|
--- |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- mteb |
|
model-index: |
|
- name: stella-base-en-v2 |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 77.19402985074628 |
|
- type: ap |
|
value: 40.43267503017359 |
|
- type: f1 |
|
value: 71.15585210518594 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 93.256675 |
|
- type: ap |
|
value: 90.00824833079179 |
|
- type: f1 |
|
value: 93.2473146151734 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 49.612 |
|
- type: f1 |
|
value: 48.530785631574304 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.411 |
|
- type: map_at_10 |
|
value: 52.673 |
|
- type: map_at_100 |
|
value: 53.410999999999994 |
|
- type: map_at_1000 |
|
value: 53.415 |
|
- type: map_at_3 |
|
value: 48.495 |
|
- type: map_at_5 |
|
value: 51.183 |
|
- type: mrr_at_1 |
|
value: 37.838 |
|
- type: mrr_at_10 |
|
value: 52.844 |
|
- type: mrr_at_100 |
|
value: 53.581999999999994 |
|
- type: mrr_at_1000 |
|
value: 53.586 |
|
- type: mrr_at_3 |
|
value: 48.672 |
|
- type: mrr_at_5 |
|
value: 51.272 |
|
- type: ndcg_at_1 |
|
value: 37.411 |
|
- type: ndcg_at_10 |
|
value: 60.626999999999995 |
|
- type: ndcg_at_100 |
|
value: 63.675000000000004 |
|
- type: ndcg_at_1000 |
|
value: 63.776999999999994 |
|
- type: ndcg_at_3 |
|
value: 52.148 |
|
- type: ndcg_at_5 |
|
value: 57.001999999999995 |
|
- type: precision_at_1 |
|
value: 37.411 |
|
- type: precision_at_10 |
|
value: 8.578 |
|
- type: precision_at_100 |
|
value: 0.989 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 20.91 |
|
- type: precision_at_5 |
|
value: 14.908 |
|
- type: recall_at_1 |
|
value: 37.411 |
|
- type: recall_at_10 |
|
value: 85.775 |
|
- type: recall_at_100 |
|
value: 98.86200000000001 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 62.731 |
|
- type: recall_at_5 |
|
value: 74.53800000000001 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 47.24219029437865 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 40.474604844291726 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.720542706366054 |
|
- type: mrr |
|
value: 75.59633733456448 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.31345008397868 |
|
- type: cos_sim_spearman |
|
value: 85.94292212320399 |
|
- type: euclidean_pearson |
|
value: 85.03974302774525 |
|
- type: euclidean_spearman |
|
value: 85.88087251659051 |
|
- type: manhattan_pearson |
|
value: 84.91900996712951 |
|
- type: manhattan_spearman |
|
value: 85.96701905781116 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.72727272727273 |
|
- type: f1 |
|
value: 84.29572512364581 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 39.55532460397536 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 35.91195973591251 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.822 |
|
- type: map_at_10 |
|
value: 44.139 |
|
- type: map_at_100 |
|
value: 45.786 |
|
- type: map_at_1000 |
|
value: 45.906000000000006 |
|
- type: map_at_3 |
|
value: 40.637 |
|
- type: map_at_5 |
|
value: 42.575 |
|
- type: mrr_at_1 |
|
value: 41.059 |
|
- type: mrr_at_10 |
|
value: 50.751000000000005 |
|
- type: mrr_at_100 |
|
value: 51.548 |
|
- type: mrr_at_1000 |
|
value: 51.583999999999996 |
|
- type: mrr_at_3 |
|
value: 48.236000000000004 |
|
- type: mrr_at_5 |
|
value: 49.838 |
|
- type: ndcg_at_1 |
|
value: 41.059 |
|
- type: ndcg_at_10 |
|
value: 50.573 |
|
- type: ndcg_at_100 |
|
value: 56.25 |
|
- type: ndcg_at_1000 |
|
value: 58.004 |
|
- type: ndcg_at_3 |
|
value: 45.995000000000005 |
|
- type: ndcg_at_5 |
|
value: 48.18 |
|
- type: precision_at_1 |
|
value: 41.059 |
|
- type: precision_at_10 |
|
value: 9.757 |
|
- type: precision_at_100 |
|
value: 1.609 |
|
- type: precision_at_1000 |
|
value: 0.20600000000000002 |
|
- type: precision_at_3 |
|
value: 22.222 |
|
- type: precision_at_5 |
|
value: 16.023 |
|
- type: recall_at_1 |
|
value: 32.822 |
|
- type: recall_at_10 |
|
value: 61.794000000000004 |
|
- type: recall_at_100 |
|
value: 85.64699999999999 |
|
- type: recall_at_1000 |
|
value: 96.836 |
|
- type: recall_at_3 |
|
value: 47.999 |
|
- type: recall_at_5 |
|
value: 54.376999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.579 |
|
- type: map_at_10 |
|
value: 39.787 |
|
- type: map_at_100 |
|
value: 40.976 |
|
- type: map_at_1000 |
|
value: 41.108 |
|
- type: map_at_3 |
|
value: 36.819 |
|
- type: map_at_5 |
|
value: 38.437 |
|
- type: mrr_at_1 |
|
value: 37.516 |
|
- type: mrr_at_10 |
|
value: 45.822 |
|
- type: mrr_at_100 |
|
value: 46.454 |
|
- type: mrr_at_1000 |
|
value: 46.495999999999995 |
|
- type: mrr_at_3 |
|
value: 43.556 |
|
- type: mrr_at_5 |
|
value: 44.814 |
|
- type: ndcg_at_1 |
|
value: 37.516 |
|
- type: ndcg_at_10 |
|
value: 45.5 |
|
- type: ndcg_at_100 |
|
value: 49.707 |
|
- type: ndcg_at_1000 |
|
value: 51.842 |
|
- type: ndcg_at_3 |
|
value: 41.369 |
|
- type: ndcg_at_5 |
|
value: 43.161 |
|
- type: precision_at_1 |
|
value: 37.516 |
|
- type: precision_at_10 |
|
value: 8.713 |
|
- type: precision_at_100 |
|
value: 1.38 |
|
- type: precision_at_1000 |
|
value: 0.188 |
|
- type: precision_at_3 |
|
value: 20.233999999999998 |
|
- type: precision_at_5 |
|
value: 14.280000000000001 |
|
- type: recall_at_1 |
|
value: 29.579 |
|
- type: recall_at_10 |
|
value: 55.458 |
|
- type: recall_at_100 |
|
value: 73.49799999999999 |
|
- type: recall_at_1000 |
|
value: 87.08200000000001 |
|
- type: recall_at_3 |
|
value: 42.858000000000004 |
|
- type: recall_at_5 |
|
value: 48.215 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.489999999999995 |
|
- type: map_at_10 |
|
value: 53.313 |
|
- type: map_at_100 |
|
value: 54.290000000000006 |
|
- type: map_at_1000 |
|
value: 54.346000000000004 |
|
- type: map_at_3 |
|
value: 49.983 |
|
- type: map_at_5 |
|
value: 51.867 |
|
- type: mrr_at_1 |
|
value: 46.27 |
|
- type: mrr_at_10 |
|
value: 56.660999999999994 |
|
- type: mrr_at_100 |
|
value: 57.274 |
|
- type: mrr_at_1000 |
|
value: 57.301 |
|
- type: mrr_at_3 |
|
value: 54.138 |
|
- type: mrr_at_5 |
|
value: 55.623999999999995 |
|
- type: ndcg_at_1 |
|
value: 46.27 |
|
- type: ndcg_at_10 |
|
value: 59.192 |
|
- type: ndcg_at_100 |
|
value: 63.026 |
|
- type: ndcg_at_1000 |
|
value: 64.079 |
|
- type: ndcg_at_3 |
|
value: 53.656000000000006 |
|
- type: ndcg_at_5 |
|
value: 56.387 |
|
- type: precision_at_1 |
|
value: 46.27 |
|
- type: precision_at_10 |
|
value: 9.511 |
|
- type: precision_at_100 |
|
value: 1.23 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 24.096 |
|
- type: precision_at_5 |
|
value: 16.476 |
|
- type: recall_at_1 |
|
value: 40.489999999999995 |
|
- type: recall_at_10 |
|
value: 73.148 |
|
- type: recall_at_100 |
|
value: 89.723 |
|
- type: recall_at_1000 |
|
value: 97.073 |
|
- type: recall_at_3 |
|
value: 58.363 |
|
- type: recall_at_5 |
|
value: 65.083 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.197 |
|
- type: map_at_10 |
|
value: 35.135 |
|
- type: map_at_100 |
|
value: 36.14 |
|
- type: map_at_1000 |
|
value: 36.216 |
|
- type: map_at_3 |
|
value: 32.358 |
|
- type: map_at_5 |
|
value: 33.814 |
|
- type: mrr_at_1 |
|
value: 28.475 |
|
- type: mrr_at_10 |
|
value: 37.096000000000004 |
|
- type: mrr_at_100 |
|
value: 38.006 |
|
- type: mrr_at_1000 |
|
value: 38.06 |
|
- type: mrr_at_3 |
|
value: 34.52 |
|
- type: mrr_at_5 |
|
value: 35.994 |
|
- type: ndcg_at_1 |
|
value: 28.475 |
|
- type: ndcg_at_10 |
|
value: 40.263 |
|
- type: ndcg_at_100 |
|
value: 45.327 |
|
- type: ndcg_at_1000 |
|
value: 47.225 |
|
- type: ndcg_at_3 |
|
value: 34.882000000000005 |
|
- type: ndcg_at_5 |
|
value: 37.347 |
|
- type: precision_at_1 |
|
value: 28.475 |
|
- type: precision_at_10 |
|
value: 6.249 |
|
- type: precision_at_100 |
|
value: 0.919 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 14.689 |
|
- type: precision_at_5 |
|
value: 10.237 |
|
- type: recall_at_1 |
|
value: 26.197 |
|
- type: recall_at_10 |
|
value: 54.17999999999999 |
|
- type: recall_at_100 |
|
value: 77.768 |
|
- type: recall_at_1000 |
|
value: 91.932 |
|
- type: recall_at_3 |
|
value: 39.804 |
|
- type: recall_at_5 |
|
value: 45.660000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.683 |
|
- type: map_at_10 |
|
value: 25.013999999999996 |
|
- type: map_at_100 |
|
value: 26.411 |
|
- type: map_at_1000 |
|
value: 26.531 |
|
- type: map_at_3 |
|
value: 22.357 |
|
- type: map_at_5 |
|
value: 23.982999999999997 |
|
- type: mrr_at_1 |
|
value: 20.896 |
|
- type: mrr_at_10 |
|
value: 29.758000000000003 |
|
- type: mrr_at_100 |
|
value: 30.895 |
|
- type: mrr_at_1000 |
|
value: 30.964999999999996 |
|
- type: mrr_at_3 |
|
value: 27.177 |
|
- type: mrr_at_5 |
|
value: 28.799999999999997 |
|
- type: ndcg_at_1 |
|
value: 20.896 |
|
- type: ndcg_at_10 |
|
value: 30.294999999999998 |
|
- type: ndcg_at_100 |
|
value: 36.68 |
|
- type: ndcg_at_1000 |
|
value: 39.519 |
|
- type: ndcg_at_3 |
|
value: 25.480999999999998 |
|
- type: ndcg_at_5 |
|
value: 28.027 |
|
- type: precision_at_1 |
|
value: 20.896 |
|
- type: precision_at_10 |
|
value: 5.56 |
|
- type: precision_at_100 |
|
value: 1.006 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 12.231 |
|
- type: precision_at_5 |
|
value: 9.104 |
|
- type: recall_at_1 |
|
value: 16.683 |
|
- type: recall_at_10 |
|
value: 41.807 |
|
- type: recall_at_100 |
|
value: 69.219 |
|
- type: recall_at_1000 |
|
value: 89.178 |
|
- type: recall_at_3 |
|
value: 28.772 |
|
- type: recall_at_5 |
|
value: 35.167 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.653000000000002 |
|
- type: map_at_10 |
|
value: 41.21 |
|
- type: map_at_100 |
|
value: 42.543 |
|
- type: map_at_1000 |
|
value: 42.657000000000004 |
|
- type: map_at_3 |
|
value: 38.094 |
|
- type: map_at_5 |
|
value: 39.966 |
|
- type: mrr_at_1 |
|
value: 37.824999999999996 |
|
- type: mrr_at_10 |
|
value: 47.087 |
|
- type: mrr_at_100 |
|
value: 47.959 |
|
- type: mrr_at_1000 |
|
value: 48.003 |
|
- type: mrr_at_3 |
|
value: 45.043 |
|
- type: mrr_at_5 |
|
value: 46.352 |
|
- type: ndcg_at_1 |
|
value: 37.824999999999996 |
|
- type: ndcg_at_10 |
|
value: 47.158 |
|
- type: ndcg_at_100 |
|
value: 52.65 |
|
- type: ndcg_at_1000 |
|
value: 54.644999999999996 |
|
- type: ndcg_at_3 |
|
value: 42.632999999999996 |
|
- type: ndcg_at_5 |
|
value: 44.994 |
|
- type: precision_at_1 |
|
value: 37.824999999999996 |
|
- type: precision_at_10 |
|
value: 8.498999999999999 |
|
- type: precision_at_100 |
|
value: 1.308 |
|
- type: precision_at_1000 |
|
value: 0.166 |
|
- type: precision_at_3 |
|
value: 20.308 |
|
- type: precision_at_5 |
|
value: 14.283000000000001 |
|
- type: recall_at_1 |
|
value: 30.653000000000002 |
|
- type: recall_at_10 |
|
value: 58.826 |
|
- type: recall_at_100 |
|
value: 81.94 |
|
- type: recall_at_1000 |
|
value: 94.71000000000001 |
|
- type: recall_at_3 |
|
value: 45.965 |
|
- type: recall_at_5 |
|
value: 52.294 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.71 |
|
- type: map_at_10 |
|
value: 36.001 |
|
- type: map_at_100 |
|
value: 37.416 |
|
- type: map_at_1000 |
|
value: 37.522 |
|
- type: map_at_3 |
|
value: 32.841 |
|
- type: map_at_5 |
|
value: 34.515 |
|
- type: mrr_at_1 |
|
value: 32.647999999999996 |
|
- type: mrr_at_10 |
|
value: 41.43 |
|
- type: mrr_at_100 |
|
value: 42.433 |
|
- type: mrr_at_1000 |
|
value: 42.482 |
|
- type: mrr_at_3 |
|
value: 39.117000000000004 |
|
- type: mrr_at_5 |
|
value: 40.35 |
|
- type: ndcg_at_1 |
|
value: 32.647999999999996 |
|
- type: ndcg_at_10 |
|
value: 41.629 |
|
- type: ndcg_at_100 |
|
value: 47.707 |
|
- type: ndcg_at_1000 |
|
value: 49.913000000000004 |
|
- type: ndcg_at_3 |
|
value: 36.598000000000006 |
|
- type: ndcg_at_5 |
|
value: 38.696000000000005 |
|
- type: precision_at_1 |
|
value: 32.647999999999996 |
|
- type: precision_at_10 |
|
value: 7.704999999999999 |
|
- type: precision_at_100 |
|
value: 1.242 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 17.314 |
|
- type: precision_at_5 |
|
value: 12.374 |
|
- type: recall_at_1 |
|
value: 26.71 |
|
- type: recall_at_10 |
|
value: 52.898 |
|
- type: recall_at_100 |
|
value: 79.08 |
|
- type: recall_at_1000 |
|
value: 93.94 |
|
- type: recall_at_3 |
|
value: 38.731 |
|
- type: recall_at_5 |
|
value: 44.433 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.510999999999996 |
|
- type: map_at_10 |
|
value: 35.755333333333326 |
|
- type: map_at_100 |
|
value: 36.97525 |
|
- type: map_at_1000 |
|
value: 37.08741666666667 |
|
- type: map_at_3 |
|
value: 32.921 |
|
- type: map_at_5 |
|
value: 34.45041666666667 |
|
- type: mrr_at_1 |
|
value: 31.578416666666666 |
|
- type: mrr_at_10 |
|
value: 40.06066666666667 |
|
- type: mrr_at_100 |
|
value: 40.93350000000001 |
|
- type: mrr_at_1000 |
|
value: 40.98716666666667 |
|
- type: mrr_at_3 |
|
value: 37.710499999999996 |
|
- type: mrr_at_5 |
|
value: 39.033249999999995 |
|
- type: ndcg_at_1 |
|
value: 31.578416666666666 |
|
- type: ndcg_at_10 |
|
value: 41.138666666666666 |
|
- type: ndcg_at_100 |
|
value: 46.37291666666666 |
|
- type: ndcg_at_1000 |
|
value: 48.587500000000006 |
|
- type: ndcg_at_3 |
|
value: 36.397083333333335 |
|
- type: ndcg_at_5 |
|
value: 38.539 |
|
- type: precision_at_1 |
|
value: 31.578416666666666 |
|
- type: precision_at_10 |
|
value: 7.221583333333332 |
|
- type: precision_at_100 |
|
value: 1.1581666666666668 |
|
- type: precision_at_1000 |
|
value: 0.15416666666666667 |
|
- type: precision_at_3 |
|
value: 16.758 |
|
- type: precision_at_5 |
|
value: 11.830916666666665 |
|
- type: recall_at_1 |
|
value: 26.510999999999996 |
|
- type: recall_at_10 |
|
value: 52.7825 |
|
- type: recall_at_100 |
|
value: 75.79675 |
|
- type: recall_at_1000 |
|
value: 91.10483333333335 |
|
- type: recall_at_3 |
|
value: 39.48233333333334 |
|
- type: recall_at_5 |
|
value: 45.07116666666667 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.564 |
|
- type: map_at_10 |
|
value: 31.235000000000003 |
|
- type: map_at_100 |
|
value: 32.124 |
|
- type: map_at_1000 |
|
value: 32.216 |
|
- type: map_at_3 |
|
value: 29.330000000000002 |
|
- type: map_at_5 |
|
value: 30.379 |
|
- type: mrr_at_1 |
|
value: 27.761000000000003 |
|
- type: mrr_at_10 |
|
value: 34.093 |
|
- type: mrr_at_100 |
|
value: 34.885 |
|
- type: mrr_at_1000 |
|
value: 34.957 |
|
- type: mrr_at_3 |
|
value: 32.388 |
|
- type: mrr_at_5 |
|
value: 33.269 |
|
- type: ndcg_at_1 |
|
value: 27.761000000000003 |
|
- type: ndcg_at_10 |
|
value: 35.146 |
|
- type: ndcg_at_100 |
|
value: 39.597 |
|
- type: ndcg_at_1000 |
|
value: 42.163000000000004 |
|
- type: ndcg_at_3 |
|
value: 31.674000000000003 |
|
- type: ndcg_at_5 |
|
value: 33.224 |
|
- type: precision_at_1 |
|
value: 27.761000000000003 |
|
- type: precision_at_10 |
|
value: 5.383 |
|
- type: precision_at_100 |
|
value: 0.836 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 13.599 |
|
- type: precision_at_5 |
|
value: 9.202 |
|
- type: recall_at_1 |
|
value: 24.564 |
|
- type: recall_at_10 |
|
value: 44.36 |
|
- type: recall_at_100 |
|
value: 64.408 |
|
- type: recall_at_1000 |
|
value: 83.892 |
|
- type: recall_at_3 |
|
value: 34.653 |
|
- type: recall_at_5 |
|
value: 38.589 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.01 |
|
- type: map_at_10 |
|
value: 24.485 |
|
- type: map_at_100 |
|
value: 25.573 |
|
- type: map_at_1000 |
|
value: 25.703 |
|
- type: map_at_3 |
|
value: 21.953 |
|
- type: map_at_5 |
|
value: 23.294999999999998 |
|
- type: mrr_at_1 |
|
value: 20.544 |
|
- type: mrr_at_10 |
|
value: 28.238000000000003 |
|
- type: mrr_at_100 |
|
value: 29.142000000000003 |
|
- type: mrr_at_1000 |
|
value: 29.219 |
|
- type: mrr_at_3 |
|
value: 25.802999999999997 |
|
- type: mrr_at_5 |
|
value: 27.105 |
|
- type: ndcg_at_1 |
|
value: 20.544 |
|
- type: ndcg_at_10 |
|
value: 29.387999999999998 |
|
- type: ndcg_at_100 |
|
value: 34.603 |
|
- type: ndcg_at_1000 |
|
value: 37.564 |
|
- type: ndcg_at_3 |
|
value: 24.731 |
|
- type: ndcg_at_5 |
|
value: 26.773000000000003 |
|
- type: precision_at_1 |
|
value: 20.544 |
|
- type: precision_at_10 |
|
value: 5.509 |
|
- type: precision_at_100 |
|
value: 0.9450000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 11.757 |
|
- type: precision_at_5 |
|
value: 8.596 |
|
- type: recall_at_1 |
|
value: 17.01 |
|
- type: recall_at_10 |
|
value: 40.392 |
|
- type: recall_at_100 |
|
value: 64.043 |
|
- type: recall_at_1000 |
|
value: 85.031 |
|
- type: recall_at_3 |
|
value: 27.293 |
|
- type: recall_at_5 |
|
value: 32.586999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.155 |
|
- type: map_at_10 |
|
value: 35.92 |
|
- type: map_at_100 |
|
value: 37.034 |
|
- type: map_at_1000 |
|
value: 37.139 |
|
- type: map_at_3 |
|
value: 33.263999999999996 |
|
- type: map_at_5 |
|
value: 34.61 |
|
- type: mrr_at_1 |
|
value: 32.183 |
|
- type: mrr_at_10 |
|
value: 40.099000000000004 |
|
- type: mrr_at_100 |
|
value: 41.001 |
|
- type: mrr_at_1000 |
|
value: 41.059 |
|
- type: mrr_at_3 |
|
value: 37.889 |
|
- type: mrr_at_5 |
|
value: 39.007999999999996 |
|
- type: ndcg_at_1 |
|
value: 32.183 |
|
- type: ndcg_at_10 |
|
value: 41.127 |
|
- type: ndcg_at_100 |
|
value: 46.464 |
|
- type: ndcg_at_1000 |
|
value: 48.67 |
|
- type: ndcg_at_3 |
|
value: 36.396 |
|
- type: ndcg_at_5 |
|
value: 38.313 |
|
- type: precision_at_1 |
|
value: 32.183 |
|
- type: precision_at_10 |
|
value: 6.847 |
|
- type: precision_at_100 |
|
value: 1.0739999999999998 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 16.356 |
|
- type: precision_at_5 |
|
value: 11.362 |
|
- type: recall_at_1 |
|
value: 27.155 |
|
- type: recall_at_10 |
|
value: 52.922000000000004 |
|
- type: recall_at_100 |
|
value: 76.39 |
|
- type: recall_at_1000 |
|
value: 91.553 |
|
- type: recall_at_3 |
|
value: 39.745999999999995 |
|
- type: recall_at_5 |
|
value: 44.637 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.523 |
|
- type: map_at_10 |
|
value: 34.268 |
|
- type: map_at_100 |
|
value: 35.835 |
|
- type: map_at_1000 |
|
value: 36.046 |
|
- type: map_at_3 |
|
value: 31.662000000000003 |
|
- type: map_at_5 |
|
value: 32.71 |
|
- type: mrr_at_1 |
|
value: 31.028 |
|
- type: mrr_at_10 |
|
value: 38.924 |
|
- type: mrr_at_100 |
|
value: 39.95 |
|
- type: mrr_at_1000 |
|
value: 40.003 |
|
- type: mrr_at_3 |
|
value: 36.594 |
|
- type: mrr_at_5 |
|
value: 37.701 |
|
- type: ndcg_at_1 |
|
value: 31.028 |
|
- type: ndcg_at_10 |
|
value: 39.848 |
|
- type: ndcg_at_100 |
|
value: 45.721000000000004 |
|
- type: ndcg_at_1000 |
|
value: 48.424 |
|
- type: ndcg_at_3 |
|
value: 35.329 |
|
- type: ndcg_at_5 |
|
value: 36.779 |
|
- type: precision_at_1 |
|
value: 31.028 |
|
- type: precision_at_10 |
|
value: 7.51 |
|
- type: precision_at_100 |
|
value: 1.478 |
|
- type: precision_at_1000 |
|
value: 0.24 |
|
- type: precision_at_3 |
|
value: 16.337 |
|
- type: precision_at_5 |
|
value: 11.383000000000001 |
|
- type: recall_at_1 |
|
value: 25.523 |
|
- type: recall_at_10 |
|
value: 50.735 |
|
- type: recall_at_100 |
|
value: 76.593 |
|
- type: recall_at_1000 |
|
value: 93.771 |
|
- type: recall_at_3 |
|
value: 37.574000000000005 |
|
- type: recall_at_5 |
|
value: 41.602 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.746000000000002 |
|
- type: map_at_10 |
|
value: 28.557 |
|
- type: map_at_100 |
|
value: 29.575000000000003 |
|
- type: map_at_1000 |
|
value: 29.659000000000002 |
|
- type: map_at_3 |
|
value: 25.753999999999998 |
|
- type: map_at_5 |
|
value: 27.254 |
|
- type: mrr_at_1 |
|
value: 22.736 |
|
- type: mrr_at_10 |
|
value: 30.769000000000002 |
|
- type: mrr_at_100 |
|
value: 31.655 |
|
- type: mrr_at_1000 |
|
value: 31.717000000000002 |
|
- type: mrr_at_3 |
|
value: 28.065 |
|
- type: mrr_at_5 |
|
value: 29.543999999999997 |
|
- type: ndcg_at_1 |
|
value: 22.736 |
|
- type: ndcg_at_10 |
|
value: 33.545 |
|
- type: ndcg_at_100 |
|
value: 38.743 |
|
- type: ndcg_at_1000 |
|
value: 41.002 |
|
- type: ndcg_at_3 |
|
value: 28.021 |
|
- type: ndcg_at_5 |
|
value: 30.586999999999996 |
|
- type: precision_at_1 |
|
value: 22.736 |
|
- type: precision_at_10 |
|
value: 5.416 |
|
- type: precision_at_100 |
|
value: 0.8710000000000001 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 11.953 |
|
- type: precision_at_5 |
|
value: 8.651 |
|
- type: recall_at_1 |
|
value: 20.746000000000002 |
|
- type: recall_at_10 |
|
value: 46.87 |
|
- type: recall_at_100 |
|
value: 71.25200000000001 |
|
- type: recall_at_1000 |
|
value: 88.26 |
|
- type: recall_at_3 |
|
value: 32.029999999999994 |
|
- type: recall_at_5 |
|
value: 38.21 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.105 |
|
- type: map_at_10 |
|
value: 20.577 |
|
- type: map_at_100 |
|
value: 22.686999999999998 |
|
- type: map_at_1000 |
|
value: 22.889 |
|
- type: map_at_3 |
|
value: 17.174 |
|
- type: map_at_5 |
|
value: 18.807 |
|
- type: mrr_at_1 |
|
value: 27.101 |
|
- type: mrr_at_10 |
|
value: 38.475 |
|
- type: mrr_at_100 |
|
value: 39.491 |
|
- type: mrr_at_1000 |
|
value: 39.525 |
|
- type: mrr_at_3 |
|
value: 34.886 |
|
- type: mrr_at_5 |
|
value: 36.922 |
|
- type: ndcg_at_1 |
|
value: 27.101 |
|
- type: ndcg_at_10 |
|
value: 29.002 |
|
- type: ndcg_at_100 |
|
value: 37.218 |
|
- type: ndcg_at_1000 |
|
value: 40.644000000000005 |
|
- type: ndcg_at_3 |
|
value: 23.464 |
|
- type: ndcg_at_5 |
|
value: 25.262 |
|
- type: precision_at_1 |
|
value: 27.101 |
|
- type: precision_at_10 |
|
value: 9.179 |
|
- type: precision_at_100 |
|
value: 1.806 |
|
- type: precision_at_1000 |
|
value: 0.244 |
|
- type: precision_at_3 |
|
value: 17.394000000000002 |
|
- type: precision_at_5 |
|
value: 13.342 |
|
- type: recall_at_1 |
|
value: 12.105 |
|
- type: recall_at_10 |
|
value: 35.143 |
|
- type: recall_at_100 |
|
value: 63.44499999999999 |
|
- type: recall_at_1000 |
|
value: 82.49499999999999 |
|
- type: recall_at_3 |
|
value: 21.489 |
|
- type: recall_at_5 |
|
value: 26.82 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.769 |
|
- type: map_at_10 |
|
value: 18.619 |
|
- type: map_at_100 |
|
value: 26.3 |
|
- type: map_at_1000 |
|
value: 28.063 |
|
- type: map_at_3 |
|
value: 13.746 |
|
- type: map_at_5 |
|
value: 16.035 |
|
- type: mrr_at_1 |
|
value: 65.25 |
|
- type: mrr_at_10 |
|
value: 73.678 |
|
- type: mrr_at_100 |
|
value: 73.993 |
|
- type: mrr_at_1000 |
|
value: 74.003 |
|
- type: mrr_at_3 |
|
value: 72.042 |
|
- type: mrr_at_5 |
|
value: 72.992 |
|
- type: ndcg_at_1 |
|
value: 53.625 |
|
- type: ndcg_at_10 |
|
value: 39.638 |
|
- type: ndcg_at_100 |
|
value: 44.601 |
|
- type: ndcg_at_1000 |
|
value: 52.80200000000001 |
|
- type: ndcg_at_3 |
|
value: 44.727 |
|
- type: ndcg_at_5 |
|
value: 42.199 |
|
- type: precision_at_1 |
|
value: 65.25 |
|
- type: precision_at_10 |
|
value: 31.025000000000002 |
|
- type: precision_at_100 |
|
value: 10.174999999999999 |
|
- type: precision_at_1000 |
|
value: 2.0740000000000003 |
|
- type: precision_at_3 |
|
value: 48.083 |
|
- type: precision_at_5 |
|
value: 40.6 |
|
- type: recall_at_1 |
|
value: 8.769 |
|
- type: recall_at_10 |
|
value: 23.910999999999998 |
|
- type: recall_at_100 |
|
value: 51.202999999999996 |
|
- type: recall_at_1000 |
|
value: 77.031 |
|
- type: recall_at_3 |
|
value: 15.387999999999998 |
|
- type: recall_at_5 |
|
value: 18.919 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 54.47 |
|
- type: f1 |
|
value: 48.21839043361556 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 63.564 |
|
- type: map_at_10 |
|
value: 74.236 |
|
- type: map_at_100 |
|
value: 74.53699999999999 |
|
- type: map_at_1000 |
|
value: 74.557 |
|
- type: map_at_3 |
|
value: 72.556 |
|
- type: map_at_5 |
|
value: 73.656 |
|
- type: mrr_at_1 |
|
value: 68.497 |
|
- type: mrr_at_10 |
|
value: 78.373 |
|
- type: mrr_at_100 |
|
value: 78.54299999999999 |
|
- type: mrr_at_1000 |
|
value: 78.549 |
|
- type: mrr_at_3 |
|
value: 77.03 |
|
- type: mrr_at_5 |
|
value: 77.938 |
|
- type: ndcg_at_1 |
|
value: 68.497 |
|
- type: ndcg_at_10 |
|
value: 79.12599999999999 |
|
- type: ndcg_at_100 |
|
value: 80.319 |
|
- type: ndcg_at_1000 |
|
value: 80.71199999999999 |
|
- type: ndcg_at_3 |
|
value: 76.209 |
|
- type: ndcg_at_5 |
|
value: 77.90700000000001 |
|
- type: precision_at_1 |
|
value: 68.497 |
|
- type: precision_at_10 |
|
value: 9.958 |
|
- type: precision_at_100 |
|
value: 1.077 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 29.908 |
|
- type: precision_at_5 |
|
value: 18.971 |
|
- type: recall_at_1 |
|
value: 63.564 |
|
- type: recall_at_10 |
|
value: 90.05199999999999 |
|
- type: recall_at_100 |
|
value: 95.028 |
|
- type: recall_at_1000 |
|
value: 97.667 |
|
- type: recall_at_3 |
|
value: 82.17999999999999 |
|
- type: recall_at_5 |
|
value: 86.388 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.042 |
|
- type: map_at_10 |
|
value: 30.764999999999997 |
|
- type: map_at_100 |
|
value: 32.678000000000004 |
|
- type: map_at_1000 |
|
value: 32.881 |
|
- type: map_at_3 |
|
value: 26.525 |
|
- type: map_at_5 |
|
value: 28.932000000000002 |
|
- type: mrr_at_1 |
|
value: 37.653999999999996 |
|
- type: mrr_at_10 |
|
value: 46.597 |
|
- type: mrr_at_100 |
|
value: 47.413 |
|
- type: mrr_at_1000 |
|
value: 47.453 |
|
- type: mrr_at_3 |
|
value: 43.775999999999996 |
|
- type: mrr_at_5 |
|
value: 45.489000000000004 |
|
- type: ndcg_at_1 |
|
value: 37.653999999999996 |
|
- type: ndcg_at_10 |
|
value: 38.615 |
|
- type: ndcg_at_100 |
|
value: 45.513999999999996 |
|
- type: ndcg_at_1000 |
|
value: 48.815999999999995 |
|
- type: ndcg_at_3 |
|
value: 34.427 |
|
- type: ndcg_at_5 |
|
value: 35.954 |
|
- type: precision_at_1 |
|
value: 37.653999999999996 |
|
- type: precision_at_10 |
|
value: 10.864 |
|
- type: precision_at_100 |
|
value: 1.7850000000000001 |
|
- type: precision_at_1000 |
|
value: 0.23800000000000002 |
|
- type: precision_at_3 |
|
value: 22.788 |
|
- type: precision_at_5 |
|
value: 17.346 |
|
- type: recall_at_1 |
|
value: 19.042 |
|
- type: recall_at_10 |
|
value: 45.707 |
|
- type: recall_at_100 |
|
value: 71.152 |
|
- type: recall_at_1000 |
|
value: 90.7 |
|
- type: recall_at_3 |
|
value: 30.814000000000004 |
|
- type: recall_at_5 |
|
value: 37.478 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.001000000000005 |
|
- type: map_at_10 |
|
value: 59.611000000000004 |
|
- type: map_at_100 |
|
value: 60.582 |
|
- type: map_at_1000 |
|
value: 60.646 |
|
- type: map_at_3 |
|
value: 56.031 |
|
- type: map_at_5 |
|
value: 58.243 |
|
- type: mrr_at_1 |
|
value: 76.003 |
|
- type: mrr_at_10 |
|
value: 82.15400000000001 |
|
- type: mrr_at_100 |
|
value: 82.377 |
|
- type: mrr_at_1000 |
|
value: 82.383 |
|
- type: mrr_at_3 |
|
value: 81.092 |
|
- type: mrr_at_5 |
|
value: 81.742 |
|
- type: ndcg_at_1 |
|
value: 76.003 |
|
- type: ndcg_at_10 |
|
value: 68.216 |
|
- type: ndcg_at_100 |
|
value: 71.601 |
|
- type: ndcg_at_1000 |
|
value: 72.821 |
|
- type: ndcg_at_3 |
|
value: 63.109 |
|
- type: ndcg_at_5 |
|
value: 65.902 |
|
- type: precision_at_1 |
|
value: 76.003 |
|
- type: precision_at_10 |
|
value: 14.379 |
|
- type: precision_at_100 |
|
value: 1.702 |
|
- type: precision_at_1000 |
|
value: 0.186 |
|
- type: precision_at_3 |
|
value: 40.396 |
|
- type: precision_at_5 |
|
value: 26.442 |
|
- type: recall_at_1 |
|
value: 38.001000000000005 |
|
- type: recall_at_10 |
|
value: 71.897 |
|
- type: recall_at_100 |
|
value: 85.105 |
|
- type: recall_at_1000 |
|
value: 93.133 |
|
- type: recall_at_3 |
|
value: 60.594 |
|
- type: recall_at_5 |
|
value: 66.104 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 91.31280000000001 |
|
- type: ap |
|
value: 87.53723467501632 |
|
- type: f1 |
|
value: 91.30282906596291 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.917 |
|
- type: map_at_10 |
|
value: 34.117999999999995 |
|
- type: map_at_100 |
|
value: 35.283 |
|
- type: map_at_1000 |
|
value: 35.333999999999996 |
|
- type: map_at_3 |
|
value: 30.330000000000002 |
|
- type: map_at_5 |
|
value: 32.461 |
|
- type: mrr_at_1 |
|
value: 22.579 |
|
- type: mrr_at_10 |
|
value: 34.794000000000004 |
|
- type: mrr_at_100 |
|
value: 35.893 |
|
- type: mrr_at_1000 |
|
value: 35.937000000000005 |
|
- type: mrr_at_3 |
|
value: 31.091 |
|
- type: mrr_at_5 |
|
value: 33.173 |
|
- type: ndcg_at_1 |
|
value: 22.579 |
|
- type: ndcg_at_10 |
|
value: 40.951 |
|
- type: ndcg_at_100 |
|
value: 46.558 |
|
- type: ndcg_at_1000 |
|
value: 47.803000000000004 |
|
- type: ndcg_at_3 |
|
value: 33.262 |
|
- type: ndcg_at_5 |
|
value: 37.036 |
|
- type: precision_at_1 |
|
value: 22.579 |
|
- type: precision_at_10 |
|
value: 6.463000000000001 |
|
- type: precision_at_100 |
|
value: 0.928 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.174000000000001 |
|
- type: precision_at_5 |
|
value: 10.421 |
|
- type: recall_at_1 |
|
value: 21.917 |
|
- type: recall_at_10 |
|
value: 61.885 |
|
- type: recall_at_100 |
|
value: 87.847 |
|
- type: recall_at_1000 |
|
value: 97.322 |
|
- type: recall_at_3 |
|
value: 41.010000000000005 |
|
- type: recall_at_5 |
|
value: 50.031000000000006 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.49521203830369 |
|
- type: f1 |
|
value: 93.30882341740241 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 71.0579115367077 |
|
- type: f1 |
|
value: 51.2368258319339 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.88029589778077 |
|
- type: f1 |
|
value: 72.34422048584663 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.2817753866846 |
|
- type: f1 |
|
value: 77.87746050004304 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.247341454119216 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.9647477166234 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.90698374676892 |
|
- type: mrr |
|
value: 33.07523683771251 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.717 |
|
- type: map_at_10 |
|
value: 14.566 |
|
- type: map_at_100 |
|
value: 18.465999999999998 |
|
- type: map_at_1000 |
|
value: 20.033 |
|
- type: map_at_3 |
|
value: 10.863 |
|
- type: map_at_5 |
|
value: 12.589 |
|
- type: mrr_at_1 |
|
value: 49.845 |
|
- type: mrr_at_10 |
|
value: 58.385 |
|
- type: mrr_at_100 |
|
value: 58.989999999999995 |
|
- type: mrr_at_1000 |
|
value: 59.028999999999996 |
|
- type: mrr_at_3 |
|
value: 56.76 |
|
- type: mrr_at_5 |
|
value: 57.766 |
|
- type: ndcg_at_1 |
|
value: 47.678 |
|
- type: ndcg_at_10 |
|
value: 37.511 |
|
- type: ndcg_at_100 |
|
value: 34.537 |
|
- type: ndcg_at_1000 |
|
value: 43.612 |
|
- type: ndcg_at_3 |
|
value: 43.713 |
|
- type: ndcg_at_5 |
|
value: 41.303 |
|
- type: precision_at_1 |
|
value: 49.845 |
|
- type: precision_at_10 |
|
value: 27.307 |
|
- type: precision_at_100 |
|
value: 8.746 |
|
- type: precision_at_1000 |
|
value: 2.182 |
|
- type: precision_at_3 |
|
value: 40.764 |
|
- type: precision_at_5 |
|
value: 35.232 |
|
- type: recall_at_1 |
|
value: 6.717 |
|
- type: recall_at_10 |
|
value: 18.107 |
|
- type: recall_at_100 |
|
value: 33.759 |
|
- type: recall_at_1000 |
|
value: 67.31 |
|
- type: recall_at_3 |
|
value: 11.68 |
|
- type: recall_at_5 |
|
value: 14.557999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.633999999999997 |
|
- type: map_at_10 |
|
value: 42.400999999999996 |
|
- type: map_at_100 |
|
value: 43.561 |
|
- type: map_at_1000 |
|
value: 43.592 |
|
- type: map_at_3 |
|
value: 37.865 |
|
- type: map_at_5 |
|
value: 40.650999999999996 |
|
- type: mrr_at_1 |
|
value: 31.286 |
|
- type: mrr_at_10 |
|
value: 44.996 |
|
- type: mrr_at_100 |
|
value: 45.889 |
|
- type: mrr_at_1000 |
|
value: 45.911 |
|
- type: mrr_at_3 |
|
value: 41.126000000000005 |
|
- type: mrr_at_5 |
|
value: 43.536 |
|
- type: ndcg_at_1 |
|
value: 31.257 |
|
- type: ndcg_at_10 |
|
value: 50.197 |
|
- type: ndcg_at_100 |
|
value: 55.062 |
|
- type: ndcg_at_1000 |
|
value: 55.81700000000001 |
|
- type: ndcg_at_3 |
|
value: 41.650999999999996 |
|
- type: ndcg_at_5 |
|
value: 46.324 |
|
- type: precision_at_1 |
|
value: 31.257 |
|
- type: precision_at_10 |
|
value: 8.508000000000001 |
|
- type: precision_at_100 |
|
value: 1.121 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 19.1 |
|
- type: precision_at_5 |
|
value: 14.16 |
|
- type: recall_at_1 |
|
value: 27.633999999999997 |
|
- type: recall_at_10 |
|
value: 71.40100000000001 |
|
- type: recall_at_100 |
|
value: 92.463 |
|
- type: recall_at_1000 |
|
value: 98.13199999999999 |
|
- type: recall_at_3 |
|
value: 49.382 |
|
- type: recall_at_5 |
|
value: 60.144 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.17099999999999 |
|
- type: map_at_10 |
|
value: 85.036 |
|
- type: map_at_100 |
|
value: 85.67099999999999 |
|
- type: map_at_1000 |
|
value: 85.68599999999999 |
|
- type: map_at_3 |
|
value: 82.086 |
|
- type: map_at_5 |
|
value: 83.956 |
|
- type: mrr_at_1 |
|
value: 82.04 |
|
- type: mrr_at_10 |
|
value: 88.018 |
|
- type: mrr_at_100 |
|
value: 88.114 |
|
- type: mrr_at_1000 |
|
value: 88.115 |
|
- type: mrr_at_3 |
|
value: 87.047 |
|
- type: mrr_at_5 |
|
value: 87.73100000000001 |
|
- type: ndcg_at_1 |
|
value: 82.03 |
|
- type: ndcg_at_10 |
|
value: 88.717 |
|
- type: ndcg_at_100 |
|
value: 89.904 |
|
- type: ndcg_at_1000 |
|
value: 89.991 |
|
- type: ndcg_at_3 |
|
value: 85.89099999999999 |
|
- type: ndcg_at_5 |
|
value: 87.485 |
|
- type: precision_at_1 |
|
value: 82.03 |
|
- type: precision_at_10 |
|
value: 13.444999999999999 |
|
- type: precision_at_100 |
|
value: 1.533 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.537 |
|
- type: precision_at_5 |
|
value: 24.692 |
|
- type: recall_at_1 |
|
value: 71.17099999999999 |
|
- type: recall_at_10 |
|
value: 95.634 |
|
- type: recall_at_100 |
|
value: 99.614 |
|
- type: recall_at_1000 |
|
value: 99.99 |
|
- type: recall_at_3 |
|
value: 87.48 |
|
- type: recall_at_5 |
|
value: 91.996 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 55.067219624685315 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 62.121822992300444 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.153 |
|
- type: map_at_10 |
|
value: 11.024000000000001 |
|
- type: map_at_100 |
|
value: 13.233 |
|
- type: map_at_1000 |
|
value: 13.62 |
|
- type: map_at_3 |
|
value: 7.779999999999999 |
|
- type: map_at_5 |
|
value: 9.529 |
|
- type: mrr_at_1 |
|
value: 20.599999999999998 |
|
- type: mrr_at_10 |
|
value: 31.361 |
|
- type: mrr_at_100 |
|
value: 32.738 |
|
- type: mrr_at_1000 |
|
value: 32.792 |
|
- type: mrr_at_3 |
|
value: 28.15 |
|
- type: mrr_at_5 |
|
value: 30.085 |
|
- type: ndcg_at_1 |
|
value: 20.599999999999998 |
|
- type: ndcg_at_10 |
|
value: 18.583 |
|
- type: ndcg_at_100 |
|
value: 27.590999999999998 |
|
- type: ndcg_at_1000 |
|
value: 34.001 |
|
- type: ndcg_at_3 |
|
value: 17.455000000000002 |
|
- type: ndcg_at_5 |
|
value: 15.588 |
|
- type: precision_at_1 |
|
value: 20.599999999999998 |
|
- type: precision_at_10 |
|
value: 9.74 |
|
- type: precision_at_100 |
|
value: 2.284 |
|
- type: precision_at_1000 |
|
value: 0.381 |
|
- type: precision_at_3 |
|
value: 16.533 |
|
- type: precision_at_5 |
|
value: 14.02 |
|
- type: recall_at_1 |
|
value: 4.153 |
|
- type: recall_at_10 |
|
value: 19.738 |
|
- type: recall_at_100 |
|
value: 46.322 |
|
- type: recall_at_1000 |
|
value: 77.378 |
|
- type: recall_at_3 |
|
value: 10.048 |
|
- type: recall_at_5 |
|
value: 14.233 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.07097501003639 |
|
- type: cos_sim_spearman |
|
value: 81.05827848407056 |
|
- type: euclidean_pearson |
|
value: 82.6279003372546 |
|
- type: euclidean_spearman |
|
value: 81.00031515279802 |
|
- type: manhattan_pearson |
|
value: 82.59338284959495 |
|
- type: manhattan_spearman |
|
value: 80.97432711064945 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.28991993621685 |
|
- type: cos_sim_spearman |
|
value: 78.71828082424351 |
|
- type: euclidean_pearson |
|
value: 83.4881331520832 |
|
- type: euclidean_spearman |
|
value: 78.51746826842316 |
|
- type: manhattan_pearson |
|
value: 83.4109223774324 |
|
- type: manhattan_spearman |
|
value: 78.431544382179 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.16651661072123 |
|
- type: cos_sim_spearman |
|
value: 84.88094386637867 |
|
- type: euclidean_pearson |
|
value: 84.3547603585416 |
|
- type: euclidean_spearman |
|
value: 84.85148665860193 |
|
- type: manhattan_pearson |
|
value: 84.29648369879266 |
|
- type: manhattan_spearman |
|
value: 84.76074870571124 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.40596254292149 |
|
- type: cos_sim_spearman |
|
value: 83.10699573133829 |
|
- type: euclidean_pearson |
|
value: 83.22794776876958 |
|
- type: euclidean_spearman |
|
value: 83.22583316084712 |
|
- type: manhattan_pearson |
|
value: 83.15899233935681 |
|
- type: manhattan_spearman |
|
value: 83.17668293648019 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.27977121352563 |
|
- type: cos_sim_spearman |
|
value: 88.73903130248591 |
|
- type: euclidean_pearson |
|
value: 88.30685958438735 |
|
- type: euclidean_spearman |
|
value: 88.79755484280406 |
|
- type: manhattan_pearson |
|
value: 88.30305607758652 |
|
- type: manhattan_spearman |
|
value: 88.80096577072784 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.08819031430218 |
|
- type: cos_sim_spearman |
|
value: 86.35414445951125 |
|
- type: euclidean_pearson |
|
value: 85.4683192388315 |
|
- type: euclidean_spearman |
|
value: 86.2079674669473 |
|
- type: manhattan_pearson |
|
value: 85.35835702257341 |
|
- type: manhattan_spearman |
|
value: 86.08483380002187 |
|
- 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: 87.36149449801478 |
|
- type: cos_sim_spearman |
|
value: 87.7102980757725 |
|
- type: euclidean_pearson |
|
value: 88.16457177837161 |
|
- type: euclidean_spearman |
|
value: 87.6598652482716 |
|
- type: manhattan_pearson |
|
value: 88.23894728971618 |
|
- type: manhattan_spearman |
|
value: 87.74470156709361 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 64.54023758394433 |
|
- type: cos_sim_spearman |
|
value: 66.28491960187773 |
|
- type: euclidean_pearson |
|
value: 67.0853128483472 |
|
- type: euclidean_spearman |
|
value: 66.10307543766307 |
|
- type: manhattan_pearson |
|
value: 66.7635365592556 |
|
- type: manhattan_spearman |
|
value: 65.76408004780167 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.15858398195317 |
|
- type: cos_sim_spearman |
|
value: 87.44850004752102 |
|
- type: euclidean_pearson |
|
value: 86.60737082550408 |
|
- type: euclidean_spearman |
|
value: 87.31591549824242 |
|
- type: manhattan_pearson |
|
value: 86.56187011429977 |
|
- type: manhattan_spearman |
|
value: 87.23854795795319 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 86.66210488769109 |
|
- type: mrr |
|
value: 96.23100664767331 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 56.094 |
|
- type: map_at_10 |
|
value: 67.486 |
|
- type: map_at_100 |
|
value: 67.925 |
|
- type: map_at_1000 |
|
value: 67.949 |
|
- type: map_at_3 |
|
value: 64.857 |
|
- type: map_at_5 |
|
value: 66.31 |
|
- type: mrr_at_1 |
|
value: 58.667 |
|
- type: mrr_at_10 |
|
value: 68.438 |
|
- type: mrr_at_100 |
|
value: 68.733 |
|
- type: mrr_at_1000 |
|
value: 68.757 |
|
- type: mrr_at_3 |
|
value: 66.389 |
|
- type: mrr_at_5 |
|
value: 67.456 |
|
- type: ndcg_at_1 |
|
value: 58.667 |
|
- type: ndcg_at_10 |
|
value: 72.506 |
|
- type: ndcg_at_100 |
|
value: 74.27 |
|
- type: ndcg_at_1000 |
|
value: 74.94800000000001 |
|
- type: ndcg_at_3 |
|
value: 67.977 |
|
- type: ndcg_at_5 |
|
value: 70.028 |
|
- type: precision_at_1 |
|
value: 58.667 |
|
- type: precision_at_10 |
|
value: 9.767000000000001 |
|
- type: precision_at_100 |
|
value: 1.073 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 27.0 |
|
- type: precision_at_5 |
|
value: 17.666999999999998 |
|
- type: recall_at_1 |
|
value: 56.094 |
|
- type: recall_at_10 |
|
value: 86.68900000000001 |
|
- type: recall_at_100 |
|
value: 94.333 |
|
- type: recall_at_1000 |
|
value: 99.667 |
|
- type: recall_at_3 |
|
value: 74.522 |
|
- type: recall_at_5 |
|
value: 79.611 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.83069306930693 |
|
- type: cos_sim_ap |
|
value: 95.69184662911199 |
|
- type: cos_sim_f1 |
|
value: 91.4027149321267 |
|
- type: cos_sim_precision |
|
value: 91.91102123356926 |
|
- type: cos_sim_recall |
|
value: 90.9 |
|
- type: dot_accuracy |
|
value: 99.69405940594059 |
|
- type: dot_ap |
|
value: 90.21674151456216 |
|
- type: dot_f1 |
|
value: 84.4489179667841 |
|
- type: dot_precision |
|
value: 85.00506585612969 |
|
- type: dot_recall |
|
value: 83.89999999999999 |
|
- type: euclidean_accuracy |
|
value: 99.83069306930693 |
|
- type: euclidean_ap |
|
value: 95.67760109671087 |
|
- type: euclidean_f1 |
|
value: 91.19754350051177 |
|
- type: euclidean_precision |
|
value: 93.39622641509435 |
|
- type: euclidean_recall |
|
value: 89.1 |
|
- type: manhattan_accuracy |
|
value: 99.83267326732673 |
|
- type: manhattan_ap |
|
value: 95.69771347732625 |
|
- type: manhattan_f1 |
|
value: 91.32420091324201 |
|
- type: manhattan_precision |
|
value: 92.68795056642637 |
|
- type: manhattan_recall |
|
value: 90.0 |
|
- type: max_accuracy |
|
value: 99.83267326732673 |
|
- type: max_ap |
|
value: 95.69771347732625 |
|
- type: max_f1 |
|
value: 91.4027149321267 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 64.47378332953092 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.79602531604151 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 53.80707639107175 |
|
- type: mrr |
|
value: 54.64886522790935 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.852448373051395 |
|
- type: cos_sim_spearman |
|
value: 32.51821499493775 |
|
- type: dot_pearson |
|
value: 30.390650062190456 |
|
- type: dot_spearman |
|
value: 30.588836159667636 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.198 |
|
- type: map_at_10 |
|
value: 1.51 |
|
- type: map_at_100 |
|
value: 8.882 |
|
- type: map_at_1000 |
|
value: 22.181 |
|
- type: map_at_3 |
|
value: 0.553 |
|
- type: map_at_5 |
|
value: 0.843 |
|
- type: mrr_at_1 |
|
value: 74.0 |
|
- type: mrr_at_10 |
|
value: 84.89999999999999 |
|
- type: mrr_at_100 |
|
value: 84.89999999999999 |
|
- type: mrr_at_1000 |
|
value: 84.89999999999999 |
|
- type: mrr_at_3 |
|
value: 84.0 |
|
- type: mrr_at_5 |
|
value: 84.89999999999999 |
|
- type: ndcg_at_1 |
|
value: 68.0 |
|
- type: ndcg_at_10 |
|
value: 64.792 |
|
- type: ndcg_at_100 |
|
value: 51.37199999999999 |
|
- type: ndcg_at_1000 |
|
value: 47.392 |
|
- type: ndcg_at_3 |
|
value: 68.46900000000001 |
|
- type: ndcg_at_5 |
|
value: 67.084 |
|
- type: precision_at_1 |
|
value: 74.0 |
|
- type: precision_at_10 |
|
value: 69.39999999999999 |
|
- type: precision_at_100 |
|
value: 53.080000000000005 |
|
- type: precision_at_1000 |
|
value: 21.258 |
|
- type: precision_at_3 |
|
value: 76.0 |
|
- type: precision_at_5 |
|
value: 73.2 |
|
- type: recall_at_1 |
|
value: 0.198 |
|
- type: recall_at_10 |
|
value: 1.7950000000000002 |
|
- type: recall_at_100 |
|
value: 12.626999999999999 |
|
- type: recall_at_1000 |
|
value: 44.84 |
|
- type: recall_at_3 |
|
value: 0.611 |
|
- type: recall_at_5 |
|
value: 0.959 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.4949999999999999 |
|
- type: map_at_10 |
|
value: 8.797 |
|
- type: map_at_100 |
|
value: 14.889 |
|
- type: map_at_1000 |
|
value: 16.309 |
|
- type: map_at_3 |
|
value: 4.389 |
|
- type: map_at_5 |
|
value: 6.776 |
|
- type: mrr_at_1 |
|
value: 18.367 |
|
- type: mrr_at_10 |
|
value: 35.844 |
|
- type: mrr_at_100 |
|
value: 37.119 |
|
- type: mrr_at_1000 |
|
value: 37.119 |
|
- type: mrr_at_3 |
|
value: 30.612000000000002 |
|
- type: mrr_at_5 |
|
value: 33.163 |
|
- type: ndcg_at_1 |
|
value: 16.326999999999998 |
|
- type: ndcg_at_10 |
|
value: 21.9 |
|
- type: ndcg_at_100 |
|
value: 34.705000000000005 |
|
- type: ndcg_at_1000 |
|
value: 45.709 |
|
- type: ndcg_at_3 |
|
value: 22.7 |
|
- type: ndcg_at_5 |
|
value: 23.197000000000003 |
|
- type: precision_at_1 |
|
value: 18.367 |
|
- type: precision_at_10 |
|
value: 21.02 |
|
- type: precision_at_100 |
|
value: 7.714 |
|
- type: precision_at_1000 |
|
value: 1.504 |
|
- type: precision_at_3 |
|
value: 26.531 |
|
- type: precision_at_5 |
|
value: 26.122 |
|
- type: recall_at_1 |
|
value: 1.4949999999999999 |
|
- type: recall_at_10 |
|
value: 15.504000000000001 |
|
- type: recall_at_100 |
|
value: 47.978 |
|
- type: recall_at_1000 |
|
value: 81.56 |
|
- type: recall_at_3 |
|
value: 5.569 |
|
- type: recall_at_5 |
|
value: 9.821 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 72.99279999999999 |
|
- type: ap |
|
value: 15.459189680101492 |
|
- type: f1 |
|
value: 56.33023271441895 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 63.070175438596486 |
|
- type: f1 |
|
value: 63.28070758709465 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 50.076231309703054 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.21463908922931 |
|
- type: cos_sim_ap |
|
value: 77.67287017966282 |
|
- type: cos_sim_f1 |
|
value: 70.34412955465588 |
|
- type: cos_sim_precision |
|
value: 67.57413709285368 |
|
- type: cos_sim_recall |
|
value: 73.35092348284961 |
|
- type: dot_accuracy |
|
value: 85.04500208618943 |
|
- type: dot_ap |
|
value: 70.4075203869744 |
|
- type: dot_f1 |
|
value: 66.18172537008678 |
|
- type: dot_precision |
|
value: 64.08798813643104 |
|
- type: dot_recall |
|
value: 68.41688654353561 |
|
- type: euclidean_accuracy |
|
value: 87.17887584192646 |
|
- type: euclidean_ap |
|
value: 77.5774128274464 |
|
- type: euclidean_f1 |
|
value: 70.09307972480777 |
|
- type: euclidean_precision |
|
value: 71.70852884349986 |
|
- type: euclidean_recall |
|
value: 68.54881266490766 |
|
- type: manhattan_accuracy |
|
value: 87.28020504261787 |
|
- type: manhattan_ap |
|
value: 77.57835820297892 |
|
- type: manhattan_f1 |
|
value: 70.23063591521131 |
|
- type: manhattan_precision |
|
value: 70.97817299919159 |
|
- type: manhattan_recall |
|
value: 69.49868073878628 |
|
- type: max_accuracy |
|
value: 87.28020504261787 |
|
- type: max_ap |
|
value: 77.67287017966282 |
|
- type: max_f1 |
|
value: 70.34412955465588 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.96650754841464 |
|
- type: cos_sim_ap |
|
value: 86.00185968965064 |
|
- type: cos_sim_f1 |
|
value: 77.95861256351718 |
|
- type: cos_sim_precision |
|
value: 74.70712773465067 |
|
- type: cos_sim_recall |
|
value: 81.50600554357868 |
|
- type: dot_accuracy |
|
value: 87.36950362867233 |
|
- type: dot_ap |
|
value: 82.22071181147555 |
|
- type: dot_f1 |
|
value: 74.85680716698488 |
|
- type: dot_precision |
|
value: 71.54688377316114 |
|
- type: dot_recall |
|
value: 78.48783492454572 |
|
- type: euclidean_accuracy |
|
value: 88.99561454573679 |
|
- type: euclidean_ap |
|
value: 86.15882097229648 |
|
- type: euclidean_f1 |
|
value: 78.18463125322332 |
|
- type: euclidean_precision |
|
value: 74.95408956067241 |
|
- type: euclidean_recall |
|
value: 81.70619032953496 |
|
- type: manhattan_accuracy |
|
value: 88.96650754841464 |
|
- type: manhattan_ap |
|
value: 86.13133111232099 |
|
- type: manhattan_f1 |
|
value: 78.10771470160115 |
|
- type: manhattan_precision |
|
value: 74.05465084184377 |
|
- type: manhattan_recall |
|
value: 82.63012011087157 |
|
- type: max_accuracy |
|
value: 88.99561454573679 |
|
- type: max_ap |
|
value: 86.15882097229648 |
|
- type: max_f1 |
|
value: 78.18463125322332 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
**新闻 | News** |
|
|
|
**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。 |
|
|
|
**[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。 |
|
|
|
**[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。 |
|
|
|
**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-09-11]** 开源stella-base-zh和stella-large-zh |
|
|
|
欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ |
|
|
|
## stella model |
|
|
|
|
|
stella是一个通用的文本编码模型，主要有以下模型： |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No | |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | |
|
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | |
|
|
|
完整的训练思路和训练过程已记录在[博客1](https://zhuanlan.zhihu.com/p/655322183)和[博客2](https://zhuanlan.zhihu.com/p/662209559)，欢迎阅读讨论。 |
|
|
|
**训练数据：** |
|
|
|
1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 |
|
2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据 |
|
|
|
**训练方法：** |
|
|
|
1. 对比学习损失函数 |
|
2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) |
|
3. EWC(Elastic Weights Consolidation)[4] |
|
4. cosent loss[5] |
|
5. 每一种类型的数据一个迭代器，分别计算loss进行更新 |
|
|
|
stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( |
|
比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。 |
|
|
|
**初始权重：**\ |
|
stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position |
|
embedding使用层次分解位置编码[7]进行初始化。\ |
|
感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。 |
|
|
|
stella is a general-purpose text encoder, which mainly includes the following models: |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No | |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | |
|
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | |
|
|
|
The training data mainly includes: |
|
|
|
1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater |
|
than 512. |
|
2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM. |
|
|
|
The loss functions mainly include: |
|
|
|
1. Contrastive learning loss function |
|
2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives) |
|
3. EWC (Elastic Weights Consolidation) |
|
4. cosent loss |
|
|
|
Model weight initialization:\ |
|
stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the |
|
512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding. |
|
|
|
Training strategy:\ |
|
One iterator for each type of data, separately calculating the loss. |
|
|
|
Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation. |
|
|
|
## Metric |
|
|
|
#### C-MTEB leaderboard (Chinese) |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:| |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.95 | 66.1 | 70.08 | 56.92 | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | |
|
| stella-base-zh | 0.2 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | |
|
|
|
#### MTEB leaderboard (English) |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | |
|
|:-----------------:|:---------------:|:---------:|:---------------:|:------------:|:-------------------:|:---------------:|:-----------------------:|:-------------:|:--------------:|:--------:|:------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | 62.61 | 75.28 | 44.9 | 86.45 | 58.77 | 50.1 | 83.02 | 32.52 | |
|
|
|
#### Reproduce our results |
|
|
|
**C-MTEB:** |
|
|
|
```python |
|
import torch |
|
import numpy as np |
|
from typing import List |
|
from mteb import MTEB |
|
from sentence_transformers import SentenceTransformer |
|
|
|
|
|
class FastTextEncoder(): |
|
def __init__(self, model_name): |
|
self.model = SentenceTransformer(model_name).cuda().half().eval() |
|
self.model.max_seq_length = 512 |
|
|
|
def encode( |
|
self, |
|
input_texts: List[str], |
|
*args, |
|
**kwargs |
|
): |
|
new_sens = list(set(input_texts)) |
|
new_sens.sort(key=lambda x: len(x), reverse=True) |
|
vecs = self.model.encode( |
|
new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256 |
|
).astype(np.float32) |
|
sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)} |
|
vecs = vecs[[sen2arrid[sen] for sen in input_texts]] |
|
torch.cuda.empty_cache() |
|
return vecs |
|
|
|
|
|
if __name__ == '__main__': |
|
model_name = "infgrad/stella-base-zh-v2" |
|
output_folder = "zh_mteb_results/stella-base-zh-v2" |
|
task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks] |
|
model = FastTextEncoder(model_name) |
|
for task in task_names: |
|
MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder) |
|
|
|
``` |
|
|
|
**MTEB:** |
|
|
|
You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) |
|
|
|
#### Evaluation for long text |
|
|
|
经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的， |
|
更致命的是那些长度大于512的文本，其重点都在前半部分 |
|
这里以CMRC2018的数据为例说明这个问题： |
|
|
|
``` |
|
question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？ |
|
|
|
passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推...... |
|
``` |
|
|
|
passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\ |
|
简言之，现有数据集的2个问题：\ |
|
1）长度大于512的过少\ |
|
2）即便大于512，对于检索而言也只需要前512的文本内容\ |
|
导致**无法准确评估模型的长文本编码能力。** |
|
|
|
为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是： |
|
|
|
- CMRC2018，通用百科 |
|
- CAIL，法律阅读理解 |
|
- DRCD，繁体百科，已转简体 |
|
- Military，军工问答 |
|
- Squad，英文阅读理解，已转中文 |
|
- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9] |
|
|
|
处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。 |
|
除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing |
|
|
|
评测指标为Recall@5, 结果如下： |
|
|
|
| Dataset | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh | |
|
|:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:| |
|
| CMRC2018 | 94.34 | 93.82 | 91.56 | 93.12 | 96.08 | 95.56 | |
|
| CAIL | 28.04 | 33.64 | 31.22 | 33.94 | 34.62 | 37.18 | |
|
| DRCD | 78.25 | 77.9 | 78.34 | 80.26 | 86.14 | 84.58 | |
|
| Military | 76.61 | 73.06 | 75.65 | 75.81 | 83.71 | 80.48 | |
|
| Squad | 91.21 | 86.61 | 87.87 | 90.38 | 93.31 | 91.21 | |
|
| Multifieldqa_zh | 81.41 | 83.92 | 83.92 | 83.42 | 79.9 | 80.4 | |
|
| **Average** | 74.98 | 74.83 | 74.76 | 76.15 | **78.96** | **78.24** | |
|
|
|
**注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。 |
|
|
|
## Usage |
|
|
|
#### stella 中文系列模型 |
|
|
|
stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致** |
|
，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。 |
|
|
|
stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。 |
|
|
|
stella中文系列模型均使用mean pooling做为文本向量。 |
|
|
|
在sentence-transformer库中的使用方法： |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
sentences = ["数据1", "数据2"] |
|
model = SentenceTransformer('infgrad/stella-base-zh-v2') |
|
print(model.max_seq_length) |
|
embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
|
|
直接使用transformers库： |
|
|
|
```python |
|
from transformers import AutoModel, AutoTokenizer |
|
from sklearn.preprocessing import normalize |
|
|
|
model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2') |
|
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2') |
|
sentences = ["数据1", "数据ABCDEFGH"] |
|
batch_data = tokenizer( |
|
batch_text_or_text_pairs=sentences, |
|
padding="longest", |
|
return_tensors="pt", |
|
max_length=1024, |
|
truncation=True, |
|
) |
|
attention_mask = batch_data["attention_mask"] |
|
model_output = model(**batch_data) |
|
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
vectors = normalize(vectors, norm="l2", axis=1, ) |
|
print(vectors.shape) # 2,768 |
|
``` |
|
|
|
#### stella models for English |
|
|
|
**Using Sentence-Transformers:** |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
sentences = ["one car come", "one car go"] |
|
model = SentenceTransformer('infgrad/stella-base-en-v2') |
|
print(model.max_seq_length) |
|
embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
|
|
**Using HuggingFace Transformers:** |
|
|
|
```python |
|
from transformers import AutoModel, AutoTokenizer |
|
from sklearn.preprocessing import normalize |
|
|
|
model = AutoModel.from_pretrained('infgrad/stella-base-en-v2') |
|
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2') |
|
sentences = ["one car come", "one car go"] |
|
batch_data = tokenizer( |
|
batch_text_or_text_pairs=sentences, |
|
padding="longest", |
|
return_tensors="pt", |
|
max_length=512, |
|
truncation=True, |
|
) |
|
attention_mask = batch_data["attention_mask"] |
|
model_output = model(**batch_data) |
|
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
vectors = normalize(vectors, norm="l2", axis=1, ) |
|
print(vectors.shape) # 2,768 |
|
``` |
|
|
|
## Training Detail |
|
|
|
**硬件：** 单卡A100-80GB |
|
|
|
**环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing |
|
|
|
**学习率：** 1e-6 |
|
|
|
**batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例 |
|
|
|
**数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。 |
|
|
|
## ToDoList |
|
|
|
**评测的稳定性：** |
|
评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。 |
|
|
|
**更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。 |
|
测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。 |
|
|
|
**OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, |
|
它们的效果均比不上BM25。 |
|
|
|
## Reference |
|
|
|
1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab |
|
2. https://github.com/wangyuxinwhy/uniem |
|
3. https://github.com/CLUEbenchmark/SimCLUE |
|
4. https://arxiv.org/abs/1612.00796 |
|
5. https://kexue.fm/archives/8847 |
|
6. https://huggingface.co/sensenova/piccolo-base-zh |
|
7. https://kexue.fm/archives/7947 |
|
8. https://github.com/FlagOpen/FlagEmbedding |
|
9. https://github.com/THUDM/LongBench |
|
|
|
|
|
|
