value: 0.14100000000000001 |
|
- type: precision_at_3 |
|
value: 12.520999999999999 |
|
- type: precision_at_5 |
|
value: 9.129 |
|
- type: recall_at_1 |
|
value: 15.767999999999999 |
|
- type: recall_at_10 |
|
value: 44.824000000000005 |
|
- type: recall_at_100 |
|
value: 70.186 |
|
- type: recall_at_1000 |
|
value: 89.934 |
|
- type: recall_at_3 |
|
value: 28.607 |
|
- type: recall_at_5 |
|
value: 34.836 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-physics |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.952 |
|
- type: map_at_10 |
|
value: 44.438 |
|
- type: map_at_100 |
|
value: 45.778 |
|
- type: map_at_1000 |
|
value: 45.883 |
|
- type: map_at_3 |
|
value: 41.044000000000004 |
|
- type: map_at_5 |
|
value: 42.986000000000004 |
|
- type: mrr_at_1 |
|
value: 39.172000000000004 |
|
- type: mrr_at_10 |
|
value: 49.76 |
|
- type: mrr_at_100 |
|
value: 50.583999999999996 |
|
- type: mrr_at_1000 |
|
value: 50.621 |
|
- type: mrr_at_3 |
|
value: 47.353 |
|
- type: mrr_at_5 |
|
value: 48.739 |
|
- type: ndcg_at_1 |
|
value: 39.172000000000004 |
|
- type: ndcg_at_10 |
|
value: 50.760000000000005 |
|
- type: ndcg_at_100 |
|
value: 56.084 |
|
- type: ndcg_at_1000 |
|
value: 57.865 |
|
- type: ndcg_at_3 |
|
value: 45.663 |
|
- type: ndcg_at_5 |
|
value: 48.178 |
|
- type: precision_at_1 |
|
value: 39.172000000000004 |
|
- type: precision_at_10 |
|
value: 9.22 |
|
- type: precision_at_100 |
|
value: 1.387 |
|
- type: precision_at_1000 |
|
value: 0.17099999999999999 |
|
- type: precision_at_3 |
|
value: 21.976000000000003 |
|
- type: precision_at_5 |
|
value: 15.457 |
|
- type: recall_at_1 |
|
value: 31.952 |
|
- type: recall_at_10 |
|
value: 63.900999999999996 |
|
- type: recall_at_100 |
|
value: 85.676 |
|
- type: recall_at_1000 |
|
value: 97.03699999999999 |
|
- type: recall_at_3 |
|
value: 49.781 |
|
- type: recall_at_5 |
|
value: 56.330000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-programmers |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.332 |
|
- type: map_at_10 |
|
value: 36.874 |
|
- type: map_at_100 |
|
value: 38.340999999999994 |
|
- type: map_at_1000 |
|
value: 38.452 |
|
- type: map_at_3 |
|
value: 33.068 |
|
- type: map_at_5 |
|
value: 35.324 |
|
- type: mrr_at_1 |
|
value: 30.822 |
|
- type: mrr_at_10 |
|
value: 41.641 |
|
- type: mrr_at_100 |
|
value: 42.519 |
|
- type: mrr_at_1000 |
|
value: 42.573 |
|
- type: mrr_at_3 |
|
value: 38.413000000000004 |
|
- type: mrr_at_5 |
|
value: 40.542 |
|
- type: ndcg_at_1 |
|
value: 30.822 |
|
- type: ndcg_at_10 |
|
value: 43.414 |
|
- type: ndcg_at_100 |
|
value: 49.196 |
|
- type: ndcg_at_1000 |
|
value: 51.237 |
|
- type: ndcg_at_3 |
|
value: 37.230000000000004 |
|
- type: ndcg_at_5 |
|
value: 40.405 |
|
- type: precision_at_1 |
|
value: 30.822 |
|
- type: precision_at_10 |
|
value: 8.379 |
|
- type: precision_at_100 |
|
value: 1.315 |
|
- type: precision_at_1000 |
|
value: 0.168 |
|
- type: precision_at_3 |
|
value: 18.417 |
|
- type: precision_at_5 |
|
value: 13.744 |
|
- type: recall_at_1 |
|
value: 25.332 |
|
- type: recall_at_10 |
|
value: 57.774 |
|
- type: recall_at_100 |
|
value: 82.071 |
|
- type: recall_at_1000 |
|
value: 95.60600000000001 |
|
- type: recall_at_3 |
|
value: 40.722 |
|
- type: recall_at_5 |
|
value: 48.754999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.91033333333334 |
|
- type: map_at_10 |
|
value: 36.23225000000001 |
|
- type: map_at_100 |
|
value: 37.55766666666667 |
|
- type: map_at_1000 |
|
value: 37.672583333333336 |
|
- type: map_at_3 |
|
value: 32.95666666666667 |
|
- type: map_at_5 |
|
value: 34.73375 |
|
- type: mrr_at_1 |
|
value: 30.634 |
|
- type: mrr_at_10 |
|
value: 40.19449999999999 |
|
- type: mrr_at_100 |
|
value: 41.099250000000005 |
|
- type: mrr_at_1000 |
|
value: 41.15091666666667 |
|
- type: mrr_at_3 |
|
value: 37.4615 |
|
- type: mrr_at_5 |
|
value: 39.00216666666667 |
|
- type: ndcg_at_1 |
|
value: 30.634 |
|
- type: ndcg_at_10 |
|
value: 42.162166666666664 |
|
- type: ndcg_at_100 |
|
value: 47.60708333333333 |
|
- type: ndcg_at_1000 |
|
value: 49.68616666666666 |
|
- type: ndcg_at_3 |
|
value: 36.60316666666666 |
|
- type: ndcg_at_5 |
|
value: 39.15616666666668 |
|
- type: precision_at_1 |
|
value: 30.634 |
|
- type: precision_at_10 |
|
value: 7.6193333333333335 |
|
- type: precision_at_100 |
|
value: 1.2198333333333333 |
|
- type: precision_at_1000 |
|
value: 0.15975000000000003 |
|
- type: precision_at_3 |
|
value: 17.087 |
|
- type: precision_at_5 |
|
value: 12.298333333333334 |
|
- type: recall_at_1 |
|
value: 25.91033333333334 |
|
- type: recall_at_10 |
|
value: 55.67300000000001 |
|
- type: recall_at_100 |
|
value: 79.20608333333334 |
|
- type: recall_at_1000 |
|
value: 93.34866666666667 |
|
- type: recall_at_3 |
|
value: 40.34858333333333 |
|
- type: recall_at_5 |
|
value: 46.834083333333325 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-stats |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.006 |
|
- type: map_at_10 |
|
value: 32.177 |
|
- type: map_at_100 |
|
value: 33.324999999999996 |
|
- type: map_at_1000 |
|
value: 33.419 |
|
- type: map_at_3 |
|
value: 29.952 |
|
- type: map_at_5 |
|
value: 31.095 |
|
- type: mrr_at_1 |
|
value: 28.066999999999997 |
|
- type: mrr_at_10 |
|
value: 34.995 |
|
- type: mrr_at_100 |
|
value: 35.978 |
|
- type: mrr_at_1000 |
|
value: 36.042 |
|
- type: mrr_at_3 |
|
value: 33.103 |
|
- type: mrr_at_5 |
|
value: 34.001 |
|
- type: ndcg_at_1 |
|
value: 28.066999999999997 |
|
- type: ndcg_at_10 |
|
value: 36.481 |
|
- type: ndcg_at_100 |
|
value: 42.022999999999996 |
|
- type: ndcg_at_1000 |
|
value: 44.377 |
|
- type: ndcg_at_3 |
|
value: 32.394 |
|
- type: ndcg_at_5 |
|
value: 34.108 |
|
- type: precision_at_1 |
|
value: 28.066999999999997 |
|
- type: precision_at_10 |
|
value: 5.736 |
|
- type: precision_at_100 |
|
value: 0.9259999999999999 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 13.804 |
|
- type: precision_at_5 |
|
value: 9.508999999999999 |
|
- type: recall_at_1 |
|
value: 25.006 |
|
- type: recall_at_10 |
|
value: 46.972 |
|
- type: recall_at_100 |
|
value: 72.138 |
|
- type: recall_at_1000 |
|
value: 89.479 |
|
- type: recall_at_3 |
|
value: 35.793 |
|
- type: recall_at_5 |
|
value: 39.947 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-tex |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: 46989137a86843e03a6195de44b09deda022eec7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.07 |
|
- type: map_at_10 |
|
value: 24.447 |
|
- type: map_at_100 |
|
value: 25.685999999999996 |
|
- type: map_at_1000 |
|
value: 25.813999999999997 |
|
- type: map_at_3 |
|
value: 21.634 |
|
- type: map_at_5 |
|
value: 23.133 |
|
- type: mrr_at_1 |
|
value: 19.580000000000002 |
|
- type: mrr_at_10 |
|
value: 28.127999999999997 |
|
- type: mrr_at_100 |
|
value: 29.119 |
|
- type: mrr_at_1000 |
|
value: 29.192 |
|
- type: mrr_at_3 |
|
value: 25.509999999999998 |
|
- type: mrr_at_5 |
|
value: 26.878 |
|
- type: ndcg_at_1 |
|
value: 19.580000000000002 |
|
- type: ndcg_at_10 |
|
value: 29.804000000000002 |
|
- type: ndcg_at_100 |
|
value: 35.555 |
|
- type: ndcg_at_1000 |
|
value: 38.421 |
|
- type: ndcg_at_3 |
|
value: 24.654999999999998 |
|
- type: ndcg_at_5 |
|
value: 26.881 |
|
- type: precision_at_1 |
|
value: 19.580000000000002 |
|
- type: precision_at_10 |
|
value: 5.736 |
|
- type: precision_at_100 |
|
value: 1.005 |
|
- type: precision_at_1000 |
|
value: 0.145 |
|
- type: precision_at_3 |
|
value: 12.033000000000001 |
|
- type: precision_at_5 |
|
value: 8.871 |
|
- type: recall_at_1 |
|
value: 16.07 |
|
- type: recall_at_10 |
|
value: 42.364000000000004 |
|
- type: recall_at_100 |
|
value: 68.01899999999999 |
|
- type: recall_at_1000 |
|
value: 88.122 |
|
- type: recall_at_3 |
|
value: 27.846 |
|
- type: recall_at_5 |
|
value: 33.638 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-unix |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.365 |
|
- type: map_at_10 |
|
value: 36.591 |
|
- type: map_at_100 |
|
value: 37.730000000000004 |
|
- type: map_at_1000 |
|
value: 37.84 |
|
- type: map_at_3 |
|
value: 33.403 |
|
- type: map_at_5 |
|
value: 35.272999999999996 |
|
- type: mrr_at_1 |
|
value: 30.503999999999998 |
|
- type: mrr_at_10 |
|
value: 39.940999999999995 |
|
- type: mrr_at_100 |
|
value: 40.818 |
|
- type: mrr_at_1000 |
|
value: 40.876000000000005 |
|
- type: mrr_at_3 |
|
value: 37.065 |
|
- type: mrr_at_5 |
|
value: 38.814 |
|
- type: ndcg_at_1 |
|
value: 30.503999999999998 |
|
- type: ndcg_at_10 |
|
value: 42.185 |
|
- type: ndcg_at_100 |
|
value: 47.416000000000004 |
|
- type: ndcg_at_1000 |
|
value: 49.705 |
|
- type: ndcg_at_3 |
|
value: 36.568 |
|
- type: ndcg_at_5 |
|
value: 39.416000000000004 |
|
- type: precision_at_1 |
|
value: 30.503999999999998 |
|
- type: precision_at_10 |
|
value: 7.276000000000001 |
|
- type: precision_at_100 |
|
value: 1.118 |
|
- type: precision_at_1000 |
|
value: 0.14300000000000002 |
|
- type: precision_at_3 |
|
value: 16.729 |
|
- type: precision_at_5 |
|
value: 12.107999999999999 |
|
- type: recall_at_1 |
|
value: 26.365 |
|
- type: recall_at_10 |
|
value: 55.616 |
|
- type: recall_at_100 |
|
value: 78.129 |
|
- type: recall_at_1000 |
|
value: 93.95599999999999 |
|
- type: recall_at_3 |
|
value: 40.686 |
|
- type: recall_at_5 |
|
value: 47.668 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-webmasters |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.750999999999998 |
|
- type: map_at_10 |
|
value: 33.446 |
|
- type: map_at_100 |
|
value: 35.235 |
|
- type: map_at_1000 |
|
value: 35.478 |
|
- type: map_at_3 |
|
value: 29.358 |
|
- type: map_at_5 |
|
value: 31.525 |
|
- type: mrr_at_1 |
|
value: 27.668 |
|
- type: mrr_at_10 |
|
value: 37.694 |
|
- type: mrr_at_100 |
|
value: 38.732 |
|
- type: mrr_at_1000 |
|
value: 38.779 |
|
- type: mrr_at_3 |
|
value: 34.223 |
|
- type: mrr_at_5 |
|
value: 36.08 |
|
- type: ndcg_at_1 |
|
value: 27.668 |
|
- type: ndcg_at_10 |
|
value: 40.557 |
|
- type: ndcg_at_100 |
|
value: 46.605999999999995 |
|
- type: ndcg_at_1000 |
|
value: 48.917 |
|
- type: ndcg_at_3 |
|
value: 33.677 |
|
- type: ndcg_at_5 |
|
value: 36.85 |
|
- type: precision_at_1 |
|
value: 27.668 |
|
- type: precision_at_10 |
|
value: 8.3 |
|
- type: precision_at_100 |
|
value: 1.6260000000000001 |
|
- type: precision_at_1000 |
|
value: 0.253 |
|
- type: precision_at_3 |
|
value: 16.008 |
|
- type: precision_at_5 |
|
value: 12.292 |
|
- type: recall_at_1 |
|
value: 22.750999999999998 |
|
- type: recall_at_10 |
|
value: 55.643 |
|
- type: recall_at_100 |
|
value: 82.151 |
|
- type: recall_at_1000 |
|
value: 95.963 |
|
- type: recall_at_3 |
|
value: 36.623 |
|
- type: recall_at_5 |
|
value: 44.708 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-wordpress |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.288999999999998 |
|
- type: map_at_10 |
|
value: 25.903 |
|
- type: map_at_100 |
|
value: 27.071 |
|
- type: map_at_1000 |
|
value: 27.173000000000002 |
|
- type: map_at_3 |
|
value: 22.935 |
|
- type: map_at_5 |
|
value: 24.573 |
|
- type: mrr_at_1 |
|
value: 18.669 |
|
- type: mrr_at_10 |
|
value: 27.682000000000002 |
|
- type: mrr_at_100 |
|
value: 28.691 |
|
- type: mrr_at_1000 |
|
value: 28.761 |
|
- type: mrr_at_3 |
|
value: 24.738 |
|
- type: mrr_at_5 |
|
value: 26.392 |
|
- type: ndcg_at_1 |
|
value: 18.669 |
|
- type: ndcg_at_10 |
|
value: 31.335 |
|
- type: ndcg_at_100 |
|
value: 36.913000000000004 |
|
- type: ndcg_at_1000 |
|
value: 39.300000000000004 |
|
- type: ndcg_at_3 |
|
value: 25.423000000000002 |
|
- type: ndcg_at_5 |
|
value: 28.262999999999998 |
|
- type: precision_at_1 |
|
value: 18.669 |
|
- type: precision_at_10 |
|
value: 5.379 |
|
- type: precision_at_100 |
|
value: 0.876 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 11.214 |
|
- type: precision_at_5 |
|
value: 8.466 |
|
- type: recall_at_1 |
|
value: 17.288999999999998 |
|
- type: recall_at_10 |
|
value: 46.377 |
|
- type: recall_at_100 |
|
value: 71.53500000000001 |
|
- type: recall_at_1000 |
|
value: 88.947 |
|
- type: recall_at_3 |
|
value: 30.581999999999997 |
|
- type: recall_at_5 |
|
value: 37.354 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.795 |
|
- type: map_at_10 |
|
value: 37.614999999999995 |
|
- type: map_at_100 |
|
value: 40.037 |
|
- type: map_at_1000 |
|
value: 40.184999999999995 |
|
- type: map_at_3 |
|
value: 32.221 |
|
- type: map_at_5 |
|
value: 35.154999999999994 |
|
- type: mrr_at_1 |
|
value: 50.358000000000004 |
|
- type: mrr_at_10 |
|
value: 62.129 |
|
- type: mrr_at_100 |
|
value: 62.613 |
|
- type: mrr_at_1000 |
|
value: 62.62 |
|
- type: mrr_at_3 |
|
value: 59.272999999999996 |
|
- type: mrr_at_5 |
|
value: 61.138999999999996 |
|
- type: ndcg_at_1 |
|
value: 50.358000000000004 |
|
- type: ndcg_at_10 |
|
value: 48.362 |
|
- type: ndcg_at_100 |
|
value: 55.932 |
|
- type: ndcg_at_1000 |
|
value: 58.062999999999995 |
|
- type: ndcg_at_3 |
|
value: 42.111 |
|
- type: ndcg_at_5 |
|
value: 44.063 |
|
- type: precision_at_1 |
|
value: 50.358000000000004 |
|
- type: precision_at_10 |
|
value: 14.677999999999999 |
|
- type: precision_at_100 |
|
value: 2.2950000000000004 |
|
- type: precision_at_1000 |
|
value: 0.271 |
|
- type: precision_at_3 |
|
value: 31.77 |
|
- type: precision_at_5 |
|
value: 23.375 |
|
- type: recall_at_1 |
|
value: 21.795 |
|
- type: recall_at_10 |
|
value: 53.846000000000004 |
|
- type: recall_at_100 |
|
value: 78.952 |
|
- type: recall_at_1000 |
|
value: 90.41900000000001 |
|
- type: recall_at_3 |
|
value: 37.257 |
|
- type: recall_at_5 |
|
value: 44.661 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/dbpedia |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.728 |
|
- type: map_at_10 |
|
value: 22.691 |
|
- type: map_at_100 |
|
value: 31.734 |
|
- type: map_at_1000 |
|
value: 33.464 |
|
- type: map_at_3 |
|
value: 16.273 |
|
- type: map_at_5 |
|
value: 19.016 |
|
- type: mrr_at_1 |
|
value: 73.25 |
|
- type: mrr_at_10 |
|
value: 80.782 |
|
- type: mrr_at_100 |
|
value: 81.01899999999999 |
|
- type: mrr_at_1000 |
|
value: 81.021 |
|
- type: mrr_at_3 |
|
value: 79.583 |
|
- type: mrr_at_5 |
|
value: 80.146 |
|
- type: ndcg_at_1 |
|
value: 59.62499999999999 |
|
- type: ndcg_at_10 |
|
value: 46.304 |
|
- type: ndcg_at_100 |
|
value: 51.23 |
|
- type: ndcg_at_1000 |
|
value: 58.048 |
|
- type: ndcg_at_3 |
|
value: 51.541000000000004 |
|
- type: ndcg_at_5 |
|
value: 48.635 |
|
- type: precision_at_1 |
|
value: 73.25 |
|
- type: precision_at_10 |
|
value: 36.375 |
|
- type: precision_at_100 |
|
value: 11.53 |
|
- type: precision_at_1000 |
|
value: 2.23 |
|
- type: precision_at_3 |
|
value: 55.583000000000006 |
|
- type: precision_at_5 |
|
value: 47.15 |
|
- type: recall_at_1 |
|
value: 9.728 |
|
- type: recall_at_10 |
|
value: 28.793999999999997 |
|
- type: recall_at_100 |
|
value: 57.885 |
|
- type: recall_at_1000 |
|
value: 78.759 |
|
- type: recall_at_3 |
|
value: 17.79 |
|
- type: recall_at_5 |
|
value: 21.733 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.775 |
|
- type: f1 |
|
value: 41.89794273264891 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
|
metrics: |
|
- type: map_at_1 |
|
value: 85.378 |
|
- type: map_at_10 |
|
value: 91.51 |
|
- type: map_at_100 |
|
value: 91.666 |
|
- type: map_at_1000 |
|
value: 91.676 |
|
- type: map_at_3 |
|
value: 90.757 |
|
- type: map_at_5 |
|
value: 91.277 |
|
- type: mrr_at_1 |
|
value: 91.839 |
|
- type: mrr_at_10 |
|
value: 95.49 |
|
- type: mrr_at_100 |
|
value: 95.493 |
|
- type: mrr_at_1000 |
|
value: 95.493 |
|
- type: mrr_at_3 |
|
value: 95.345 |
|
- type: mrr_at_5 |
|
value: 95.47200000000001 |
|
- type: ndcg_at_1 |
|
value: 91.839 |
|
- type: ndcg_at_10 |
|
value: 93.806 |
|
- type: ndcg_at_100 |
|
value: 94.255 |
|
- type: ndcg_at_1000 |
|
value: 94.399 |
|
- type: ndcg_at_3 |
|
value: 93.027 |
|
- type: ndcg_at_5 |
|
value: 93.51 |
|
- type: precision_at_1 |
|
value: 91.839 |
|
- type: precision_at_10 |
|
value: 10.93 |
|
- type: precision_at_100 |
|
value: 1.1400000000000001 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 34.873 |
|
- type: precision_at_5 |
|
value: 21.44 |
|
- type: recall_at_1 |
|
value: 85.378 |
|
- type: recall_at_10 |
|
value: 96.814 |
|
- type: recall_at_100 |
|
value: 98.386 |
|
- type: recall_at_1000 |
|
value: 99.21600000000001 |
|
- type: recall_at_3 |
|
value: 94.643 |
|
- type: recall_at_5 |
|
value: 95.976 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.190000000000005 |
|
- type: map_at_10 |
|
value: 53.605000000000004 |
|
- type: map_at_100 |
|
value: 55.550999999999995 |
|
- type: map_at_1000 |
|
value: 55.665 |
|
- type: map_at_3 |
|
value: 46.62 |
|
- type: map_at_5 |
|
value: 50.517999999999994 |
|
- type: mrr_at_1 |
|
value: 60.34 |
|
- type: mrr_at_10 |
|
value: 70.775 |
|
- type: mrr_at_100 |
|
value: 71.238 |
|
- type: mrr_at_1000 |
|
value: 71.244 |
|
- type: mrr_at_3 |
|
value: 68.72399999999999 |
|
- type: mrr_at_5 |
|
value: 69.959 |
|
- type: ndcg_at_1 |
|
value: 60.34 |
|
- type: ndcg_at_10 |
|
value: 63.226000000000006 |
|
- type: ndcg_at_100 |
|
value: 68.60300000000001 |
|
- type: ndcg_at_1000 |
|
value: 69.901 |
|
- type: ndcg_at_3 |
|
value: 58.048 |
|
- type: ndcg_at_5 |
|
value: 59.789 |
|
- type: precision_at_1 |
|
value: 60.34 |
|
- type: precision_at_10 |
|
value: 17.130000000000003 |
|
- type: precision_at_100 |
|
value: 2.29 |
|
- type: precision_at_1000 |
|
value: 0.256 |
|
- type: precision_at_3 |
|
value: 38.323 |
|
- type: precision_at_5 |
|
value: 27.87 |
|
- type: recall_at_1 |
|
value: 32.190000000000005 |
|
- type: recall_at_10 |
|
value: 73.041 |
|
- type: recall_at_100 |
|
value: 91.31 |
|
- type: recall_at_1000 |
|
value: 98.104 |
|
- type: recall_at_3 |
|
value: 53.70399999999999 |
|
- type: recall_at_5 |
|
value: 62.358999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
|
metrics: |
|
- type: map_at_1 |
|
value: 43.511 |
|
- type: map_at_10 |
|
value: 58.15 |
|
- type: map_at_100 |
|
value: 58.95399999999999 |
|
- type: map_at_1000 |
|
value: 59.018 |
|
- type: map_at_3 |
|
value: 55.31700000000001 |
|
- type: map_at_5 |
|
value: 57.04900000000001 |
|
- type: mrr_at_1 |
|
value: 87.022 |
|
- type: mrr_at_10 |
|
value: 91.32000000000001 |
|
- type: mrr_at_100 |
|
value: 91.401 |
|
- type: mrr_at_1000 |
|
value: 91.403 |
|
- type: mrr_at_3 |
|
value: 90.77 |
|
- type: mrr_at_5 |
|
value: 91.156 |
|
- type: ndcg_at_1 |
|
value: 87.022 |
|
- type: ndcg_at_10 |
|
value: 68.183 |
|
- type: ndcg_at_100 |
|
value: 70.781 |
|
- type: ndcg_at_1000 |
|
value: 72.009 |
|
- type: ndcg_at_3 |
|
value: 64.334 |
|
- type: ndcg_at_5 |
|
value: 66.449 |
|
- type: precision_at_1 |
|
value: 87.022 |
|
- type: precision_at_10 |
|
value: 13.406 |
|
- type: precision_at_100 |
|
value: 1.542 |
|
- type: precision_at_1000 |
|
value: 0.17099999999999999 |
|
- type: precision_at_3 |
|
value: 39.023 |
|
- type: precision_at_5 |
|
value: 25.080000000000002 |
|
- type: recall_at_1 |
|
value: 43.511 |
|
- type: recall_at_10 |
|
value: 67.02900000000001 |
|
- type: recall_at_100 |
|
value: 77.11 |
|
- type: recall_at_1000 |
|
value: 85.294 |
|
- type: recall_at_3 |
|
value: 58.535000000000004 |
|
- type: recall_at_5 |
|
value: 62.70099999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 92.0996 |
|
- type: ap |
|
value: 87.86206089096373 |
|
- type: f1 |
|
value: 92.07554547510763 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: c5a29a104738b98a9e76336939199e264163d4a0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.179 |
|
- type: map_at_10 |
|
value: 35.86 |
|
- type: map_at_100 |
|
value: 37.025999999999996 |
|
- type: map_at_1000 |
|
value: 37.068 |
|
- type: map_at_3 |
|
value: 31.921 |
|
- type: map_at_5 |
|
value: 34.172000000000004 |
|
- type: mrr_at_1 |
|
value: 23.926 |
|
- type: mrr_at_10 |
|
value: 36.525999999999996 |
|
- type: mrr_at_100 |
|
value: 37.627 |
|
- type: mrr_at_1000 |
|
value: 37.665 |
|
- type: mrr_at_3 |
|
value: 32.653 |
|
- type: mrr_at_5 |
|
value: 34.897 |
|
- type: ndcg_at_1 |
|
value: 23.910999999999998 |
|
- type: ndcg_at_10 |
|
value: 42.927 |
|
- type: ndcg_at_100 |
|
value: 48.464 |
|
- type: ndcg_at_1000 |
|
value: 49.533 |
|
- type: ndcg_at_3 |
|
value: 34.910000000000004 |
|
- type: ndcg_at_5 |
|
value: 38.937 |
|
- type: precision_at_1 |
|
value: 23.910999999999998 |
|
- type: precision_at_10 |
|
value: 6.758 |
|
- type: precision_at_100 |
|
value: 0.9520000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.838000000000001 |
|
- type: precision_at_5 |
|
value: 10.934000000000001 |
|
- type: recall_at_1 |
|
value: 23.179 |
|
- type: recall_at_10 |
|
value: 64.622 |
|
- type: recall_at_100 |
|
value: 90.135 |
|
- type: recall_at_1000 |
|
value: 98.301 |
|
- type: recall_at_3 |
|
value: 42.836999999999996 |
|
- type: recall_at_5 |
|
value: 52.512 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 96.59598723210215 |
|
- type: f1 |
|
value: 96.41913500001952 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 82.89557683538533 |
|
- type: f1 |
|
value: 63.379319722356264 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 78.93745796906524 |
|
- type: f1 |
|
value: 75.71616541785902 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 81.41223940820443 |
|
- type: f1 |
|
value: 81.2877893719078 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 35.03682528325662 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 32.942529406124 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.459949660460317 |
|
- type: mrr |
|
value: 32.70509582031616 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.497 |
|
- type: map_at_10 |
|
value: 13.843 |
|
- type: map_at_100 |
|
value: 17.713 |
|
- type: map_at_1000 |
|
value: 19.241 |
|
- type: map_at_3 |
|
value: 10.096 |
|
- type: map_at_5 |
|
value: 11.85 |
|
- type: mrr_at_1 |
|
value: 48.916 |
|
- type: mrr_at_10 |
|
value: 57.764 |
|
- type: mrr_at_100 |
|
value: 58.251 |
|
- type: mrr_at_1000 |
|
value: 58.282999999999994 |
|
- type: mrr_at_3 |
|
value: 55.623999999999995 |
|
- type: mrr_at_5 |
|
value: 57.018 |
|
- type: ndcg_at_1 |
|
value: 46.594 |
|
- type: ndcg_at_10 |
|
value: 36.945 |
|
- type: ndcg_at_100 |
|
value: 34.06 |
|
- type: ndcg_at_1000 |
|
value: 43.05 |
|
- type: ndcg_at_3 |
|
value: 41.738 |
|
- type: ndcg_at_5 |
|
value: 39.330999999999996 |
|
- type: precision_at_1 |
|
value: 48.916 |
|
- type: precision_at_10 |
|
value: 27.43 |
|
- type: precision_at_100 |
|
value: 8.616 |
|
- type: precision_at_1000 |
|
value: 2.155 |
|
- type: precision_at_3 |
|
value: 39.112 |
|
- type: precision_at_5 |
|
value: 33.808 |
|
- type: recall_at_1 |
|
value: 6.497 |
|
- type: recall_at_10 |
|
value: 18.163 |
|
- type: recall_at_100 |
|
value: 34.566 |
|
- type: recall_at_1000 |
|
value: 67.15 |
|
- type: recall_at_3 |
|
value: 11.100999999999999 |
|
- type: recall_at_5 |
|
value: 14.205000000000002 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.916 |
|
- type: map_at_10 |
|
value: 48.123 |
|
- type: map_at_100 |
|
value: 49.103 |
|
- type: map_at_1000 |
|
value: 49.131 |
|
- type: map_at_3 |
|
value: 43.711 |
|
- type: map_at_5 |
|
value: 46.323 |
|
- type: mrr_at_1 |
|
value: 36.181999999999995 |
|
- type: mrr_at_10 |
|
value: 50.617999999999995 |
|
- type: mrr_at_100 |
|
value: 51.329 |
|
- type: mrr_at_1000 |
|
value: 51.348000000000006 |
|
- type: mrr_at_3 |
|
value: 47.010999999999996 |
|
- type: mrr_at_5 |
|
value: 49.175000000000004 |
|
- type: ndcg_at_1 |
|
value: 36.181999999999995 |
|
- type: ndcg_at_10 |
|
value: 56.077999999999996 |
|
- type: ndcg_at_100 |
|
value: 60.037 |
|
- type: ndcg_at_1000 |
|
value: 60.63499999999999 |
|
- type: ndcg_at_3 |
|
value: 47.859 |
|
- type: ndcg_at_5 |
|
value: 52.178999999999995 |
|
- type: precision_at_1 |
|
value: 36.181999999999995 |
|
- type: precision_at_10 |
|
value: 9.284 |
|
- type: precision_at_100 |
|
value: 1.149 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 22.006999999999998 |
|
- type: precision_at_5 |
|
value: 15.695 |
|
- type: recall_at_1 |
|
value: 31.916 |
|
- type: recall_at_10 |
|
value: 77.771 |
|
- type: recall_at_100 |
|
value: 94.602 |
|
- type: recall_at_1000 |
|
value: 98.967 |
|
- type: recall_at_3 |
|
value: 56.528 |
|
- type: recall_at_5 |
|
value: 66.527 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.486 |
|
- type: map_at_10 |
|
value: 85.978 |
|
- type: map_at_100 |
|
value: 86.587 |
|
- type: map_at_1000 |
|
value: 86.598 |
|
- type: map_at_3 |
|
value: 83.04899999999999 |
|
- type: map_at_5 |
|
value: 84.857 |
|
- type: mrr_at_1 |
|
value: 82.32000000000001 |
|
- type: mrr_at_10 |
|
value: 88.64 |
|
- type: mrr_at_100 |
|
value: 88.702 |
|
- type: mrr_at_1000 |
|
value: 88.702 |
|
- type: mrr_at_3 |
|
value: 87.735 |
|
- type: mrr_at_5 |
|
value: 88.36 |
|
- type: ndcg_at_1 |
|
value: 82.34 |
|
- type: ndcg_at_10 |
|
value: 89.67 |
|
- type: ndcg_at_100 |
|
value: 90.642 |
|
- type: ndcg_at_1000 |
|
value: 90.688 |
|
- type: ndcg_at_3 |
|
value: 86.932 |
|
- type: ndcg_at_5 |
|
value: 88.408 |
|
- type: precision_at_1 |
|
value: 82.34 |
|
- type: precision_at_10 |
|
value: 13.675999999999998 |
|
- type: precision_at_100 |
|
value: 1.544 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 38.24 |
|
- type: precision_at_5 |
|
value: 25.068 |
|
- type: recall_at_1 |
|
value: 71.486 |
|
- type: recall_at_10 |
|
value: 96.844 |
|
- type: recall_at_100 |
|
value: 99.843 |
|
- type: recall_at_1000 |
|
value: 99.996 |
|
- type: recall_at_3 |
|
value: 88.92099999999999 |
|
- type: recall_at_5 |
|
value: 93.215 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 59.75758437908334 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 68.03497914092789 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.808 |
|
- type: map_at_10 |
|
value: 16.059 |
|
- type: map_at_100 |
|
value: 19.048000000000002 |
|
- type: map_at_1000 |
|
value: 19.43 |
|
- type: map_at_3 |
|
value: 10.953 |
|
- type: map_at_5 |
|
value: 13.363 |
|
- type: mrr_at_1 |
|
value: 28.7 |
|
- type: mrr_at_10 |
|
value: 42.436 |
|
- type: mrr_at_100 |
|
value: 43.599 |
|
- type: mrr_at_1000 |
|
value: 43.62 |
|
- type: mrr_at_3 |
|
value: 38.45 |
|
- type: mrr_at_5 |
|
value: 40.89 |
|
- type: ndcg_at_1 |
|
value: 28.7 |
|
- type: ndcg_at_10 |
|
value: 26.346000000000004 |
|
- type: ndcg_at_100 |
|
value: 36.758 |
|
- type: ndcg_at_1000 |
|
value: 42.113 |
|
- type: ndcg_at_3 |
|
value: 24.254 |
|
- type: ndcg_at_5 |
|
value: 21.506 |
|
- type: precision_at_1 |
|
value: 28.7 |
|
- type: precision_at_10 |
|
value: 13.969999999999999 |
|
- type: precision_at_100 |
|
value: 2.881 |
|
- type: precision_at_1000 |
|
value: 0.414 |
|
- type: precision_at_3 |
|
value: 22.933 |
|
- type: precision_at_5 |
|
value: 19.220000000000002 |
|
- type: recall_at_1 |
|
value: 5.808 |
|
- type: recall_at_10 |
|
value: 28.310000000000002 |
|
- type: recall_at_100 |
|
value: 58.475 |
|
- type: recall_at_1000 |
|
value: 84.072 |
|
- type: recall_at_3 |
|
value: 13.957 |
|
- type: recall_at_5 |
|
value: 19.515 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.39274129958557 |
|
- type: cos_sim_spearman |
|
value: 79.78021235170053 |
|
- type: euclidean_pearson |
|
value: 79.35335401300166 |
|
- type: euclidean_spearman |
|
value: 79.7271870968275 |
|
- type: manhattan_pearson |
|
value: 79.35256263340601 |
|
- type: manhattan_spearman |
|
value: 79.76036386976321 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.99130429246708 |
|
- type: cos_sim_spearman |
|
value: 73.88322811171203 |
|
- type: euclidean_pearson |
|
value: 80.7569419170376 |
|
- type: euclidean_spearman |
|
value: 73.82542155409597 |
|
- type: manhattan_pearson |
|
value: 80.79468183847625 |
|
- type: manhattan_spearman |
|
value: 73.87027144047784 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.88548789489907 |
|
- type: cos_sim_spearman |
|
value: 85.07535893847255 |
|
- type: euclidean_pearson |
|
value: 84.6637222061494 |
|
- type: euclidean_spearman |
|
value: 85.14200626702456 |
|
- type: manhattan_pearson |
|
value: 84.75327892344734 |
|
- type: manhattan_spearman |
|
value: 85.24406181838596 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.88140039325008 |
|
- type: cos_sim_spearman |
|
value: 79.61211268112362 |
|
- type: euclidean_pearson |
|
value: 81.29639728816458 |
|
- type: euclidean_spearman |
|
value: 79.51284578041442 |
|
- type: manhattan_pearson |
|
value: 81.3381797137111 |
|
- type: manhattan_spearman |
|
value: 79.55683684039808 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.16716737270485 |
|
- type: cos_sim_spearman |
|
value: 86.14823841857738 |
|
- type: euclidean_pearson |
|
value: 85.36325733440725 |
|
- type: euclidean_spearman |
|
value: 86.04919691402029 |
|
- type: manhattan_pearson |
|
value: 85.3147511385052 |
|
- type: manhattan_spearman |
|
value: 86.00676205857764 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.34266645861588 |
|
- type: cos_sim_spearman |
|
value: 81.59914035005882 |
|
- type: euclidean_pearson |
|
value: 81.15053076245988 |
|
- type: euclidean_spearman |
|
value: 81.52776915798489 |
|
- type: manhattan_pearson |
|
value: 81.1819647418673 |
|
- type: manhattan_spearman |
|
value: 81.57479527353556 |
|
- 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: 89.38263326821439 |
|
- type: cos_sim_spearman |
|
value: 89.10946308202642 |
|
- type: euclidean_pearson |
|
value: 88.87831312540068 |
|
- type: euclidean_spearman |
|
value: 89.03615865973664 |
|
- type: manhattan_pearson |
|
value: 88.79835539970384 |
|
- type: manhattan_spearman |
|
value: 88.9766156339753 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 70.1574915581685 |
|
- type: cos_sim_spearman |
|
value: 70.59144980004054 |
|
- type: euclidean_pearson |
|
value: 71.43246306918755 |
|
- type: euclidean_spearman |
|
value: 70.5544189562984 |
|
- type: manhattan_pearson |
|
value: 71.4071414609503 |
|
- type: manhattan_spearman |
|
value: 70.31799126163712 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.36215796635351 |
|
- type: cos_sim_spearman |
|
value: 83.07276756467208 |
|
- type: euclidean_pearson |
|
value: 83.06690453635584 |
|
- type: euclidean_spearman |
|
value: 82.9635366303289 |
|
- type: manhattan_pearson |
|
value: 83.04994049700815 |
|
- type: manhattan_spearman |
|
value: 82.98120125356036 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 86.92530011616722 |
|
- type: mrr |
|
value: 96.21826793395421 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 65.75 |
|
- type: map_at_10 |
|
value: 77.701 |
|
- type: map_at_100 |
|
value: 78.005 |
|
- type: map_at_1000 |
|
value: 78.006 |
|
- type: map_at_3 |
|
value: 75.48 |
|
- type: map_at_5 |
|
value: 76.927 |
|
- type: mrr_at_1 |
|
value: 68.333 |
|
- type: mrr_at_10 |
|
value: 78.511 |
|
- type: mrr_at_100 |
|
value: 78.704 |
|
- type: mrr_at_1000 |
|
value: 78.704 |
|
- type: mrr_at_3 |
|
value: 77 |
|
- type: mrr_at_5 |
|
value: 78.083 |
|
- type: ndcg_at_1 |
|
value: 68.333 |
|
- type: ndcg_at_10 |
|
value: 82.42699999999999 |
|
- type: ndcg_at_100 |
|
value: 83.486 |
|
- type: ndcg_at_1000 |
|
value: 83.511 |
|
- type: ndcg_at_3 |
|
value: 78.96300000000001 |
|
- type: ndcg_at_5 |
|
value: 81.028 |
|
- type: precision_at_1 |
|
value: 68.333 |
|
- type: precision_at_10 |
|
value: 10.667 |
|
- type: precision_at_100 |
|
value: 1.127 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 31.333 |
|
- type: precision_at_5 |
|
value: 20.133000000000003 |
|
- type: recall_at_1 |
|
value: 65.75 |
|
- type: recall_at_10 |
|
value: 95.578 |
|
- type: recall_at_100 |
|
value: 99.833 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 86.506 |
|
- type: recall_at_5 |
|
value: 91.75 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.75247524752476 |
|
- type: cos_sim_ap |
|
value: 94.16065078045173 |
|
- type: cos_sim_f1 |
|
value: 87.22986247544205 |
|
- type: cos_sim_precision |
|
value: 85.71428571428571 |
|
- type: cos_sim_recall |
|
value: 88.8 |
|
- type: dot_accuracy |
|
value: 99.74554455445545 |
|
- type: dot_ap |
|
value: 93.90633887037264 |
|
- type: dot_f1 |
|
value: 86.9873417721519 |
|
- type: dot_precision |
|
value: 88.1025641025641 |
|
- type: dot_recall |
|
value: 85.9 |
|
- type: euclidean_accuracy |
|
value: 99.75247524752476 |
|
- type: euclidean_ap |
|
value: 94.17466319018055 |
|
- type: euclidean_f1 |
|
value: 87.3405299313052 |
|
- type: euclidean_precision |
|
value: 85.74181117533719 |
|
- type: euclidean_recall |
|
value: 89 |
|
- type: manhattan_accuracy |
|
value: 99.75445544554455 |
|
- type: manhattan_ap |
|
value: 94.27688371923577 |
|
- type: manhattan_f1 |
|
value: 87.74002954209749 |
|
- type: manhattan_precision |
|
value: 86.42095053346266 |
|
- type: manhattan_recall |
|
value: 89.1 |
|
- type: max_accuracy |
|
value: 99.75445544554455 |
|
- type: max_ap |
|
value: 94.27688371923577 |
|
- type: max_f1 |
|
value: 87.74002954209749 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 71.26500637517056 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 39.17507906280528 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 52.4848744828509 |
|
- type: mrr |
|
value: 53.33678168236992 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.599864323827887 |
|
- type: cos_sim_spearman |
|
value: 30.91116204665598 |
|
- type: dot_pearson |
|
value: 30.82637894269936 |
|
- type: dot_spearman |
|
value: 30.957573868416066 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.23600000000000002 |
|
- type: map_at_10 |
|
value: 1.892 |
|
- type: map_at_100 |
|
value: 11.586 |
|
- type: map_at_1000 |
|
value: 27.761999999999997 |
|
- type: map_at_3 |
|
value: 0.653 |
|
- type: map_at_5 |
|
value: 1.028 |
|
- type: mrr_at_1 |
|
value: 88 |
|
- type: mrr_at_10 |
|
value: 94 |
|
- type: mrr_at_100 |
|
value: 94 |
|
- type: mrr_at_1000 |
|
value: 94 |
|
- type: mrr_at_3 |
|
value: 94 |
|
- type: mrr_at_5 |
|
value: 94 |
|
- type: ndcg_at_1 |
|
value: 82 |
|
- type: ndcg_at_10 |
|
value: 77.48899999999999 |
|
- type: ndcg_at_100 |
|
value: 60.141 |
|
- type: ndcg_at_1000 |
|
value: 54.228 |
|
- type: ndcg_at_3 |
|
value: 82.358 |
|
- type: ndcg_at_5 |
|
value: 80.449 |
|
- type: precision_at_1 |
|
value: 88 |
|
- type: precision_at_10 |
|
value: 82.19999999999999 |
|
- type: precision_at_100 |
|
value: 61.760000000000005 |
|
- type: precision_at_1000 |
|
value: 23.684 |
|
- type: precision_at_3 |
|
value: 88 |
|
- type: precision_at_5 |
|
value: 85.6 |
|
- type: recall_at_1 |
|
value: 0.23600000000000002 |
|
- type: recall_at_10 |
|
value: 2.117 |
|
- type: recall_at_100 |
|
value: 14.985000000000001 |
|
- type: recall_at_1000 |
|
value: 51.107 |
|
- type: recall_at_3 |
|
value: 0.688 |
|
- type: recall_at_5 |
|
value: 1.1039999999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.3040000000000003 |
|
- type: map_at_10 |
|
value: 9.025 |
|
- type: map_at_100 |
|
value: 15.312999999999999 |
|
- type: map_at_1000 |
|
value: 16.954 |
|
- type: map_at_3 |
|
value: 4.981 |
|
- type: map_at_5 |
|
value: 6.32 |
|
- type: mrr_at_1 |
|
value: 24.490000000000002 |
|
- type: mrr_at_10 |
|
value: 39.835 |
|
- type: mrr_at_100 |
|
value: 40.8 |
|
- type: mrr_at_1000 |
|
value: 40.8 |
|
- type: mrr_at_3 |
|
value: 35.034 |
|
- type: mrr_at_5 |
|
value: 37.687 |
|
- type: ndcg_at_1 |
|
value: 22.448999999999998 |
|
- type: ndcg_at_10 |
|
value: 22.545 |
|
- type: ndcg_at_100 |
|
value: 35.931999999999995 |
|
- type: ndcg_at_1000 |
|
value: 47.665 |
|
- type: ndcg_at_3 |
|
value: 23.311 |
|
- type: ndcg_at_5 |
|
value: 22.421 |
|
- type: precision_at_1 |
|
value: 24.490000000000002 |
|
- type: precision_at_10 |
|
value: 20.408 |
|
- type: precision_at_100 |
|
value: 7.815999999999999 |
|
- type: precision_at_1000 |
|
value: 1.553 |
|
- type: precision_at_3 |
|
value: 25.169999999999998 |
|
- type: precision_at_5 |
|
value: 23.265 |
|
- type: recall_at_1 |
|
value: 2.3040000000000003 |
|
- type: recall_at_10 |
|
value: 15.693999999999999 |
|
- type: recall_at_100 |
|
value: 48.917 |
|
- type: recall_at_1000 |
|
value: 84.964 |
|
- type: recall_at_3 |
|
value: 6.026 |
|
- type: recall_at_5 |
|
value: 9.066 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 82.6074 |
|
- type: ap |
|
value: 23.187467098602013 |
|
- type: f1 |
|
value: 65.36829506379657 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 63.16355404640635 |
|
- type: f1 |
|
value: 63.534725639863346 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 50.91004094411276 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.55301901412649 |
|
- type: cos_sim_ap |
|
value: 75.25312618556728 |
|
- type: cos_sim_f1 |
|
value: 68.76561719140429 |
|
- type: cos_sim_precision |
|
value: 65.3061224489796 |
|
- type: cos_sim_recall |
|
value: 72.61213720316623 |
|
- type: dot_accuracy |
|
value: 86.29671574178936 |
|
- type: dot_ap |
|
value: 75.11910195501207 |
|
- type: dot_f1 |
|
value: 68.44048376830045 |
|
- type: dot_precision |
|
value: 66.12546125461255 |
|
- type: dot_recall |
|
value: 70.92348284960423 |
|
- type: euclidean_accuracy |
|
value: 86.5828217202122 |
|
- type: euclidean_ap |
|
value: 75.22986344900924 |
|
- type: euclidean_f1 |
|
value: 68.81267797449549 |
|
- type: euclidean_precision |
|
value: 64.8238861674831 |
|
- type: euclidean_recall |
|
value: 73.3245382585752 |
|
- type: manhattan_accuracy |
|
value: 86.61262442629791 |
|
- type: manhattan_ap |
|
value: 75.24401608557328 |
|
- type: manhattan_f1 |
|
value: 68.80473982483257 |
|
- type: manhattan_precision |
|
value: 67.21187720181177 |
|
- type: manhattan_recall |
|
value: 70.47493403693932 |
|
- type: max_accuracy |
|
value: 86.61262442629791 |
|
- type: max_ap |
|
value: 75.25312618556728 |
|
- type: max_f1 |
|
value: 68.81267797449549 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.10688089416696 |
|
- type: cos_sim_ap |
|
value: 84.17862178779863 |
|
- type: cos_sim_f1 |
|
value: 76.17305208781748 |
|
- type: cos_sim_precision |
|
value: 71.31246641590543 |
|
- type: cos_sim_recall |
|
value: 81.74468740375731 |
|
- type: dot_accuracy |
|
value: 88.1844995536927 |
|
- type: dot_ap |
|
value: 84.33816725235876 |
|
- type: dot_f1 |
|
value: 76.43554032918746 |
|
- type: dot_precision |
|
value: 74.01557767200346 |
|
- type: dot_recall |
|
value: 79.0190945488143 |
|
- type: euclidean_accuracy |
|
value: 88.07001203089223 |
|
- type: euclidean_ap |
|
value: 84.12267000814985 |
|
- type: euclidean_f1 |
|
value: 76.12232600180778 |
|
- type: euclidean_precision |
|
value: 74.50604541433205 |
|
- type: euclidean_recall |
|
value: 77.81028641823221 |
|
- type: manhattan_accuracy |
|
value: 88.06419063142779 |
|
- type: manhattan_ap |
|
value: 84.11648917164187 |
|
- type: manhattan_f1 |
|
value: 76.20579953925474 |
|
- type: manhattan_precision |
|
value: 72.56772755762935 |
|
- type: manhattan_recall |
|
value: 80.22790267939637 |
|
- type: max_accuracy |
|
value: 88.1844995536927 |
|
- type: max_ap |
|
value: 84.33816725235876 |
|
- type: max_f1 |
|
value: 76.43554032918746 |
|
--- |
|
|
|
<!-- **English** | [中文](./README_zh.md) --> |
|
|
|
# gte-large-en-v1.5 |
|
|
|
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. |
|
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). |
|
|
|
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). |
|
|
|
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct), |
|
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
- **Developed by:** Institute for Intelligent Computing, Alibaba Group |
|
- **Model type:** Text Embeddings |
|
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking |
|
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) |
|
|
|
<!-- - **Demo [optional]:** [More Information Needed] --> |
|
|
|
### Model list |
|
|
|
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |
|
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |
|
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 | |
|
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |
|
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | |
|
|
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
```python |
|
# Requires transformers>=4.36.0 |
|
|
|
import torch.nn.functional as F |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
input_texts = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
|
|
model_path = 'Alibaba-NLP/gte-large-en-v1.5' |
|
tokenizer = AutoTokenizer.from_pretrained(model_path) |
|
model = AutoModel.from_pretrained(model_path, trust_remote_code=True) |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = outputs.last_hidden_state[:, 0] |
|
|
|
# (Optionally) normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** |
|
|
|
|
|
Use with sentence-transformers: |
|
|
|
```python |
|
# Requires sentence_transformers>=2.7.0 |
|
|
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['That is a happy person', 'That is a very happy person'] |
|
|
|
model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True) |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
Use with `transformers.js`: |
|
|
|
```js |
|
// npm i @xenova/transformers |
|
import { pipeline, dot } from '@xenova/transformers'; |
|
|
|
// Create feature extraction pipeline |
|
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', { |
|
quantized: false, // Comment out this line to use the quantized version |
|
}); |
|
|
|
// Generate sentence embeddings |
|
const sentences = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); |
|
|
|
// Compute similarity scores |
|
const [source_embeddings, ...document_embeddings ] = output.tolist(); |
|
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x)); |
|
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899] |
|
``` |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
- Masked language modeling (MLM): `c4-en` |
|
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data |
|
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data |
|
|
|
### Training Procedure |
|
|
|
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. |
|
The model first undergoes preliminary MLM pre-training on shorter lengths. |
|
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. |
|
|
|
The entire training process is as follows: |
|
- MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000 |
|
- MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000 |
|
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-large): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000 |
|
- CPT: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000 |
|
- Fine-tuning: TODO |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### MTEB |
|
|
|
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). |
|
|
|
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). |
|
|
|
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | |
|
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | |
|
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | |
|
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | |
|
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | |
|
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | |
|
|
|
|
|
### LoCo |
|
|
|
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | |
|
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | |
|
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | |
|
|
|
|
|
|
|
## Citation |
|
|
|
If you find our paper or models helpful, please consider citing them as follows: |
|
|
|
``` |
|
@article{zhang2024mgte, |
|
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, |
|
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others}, |
|
journal={arXiv preprint arXiv:2407.19669}, |
|
year={2024} |
|
} |
|
|
|
@article{li2023towards, |
|
title={Towards general text embeddings with multi-stage contrastive learning}, |
|
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, |
|
journal={arXiv preprint arXiv:2308.03281}, |
|
year={2023} |
|
} |
|
``` |
