|
--- |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: mit |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- feature-extraction |
|
- mteb |
|
- sentence-similarity |
|
- sentence-transformers |
|
|
|
model-index: |
|
- name: GIST-small-Embedding-v0 |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 73.40298507462688 |
|
- type: ap |
|
value: 36.01661955459773 |
|
- type: f1 |
|
value: 67.35688942295793 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 92.71195000000002 |
|
- type: ap |
|
value: 89.33528835459364 |
|
- type: f1 |
|
value: 92.69653287380515 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 49.007999999999996 |
|
- type: f1 |
|
value: 48.44310279702607 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 36.272999999999996 |
|
- type: map_at_10 |
|
value: 52.059999999999995 |
|
- type: map_at_100 |
|
value: 52.75300000000001 |
|
- type: map_at_1000 |
|
value: 52.756 |
|
- type: map_at_3 |
|
value: 47.57 |
|
- type: map_at_5 |
|
value: 50.236999999999995 |
|
- type: mrr_at_1 |
|
value: 36.272999999999996 |
|
- type: mrr_at_10 |
|
value: 51.942 |
|
- type: mrr_at_100 |
|
value: 52.634 |
|
- type: mrr_at_1000 |
|
value: 52.637 |
|
- type: mrr_at_3 |
|
value: 47.475 |
|
- type: mrr_at_5 |
|
value: 50.11 |
|
- type: ndcg_at_1 |
|
value: 36.272999999999996 |
|
- type: ndcg_at_10 |
|
value: 60.558 |
|
- type: ndcg_at_100 |
|
value: 63.293 |
|
- type: ndcg_at_1000 |
|
value: 63.375 |
|
- type: ndcg_at_3 |
|
value: 51.364 |
|
- type: ndcg_at_5 |
|
value: 56.154 |
|
- type: precision_at_1 |
|
value: 36.272999999999996 |
|
- type: precision_at_10 |
|
value: 8.755 |
|
- type: precision_at_100 |
|
value: 0.9900000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 20.791999999999998 |
|
- type: precision_at_5 |
|
value: 14.793999999999999 |
|
- type: recall_at_1 |
|
value: 36.272999999999996 |
|
- type: recall_at_10 |
|
value: 87.553 |
|
- type: recall_at_100 |
|
value: 99.004 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 62.376 |
|
- type: recall_at_5 |
|
value: 73.969 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 47.79137102109872 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 40.03049595085257 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.868157850825256 |
|
- type: mrr |
|
value: 75.33922525612276 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.96056116438724 |
|
- type: cos_sim_spearman |
|
value: 87.32608616965557 |
|
- type: euclidean_pearson |
|
value: 87.40536769084146 |
|
- type: euclidean_spearman |
|
value: 87.39235273982528 |
|
- type: manhattan_pearson |
|
value: 87.4496043849794 |
|
- type: manhattan_spearman |
|
value: 87.1128282983821 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 86.16883116883116 |
|
- type: f1 |
|
value: 86.1338488750026 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 38.950791675044 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 35.40686850755838 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.891000000000002 |
|
- type: map_at_10 |
|
value: 42.624 |
|
- type: map_at_100 |
|
value: 44.205 |
|
- type: map_at_1000 |
|
value: 44.336999999999996 |
|
- type: map_at_3 |
|
value: 38.81 |
|
- type: map_at_5 |
|
value: 41.152 |
|
- type: mrr_at_1 |
|
value: 38.196999999999996 |
|
- type: mrr_at_10 |
|
value: 48.641 |
|
- type: mrr_at_100 |
|
value: 49.329 |
|
- type: mrr_at_1000 |
|
value: 49.376 |
|
- type: mrr_at_3 |
|
value: 45.637 |
|
- type: mrr_at_5 |
|
value: 47.611 |
|
- type: ndcg_at_1 |
|
value: 38.196999999999996 |
|
- type: ndcg_at_10 |
|
value: 49.274 |
|
- type: ndcg_at_100 |
|
value: 54.716 |
|
- type: ndcg_at_1000 |
|
value: 56.654 |
|
- type: ndcg_at_3 |
|
value: 43.787 |
|
- type: ndcg_at_5 |
|
value: 46.719 |
|
- type: precision_at_1 |
|
value: 38.196999999999996 |
|
- type: precision_at_10 |
|
value: 9.585 |
|
- type: precision_at_100 |
|
value: 1.545 |
|
- type: precision_at_1000 |
|
value: 0.20400000000000001 |
|
- type: precision_at_3 |
|
value: 21.173000000000002 |
|
- type: precision_at_5 |
|
value: 15.536 |
|
- type: recall_at_1 |
|
value: 30.891000000000002 |
|
- type: recall_at_10 |
|
value: 61.792 |
|
- type: recall_at_100 |
|
value: 84.526 |
|
- type: recall_at_1000 |
|
value: 96.717 |
|
- type: recall_at_3 |
|
value: 46.472 |
|
- type: recall_at_5 |
|
value: 54.391999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.266 |
|
- type: map_at_10 |
|
value: 39.717999999999996 |
|
- type: map_at_100 |
|
value: 40.971000000000004 |
|
- type: map_at_1000 |
|
value: 41.097 |
|
- type: map_at_3 |
|
value: 36.858999999999995 |
|
- type: map_at_5 |
|
value: 38.405 |
|
- type: mrr_at_1 |
|
value: 37.452000000000005 |
|
- type: mrr_at_10 |
|
value: 45.528 |
|
- type: mrr_at_100 |
|
value: 46.178000000000004 |
|
- type: mrr_at_1000 |
|
value: 46.221000000000004 |
|
- type: mrr_at_3 |
|
value: 43.089 |
|
- type: mrr_at_5 |
|
value: 44.497 |
|
- type: ndcg_at_1 |
|
value: 37.452000000000005 |
|
- type: ndcg_at_10 |
|
value: 45.282 |
|
- type: ndcg_at_100 |
|
value: 49.742 |
|
- type: ndcg_at_1000 |
|
value: 51.754999999999995 |
|
- type: ndcg_at_3 |
|
value: 41.024 |
|
- type: ndcg_at_5 |
|
value: 42.912 |
|
- type: precision_at_1 |
|
value: 37.452000000000005 |
|
- type: precision_at_10 |
|
value: 8.516 |
|
- type: precision_at_100 |
|
value: 1.3679999999999999 |
|
- type: precision_at_1000 |
|
value: 0.184 |
|
- type: precision_at_3 |
|
value: 19.575 |
|
- type: precision_at_5 |
|
value: 13.771 |
|
- type: recall_at_1 |
|
value: 30.266 |
|
- type: recall_at_10 |
|
value: 55.086 |
|
- type: recall_at_100 |
|
value: 74.083 |
|
- type: recall_at_1000 |
|
value: 86.722 |
|
- type: recall_at_3 |
|
value: 42.449999999999996 |
|
- type: recall_at_5 |
|
value: 47.975 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.217 |
|
- type: map_at_10 |
|
value: 51.466 |
|
- type: map_at_100 |
|
value: 52.531000000000006 |
|
- type: map_at_1000 |
|
value: 52.586 |
|
- type: map_at_3 |
|
value: 47.942 |
|
- type: map_at_5 |
|
value: 49.988 |
|
- type: mrr_at_1 |
|
value: 44.765 |
|
- type: mrr_at_10 |
|
value: 54.748 |
|
- type: mrr_at_100 |
|
value: 55.41199999999999 |
|
- type: mrr_at_1000 |
|
value: 55.437999999999995 |
|
- type: mrr_at_3 |
|
value: 52.017 |
|
- type: mrr_at_5 |
|
value: 53.693999999999996 |
|
- type: ndcg_at_1 |
|
value: 44.765 |
|
- type: ndcg_at_10 |
|
value: 57.397 |
|
- type: ndcg_at_100 |
|
value: 61.526 |
|
- type: ndcg_at_1000 |
|
value: 62.577000000000005 |
|
- type: ndcg_at_3 |
|
value: 51.414 |
|
- type: ndcg_at_5 |
|
value: 54.486999999999995 |
|
- type: precision_at_1 |
|
value: 44.765 |
|
- type: precision_at_10 |
|
value: 9.354 |
|
- type: precision_at_100 |
|
value: 1.2309999999999999 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 22.820999999999998 |
|
- type: precision_at_5 |
|
value: 16.012999999999998 |
|
- type: recall_at_1 |
|
value: 39.217 |
|
- type: recall_at_10 |
|
value: 71.588 |
|
- type: recall_at_100 |
|
value: 89.473 |
|
- type: recall_at_1000 |
|
value: 96.863 |
|
- type: recall_at_3 |
|
value: 55.943 |
|
- type: recall_at_5 |
|
value: 63.14999999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.451 |
|
- type: map_at_10 |
|
value: 34.738 |
|
- type: map_at_100 |
|
value: 35.769 |
|
- type: map_at_1000 |
|
value: 35.851 |
|
- type: map_at_3 |
|
value: 32.002 |
|
- type: map_at_5 |
|
value: 33.800999999999995 |
|
- type: mrr_at_1 |
|
value: 28.814 |
|
- type: mrr_at_10 |
|
value: 36.992000000000004 |
|
- type: mrr_at_100 |
|
value: 37.901 |
|
- type: mrr_at_1000 |
|
value: 37.964 |
|
- type: mrr_at_3 |
|
value: 34.426 |
|
- type: mrr_at_5 |
|
value: 36.075 |
|
- type: ndcg_at_1 |
|
value: 28.814 |
|
- type: ndcg_at_10 |
|
value: 39.667 |
|
- type: ndcg_at_100 |
|
value: 44.741 |
|
- type: ndcg_at_1000 |
|
value: 46.763 |
|
- type: ndcg_at_3 |
|
value: 34.461999999999996 |
|
- type: ndcg_at_5 |
|
value: 37.472 |
|
- type: precision_at_1 |
|
value: 28.814 |
|
- type: precision_at_10 |
|
value: 6.045 |
|
- type: precision_at_100 |
|
value: 0.903 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 14.463000000000001 |
|
- type: precision_at_5 |
|
value: 10.418 |
|
- type: recall_at_1 |
|
value: 26.451 |
|
- type: recall_at_10 |
|
value: 52.751999999999995 |
|
- type: recall_at_100 |
|
value: 75.971 |
|
- type: recall_at_1000 |
|
value: 91.02 |
|
- type: recall_at_3 |
|
value: 38.896 |
|
- type: recall_at_5 |
|
value: 46.126 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.03 |
|
- type: map_at_10 |
|
value: 24.474999999999998 |
|
- type: map_at_100 |
|
value: 25.650000000000002 |
|
- type: map_at_1000 |
|
value: 25.764 |
|
- type: map_at_3 |
|
value: 21.656 |
|
- type: map_at_5 |
|
value: 23.269000000000002 |
|
- type: mrr_at_1 |
|
value: 20.025000000000002 |
|
- type: mrr_at_10 |
|
value: 29.325000000000003 |
|
- type: mrr_at_100 |
|
value: 30.264999999999997 |
|
- type: mrr_at_1000 |
|
value: 30.325000000000003 |
|
- type: mrr_at_3 |
|
value: 26.493 |
|
- type: mrr_at_5 |
|
value: 28.197 |
|
- type: ndcg_at_1 |
|
value: 20.025000000000002 |
|
- type: ndcg_at_10 |
|
value: 30.012 |
|
- type: ndcg_at_100 |
|
value: 35.760999999999996 |
|
- type: ndcg_at_1000 |
|
value: 38.53 |
|
- type: ndcg_at_3 |
|
value: 24.863 |
|
- type: ndcg_at_5 |
|
value: 27.36 |
|
- type: precision_at_1 |
|
value: 20.025000000000002 |
|
- type: precision_at_10 |
|
value: 5.721 |
|
- type: precision_at_100 |
|
value: 0.9809999999999999 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 12.189 |
|
- type: precision_at_5 |
|
value: 9.08 |
|
- type: recall_at_1 |
|
value: 16.03 |
|
- type: recall_at_10 |
|
value: 42.263 |
|
- type: recall_at_100 |
|
value: 67.868 |
|
- type: recall_at_1000 |
|
value: 87.77000000000001 |
|
- type: recall_at_3 |
|
value: 27.932000000000002 |
|
- type: recall_at_5 |
|
value: 34.46 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.358 |
|
- type: map_at_10 |
|
value: 39.753 |
|
- type: map_at_100 |
|
value: 41.031 |
|
- type: map_at_1000 |
|
value: 41.135 |
|
- type: map_at_3 |
|
value: 36.515 |
|
- type: map_at_5 |
|
value: 38.346999999999994 |
|
- type: mrr_at_1 |
|
value: 35.9 |
|
- type: mrr_at_10 |
|
value: 45.336 |
|
- type: mrr_at_100 |
|
value: 46.087 |
|
- type: mrr_at_1000 |
|
value: 46.129999999999995 |
|
- type: mrr_at_3 |
|
value: 42.620999999999995 |
|
- type: mrr_at_5 |
|
value: 44.224000000000004 |
|
- type: ndcg_at_1 |
|
value: 35.9 |
|
- type: ndcg_at_10 |
|
value: 45.85 |
|
- type: ndcg_at_100 |
|
value: 51.186 |
|
- type: ndcg_at_1000 |
|
value: 53.154999999999994 |
|
- type: ndcg_at_3 |
|
value: 40.594 |
|
- type: ndcg_at_5 |
|
value: 43.169999999999995 |
|
- type: precision_at_1 |
|
value: 35.9 |
|
- type: precision_at_10 |
|
value: 8.402 |
|
- type: precision_at_100 |
|
value: 1.2850000000000001 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 19.249 |
|
- type: precision_at_5 |
|
value: 13.763 |
|
- type: recall_at_1 |
|
value: 29.358 |
|
- type: recall_at_10 |
|
value: 58.257000000000005 |
|
- type: recall_at_100 |
|
value: 81.22200000000001 |
|
- type: recall_at_1000 |
|
value: 94.045 |
|
- type: recall_at_3 |
|
value: 43.599 |
|
- type: recall_at_5 |
|
value: 50.232 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.954 |
|
- type: map_at_10 |
|
value: 33.767 |
|
- type: map_at_100 |
|
value: 35.225 |
|
- type: map_at_1000 |
|
value: 35.339 |
|
- type: map_at_3 |
|
value: 30.746000000000002 |
|
- type: map_at_5 |
|
value: 32.318000000000005 |
|
- type: mrr_at_1 |
|
value: 30.137000000000004 |
|
- type: mrr_at_10 |
|
value: 39.24 |
|
- type: mrr_at_100 |
|
value: 40.235 |
|
- type: mrr_at_1000 |
|
value: 40.294999999999995 |
|
- type: mrr_at_3 |
|
value: 36.758 |
|
- type: mrr_at_5 |
|
value: 38.031 |
|
- type: ndcg_at_1 |
|
value: 30.137000000000004 |
|
- type: ndcg_at_10 |
|
value: 39.711999999999996 |
|
- type: ndcg_at_100 |
|
value: 45.795 |
|
- type: ndcg_at_1000 |
|
value: 48.178 |
|
- type: ndcg_at_3 |
|
value: 34.768 |
|
- type: ndcg_at_5 |
|
value: 36.756 |
|
- type: precision_at_1 |
|
value: 30.137000000000004 |
|
- type: precision_at_10 |
|
value: 7.443 |
|
- type: precision_at_100 |
|
value: 1.221 |
|
- type: precision_at_1000 |
|
value: 0.159 |
|
- type: precision_at_3 |
|
value: 16.933 |
|
- type: precision_at_5 |
|
value: 11.918 |
|
- type: recall_at_1 |
|
value: 23.954 |
|
- type: recall_at_10 |
|
value: 52.234 |
|
- type: recall_at_100 |
|
value: 77.75800000000001 |
|
- type: recall_at_1000 |
|
value: 94.072 |
|
- type: recall_at_3 |
|
value: 37.876 |
|
- type: recall_at_5 |
|
value: 43.494 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.478416666666664 |
|
- type: map_at_10 |
|
value: 34.483999999999995 |
|
- type: map_at_100 |
|
value: 35.71641666666667 |
|
- type: map_at_1000 |
|
value: 35.8315 |
|
- type: map_at_3 |
|
value: 31.571083333333334 |
|
- type: map_at_5 |
|
value: 33.229749999999996 |
|
- type: mrr_at_1 |
|
value: 30.122416666666663 |
|
- type: mrr_at_10 |
|
value: 38.608333333333334 |
|
- type: mrr_at_100 |
|
value: 39.465500000000006 |
|
- type: mrr_at_1000 |
|
value: 39.52375 |
|
- type: mrr_at_3 |
|
value: 36.047916666666666 |
|
- type: mrr_at_5 |
|
value: 37.53833333333333 |
|
- type: ndcg_at_1 |
|
value: 30.122416666666663 |
|
- type: ndcg_at_10 |
|
value: 39.87575 |
|
- type: ndcg_at_100 |
|
value: 45.15691666666666 |
|
- type: ndcg_at_1000 |
|
value: 47.43891666666667 |
|
- type: ndcg_at_3 |
|
value: 34.88666666666666 |
|
- type: ndcg_at_5 |
|
value: 37.30966666666667 |
|
- type: precision_at_1 |
|
value: 30.122416666666663 |
|
- type: precision_at_10 |
|
value: 7.056500000000001 |
|
- type: precision_at_100 |
|
value: 1.1415000000000002 |
|
- type: precision_at_1000 |
|
value: 0.15308333333333332 |
|
- type: precision_at_3 |
|
value: 16.03525 |
|
- type: precision_at_5 |
|
value: 11.51125 |
|
- type: recall_at_1 |
|
value: 25.478416666666664 |
|
- type: recall_at_10 |
|
value: 51.72658333333333 |
|
- type: recall_at_100 |
|
value: 74.94641666666666 |
|
- type: recall_at_1000 |
|
value: 90.75300000000001 |
|
- type: recall_at_3 |
|
value: 37.93833333333333 |
|
- type: recall_at_5 |
|
value: 44.15625 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.697 |
|
- type: map_at_10 |
|
value: 30.919999999999998 |
|
- type: map_at_100 |
|
value: 31.889 |
|
- type: map_at_1000 |
|
value: 31.985000000000003 |
|
- type: map_at_3 |
|
value: 29.046 |
|
- type: map_at_5 |
|
value: 29.902 |
|
- type: mrr_at_1 |
|
value: 27.454 |
|
- type: mrr_at_10 |
|
value: 33.517 |
|
- type: mrr_at_100 |
|
value: 34.381 |
|
- type: mrr_at_1000 |
|
value: 34.452 |
|
- type: mrr_at_3 |
|
value: 31.747999999999998 |
|
- type: mrr_at_5 |
|
value: 32.561 |
|
- type: ndcg_at_1 |
|
value: 27.454 |
|
- type: ndcg_at_10 |
|
value: 34.687 |
|
- type: ndcg_at_100 |
|
value: 39.395 |
|
- type: ndcg_at_1000 |
|
value: 41.826 |
|
- type: ndcg_at_3 |
|
value: 31.102 |
|
- type: ndcg_at_5 |
|
value: 32.435 |
|
- type: precision_at_1 |
|
value: 27.454 |
|
- type: precision_at_10 |
|
value: 5.322 |
|
- type: precision_at_100 |
|
value: 0.83 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 13.088 |
|
- type: precision_at_5 |
|
value: 8.803999999999998 |
|
- type: recall_at_1 |
|
value: 24.697 |
|
- type: recall_at_10 |
|
value: 43.688 |
|
- type: recall_at_100 |
|
value: 64.893 |
|
- type: recall_at_1000 |
|
value: 82.755 |
|
- type: recall_at_3 |
|
value: 33.896 |
|
- type: recall_at_5 |
|
value: 37.174 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.525000000000002 |
|
- type: map_at_10 |
|
value: 23.435 |
|
- type: map_at_100 |
|
value: 24.535999999999998 |
|
- type: map_at_1000 |
|
value: 24.672 |
|
- type: map_at_3 |
|
value: 21.095 |
|
- type: map_at_5 |
|
value: 22.308 |
|
- type: mrr_at_1 |
|
value: 19.993 |
|
- type: mrr_at_10 |
|
value: 27.096999999999998 |
|
- type: mrr_at_100 |
|
value: 28.036 |
|
- type: mrr_at_1000 |
|
value: 28.119 |
|
- type: mrr_at_3 |
|
value: 24.971 |
|
- type: mrr_at_5 |
|
value: 26.062 |
|
- type: ndcg_at_1 |
|
value: 19.993 |
|
- type: ndcg_at_10 |
|
value: 28.002 |
|
- type: ndcg_at_100 |
|
value: 33.288000000000004 |
|
- type: ndcg_at_1000 |
|
value: 36.416 |
|
- type: ndcg_at_3 |
|
value: 23.768 |
|
- type: ndcg_at_5 |
|
value: 25.579 |
|
- type: precision_at_1 |
|
value: 19.993 |
|
- type: precision_at_10 |
|
value: 5.196 |
|
- type: precision_at_100 |
|
value: 0.922 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 11.241 |
|
- type: precision_at_5 |
|
value: 8.176 |
|
- type: recall_at_1 |
|
value: 16.525000000000002 |
|
- type: recall_at_10 |
|
value: 38.082 |
|
- type: recall_at_100 |
|
value: 61.866 |
|
- type: recall_at_1000 |
|
value: 84.20100000000001 |
|
- type: recall_at_3 |
|
value: 26.228 |
|
- type: recall_at_5 |
|
value: 30.86 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.480999999999998 |
|
- type: map_at_10 |
|
value: 34.319 |
|
- type: map_at_100 |
|
value: 35.54 |
|
- type: map_at_1000 |
|
value: 35.648 |
|
- type: map_at_3 |
|
value: 31.533 |
|
- type: map_at_5 |
|
value: 33.058 |
|
- type: mrr_at_1 |
|
value: 29.851 |
|
- type: mrr_at_10 |
|
value: 38.243 |
|
- type: mrr_at_100 |
|
value: 39.172000000000004 |
|
- type: mrr_at_1000 |
|
value: 39.235 |
|
- type: mrr_at_3 |
|
value: 35.697 |
|
- type: mrr_at_5 |
|
value: 37.147000000000006 |
|
- type: ndcg_at_1 |
|
value: 29.851 |
|
- type: ndcg_at_10 |
|
value: 39.653 |
|
- type: ndcg_at_100 |
|
value: 45.065 |
|
- type: ndcg_at_1000 |
|
value: 47.477999999999994 |
|
- type: ndcg_at_3 |
|
value: 34.481 |
|
- type: ndcg_at_5 |
|
value: 36.870999999999995 |
|
- type: precision_at_1 |
|
value: 29.851 |
|
- type: precision_at_10 |
|
value: 6.679 |
|
- type: precision_at_100 |
|
value: 1.053 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 15.485 |
|
- type: precision_at_5 |
|
value: 10.989 |
|
- type: recall_at_1 |
|
value: 25.480999999999998 |
|
- type: recall_at_10 |
|
value: 52.032000000000004 |
|
- type: recall_at_100 |
|
value: 75.193 |
|
- type: recall_at_1000 |
|
value: 91.958 |
|
- type: recall_at_3 |
|
value: 38.089 |
|
- type: recall_at_5 |
|
value: 43.947 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.148 |
|
- type: map_at_10 |
|
value: 33.007 |
|
- type: map_at_100 |
|
value: 34.602 |
|
- type: map_at_1000 |
|
value: 34.809 |
|
- type: map_at_3 |
|
value: 30.014000000000003 |
|
- type: map_at_5 |
|
value: 31.728 |
|
- type: mrr_at_1 |
|
value: 29.842000000000002 |
|
- type: mrr_at_10 |
|
value: 37.318 |
|
- type: mrr_at_100 |
|
value: 38.353 |
|
- type: mrr_at_1000 |
|
value: 38.41 |
|
- type: mrr_at_3 |
|
value: 34.75 |
|
- type: mrr_at_5 |
|
value: 36.163000000000004 |
|
- type: ndcg_at_1 |
|
value: 29.842000000000002 |
|
- type: ndcg_at_10 |
|
value: 38.462 |
|
- type: ndcg_at_100 |
|
value: 44.86 |
|
- type: ndcg_at_1000 |
|
value: 47.375 |
|
- type: ndcg_at_3 |
|
value: 33.614 |
|
- type: ndcg_at_5 |
|
value: 36.032 |
|
- type: precision_at_1 |
|
value: 29.842000000000002 |
|
- type: precision_at_10 |
|
value: 7.332 |
|
- type: precision_at_100 |
|
value: 1.52 |
|
- type: precision_at_1000 |
|
value: 0.23900000000000002 |
|
- type: precision_at_3 |
|
value: 15.547 |
|
- type: precision_at_5 |
|
value: 11.423 |
|
- type: recall_at_1 |
|
value: 25.148 |
|
- type: recall_at_10 |
|
value: 48.894 |
|
- type: recall_at_100 |
|
value: 77.845 |
|
- type: recall_at_1000 |
|
value: 93.74900000000001 |
|
- type: recall_at_3 |
|
value: 35.17 |
|
- type: recall_at_5 |
|
value: 41.734 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.723 |
|
- type: map_at_10 |
|
value: 25.586 |
|
- type: map_at_100 |
|
value: 26.648 |
|
- type: map_at_1000 |
|
value: 26.755000000000003 |
|
- type: map_at_3 |
|
value: 22.634999999999998 |
|
- type: map_at_5 |
|
value: 24.481 |
|
- type: mrr_at_1 |
|
value: 19.039 |
|
- type: mrr_at_10 |
|
value: 27.315 |
|
- type: mrr_at_100 |
|
value: 28.237000000000002 |
|
- type: mrr_at_1000 |
|
value: 28.32 |
|
- type: mrr_at_3 |
|
value: 24.368000000000002 |
|
- type: mrr_at_5 |
|
value: 26.198 |
|
- type: ndcg_at_1 |
|
value: 19.039 |
|
- type: ndcg_at_10 |
|
value: 30.511 |
|
- type: ndcg_at_100 |
|
value: 35.808 |
|
- type: ndcg_at_1000 |
|
value: 38.56 |
|
- type: ndcg_at_3 |
|
value: 24.762999999999998 |
|
- type: ndcg_at_5 |
|
value: 27.923 |
|
- type: precision_at_1 |
|
value: 19.039 |
|
- type: precision_at_10 |
|
value: 5.083 |
|
- type: precision_at_100 |
|
value: 0.839 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 10.659 |
|
- type: precision_at_5 |
|
value: 8.244 |
|
- type: recall_at_1 |
|
value: 17.723 |
|
- type: recall_at_10 |
|
value: 44.051 |
|
- type: recall_at_100 |
|
value: 68.659 |
|
- type: recall_at_1000 |
|
value: 89.164 |
|
- type: recall_at_3 |
|
value: 28.709 |
|
- type: recall_at_5 |
|
value: 36.331 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.669999999999998 |
|
- type: map_at_10 |
|
value: 23.46 |
|
- type: map_at_100 |
|
value: 25.304 |
|
- type: map_at_1000 |
|
value: 25.497999999999998 |
|
- type: map_at_3 |
|
value: 19.702 |
|
- type: map_at_5 |
|
value: 21.642 |
|
- type: mrr_at_1 |
|
value: 31.269999999999996 |
|
- type: mrr_at_10 |
|
value: 43.264 |
|
- type: mrr_at_100 |
|
value: 44.1 |
|
- type: mrr_at_1000 |
|
value: 44.134 |
|
- type: mrr_at_3 |
|
value: 40.011 |
|
- type: mrr_at_5 |
|
value: 42.079 |
|
- type: ndcg_at_1 |
|
value: 31.269999999999996 |
|
- type: ndcg_at_10 |
|
value: 32.385000000000005 |
|
- type: ndcg_at_100 |
|
value: 39.282000000000004 |
|
- type: ndcg_at_1000 |
|
value: 42.628 |
|
- type: ndcg_at_3 |
|
value: 26.942 |
|
- type: ndcg_at_5 |
|
value: 28.832 |
|
- type: precision_at_1 |
|
value: 31.269999999999996 |
|
- type: precision_at_10 |
|
value: 10.123999999999999 |
|
- type: precision_at_100 |
|
value: 1.748 |
|
- type: precision_at_1000 |
|
value: 0.23700000000000002 |
|
- type: precision_at_3 |
|
value: 20.282 |
|
- type: precision_at_5 |
|
value: 15.479000000000001 |
|
- type: recall_at_1 |
|
value: 13.669999999999998 |
|
- type: recall_at_10 |
|
value: 38.078 |
|
- type: recall_at_100 |
|
value: 61.651 |
|
- type: recall_at_1000 |
|
value: 80.279 |
|
- type: recall_at_3 |
|
value: 24.438 |
|
- type: recall_at_5 |
|
value: 30.244 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.103 |
|
- type: map_at_10 |
|
value: 19.238 |
|
- type: map_at_100 |
|
value: 26.451999999999998 |
|
- type: map_at_1000 |
|
value: 27.987000000000002 |
|
- type: map_at_3 |
|
value: 14.069999999999999 |
|
- type: map_at_5 |
|
value: 16.434 |
|
- type: mrr_at_1 |
|
value: 67.5 |
|
- type: mrr_at_10 |
|
value: 75.64800000000001 |
|
- type: mrr_at_100 |
|
value: 75.847 |
|
- type: mrr_at_1000 |
|
value: 75.85499999999999 |
|
- type: mrr_at_3 |
|
value: 73.833 |
|
- type: mrr_at_5 |
|
value: 74.933 |
|
- type: ndcg_at_1 |
|
value: 55.625 |
|
- type: ndcg_at_10 |
|
value: 40.505 |
|
- type: ndcg_at_100 |
|
value: 44.505 |
|
- type: ndcg_at_1000 |
|
value: 52.005 |
|
- type: ndcg_at_3 |
|
value: 45.841 |
|
- type: ndcg_at_5 |
|
value: 42.945 |
|
- type: precision_at_1 |
|
value: 67.5 |
|
- type: precision_at_10 |
|
value: 31.6 |
|
- type: precision_at_100 |
|
value: 9.83 |
|
- type: precision_at_1000 |
|
value: 1.9619999999999997 |
|
- type: precision_at_3 |
|
value: 49.083 |
|
- type: precision_at_5 |
|
value: 41.15 |
|
- type: recall_at_1 |
|
value: 9.103 |
|
- type: recall_at_10 |
|
value: 24.6 |
|
- type: recall_at_100 |
|
value: 50.075 |
|
- type: recall_at_1000 |
|
value: 73.516 |
|
- type: recall_at_3 |
|
value: 15.35 |
|
- type: recall_at_5 |
|
value: 19.217000000000002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 50.595 |
|
- type: f1 |
|
value: 45.43005573726517 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 76.08200000000001 |
|
- type: map_at_10 |
|
value: 83.697 |
|
- type: map_at_100 |
|
value: 83.891 |
|
- type: map_at_1000 |
|
value: 83.905 |
|
- type: map_at_3 |
|
value: 82.69 |
|
- type: map_at_5 |
|
value: 83.35900000000001 |
|
- type: mrr_at_1 |
|
value: 82.148 |
|
- type: mrr_at_10 |
|
value: 88.727 |
|
- type: mrr_at_100 |
|
value: 88.787 |
|
- type: mrr_at_1000 |
|
value: 88.788 |
|
- type: mrr_at_3 |
|
value: 88.054 |
|
- type: mrr_at_5 |
|
value: 88.547 |
|
- type: ndcg_at_1 |
|
value: 82.148 |
|
- type: ndcg_at_10 |
|
value: 87.274 |
|
- type: ndcg_at_100 |
|
value: 87.957 |
|
- type: ndcg_at_1000 |
|
value: 88.203 |
|
- type: ndcg_at_3 |
|
value: 85.744 |
|
- type: ndcg_at_5 |
|
value: 86.664 |
|
- type: precision_at_1 |
|
value: 82.148 |
|
- type: precision_at_10 |
|
value: 10.315000000000001 |
|
- type: precision_at_100 |
|
value: 1.086 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 32.458 |
|
- type: precision_at_5 |
|
value: 20.09 |
|
- type: recall_at_1 |
|
value: 76.08200000000001 |
|
- type: recall_at_10 |
|
value: 93.408 |
|
- type: recall_at_100 |
|
value: 96.11 |
|
- type: recall_at_1000 |
|
value: 97.626 |
|
- type: recall_at_3 |
|
value: 89.172 |
|
- type: recall_at_5 |
|
value: 91.604 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.377 |
|
- type: map_at_10 |
|
value: 31.785000000000004 |
|
- type: map_at_100 |
|
value: 33.511 |
|
- type: map_at_1000 |
|
value: 33.713 |
|
- type: map_at_3 |
|
value: 27.811999999999998 |
|
- type: map_at_5 |
|
value: 30.148000000000003 |
|
- type: mrr_at_1 |
|
value: 38.426 |
|
- type: mrr_at_10 |
|
value: 47.233000000000004 |
|
- type: mrr_at_100 |
|
value: 47.980000000000004 |
|
- type: mrr_at_1000 |
|
value: 48.022 |
|
- type: mrr_at_3 |
|
value: 44.856 |
|
- type: mrr_at_5 |
|
value: 46.322 |
|
- type: ndcg_at_1 |
|
value: 38.426 |
|
- type: ndcg_at_10 |
|
value: 39.326 |
|
- type: ndcg_at_100 |
|
value: 45.769999999999996 |
|
- type: ndcg_at_1000 |
|
value: 49.131 |
|
- type: ndcg_at_3 |
|
value: 36.1 |
|
- type: ndcg_at_5 |
|
value: 37.271 |
|
- type: precision_at_1 |
|
value: 38.426 |
|
- type: precision_at_10 |
|
value: 11.126999999999999 |
|
- type: precision_at_100 |
|
value: 1.7870000000000001 |
|
- type: precision_at_1000 |
|
value: 0.23700000000000002 |
|
- type: precision_at_3 |
|
value: 24.587999999999997 |
|
- type: precision_at_5 |
|
value: 18.21 |
|
- type: recall_at_1 |
|
value: 19.377 |
|
- type: recall_at_10 |
|
value: 45.484 |
|
- type: recall_at_100 |
|
value: 69.968 |
|
- type: recall_at_1000 |
|
value: 90.30799999999999 |
|
- type: recall_at_3 |
|
value: 32.72 |
|
- type: recall_at_5 |
|
value: 38.856 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.475 |
|
- type: map_at_10 |
|
value: 58.662000000000006 |
|
- type: map_at_100 |
|
value: 59.561 |
|
- type: map_at_1000 |
|
value: 59.626999999999995 |
|
- type: map_at_3 |
|
value: 55.496 |
|
- type: map_at_5 |
|
value: 57.464000000000006 |
|
- type: mrr_at_1 |
|
value: 74.949 |
|
- type: mrr_at_10 |
|
value: 80.976 |
|
- type: mrr_at_100 |
|
value: 81.215 |
|
- type: mrr_at_1000 |
|
value: 81.22399999999999 |
|
- type: mrr_at_3 |
|
value: 79.892 |
|
- type: mrr_at_5 |
|
value: 80.57 |
|
- type: ndcg_at_1 |
|
value: 74.949 |
|
- type: ndcg_at_10 |
|
value: 66.93599999999999 |
|
- type: ndcg_at_100 |
|
value: 70.137 |
|
- type: ndcg_at_1000 |
|
value: 71.452 |
|
- type: ndcg_at_3 |
|
value: 62.319 |
|
- type: ndcg_at_5 |
|
value: 64.866 |
|
- type: precision_at_1 |
|
value: 74.949 |
|
- type: precision_at_10 |
|
value: 13.988999999999999 |
|
- type: precision_at_100 |
|
value: 1.6500000000000001 |
|
- type: precision_at_1000 |
|
value: 0.182 |
|
- type: precision_at_3 |
|
value: 39.806000000000004 |
|
- type: precision_at_5 |
|
value: 25.899 |
|
- type: recall_at_1 |
|
value: 37.475 |
|
- type: recall_at_10 |
|
value: 69.946 |
|
- type: recall_at_100 |
|
value: 82.478 |
|
- type: recall_at_1000 |
|
value: 91.202 |
|
- type: recall_at_3 |
|
value: 59.709999999999994 |
|
- type: recall_at_5 |
|
value: 64.747 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 89.2272 |
|
- type: ap |
|
value: 84.69017509523854 |
|
- type: f1 |
|
value: 89.20673182133066 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.471999999999998 |
|
- type: map_at_10 |
|
value: 33.287 |
|
- type: map_at_100 |
|
value: 34.486 |
|
- type: map_at_1000 |
|
value: 34.536 |
|
- type: map_at_3 |
|
value: 29.520999999999997 |
|
- type: map_at_5 |
|
value: 31.647 |
|
- type: mrr_at_1 |
|
value: 22.076999999999998 |
|
- type: mrr_at_10 |
|
value: 33.902 |
|
- type: mrr_at_100 |
|
value: 35.037 |
|
- type: mrr_at_1000 |
|
value: 35.081 |
|
- type: mrr_at_3 |
|
value: 30.174 |
|
- type: mrr_at_5 |
|
value: 32.302 |
|
- type: ndcg_at_1 |
|
value: 22.092 |
|
- type: ndcg_at_10 |
|
value: 40.073 |
|
- type: ndcg_at_100 |
|
value: 45.82 |
|
- type: ndcg_at_1000 |
|
value: 47.097 |
|
- type: ndcg_at_3 |
|
value: 32.364 |
|
- type: ndcg_at_5 |
|
value: 36.179 |
|
- type: precision_at_1 |
|
value: 22.092 |
|
- type: precision_at_10 |
|
value: 6.36 |
|
- type: precision_at_100 |
|
value: 0.924 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 13.806 |
|
- type: precision_at_5 |
|
value: 10.223 |
|
- type: recall_at_1 |
|
value: 21.471999999999998 |
|
- type: recall_at_10 |
|
value: 60.971 |
|
- type: recall_at_100 |
|
value: 87.518 |
|
- type: recall_at_1000 |
|
value: 97.333 |
|
- type: recall_at_3 |
|
value: 39.961999999999996 |
|
- type: recall_at_5 |
|
value: 49.126 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 94.44596443228454 |
|
- type: f1 |
|
value: 94.19326360848854 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 75.7934336525308 |
|
- type: f1 |
|
value: 57.619082395865604 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 74.70410221923336 |
|
- type: f1 |
|
value: 72.82854233810865 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.61802286482852 |
|
- type: f1 |
|
value: 78.76695988384789 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 34.212621347614174 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.899728392028948 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.190245086632466 |
|
- type: mrr |
|
value: 33.424442963159876 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.141 |
|
- type: map_at_10 |
|
value: 13.558 |
|
- type: map_at_100 |
|
value: 17.238 |
|
- type: map_at_1000 |
|
value: 18.727 |
|
- type: map_at_3 |
|
value: 9.803 |
|
- type: map_at_5 |
|
value: 11.517 |
|
- type: mrr_at_1 |
|
value: 46.129999999999995 |
|
- type: mrr_at_10 |
|
value: 54.876999999999995 |
|
- type: mrr_at_100 |
|
value: 55.428999999999995 |
|
- type: mrr_at_1000 |
|
value: 55.47 |
|
- type: mrr_at_3 |
|
value: 52.993 |
|
- type: mrr_at_5 |
|
value: 54.107000000000006 |
|
- type: ndcg_at_1 |
|
value: 43.963 |
|
- type: ndcg_at_10 |
|
value: 35.72 |
|
- type: ndcg_at_100 |
|
value: 32.792 |
|
- type: ndcg_at_1000 |
|
value: 41.52 |
|
- type: ndcg_at_3 |
|
value: 40.929 |
|
- type: ndcg_at_5 |
|
value: 38.664 |
|
- type: precision_at_1 |
|
value: 45.82 |
|
- type: precision_at_10 |
|
value: 26.625 |
|
- type: precision_at_100 |
|
value: 8.387 |
|
- type: precision_at_1000 |
|
value: 2.131 |
|
- type: precision_at_3 |
|
value: 38.39 |
|
- type: precision_at_5 |
|
value: 33.56 |
|
- type: recall_at_1 |
|
value: 6.141 |
|
- type: recall_at_10 |
|
value: 17.598 |
|
- type: recall_at_100 |
|
value: 33.619 |
|
- type: recall_at_1000 |
|
value: 64.455 |
|
- type: recall_at_3 |
|
value: 10.667 |
|
- type: recall_at_5 |
|
value: 13.492999999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.019 |
|
- type: map_at_10 |
|
value: 40.644999999999996 |
|
- type: map_at_100 |
|
value: 41.870000000000005 |
|
- type: map_at_1000 |
|
value: 41.904 |
|
- type: map_at_3 |
|
value: 36.28 |
|
- type: map_at_5 |
|
value: 38.830999999999996 |
|
- type: mrr_at_1 |
|
value: 29.664 |
|
- type: mrr_at_10 |
|
value: 43.168 |
|
- type: mrr_at_100 |
|
value: 44.126 |
|
- type: mrr_at_1000 |
|
value: 44.151 |
|
- type: mrr_at_3 |
|
value: 39.484 |
|
- type: mrr_at_5 |
|
value: 41.702 |
|
- type: ndcg_at_1 |
|
value: 29.635 |
|
- type: ndcg_at_10 |
|
value: 48.284 |
|
- type: ndcg_at_100 |
|
value: 53.522999999999996 |
|
- type: ndcg_at_1000 |
|
value: 54.344 |
|
- type: ndcg_at_3 |
|
value: 40.048 |
|
- type: ndcg_at_5 |
|
value: 44.329 |
|
- type: precision_at_1 |
|
value: 29.635 |
|
- type: precision_at_10 |
|
value: 8.262 |
|
- type: precision_at_100 |
|
value: 1.1159999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 18.54 |
|
- type: precision_at_5 |
|
value: 13.586 |
|
- type: recall_at_1 |
|
value: 26.019 |
|
- type: recall_at_10 |
|
value: 69.049 |
|
- type: recall_at_100 |
|
value: 91.89399999999999 |
|
- type: recall_at_1000 |
|
value: 98.095 |
|
- type: recall_at_3 |
|
value: 47.81 |
|
- type: recall_at_5 |
|
value: 57.645 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.952 |
|
- type: map_at_10 |
|
value: 84.895 |
|
- type: map_at_100 |
|
value: 85.51299999999999 |
|
- type: map_at_1000 |
|
value: 85.529 |
|
- type: map_at_3 |
|
value: 81.94500000000001 |
|
- type: map_at_5 |
|
value: 83.83500000000001 |
|
- type: mrr_at_1 |
|
value: 81.65 |
|
- type: mrr_at_10 |
|
value: 87.756 |
|
- type: mrr_at_100 |
|
value: 87.855 |
|
- type: mrr_at_1000 |
|
value: 87.856 |
|
- type: mrr_at_3 |
|
value: 86.822 |
|
- type: mrr_at_5 |
|
value: 87.473 |
|
- type: ndcg_at_1 |
|
value: 81.65 |
|
- type: ndcg_at_10 |
|
value: 88.563 |
|
- type: ndcg_at_100 |
|
value: 89.74499999999999 |
|
- type: ndcg_at_1000 |
|
value: 89.84400000000001 |
|
- type: ndcg_at_3 |
|
value: 85.782 |
|
- type: ndcg_at_5 |
|
value: 87.381 |
|
- type: precision_at_1 |
|
value: 81.65 |
|
- type: precision_at_10 |
|
value: 13.435 |
|
- type: precision_at_100 |
|
value: 1.529 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.523 |
|
- type: precision_at_5 |
|
value: 24.72 |
|
- type: recall_at_1 |
|
value: 70.952 |
|
- type: recall_at_10 |
|
value: 95.521 |
|
- type: recall_at_100 |
|
value: 99.53699999999999 |
|
- type: recall_at_1000 |
|
value: 99.983 |
|
- type: recall_at_3 |
|
value: 87.559 |
|
- type: recall_at_5 |
|
value: 92.038 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 54.61973943122806 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 60.92179806944469 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.993 |
|
- type: map_at_10 |
|
value: 13.175999999999998 |
|
- type: map_at_100 |
|
value: 15.689 |
|
- type: map_at_1000 |
|
value: 16.054 |
|
- type: map_at_3 |
|
value: 9.325999999999999 |
|
- type: map_at_5 |
|
value: 11.283 |
|
- type: mrr_at_1 |
|
value: 24.7 |
|
- type: mrr_at_10 |
|
value: 36.568 |
|
- type: mrr_at_100 |
|
value: 37.667 |
|
- type: mrr_at_1000 |
|
value: 37.714 |
|
- type: mrr_at_3 |
|
value: 32.933 |
|
- type: mrr_at_5 |
|
value: 34.963 |
|
- type: ndcg_at_1 |
|
value: 24.7 |
|
- type: ndcg_at_10 |
|
value: 21.839 |
|
- type: ndcg_at_100 |
|
value: 31.057000000000002 |
|
- type: ndcg_at_1000 |
|
value: 36.962 |
|
- type: ndcg_at_3 |
|
value: 20.623 |
|
- type: ndcg_at_5 |
|
value: 18.107 |
|
- type: precision_at_1 |
|
value: 24.7 |
|
- type: precision_at_10 |
|
value: 11.360000000000001 |
|
- type: precision_at_100 |
|
value: 2.4619999999999997 |
|
- type: precision_at_1000 |
|
value: 0.388 |
|
- type: precision_at_3 |
|
value: 19.267 |
|
- type: precision_at_5 |
|
value: 15.959999999999999 |
|
- type: recall_at_1 |
|
value: 4.993 |
|
- type: recall_at_10 |
|
value: 22.982 |
|
- type: recall_at_100 |
|
value: 49.97 |
|
- type: recall_at_1000 |
|
value: 78.623 |
|
- type: recall_at_3 |
|
value: 11.716999999999999 |
|
- type: recall_at_5 |
|
value: 16.172 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.71899431421795 |
|
- type: cos_sim_spearman |
|
value: 80.46430776062674 |
|
- type: euclidean_pearson |
|
value: 83.02871101280735 |
|
- type: euclidean_spearman |
|
value: 80.49525009964952 |
|
- type: manhattan_pearson |
|
value: 82.96176477360466 |
|
- type: manhattan_spearman |
|
value: 80.4038922852272 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.4473643076464 |
|
- type: cos_sim_spearman |
|
value: 76.2648833265373 |
|
- type: euclidean_pearson |
|
value: 82.5498605585181 |
|
- type: euclidean_spearman |
|
value: 76.06458177068038 |
|
- type: manhattan_pearson |
|
value: 82.55572570767087 |
|
- type: manhattan_spearman |
|
value: 76.1267237133785 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.24858438337428 |
|
- type: cos_sim_spearman |
|
value: 86.42907705680409 |
|
- type: euclidean_pearson |
|
value: 85.50673274898077 |
|
- type: euclidean_spearman |
|
value: 86.50066760759493 |
|
- type: manhattan_pearson |
|
value: 85.38098024332331 |
|
- type: manhattan_spearman |
|
value: 86.3179935859058 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.97052112858252 |
|
- type: cos_sim_spearman |
|
value: 82.97007079944963 |
|
- type: euclidean_pearson |
|
value: 84.49118913390151 |
|
- type: euclidean_spearman |
|
value: 82.89912124589944 |
|
- type: manhattan_pearson |
|
value: 84.45725470158602 |
|
- type: manhattan_spearman |
|
value: 82.89422444440467 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.44702160696032 |
|
- type: cos_sim_spearman |
|
value: 88.75678661413305 |
|
- type: euclidean_pearson |
|
value: 88.22046240533754 |
|
- type: euclidean_spearman |
|
value: 88.78103010580752 |
|
- type: manhattan_pearson |
|
value: 88.15576644132916 |
|
- type: manhattan_spearman |
|
value: 88.72891963379698 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.25112584874732 |
|
- type: cos_sim_spearman |
|
value: 85.0642487018319 |
|
- type: euclidean_pearson |
|
value: 84.37279427321502 |
|
- type: euclidean_spearman |
|
value: 85.074198902509 |
|
- type: manhattan_pearson |
|
value: 84.19323050597049 |
|
- type: manhattan_spearman |
|
value: 84.88383717319327 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.87357291874198 |
|
- type: cos_sim_spearman |
|
value: 89.1113081854716 |
|
- type: euclidean_pearson |
|
value: 89.61137598923361 |
|
- type: euclidean_spearman |
|
value: 89.13391070267475 |
|
- type: manhattan_pearson |
|
value: 89.62382071829829 |
|
- type: manhattan_spearman |
|
value: 89.1997962715288 |
|
- 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: 67.1205707180893 |
|
- type: cos_sim_spearman |
|
value: 68.16260851835224 |
|
- type: euclidean_pearson |
|
value: 68.87294373141141 |
|
- type: euclidean_spearman |
|
value: 67.98447223948163 |
|
- type: manhattan_pearson |
|
value: 68.98950941915248 |
|
- type: manhattan_spearman |
|
value: 68.29388343776796 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.9949201588004 |
|
- type: cos_sim_spearman |
|
value: 87.31663820432567 |
|
- type: euclidean_pearson |
|
value: 87.27979534770259 |
|
- type: euclidean_spearman |
|
value: 87.31872069375427 |
|
- type: manhattan_pearson |
|
value: 87.0783256942344 |
|
- type: manhattan_spearman |
|
value: 87.16038562428714 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 86.08173708317305 |
|
- type: mrr |
|
value: 95.93575179359493 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.65 |
|
- type: map_at_10 |
|
value: 67.19000000000001 |
|
- type: map_at_100 |
|
value: 67.772 |
|
- type: map_at_1000 |
|
value: 67.805 |
|
- type: map_at_3 |
|
value: 64.14800000000001 |
|
- type: map_at_5 |
|
value: 65.745 |
|
- type: mrr_at_1 |
|
value: 60.333000000000006 |
|
- type: mrr_at_10 |
|
value: 68.158 |
|
- type: mrr_at_100 |
|
value: 68.583 |
|
- type: mrr_at_1000 |
|
value: 68.613 |
|
- type: mrr_at_3 |
|
value: 65.72200000000001 |
|
- type: mrr_at_5 |
|
value: 67.039 |
|
- type: ndcg_at_1 |
|
value: 60.333000000000006 |
|
- type: ndcg_at_10 |
|
value: 71.69200000000001 |
|
- type: ndcg_at_100 |
|
value: 74.064 |
|
- type: ndcg_at_1000 |
|
value: 74.694 |
|
- type: ndcg_at_3 |
|
value: 66.378 |
|
- type: ndcg_at_5 |
|
value: 68.73 |
|
- type: precision_at_1 |
|
value: 60.333000000000006 |
|
- type: precision_at_10 |
|
value: 9.533 |
|
- type: precision_at_100 |
|
value: 1.08 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 25.556 |
|
- type: precision_at_5 |
|
value: 17.0 |
|
- type: recall_at_1 |
|
value: 57.65 |
|
- type: recall_at_10 |
|
value: 84.56700000000001 |
|
- type: recall_at_100 |
|
value: 95.167 |
|
- type: recall_at_1000 |
|
value: 99.667 |
|
- type: recall_at_3 |
|
value: 70.272 |
|
- type: recall_at_5 |
|
value: 76.11099999999999 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.83663366336634 |
|
- type: cos_sim_ap |
|
value: 96.13854487816917 |
|
- type: cos_sim_f1 |
|
value: 91.77057356608479 |
|
- type: cos_sim_precision |
|
value: 91.54228855721394 |
|
- type: cos_sim_recall |
|
value: 92.0 |
|
- type: dot_accuracy |
|
value: 99.83663366336634 |
|
- type: dot_ap |
|
value: 96.29459284844314 |
|
- type: dot_f1 |
|
value: 91.6030534351145 |
|
- type: dot_precision |
|
value: 93.26424870466322 |
|
- type: dot_recall |
|
value: 90.0 |
|
- type: euclidean_accuracy |
|
value: 99.83564356435643 |
|
- type: euclidean_ap |
|
value: 96.09957152523418 |
|
- type: euclidean_f1 |
|
value: 91.7 |
|
- type: euclidean_precision |
|
value: 91.7 |
|
- type: euclidean_recall |
|
value: 91.7 |
|
- type: manhattan_accuracy |
|
value: 99.83663366336634 |
|
- type: manhattan_ap |
|
value: 96.09579952373399 |
|
- type: manhattan_f1 |
|
value: 91.72932330827068 |
|
- type: manhattan_precision |
|
value: 91.95979899497488 |
|
- type: manhattan_recall |
|
value: 91.5 |
|
- type: max_accuracy |
|
value: 99.83663366336634 |
|
- type: max_ap |
|
value: 96.29459284844314 |
|
- type: max_f1 |
|
value: 91.77057356608479 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 61.270213664772385 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 35.23973443659002 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 53.40061413824656 |
|
- type: mrr |
|
value: 54.28819444444445 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.59314409717665 |
|
- type: cos_sim_spearman |
|
value: 30.573109955748677 |
|
- type: dot_pearson |
|
value: 30.884662900409722 |
|
- type: dot_spearman |
|
value: 30.778591618272262 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.20400000000000001 |
|
- type: map_at_10 |
|
value: 1.7229999999999999 |
|
- type: map_at_100 |
|
value: 9.185 |
|
- type: map_at_1000 |
|
value: 23.019000000000002 |
|
- type: map_at_3 |
|
value: 0.596 |
|
- type: map_at_5 |
|
value: 0.9339999999999999 |
|
- type: mrr_at_1 |
|
value: 78.0 |
|
- type: mrr_at_10 |
|
value: 85.5 |
|
- type: mrr_at_100 |
|
value: 85.682 |
|
- type: mrr_at_1000 |
|
value: 85.682 |
|
- type: mrr_at_3 |
|
value: 84.0 |
|
- type: mrr_at_5 |
|
value: 85.5 |
|
- type: ndcg_at_1 |
|
value: 73.0 |
|
- type: ndcg_at_10 |
|
value: 68.28 |
|
- type: ndcg_at_100 |
|
value: 52.239000000000004 |
|
- type: ndcg_at_1000 |
|
value: 48.217 |
|
- type: ndcg_at_3 |
|
value: 72.603 |
|
- type: ndcg_at_5 |
|
value: 70.64099999999999 |
|
- type: precision_at_1 |
|
value: 78.0 |
|
- type: precision_at_10 |
|
value: 72.39999999999999 |
|
- type: precision_at_100 |
|
value: 53.459999999999994 |
|
- type: precision_at_1000 |
|
value: 21.254 |
|
- type: precision_at_3 |
|
value: 78.0 |
|
- type: precision_at_5 |
|
value: 74.8 |
|
- type: recall_at_1 |
|
value: 0.20400000000000001 |
|
- type: recall_at_10 |
|
value: 1.939 |
|
- type: recall_at_100 |
|
value: 12.831000000000001 |
|
- type: recall_at_1000 |
|
value: 45.572 |
|
- type: recall_at_3 |
|
value: 0.628 |
|
- type: recall_at_5 |
|
value: 1.004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.693 |
|
- type: map_at_10 |
|
value: 7.7410000000000005 |
|
- type: map_at_100 |
|
value: 13.778000000000002 |
|
- type: map_at_1000 |
|
value: 15.328 |
|
- type: map_at_3 |
|
value: 4.361000000000001 |
|
- type: map_at_5 |
|
value: 5.534 |
|
- type: mrr_at_1 |
|
value: 20.408 |
|
- type: mrr_at_10 |
|
value: 37.008 |
|
- type: mrr_at_100 |
|
value: 38.198 |
|
- type: mrr_at_1000 |
|
value: 38.216 |
|
- type: mrr_at_3 |
|
value: 32.993 |
|
- type: mrr_at_5 |
|
value: 34.83 |
|
- type: ndcg_at_1 |
|
value: 18.367 |
|
- type: ndcg_at_10 |
|
value: 19.676 |
|
- type: ndcg_at_100 |
|
value: 33.421 |
|
- type: ndcg_at_1000 |
|
value: 45.123999999999995 |
|
- type: ndcg_at_3 |
|
value: 22.109 |
|
- type: ndcg_at_5 |
|
value: 20.166999999999998 |
|
- type: precision_at_1 |
|
value: 20.408 |
|
- type: precision_at_10 |
|
value: 17.551 |
|
- type: precision_at_100 |
|
value: 7.286 |
|
- type: precision_at_1000 |
|
value: 1.516 |
|
- type: precision_at_3 |
|
value: 23.810000000000002 |
|
- type: precision_at_5 |
|
value: 20.408 |
|
- type: recall_at_1 |
|
value: 1.693 |
|
- type: recall_at_10 |
|
value: 13.485 |
|
- type: recall_at_100 |
|
value: 46.361000000000004 |
|
- type: recall_at_1000 |
|
value: 81.997 |
|
- type: recall_at_3 |
|
value: 5.432 |
|
- type: recall_at_5 |
|
value: 7.797 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 70.6774 |
|
- type: ap |
|
value: 14.243691983984998 |
|
- type: f1 |
|
value: 54.45105895755751 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 60.0509337860781 |
|
- type: f1 |
|
value: 60.424197644605236 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 49.94452711339773 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.75430649102938 |
|
- type: cos_sim_ap |
|
value: 73.38576407567363 |
|
- type: cos_sim_f1 |
|
value: 67.47549019607844 |
|
- type: cos_sim_precision |
|
value: 62.99771167048055 |
|
- type: cos_sim_recall |
|
value: 72.63852242744063 |
|
- type: dot_accuracy |
|
value: 85.67681945520653 |
|
- type: dot_ap |
|
value: 73.37650773516077 |
|
- type: dot_f1 |
|
value: 67.56520653937352 |
|
- type: dot_precision |
|
value: 64.1013497513616 |
|
- type: dot_recall |
|
value: 71.42480211081794 |
|
- type: euclidean_accuracy |
|
value: 85.76622757346367 |
|
- type: euclidean_ap |
|
value: 73.31834510956003 |
|
- type: euclidean_f1 |
|
value: 67.40331491712708 |
|
- type: euclidean_precision |
|
value: 60.780156879372484 |
|
- type: euclidean_recall |
|
value: 75.64643799472296 |
|
- type: manhattan_accuracy |
|
value: 85.73046432616081 |
|
- type: manhattan_ap |
|
value: 73.10120518588954 |
|
- type: manhattan_f1 |
|
value: 67.34183545886471 |
|
- type: manhattan_precision |
|
value: 63.997148288973385 |
|
- type: manhattan_recall |
|
value: 71.05540897097626 |
|
- type: max_accuracy |
|
value: 85.76622757346367 |
|
- type: max_ap |
|
value: 73.38576407567363 |
|
- type: max_f1 |
|
value: 67.56520653937352 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.71424690495596 |
|
- type: cos_sim_ap |
|
value: 85.42819672981983 |
|
- type: cos_sim_f1 |
|
value: 77.76150014649868 |
|
- type: cos_sim_precision |
|
value: 74.15479184129646 |
|
- type: cos_sim_recall |
|
value: 81.73698798891284 |
|
- type: dot_accuracy |
|
value: 88.45810532852097 |
|
- type: dot_ap |
|
value: 84.78667227857513 |
|
- type: dot_f1 |
|
value: 77.29539996305192 |
|
- type: dot_precision |
|
value: 74.30560488740498 |
|
- type: dot_recall |
|
value: 80.53587927317524 |
|
- type: euclidean_accuracy |
|
value: 88.73171110334924 |
|
- type: euclidean_ap |
|
value: 85.46052151213301 |
|
- type: euclidean_f1 |
|
value: 77.79939075861563 |
|
- type: euclidean_precision |
|
value: 74.33200084157374 |
|
- type: euclidean_recall |
|
value: 81.60609793655682 |
|
- type: manhattan_accuracy |
|
value: 88.75111576823068 |
|
- type: manhattan_ap |
|
value: 85.4412901701619 |
|
- type: manhattan_f1 |
|
value: 77.72423325488437 |
|
- type: manhattan_precision |
|
value: 75.48799071184965 |
|
- type: manhattan_recall |
|
value: 80.09701262704034 |
|
- type: max_accuracy |
|
value: 88.75111576823068 |
|
- type: max_ap |
|
value: 85.46052151213301 |
|
- type: max_f1 |
|
value: 77.79939075861563 |
|
--- |
|
<h1 align="center">GIST small Embedding v0</h1> |
|
|
|
*GIST Embedding: Guided In-sample Selection of Training Negatives for Text Embedding* |
|
|
|
The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). |
|
|
|
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. |
|
|
|
Technical details of the model will be published shortly. |
|
|
|
# Data |
|
|
|
The dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: |
|
|
|
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) |
|
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb |
|
|
|
The dataset contains a `task_type` key which can be used to select only the mteb classification tasks (prefixed with `mteb_`). |
|
|
|
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). |
|
|
|
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. |
|
|
|
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis. |
|
|
|
# Usage |
|
|
|
The model can be easily loaded using the Sentence Transformers library. |
|
|
|
```Python |
|
import torch.nn.functional as F |
|
from sentence_transformers import SentenceTransformer |
|
|
|
revision = None # Replace with the specific revision to ensure reproducibility in case the model is updated. |
|
|
|
model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) |
|
|
|
texts = [ |
|
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", |
|
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", |
|
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" |
|
] |
|
|
|
# Compute embeddings |
|
embeddings = model.encode(texts, convert_to_tensor=True) |
|
|
|
# Compute cosine-similarity for each pair of sentences |
|
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) |
|
|
|
print(scores.cpu().numpy()) |
|
``` |
|
|
|
# Training Parameters |
|
|
|
Below are the training parameters used to fine-tune the model: |
|
|
|
``` |
|
Epochs = 40 |
|
Warmup ratio = 0.1 |
|
Learning rate = 5e-6 |
|
Batch size = 16 |
|
Checkpoint step = 102000 |
|
Contrastive loss temperature = 0.01 |
|
``` |
|
|
|
Specific training details and strategies will be published shortly. |
|
|
|
# Evaluation |
|
|
|
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. |
|
|
|
|
|
# Acknowledgements |
|
|
|
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. |
|
|
|
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. |
