|
--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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model-index: |
|
- name: SGPT-1.3B-weightedmean-msmarco-specb-bitfit |
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results: |
|
- task: |
|
type: Classification |
|
dataset: |
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type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 65.20895522388061 |
|
- type: ap |
|
value: 29.59212705444778 |
|
- type: f1 |
|
value: 59.97099864321921 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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metrics: |
|
- type: accuracy |
|
value: 73.20565 |
|
- type: ap |
|
value: 67.36680643550963 |
|
- type: f1 |
|
value: 72.90420520325125 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 34.955999999999996 |
|
- type: f1 |
|
value: 34.719324437696955 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.101999999999997 |
|
- type: map_at_10 |
|
value: 40.958 |
|
- type: map_at_100 |
|
value: 42.033 |
|
- type: map_at_1000 |
|
value: 42.042 |
|
- type: map_at_3 |
|
value: 36.332 |
|
- type: map_at_5 |
|
value: 38.608 |
|
- type: mrr_at_1 |
|
value: 26.387 |
|
- type: mrr_at_10 |
|
value: 41.051 |
|
- type: mrr_at_100 |
|
value: 42.118 |
|
- type: mrr_at_1000 |
|
value: 42.126999999999995 |
|
- type: mrr_at_3 |
|
value: 36.415 |
|
- type: mrr_at_5 |
|
value: 38.72 |
|
- type: ndcg_at_1 |
|
value: 26.101999999999997 |
|
- type: ndcg_at_10 |
|
value: 49.68 |
|
- type: ndcg_at_100 |
|
value: 54.257999999999996 |
|
- type: ndcg_at_1000 |
|
value: 54.486000000000004 |
|
- type: ndcg_at_3 |
|
value: 39.864 |
|
- type: ndcg_at_5 |
|
value: 43.980000000000004 |
|
- type: precision_at_1 |
|
value: 26.101999999999997 |
|
- type: precision_at_10 |
|
value: 7.781000000000001 |
|
- type: precision_at_100 |
|
value: 0.979 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.714000000000002 |
|
- type: precision_at_5 |
|
value: 12.034 |
|
- type: recall_at_1 |
|
value: 26.101999999999997 |
|
- type: recall_at_10 |
|
value: 77.809 |
|
- type: recall_at_100 |
|
value: 97.866 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 50.141999999999996 |
|
- type: recall_at_5 |
|
value: 60.171 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/arxiv-clustering-p2p |
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name: MTEB ArxivClusteringP2P |
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metrics: |
|
- type: v_measure |
|
value: 43.384194916953774 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
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name: MTEB ArxivClusteringS2S |
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metrics: |
|
- type: v_measure |
|
value: 33.70962633433912 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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metrics: |
|
- type: map |
|
value: 58.133058996870076 |
|
- type: mrr |
|
value: 72.10922041946972 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.62153841660047 |
|
- type: cos_sim_spearman |
|
value: 83.01514456843276 |
|
- type: euclidean_pearson |
|
value: 86.00431518427241 |
|
- type: euclidean_spearman |
|
value: 83.85552516285783 |
|
- type: manhattan_pearson |
|
value: 85.83025803351181 |
|
- type: manhattan_spearman |
|
value: 83.86636878343106 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
metrics: |
|
- type: accuracy |
|
value: 82.05844155844156 |
|
- type: f1 |
|
value: 82.0185837884764 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
metrics: |
|
- type: v_measure |
|
value: 35.05918333141837 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
metrics: |
|
- type: v_measure |
|
value: 30.71055028830579 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.519 |
|
- type: map_at_10 |
|
value: 35.634 |
|
- type: map_at_100 |
|
value: 36.961 |
|
- type: map_at_1000 |
|
value: 37.088 |
|
- type: map_at_3 |
|
value: 32.254 |
|
- type: map_at_5 |
|
value: 34.22 |
|
- type: mrr_at_1 |
|
value: 32.332 |
|
- type: mrr_at_10 |
|
value: 41.168 |
|
- type: mrr_at_100 |
|
value: 41.977 |
|
- type: mrr_at_1000 |
|
value: 42.028999999999996 |
|
- type: mrr_at_3 |
|
value: 38.196999999999996 |
|
- type: mrr_at_5 |
|
value: 40.036 |
|
- type: ndcg_at_1 |
|
value: 32.332 |
|
- type: ndcg_at_10 |
|
value: 41.471000000000004 |
|
- type: ndcg_at_100 |
|
value: 46.955999999999996 |
|
- type: ndcg_at_1000 |
|
value: 49.262 |
|
- type: ndcg_at_3 |
|
value: 35.937999999999995 |
|
- type: ndcg_at_5 |
|
value: 38.702999999999996 |
|
- type: precision_at_1 |
|
value: 32.332 |
|
- type: precision_at_10 |
|
value: 7.7829999999999995 |
|
- type: precision_at_100 |
|
value: 1.29 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_3 |
|
value: 16.834 |
|
- type: precision_at_5 |
|
value: 12.418 |
|
- type: recall_at_1 |
|
value: 26.519 |
|
- type: recall_at_10 |
|
value: 53.190000000000005 |
|
- type: recall_at_100 |
|
value: 76.56500000000001 |
|
- type: recall_at_1000 |
|
value: 91.47800000000001 |
|
- type: recall_at_3 |
|
value: 38.034 |
|
- type: recall_at_5 |
|
value: 45.245999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.356 |
|
- type: map_at_10 |
|
value: 34.596 |
|
- type: map_at_100 |
|
value: 35.714 |
|
- type: map_at_1000 |
|
value: 35.839999999999996 |
|
- type: map_at_3 |
|
value: 32.073 |
|
- type: map_at_5 |
|
value: 33.475 |
|
- type: mrr_at_1 |
|
value: 31.274 |
|
- type: mrr_at_10 |
|
value: 39.592 |
|
- type: mrr_at_100 |
|
value: 40.284 |
|
- type: mrr_at_1000 |
|
value: 40.339999999999996 |
|
- type: mrr_at_3 |
|
value: 37.378 |
|
- type: mrr_at_5 |
|
value: 38.658 |
|
- type: ndcg_at_1 |
|
value: 31.274 |
|
- type: ndcg_at_10 |
|
value: 39.766 |
|
- type: ndcg_at_100 |
|
value: 44.028 |
|
- type: ndcg_at_1000 |
|
value: 46.445 |
|
- type: ndcg_at_3 |
|
value: 35.934 |
|
- type: ndcg_at_5 |
|
value: 37.751000000000005 |
|
- type: precision_at_1 |
|
value: 31.274 |
|
- type: precision_at_10 |
|
value: 7.452 |
|
- type: precision_at_100 |
|
value: 1.217 |
|
- type: precision_at_1000 |
|
value: 0.16999999999999998 |
|
- type: precision_at_3 |
|
value: 17.431 |
|
- type: precision_at_5 |
|
value: 12.306000000000001 |
|
- type: recall_at_1 |
|
value: 25.356 |
|
- type: recall_at_10 |
|
value: 49.344 |
|
- type: recall_at_100 |
|
value: 67.497 |
|
- type: recall_at_1000 |
|
value: 83.372 |
|
- type: recall_at_3 |
|
value: 38.227 |
|
- type: recall_at_5 |
|
value: 43.187999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.759 |
|
- type: map_at_10 |
|
value: 43.937 |
|
- type: map_at_100 |
|
value: 45.004 |
|
- type: map_at_1000 |
|
value: 45.07 |
|
- type: map_at_3 |
|
value: 40.805 |
|
- type: map_at_5 |
|
value: 42.497 |
|
- type: mrr_at_1 |
|
value: 37.367 |
|
- type: mrr_at_10 |
|
value: 47.237 |
|
- type: mrr_at_100 |
|
value: 47.973 |
|
- type: mrr_at_1000 |
|
value: 48.010999999999996 |
|
- type: mrr_at_3 |
|
value: 44.65 |
|
- type: mrr_at_5 |
|
value: 46.050999999999995 |
|
- type: ndcg_at_1 |
|
value: 37.367 |
|
- type: ndcg_at_10 |
|
value: 49.659 |
|
- type: ndcg_at_100 |
|
value: 54.069 |
|
- type: ndcg_at_1000 |
|
value: 55.552 |
|
- type: ndcg_at_3 |
|
value: 44.169000000000004 |
|
- type: ndcg_at_5 |
|
value: 46.726 |
|
- type: precision_at_1 |
|
value: 37.367 |
|
- type: precision_at_10 |
|
value: 8.163 |
|
- type: precision_at_100 |
|
value: 1.133 |
|
- type: precision_at_1000 |
|
value: 0.131 |
|
- type: precision_at_3 |
|
value: 19.707 |
|
- type: precision_at_5 |
|
value: 13.718 |
|
- type: recall_at_1 |
|
value: 32.759 |
|
- type: recall_at_10 |
|
value: 63.341 |
|
- type: recall_at_100 |
|
value: 82.502 |
|
- type: recall_at_1000 |
|
value: 93.259 |
|
- type: recall_at_3 |
|
value: 48.796 |
|
- type: recall_at_5 |
|
value: 54.921 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.962 |
|
- type: map_at_10 |
|
value: 25.863000000000003 |
|
- type: map_at_100 |
|
value: 26.817999999999998 |
|
- type: map_at_1000 |
|
value: 26.918 |
|
- type: map_at_3 |
|
value: 23.043 |
|
- type: map_at_5 |
|
value: 24.599 |
|
- type: mrr_at_1 |
|
value: 20.452 |
|
- type: mrr_at_10 |
|
value: 27.301 |
|
- type: mrr_at_100 |
|
value: 28.233000000000004 |
|
- type: mrr_at_1000 |
|
value: 28.310000000000002 |
|
- type: mrr_at_3 |
|
value: 24.539 |
|
- type: mrr_at_5 |
|
value: 26.108999999999998 |
|
- type: ndcg_at_1 |
|
value: 20.452 |
|
- type: ndcg_at_10 |
|
value: 30.354999999999997 |
|
- type: ndcg_at_100 |
|
value: 35.336 |
|
- type: ndcg_at_1000 |
|
value: 37.927 |
|
- type: ndcg_at_3 |
|
value: 24.705 |
|
- type: ndcg_at_5 |
|
value: 27.42 |
|
- type: precision_at_1 |
|
value: 20.452 |
|
- type: precision_at_10 |
|
value: 4.949 |
|
- type: precision_at_100 |
|
value: 0.7799999999999999 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 10.358 |
|
- type: precision_at_5 |
|
value: 7.774 |
|
- type: recall_at_1 |
|
value: 18.962 |
|
- type: recall_at_10 |
|
value: 43.056 |
|
- type: recall_at_100 |
|
value: 66.27300000000001 |
|
- type: recall_at_1000 |
|
value: 85.96000000000001 |
|
- type: recall_at_3 |
|
value: 27.776 |
|
- type: recall_at_5 |
|
value: 34.287 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 11.24 |
|
- type: map_at_10 |
|
value: 18.503 |
|
- type: map_at_100 |
|
value: 19.553 |
|
- type: map_at_1000 |
|
value: 19.689999999999998 |
|
- type: map_at_3 |
|
value: 16.150000000000002 |
|
- type: map_at_5 |
|
value: 17.254 |
|
- type: mrr_at_1 |
|
value: 13.806 |
|
- type: mrr_at_10 |
|
value: 21.939 |
|
- type: mrr_at_100 |
|
value: 22.827 |
|
- type: mrr_at_1000 |
|
value: 22.911 |
|
- type: mrr_at_3 |
|
value: 19.32 |
|
- type: mrr_at_5 |
|
value: 20.558 |
|
- type: ndcg_at_1 |
|
value: 13.806 |
|
- type: ndcg_at_10 |
|
value: 23.383000000000003 |
|
- type: ndcg_at_100 |
|
value: 28.834 |
|
- type: ndcg_at_1000 |
|
value: 32.175 |
|
- type: ndcg_at_3 |
|
value: 18.651999999999997 |
|
- type: ndcg_at_5 |
|
value: 20.505000000000003 |
|
- type: precision_at_1 |
|
value: 13.806 |
|
- type: precision_at_10 |
|
value: 4.714 |
|
- type: precision_at_100 |
|
value: 0.864 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 9.328 |
|
- type: precision_at_5 |
|
value: 6.841 |
|
- type: recall_at_1 |
|
value: 11.24 |
|
- type: recall_at_10 |
|
value: 34.854 |
|
- type: recall_at_100 |
|
value: 59.50299999999999 |
|
- type: recall_at_1000 |
|
value: 83.25 |
|
- type: recall_at_3 |
|
value: 22.02 |
|
- type: recall_at_5 |
|
value: 26.715 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.012 |
|
- type: map_at_10 |
|
value: 33.048 |
|
- type: map_at_100 |
|
value: 34.371 |
|
- type: map_at_1000 |
|
value: 34.489 |
|
- type: map_at_3 |
|
value: 29.942999999999998 |
|
- type: map_at_5 |
|
value: 31.602000000000004 |
|
- type: mrr_at_1 |
|
value: 28.104000000000003 |
|
- type: mrr_at_10 |
|
value: 37.99 |
|
- type: mrr_at_100 |
|
value: 38.836 |
|
- type: mrr_at_1000 |
|
value: 38.891 |
|
- type: mrr_at_3 |
|
value: 35.226 |
|
- type: mrr_at_5 |
|
value: 36.693999999999996 |
|
- type: ndcg_at_1 |
|
value: 28.104000000000003 |
|
- type: ndcg_at_10 |
|
value: 39.037 |
|
- type: ndcg_at_100 |
|
value: 44.643 |
|
- type: ndcg_at_1000 |
|
value: 46.939 |
|
- type: ndcg_at_3 |
|
value: 33.784 |
|
- type: ndcg_at_5 |
|
value: 36.126000000000005 |
|
- type: precision_at_1 |
|
value: 28.104000000000003 |
|
- type: precision_at_10 |
|
value: 7.2669999999999995 |
|
- type: precision_at_100 |
|
value: 1.193 |
|
- type: precision_at_1000 |
|
value: 0.159 |
|
- type: precision_at_3 |
|
value: 16.298000000000002 |
|
- type: precision_at_5 |
|
value: 11.684 |
|
- type: recall_at_1 |
|
value: 23.012 |
|
- type: recall_at_10 |
|
value: 52.054 |
|
- type: recall_at_100 |
|
value: 75.622 |
|
- type: recall_at_1000 |
|
value: 90.675 |
|
- type: recall_at_3 |
|
value: 37.282 |
|
- type: recall_at_5 |
|
value: 43.307 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.624 |
|
- type: map_at_10 |
|
value: 30.209999999999997 |
|
- type: map_at_100 |
|
value: 31.52 |
|
- type: map_at_1000 |
|
value: 31.625999999999998 |
|
- type: map_at_3 |
|
value: 26.951000000000004 |
|
- type: map_at_5 |
|
value: 28.938999999999997 |
|
- type: mrr_at_1 |
|
value: 26.941 |
|
- type: mrr_at_10 |
|
value: 35.13 |
|
- type: mrr_at_100 |
|
value: 36.15 |
|
- type: mrr_at_1000 |
|
value: 36.204 |
|
- type: mrr_at_3 |
|
value: 32.42 |
|
- type: mrr_at_5 |
|
value: 34.155 |
|
- type: ndcg_at_1 |
|
value: 26.941 |
|
- type: ndcg_at_10 |
|
value: 35.726 |
|
- type: ndcg_at_100 |
|
value: 41.725 |
|
- type: ndcg_at_1000 |
|
value: 44.105 |
|
- type: ndcg_at_3 |
|
value: 30.184 |
|
- type: ndcg_at_5 |
|
value: 33.176 |
|
- type: precision_at_1 |
|
value: 26.941 |
|
- type: precision_at_10 |
|
value: 6.654999999999999 |
|
- type: precision_at_100 |
|
value: 1.1520000000000001 |
|
- type: precision_at_1000 |
|
value: 0.152 |
|
- type: precision_at_3 |
|
value: 14.346 |
|
- type: precision_at_5 |
|
value: 10.868 |
|
- type: recall_at_1 |
|
value: 21.624 |
|
- type: recall_at_10 |
|
value: 47.359 |
|
- type: recall_at_100 |
|
value: 73.436 |
|
- type: recall_at_1000 |
|
value: 89.988 |
|
- type: recall_at_3 |
|
value: 32.34 |
|
- type: recall_at_5 |
|
value: 39.856 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.67566666666667 |
|
- type: map_at_10 |
|
value: 28.479333333333333 |
|
- type: map_at_100 |
|
value: 29.612249999999996 |
|
- type: map_at_1000 |
|
value: 29.731166666666663 |
|
- type: map_at_3 |
|
value: 25.884 |
|
- type: map_at_5 |
|
value: 27.298916666666667 |
|
- type: mrr_at_1 |
|
value: 24.402583333333332 |
|
- type: mrr_at_10 |
|
value: 32.07041666666667 |
|
- type: mrr_at_100 |
|
value: 32.95841666666667 |
|
- type: mrr_at_1000 |
|
value: 33.025416666666665 |
|
- type: mrr_at_3 |
|
value: 29.677749999999996 |
|
- type: mrr_at_5 |
|
value: 31.02391666666667 |
|
- type: ndcg_at_1 |
|
value: 24.402583333333332 |
|
- type: ndcg_at_10 |
|
value: 33.326166666666666 |
|
- type: ndcg_at_100 |
|
value: 38.51566666666667 |
|
- type: ndcg_at_1000 |
|
value: 41.13791666666667 |
|
- type: ndcg_at_3 |
|
value: 28.687749999999994 |
|
- type: ndcg_at_5 |
|
value: 30.84766666666667 |
|
- type: precision_at_1 |
|
value: 24.402583333333332 |
|
- type: precision_at_10 |
|
value: 5.943749999999999 |
|
- type: precision_at_100 |
|
value: 1.0098333333333334 |
|
- type: precision_at_1000 |
|
value: 0.14183333333333334 |
|
- type: precision_at_3 |
|
value: 13.211500000000001 |
|
- type: precision_at_5 |
|
value: 9.548416666666668 |
|
- type: recall_at_1 |
|
value: 20.67566666666667 |
|
- type: recall_at_10 |
|
value: 44.245583333333336 |
|
- type: recall_at_100 |
|
value: 67.31116666666667 |
|
- type: recall_at_1000 |
|
value: 85.87841666666665 |
|
- type: recall_at_3 |
|
value: 31.49258333333333 |
|
- type: recall_at_5 |
|
value: 36.93241666666667 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.34 |
|
- type: map_at_10 |
|
value: 23.988 |
|
- type: map_at_100 |
|
value: 24.895 |
|
- type: map_at_1000 |
|
value: 24.992 |
|
- type: map_at_3 |
|
value: 21.831 |
|
- type: map_at_5 |
|
value: 23.0 |
|
- type: mrr_at_1 |
|
value: 20.399 |
|
- type: mrr_at_10 |
|
value: 26.186 |
|
- type: mrr_at_100 |
|
value: 27.017999999999997 |
|
- type: mrr_at_1000 |
|
value: 27.090999999999998 |
|
- type: mrr_at_3 |
|
value: 24.08 |
|
- type: mrr_at_5 |
|
value: 25.230000000000004 |
|
- type: ndcg_at_1 |
|
value: 20.399 |
|
- type: ndcg_at_10 |
|
value: 27.799000000000003 |
|
- type: ndcg_at_100 |
|
value: 32.579 |
|
- type: ndcg_at_1000 |
|
value: 35.209 |
|
- type: ndcg_at_3 |
|
value: 23.684 |
|
- type: ndcg_at_5 |
|
value: 25.521 |
|
- type: precision_at_1 |
|
value: 20.399 |
|
- type: precision_at_10 |
|
value: 4.585999999999999 |
|
- type: precision_at_100 |
|
value: 0.755 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 10.276 |
|
- type: precision_at_5 |
|
value: 7.362 |
|
- type: recall_at_1 |
|
value: 18.34 |
|
- type: recall_at_10 |
|
value: 37.456 |
|
- type: recall_at_100 |
|
value: 59.86 |
|
- type: recall_at_1000 |
|
value: 79.703 |
|
- type: recall_at_3 |
|
value: 26.163999999999998 |
|
- type: recall_at_5 |
|
value: 30.652 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.327 |
|
- type: map_at_10 |
|
value: 17.572 |
|
- type: map_at_100 |
|
value: 18.534 |
|
- type: map_at_1000 |
|
value: 18.653 |
|
- type: map_at_3 |
|
value: 15.703 |
|
- type: map_at_5 |
|
value: 16.752 |
|
- type: mrr_at_1 |
|
value: 15.038000000000002 |
|
- type: mrr_at_10 |
|
value: 20.726 |
|
- type: mrr_at_100 |
|
value: 21.61 |
|
- type: mrr_at_1000 |
|
value: 21.695 |
|
- type: mrr_at_3 |
|
value: 18.829 |
|
- type: mrr_at_5 |
|
value: 19.885 |
|
- type: ndcg_at_1 |
|
value: 15.038000000000002 |
|
- type: ndcg_at_10 |
|
value: 21.241 |
|
- type: ndcg_at_100 |
|
value: 26.179000000000002 |
|
- type: ndcg_at_1000 |
|
value: 29.316 |
|
- type: ndcg_at_3 |
|
value: 17.762 |
|
- type: ndcg_at_5 |
|
value: 19.413 |
|
- type: precision_at_1 |
|
value: 15.038000000000002 |
|
- type: precision_at_10 |
|
value: 3.8920000000000003 |
|
- type: precision_at_100 |
|
value: 0.75 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 8.351 |
|
- type: precision_at_5 |
|
value: 6.187 |
|
- type: recall_at_1 |
|
value: 12.327 |
|
- type: recall_at_10 |
|
value: 29.342000000000002 |
|
- type: recall_at_100 |
|
value: 51.854 |
|
- type: recall_at_1000 |
|
value: 74.648 |
|
- type: recall_at_3 |
|
value: 19.596 |
|
- type: recall_at_5 |
|
value: 23.899 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.594 |
|
- type: map_at_10 |
|
value: 27.878999999999998 |
|
- type: map_at_100 |
|
value: 28.926000000000002 |
|
- type: map_at_1000 |
|
value: 29.041 |
|
- type: map_at_3 |
|
value: 25.668999999999997 |
|
- type: map_at_5 |
|
value: 26.773999999999997 |
|
- type: mrr_at_1 |
|
value: 23.694000000000003 |
|
- type: mrr_at_10 |
|
value: 31.335 |
|
- type: mrr_at_100 |
|
value: 32.218 |
|
- type: mrr_at_1000 |
|
value: 32.298 |
|
- type: mrr_at_3 |
|
value: 29.26 |
|
- type: mrr_at_5 |
|
value: 30.328 |
|
- type: ndcg_at_1 |
|
value: 23.694000000000003 |
|
- type: ndcg_at_10 |
|
value: 32.456 |
|
- type: ndcg_at_100 |
|
value: 37.667 |
|
- type: ndcg_at_1000 |
|
value: 40.571 |
|
- type: ndcg_at_3 |
|
value: 28.283 |
|
- type: ndcg_at_5 |
|
value: 29.986 |
|
- type: precision_at_1 |
|
value: 23.694000000000003 |
|
- type: precision_at_10 |
|
value: 5.448 |
|
- type: precision_at_100 |
|
value: 0.9119999999999999 |
|
- type: precision_at_1000 |
|
value: 0.127 |
|
- type: precision_at_3 |
|
value: 12.717999999999998 |
|
- type: precision_at_5 |
|
value: 8.843 |
|
- type: recall_at_1 |
|
value: 20.594 |
|
- type: recall_at_10 |
|
value: 43.004999999999995 |
|
- type: recall_at_100 |
|
value: 66.228 |
|
- type: recall_at_1000 |
|
value: 87.17099999999999 |
|
- type: recall_at_3 |
|
value: 31.554 |
|
- type: recall_at_5 |
|
value: 35.838 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.855999999999998 |
|
- type: map_at_10 |
|
value: 28.372000000000003 |
|
- type: map_at_100 |
|
value: 29.87 |
|
- type: map_at_1000 |
|
value: 30.075000000000003 |
|
- type: map_at_3 |
|
value: 26.054 |
|
- type: map_at_5 |
|
value: 27.128999999999998 |
|
- type: mrr_at_1 |
|
value: 25.494 |
|
- type: mrr_at_10 |
|
value: 32.735 |
|
- type: mrr_at_100 |
|
value: 33.794000000000004 |
|
- type: mrr_at_1000 |
|
value: 33.85 |
|
- type: mrr_at_3 |
|
value: 30.731 |
|
- type: mrr_at_5 |
|
value: 31.897 |
|
- type: ndcg_at_1 |
|
value: 25.494 |
|
- type: ndcg_at_10 |
|
value: 33.385 |
|
- type: ndcg_at_100 |
|
value: 39.436 |
|
- type: ndcg_at_1000 |
|
value: 42.313 |
|
- type: ndcg_at_3 |
|
value: 29.612 |
|
- type: ndcg_at_5 |
|
value: 31.186999999999998 |
|
- type: precision_at_1 |
|
value: 25.494 |
|
- type: precision_at_10 |
|
value: 6.422999999999999 |
|
- type: precision_at_100 |
|
value: 1.383 |
|
- type: precision_at_1000 |
|
value: 0.22399999999999998 |
|
- type: precision_at_3 |
|
value: 13.834 |
|
- type: precision_at_5 |
|
value: 10.0 |
|
- type: recall_at_1 |
|
value: 20.855999999999998 |
|
- type: recall_at_10 |
|
value: 42.678 |
|
- type: recall_at_100 |
|
value: 70.224 |
|
- type: recall_at_1000 |
|
value: 89.369 |
|
- type: recall_at_3 |
|
value: 31.957 |
|
- type: recall_at_5 |
|
value: 36.026 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.519000000000002 |
|
- type: map_at_10 |
|
value: 22.15 |
|
- type: map_at_100 |
|
value: 23.180999999999997 |
|
- type: map_at_1000 |
|
value: 23.291999999999998 |
|
- type: map_at_3 |
|
value: 20.132 |
|
- type: map_at_5 |
|
value: 21.346 |
|
- type: mrr_at_1 |
|
value: 17.93 |
|
- type: mrr_at_10 |
|
value: 23.506 |
|
- type: mrr_at_100 |
|
value: 24.581 |
|
- type: mrr_at_1000 |
|
value: 24.675 |
|
- type: mrr_at_3 |
|
value: 21.503 |
|
- type: mrr_at_5 |
|
value: 22.686 |
|
- type: ndcg_at_1 |
|
value: 17.93 |
|
- type: ndcg_at_10 |
|
value: 25.636 |
|
- type: ndcg_at_100 |
|
value: 30.736 |
|
- type: ndcg_at_1000 |
|
value: 33.841 |
|
- type: ndcg_at_3 |
|
value: 21.546000000000003 |
|
- type: ndcg_at_5 |
|
value: 23.658 |
|
- type: precision_at_1 |
|
value: 17.93 |
|
- type: precision_at_10 |
|
value: 3.993 |
|
- type: precision_at_100 |
|
value: 0.6890000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 9.057 |
|
- type: precision_at_5 |
|
value: 6.58 |
|
- type: recall_at_1 |
|
value: 16.519000000000002 |
|
- type: recall_at_10 |
|
value: 35.268 |
|
- type: recall_at_100 |
|
value: 58.17 |
|
- type: recall_at_1000 |
|
value: 81.66799999999999 |
|
- type: recall_at_3 |
|
value: 24.165 |
|
- type: recall_at_5 |
|
value: 29.254 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.363 |
|
- type: map_at_10 |
|
value: 18.301000000000002 |
|
- type: map_at_100 |
|
value: 20.019000000000002 |
|
- type: map_at_1000 |
|
value: 20.207 |
|
- type: map_at_3 |
|
value: 14.877 |
|
- type: map_at_5 |
|
value: 16.544 |
|
- type: mrr_at_1 |
|
value: 22.866 |
|
- type: mrr_at_10 |
|
value: 34.935 |
|
- type: mrr_at_100 |
|
value: 35.802 |
|
- type: mrr_at_1000 |
|
value: 35.839999999999996 |
|
- type: mrr_at_3 |
|
value: 30.965999999999998 |
|
- type: mrr_at_5 |
|
value: 33.204 |
|
- type: ndcg_at_1 |
|
value: 22.866 |
|
- type: ndcg_at_10 |
|
value: 26.595000000000002 |
|
- type: ndcg_at_100 |
|
value: 33.513999999999996 |
|
- type: ndcg_at_1000 |
|
value: 36.872 |
|
- type: ndcg_at_3 |
|
value: 20.666999999999998 |
|
- type: ndcg_at_5 |
|
value: 22.728 |
|
- type: precision_at_1 |
|
value: 22.866 |
|
- type: precision_at_10 |
|
value: 8.632 |
|
- type: precision_at_100 |
|
value: 1.6119999999999999 |
|
- type: precision_at_1000 |
|
value: 0.22399999999999998 |
|
- type: precision_at_3 |
|
value: 15.504999999999999 |
|
- type: precision_at_5 |
|
value: 12.404 |
|
- type: recall_at_1 |
|
value: 10.363 |
|
- type: recall_at_10 |
|
value: 33.494 |
|
- type: recall_at_100 |
|
value: 57.593 |
|
- type: recall_at_1000 |
|
value: 76.342 |
|
- type: recall_at_3 |
|
value: 19.157 |
|
- type: recall_at_5 |
|
value: 24.637999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.436 |
|
- type: map_at_10 |
|
value: 14.760000000000002 |
|
- type: map_at_100 |
|
value: 19.206 |
|
- type: map_at_1000 |
|
value: 20.267 |
|
- type: map_at_3 |
|
value: 10.894 |
|
- type: map_at_5 |
|
value: 12.828999999999999 |
|
- type: mrr_at_1 |
|
value: 54.25 |
|
- type: mrr_at_10 |
|
value: 63.769 |
|
- type: mrr_at_100 |
|
value: 64.193 |
|
- type: mrr_at_1000 |
|
value: 64.211 |
|
- type: mrr_at_3 |
|
value: 61.458 |
|
- type: mrr_at_5 |
|
value: 63.096 |
|
- type: ndcg_at_1 |
|
value: 42.875 |
|
- type: ndcg_at_10 |
|
value: 31.507 |
|
- type: ndcg_at_100 |
|
value: 34.559 |
|
- type: ndcg_at_1000 |
|
value: 41.246 |
|
- type: ndcg_at_3 |
|
value: 35.058 |
|
- type: ndcg_at_5 |
|
value: 33.396 |
|
- type: precision_at_1 |
|
value: 54.25 |
|
- type: precision_at_10 |
|
value: 24.45 |
|
- type: precision_at_100 |
|
value: 7.383000000000001 |
|
- type: precision_at_1000 |
|
value: 1.582 |
|
- type: precision_at_3 |
|
value: 38.083 |
|
- type: precision_at_5 |
|
value: 32.6 |
|
- type: recall_at_1 |
|
value: 7.436 |
|
- type: recall_at_10 |
|
value: 19.862 |
|
- type: recall_at_100 |
|
value: 38.981 |
|
- type: recall_at_1000 |
|
value: 61.038000000000004 |
|
- type: recall_at_3 |
|
value: 11.949 |
|
- type: recall_at_5 |
|
value: 15.562000000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
metrics: |
|
- type: accuracy |
|
value: 46.39 |
|
- type: f1 |
|
value: 42.26424885856703 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
metrics: |
|
- type: map_at_1 |
|
value: 50.916 |
|
- type: map_at_10 |
|
value: 62.258 |
|
- type: map_at_100 |
|
value: 62.741 |
|
- type: map_at_1000 |
|
value: 62.763000000000005 |
|
- type: map_at_3 |
|
value: 60.01800000000001 |
|
- type: map_at_5 |
|
value: 61.419999999999995 |
|
- type: mrr_at_1 |
|
value: 54.964999999999996 |
|
- type: mrr_at_10 |
|
value: 66.554 |
|
- type: mrr_at_100 |
|
value: 66.96600000000001 |
|
- type: mrr_at_1000 |
|
value: 66.97800000000001 |
|
- type: mrr_at_3 |
|
value: 64.414 |
|
- type: mrr_at_5 |
|
value: 65.77 |
|
- type: ndcg_at_1 |
|
value: 54.964999999999996 |
|
- type: ndcg_at_10 |
|
value: 68.12 |
|
- type: ndcg_at_100 |
|
value: 70.282 |
|
- type: ndcg_at_1000 |
|
value: 70.788 |
|
- type: ndcg_at_3 |
|
value: 63.861999999999995 |
|
- type: ndcg_at_5 |
|
value: 66.216 |
|
- type: precision_at_1 |
|
value: 54.964999999999996 |
|
- type: precision_at_10 |
|
value: 8.998000000000001 |
|
- type: precision_at_100 |
|
value: 1.016 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_3 |
|
value: 25.618000000000002 |
|
- type: precision_at_5 |
|
value: 16.676 |
|
- type: recall_at_1 |
|
value: 50.916 |
|
- type: recall_at_10 |
|
value: 82.04 |
|
- type: recall_at_100 |
|
value: 91.689 |
|
- type: recall_at_1000 |
|
value: 95.34899999999999 |
|
- type: recall_at_3 |
|
value: 70.512 |
|
- type: recall_at_5 |
|
value: 76.29899999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.568 |
|
- type: map_at_10 |
|
value: 23.264000000000003 |
|
- type: map_at_100 |
|
value: 24.823999999999998 |
|
- type: map_at_1000 |
|
value: 25.013999999999996 |
|
- type: map_at_3 |
|
value: 19.724 |
|
- type: map_at_5 |
|
value: 21.772 |
|
- type: mrr_at_1 |
|
value: 27.315 |
|
- type: mrr_at_10 |
|
value: 35.935 |
|
- type: mrr_at_100 |
|
value: 36.929 |
|
- type: mrr_at_1000 |
|
value: 36.985 |
|
- type: mrr_at_3 |
|
value: 33.591 |
|
- type: mrr_at_5 |
|
value: 34.848 |
|
- type: ndcg_at_1 |
|
value: 27.315 |
|
- type: ndcg_at_10 |
|
value: 29.988 |
|
- type: ndcg_at_100 |
|
value: 36.41 |
|
- type: ndcg_at_1000 |
|
value: 40.184999999999995 |
|
- type: ndcg_at_3 |
|
value: 26.342 |
|
- type: ndcg_at_5 |
|
value: 27.68 |
|
- type: precision_at_1 |
|
value: 27.315 |
|
- type: precision_at_10 |
|
value: 8.565000000000001 |
|
- type: precision_at_100 |
|
value: 1.508 |
|
- type: precision_at_1000 |
|
value: 0.219 |
|
- type: precision_at_3 |
|
value: 17.849999999999998 |
|
- type: precision_at_5 |
|
value: 13.672999999999998 |
|
- type: recall_at_1 |
|
value: 13.568 |
|
- type: recall_at_10 |
|
value: 37.133 |
|
- type: recall_at_100 |
|
value: 61.475 |
|
- type: recall_at_1000 |
|
value: 84.372 |
|
- type: recall_at_3 |
|
value: 24.112000000000002 |
|
- type: recall_at_5 |
|
value: 29.507 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.878 |
|
- type: map_at_10 |
|
value: 40.868 |
|
- type: map_at_100 |
|
value: 41.693999999999996 |
|
- type: map_at_1000 |
|
value: 41.775 |
|
- type: map_at_3 |
|
value: 38.56 |
|
- type: map_at_5 |
|
value: 39.947 |
|
- type: mrr_at_1 |
|
value: 61.756 |
|
- type: mrr_at_10 |
|
value: 68.265 |
|
- type: mrr_at_100 |
|
value: 68.671 |
|
- type: mrr_at_1000 |
|
value: 68.694 |
|
- type: mrr_at_3 |
|
value: 66.78399999999999 |
|
- type: mrr_at_5 |
|
value: 67.704 |
|
- type: ndcg_at_1 |
|
value: 61.756 |
|
- type: ndcg_at_10 |
|
value: 49.931 |
|
- type: ndcg_at_100 |
|
value: 53.179 |
|
- type: ndcg_at_1000 |
|
value: 54.94799999999999 |
|
- type: ndcg_at_3 |
|
value: 46.103 |
|
- type: ndcg_at_5 |
|
value: 48.147 |
|
- type: precision_at_1 |
|
value: 61.756 |
|
- type: precision_at_10 |
|
value: 10.163 |
|
- type: precision_at_100 |
|
value: 1.2710000000000001 |
|
- type: precision_at_1000 |
|
value: 0.151 |
|
- type: precision_at_3 |
|
value: 28.179 |
|
- type: precision_at_5 |
|
value: 18.528 |
|
- type: recall_at_1 |
|
value: 30.878 |
|
- type: recall_at_10 |
|
value: 50.817 |
|
- type: recall_at_100 |
|
value: 63.544999999999995 |
|
- type: recall_at_1000 |
|
value: 75.361 |
|
- type: recall_at_3 |
|
value: 42.269 |
|
- type: recall_at_5 |
|
value: 46.32 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
metrics: |
|
- type: accuracy |
|
value: 64.04799999999999 |
|
- type: ap |
|
value: 59.185251455339284 |
|
- type: f1 |
|
value: 63.947123181349255 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.9 |
|
- type: map_at_10 |
|
value: 29.748 |
|
- type: map_at_100 |
|
value: 30.976 |
|
- type: map_at_1000 |
|
value: 31.041 |
|
- type: map_at_3 |
|
value: 26.112999999999996 |
|
- type: map_at_5 |
|
value: 28.197 |
|
- type: mrr_at_1 |
|
value: 19.413 |
|
- type: mrr_at_10 |
|
value: 30.322 |
|
- type: mrr_at_100 |
|
value: 31.497000000000003 |
|
- type: mrr_at_1000 |
|
value: 31.555 |
|
- type: mrr_at_3 |
|
value: 26.729000000000003 |
|
- type: mrr_at_5 |
|
value: 28.788999999999998 |
|
- type: ndcg_at_1 |
|
value: 19.413 |
|
- type: ndcg_at_10 |
|
value: 36.048 |
|
- type: ndcg_at_100 |
|
value: 42.152 |
|
- type: ndcg_at_1000 |
|
value: 43.772 |
|
- type: ndcg_at_3 |
|
value: 28.642 |
|
- type: ndcg_at_5 |
|
value: 32.358 |
|
- type: precision_at_1 |
|
value: 19.413 |
|
- type: precision_at_10 |
|
value: 5.785 |
|
- type: precision_at_100 |
|
value: 0.8869999999999999 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 12.192 |
|
- type: precision_at_5 |
|
value: 9.189 |
|
- type: recall_at_1 |
|
value: 18.9 |
|
- type: recall_at_10 |
|
value: 55.457 |
|
- type: recall_at_100 |
|
value: 84.09100000000001 |
|
- type: recall_at_1000 |
|
value: 96.482 |
|
- type: recall_at_3 |
|
value: 35.359 |
|
- type: recall_at_5 |
|
value: 44.275 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 92.07706338349293 |
|
- type: f1 |
|
value: 91.56680443236652 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 71.18559051527589 |
|
- type: f1 |
|
value: 52.42887061726789 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 68.64828513786148 |
|
- type: f1 |
|
value: 66.54281381596097 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
metrics: |
|
- type: accuracy |
|
value: 76.04236718224612 |
|
- type: f1 |
|
value: 75.89170458655639 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
metrics: |
|
- type: v_measure |
|
value: 32.0840369055247 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
metrics: |
|
- type: v_measure |
|
value: 29.448729560244537 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
metrics: |
|
- type: map |
|
value: 31.340856463122375 |
|
- type: mrr |
|
value: 32.398547669840916 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.526 |
|
- type: map_at_10 |
|
value: 11.745 |
|
- type: map_at_100 |
|
value: 14.831 |
|
- type: map_at_1000 |
|
value: 16.235 |
|
- type: map_at_3 |
|
value: 8.716 |
|
- type: map_at_5 |
|
value: 10.101 |
|
- type: mrr_at_1 |
|
value: 43.653 |
|
- type: mrr_at_10 |
|
value: 51.06699999999999 |
|
- type: mrr_at_100 |
|
value: 51.881 |
|
- type: mrr_at_1000 |
|
value: 51.912000000000006 |
|
- type: mrr_at_3 |
|
value: 49.02 |
|
- type: mrr_at_5 |
|
value: 50.288999999999994 |
|
- type: ndcg_at_1 |
|
value: 41.949999999999996 |
|
- type: ndcg_at_10 |
|
value: 32.083 |
|
- type: ndcg_at_100 |
|
value: 30.049999999999997 |
|
- type: ndcg_at_1000 |
|
value: 38.661 |
|
- type: ndcg_at_3 |
|
value: 37.940000000000005 |
|
- type: ndcg_at_5 |
|
value: 35.455999999999996 |
|
- type: precision_at_1 |
|
value: 43.344 |
|
- type: precision_at_10 |
|
value: 23.437 |
|
- type: precision_at_100 |
|
value: 7.829999999999999 |
|
- type: precision_at_1000 |
|
value: 2.053 |
|
- type: precision_at_3 |
|
value: 35.501 |
|
- type: precision_at_5 |
|
value: 30.464000000000002 |
|
- type: recall_at_1 |
|
value: 5.526 |
|
- type: recall_at_10 |
|
value: 15.445999999999998 |
|
- type: recall_at_100 |
|
value: 31.179000000000002 |
|
- type: recall_at_1000 |
|
value: 61.578 |
|
- type: recall_at_3 |
|
value: 9.71 |
|
- type: recall_at_5 |
|
value: 12.026 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.467 |
|
- type: map_at_10 |
|
value: 36.041000000000004 |
|
- type: map_at_100 |
|
value: 37.268 |
|
- type: map_at_1000 |
|
value: 37.322 |
|
- type: map_at_3 |
|
value: 32.09 |
|
- type: map_at_5 |
|
value: 34.414 |
|
- type: mrr_at_1 |
|
value: 26.738 |
|
- type: mrr_at_10 |
|
value: 38.665 |
|
- type: mrr_at_100 |
|
value: 39.64 |
|
- type: mrr_at_1000 |
|
value: 39.681 |
|
- type: mrr_at_3 |
|
value: 35.207 |
|
- type: mrr_at_5 |
|
value: 37.31 |
|
- type: ndcg_at_1 |
|
value: 26.709 |
|
- type: ndcg_at_10 |
|
value: 42.942 |
|
- type: ndcg_at_100 |
|
value: 48.296 |
|
- type: ndcg_at_1000 |
|
value: 49.651 |
|
- type: ndcg_at_3 |
|
value: 35.413 |
|
- type: ndcg_at_5 |
|
value: 39.367999999999995 |
|
- type: precision_at_1 |
|
value: 26.709 |
|
- type: precision_at_10 |
|
value: 7.306 |
|
- type: precision_at_100 |
|
value: 1.0290000000000001 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 16.348 |
|
- type: precision_at_5 |
|
value: 12.068 |
|
- type: recall_at_1 |
|
value: 23.467 |
|
- type: recall_at_10 |
|
value: 61.492999999999995 |
|
- type: recall_at_100 |
|
value: 85.01100000000001 |
|
- type: recall_at_1000 |
|
value: 95.261 |
|
- type: recall_at_3 |
|
value: 41.952 |
|
- type: recall_at_5 |
|
value: 51.105999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 67.51700000000001 |
|
- type: map_at_10 |
|
value: 81.054 |
|
- type: map_at_100 |
|
value: 81.727 |
|
- type: map_at_1000 |
|
value: 81.75200000000001 |
|
- type: map_at_3 |
|
value: 78.018 |
|
- type: map_at_5 |
|
value: 79.879 |
|
- type: mrr_at_1 |
|
value: 77.52 |
|
- type: mrr_at_10 |
|
value: 84.429 |
|
- type: mrr_at_100 |
|
value: 84.58200000000001 |
|
- type: mrr_at_1000 |
|
value: 84.584 |
|
- type: mrr_at_3 |
|
value: 83.268 |
|
- type: mrr_at_5 |
|
value: 84.013 |
|
- type: ndcg_at_1 |
|
value: 77.53 |
|
- type: ndcg_at_10 |
|
value: 85.277 |
|
- type: ndcg_at_100 |
|
value: 86.80499999999999 |
|
- type: ndcg_at_1000 |
|
value: 87.01 |
|
- type: ndcg_at_3 |
|
value: 81.975 |
|
- type: ndcg_at_5 |
|
value: 83.723 |
|
- type: precision_at_1 |
|
value: 77.53 |
|
- type: precision_at_10 |
|
value: 12.961 |
|
- type: precision_at_100 |
|
value: 1.502 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 35.713 |
|
- type: precision_at_5 |
|
value: 23.574 |
|
- type: recall_at_1 |
|
value: 67.51700000000001 |
|
- type: recall_at_10 |
|
value: 93.486 |
|
- type: recall_at_100 |
|
value: 98.9 |
|
- type: recall_at_1000 |
|
value: 99.92999999999999 |
|
- type: recall_at_3 |
|
value: 84.17999999999999 |
|
- type: recall_at_5 |
|
value: 88.97500000000001 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
metrics: |
|
- type: v_measure |
|
value: 48.225994608749915 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
metrics: |
|
- type: v_measure |
|
value: 53.17635557157765 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.988 |
|
- type: map_at_10 |
|
value: 9.4 |
|
- type: map_at_100 |
|
value: 10.968 |
|
- type: map_at_1000 |
|
value: 11.257 |
|
- type: map_at_3 |
|
value: 7.123 |
|
- type: map_at_5 |
|
value: 8.221 |
|
- type: mrr_at_1 |
|
value: 19.7 |
|
- type: mrr_at_10 |
|
value: 29.098000000000003 |
|
- type: mrr_at_100 |
|
value: 30.247 |
|
- type: mrr_at_1000 |
|
value: 30.318 |
|
- type: mrr_at_3 |
|
value: 26.55 |
|
- type: mrr_at_5 |
|
value: 27.915 |
|
- type: ndcg_at_1 |
|
value: 19.7 |
|
- type: ndcg_at_10 |
|
value: 16.176 |
|
- type: ndcg_at_100 |
|
value: 22.931 |
|
- type: ndcg_at_1000 |
|
value: 28.301 |
|
- type: ndcg_at_3 |
|
value: 16.142 |
|
- type: ndcg_at_5 |
|
value: 13.633999999999999 |
|
- type: precision_at_1 |
|
value: 19.7 |
|
- type: precision_at_10 |
|
value: 8.18 |
|
- type: precision_at_100 |
|
value: 1.8010000000000002 |
|
- type: precision_at_1000 |
|
value: 0.309 |
|
- type: precision_at_3 |
|
value: 15.1 |
|
- type: precision_at_5 |
|
value: 11.74 |
|
- type: recall_at_1 |
|
value: 3.988 |
|
- type: recall_at_10 |
|
value: 16.625 |
|
- type: recall_at_100 |
|
value: 36.61 |
|
- type: recall_at_1000 |
|
value: 62.805 |
|
- type: recall_at_3 |
|
value: 9.168 |
|
- type: recall_at_5 |
|
value: 11.902 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.29330379162072 |
|
- type: cos_sim_spearman |
|
value: 67.22953551111448 |
|
- type: euclidean_pearson |
|
value: 71.44682700059415 |
|
- type: euclidean_spearman |
|
value: 66.33178012153247 |
|
- type: manhattan_pearson |
|
value: 71.46941734657887 |
|
- type: manhattan_spearman |
|
value: 66.43234359835814 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 75.40943196466576 |
|
- type: cos_sim_spearman |
|
value: 66.59241013465915 |
|
- type: euclidean_pearson |
|
value: 71.32500540796616 |
|
- type: euclidean_spearman |
|
value: 67.86667467202591 |
|
- type: manhattan_pearson |
|
value: 71.48209832089134 |
|
- type: manhattan_spearman |
|
value: 67.94511626964879 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.08302398877518 |
|
- type: cos_sim_spearman |
|
value: 77.33151317062642 |
|
- type: euclidean_pearson |
|
value: 76.77020279715008 |
|
- type: euclidean_spearman |
|
value: 77.13893776083225 |
|
- type: manhattan_pearson |
|
value: 76.76732290707477 |
|
- type: manhattan_spearman |
|
value: 77.14500877396631 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.46886184932168 |
|
- type: cos_sim_spearman |
|
value: 71.82815265534886 |
|
- type: euclidean_pearson |
|
value: 75.19783284299076 |
|
- type: euclidean_spearman |
|
value: 71.36479611710412 |
|
- type: manhattan_pearson |
|
value: 75.30375233959337 |
|
- type: manhattan_spearman |
|
value: 71.46280266488021 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.093017609484 |
|
- type: cos_sim_spearman |
|
value: 80.65931167868882 |
|
- type: euclidean_pearson |
|
value: 80.36786337117047 |
|
- type: euclidean_spearman |
|
value: 81.30521389642827 |
|
- type: manhattan_pearson |
|
value: 80.37922433220973 |
|
- type: manhattan_spearman |
|
value: 81.30496664496285 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.98998347238742 |
|
- type: cos_sim_spearman |
|
value: 78.91151365939403 |
|
- type: euclidean_pearson |
|
value: 76.40510899217841 |
|
- type: euclidean_spearman |
|
value: 76.8551459824213 |
|
- type: manhattan_pearson |
|
value: 76.3986079603294 |
|
- type: manhattan_spearman |
|
value: 76.8848053254288 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.63510653472044 |
|
- type: cos_sim_spearman |
|
value: 86.98674844768605 |
|
- type: euclidean_pearson |
|
value: 85.205080538809 |
|
- type: euclidean_spearman |
|
value: 85.53630494151886 |
|
- type: manhattan_pearson |
|
value: 85.48612469885626 |
|
- type: manhattan_spearman |
|
value: 85.81741413931921 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.7257987615171 |
|
- type: cos_sim_spearman |
|
value: 67.30387805090024 |
|
- type: euclidean_pearson |
|
value: 69.46877227885867 |
|
- type: euclidean_spearman |
|
value: 69.33161798704344 |
|
- type: manhattan_pearson |
|
value: 69.82773311626424 |
|
- type: manhattan_spearman |
|
value: 69.57199940498796 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.37322139418472 |
|
- type: cos_sim_spearman |
|
value: 77.5887175717799 |
|
- type: euclidean_pearson |
|
value: 78.23006410562164 |
|
- type: euclidean_spearman |
|
value: 77.18470385673044 |
|
- type: manhattan_pearson |
|
value: 78.40868369362455 |
|
- type: manhattan_spearman |
|
value: 77.36675823897656 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
metrics: |
|
- type: map |
|
value: 77.21233007730808 |
|
- type: mrr |
|
value: 93.0502386139641 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.567 |
|
- type: map_at_10 |
|
value: 63.653000000000006 |
|
- type: map_at_100 |
|
value: 64.282 |
|
- type: map_at_1000 |
|
value: 64.31099999999999 |
|
- type: map_at_3 |
|
value: 60.478 |
|
- type: map_at_5 |
|
value: 62.322 |
|
- type: mrr_at_1 |
|
value: 56.99999999999999 |
|
- type: mrr_at_10 |
|
value: 64.759 |
|
- type: mrr_at_100 |
|
value: 65.274 |
|
- type: mrr_at_1000 |
|
value: 65.301 |
|
- type: mrr_at_3 |
|
value: 62.333000000000006 |
|
- type: mrr_at_5 |
|
value: 63.817 |
|
- type: ndcg_at_1 |
|
value: 56.99999999999999 |
|
- type: ndcg_at_10 |
|
value: 68.28699999999999 |
|
- type: ndcg_at_100 |
|
value: 70.98400000000001 |
|
- type: ndcg_at_1000 |
|
value: 71.695 |
|
- type: ndcg_at_3 |
|
value: 62.656 |
|
- type: ndcg_at_5 |
|
value: 65.523 |
|
- type: precision_at_1 |
|
value: 56.99999999999999 |
|
- type: precision_at_10 |
|
value: 9.232999999999999 |
|
- type: precision_at_100 |
|
value: 1.0630000000000002 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 24.221999999999998 |
|
- type: precision_at_5 |
|
value: 16.333000000000002 |
|
- type: recall_at_1 |
|
value: 54.567 |
|
- type: recall_at_10 |
|
value: 81.45599999999999 |
|
- type: recall_at_100 |
|
value: 93.5 |
|
- type: recall_at_1000 |
|
value: 99.0 |
|
- type: recall_at_3 |
|
value: 66.228 |
|
- type: recall_at_5 |
|
value: 73.489 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.74455445544554 |
|
- type: cos_sim_ap |
|
value: 92.57836032673468 |
|
- type: cos_sim_f1 |
|
value: 87.0471464019851 |
|
- type: cos_sim_precision |
|
value: 86.4039408866995 |
|
- type: cos_sim_recall |
|
value: 87.7 |
|
- type: dot_accuracy |
|
value: 99.56039603960396 |
|
- type: dot_ap |
|
value: 82.47233353407186 |
|
- type: dot_f1 |
|
value: 76.78207739307537 |
|
- type: dot_precision |
|
value: 78.21576763485477 |
|
- type: dot_recall |
|
value: 75.4 |
|
- type: euclidean_accuracy |
|
value: 99.73069306930694 |
|
- type: euclidean_ap |
|
value: 91.70507666665775 |
|
- type: euclidean_f1 |
|
value: 86.26262626262626 |
|
- type: euclidean_precision |
|
value: 87.14285714285714 |
|
- type: euclidean_recall |
|
value: 85.39999999999999 |
|
- type: manhattan_accuracy |
|
value: 99.73861386138614 |
|
- type: manhattan_ap |
|
value: 91.96809459281754 |
|
- type: manhattan_f1 |
|
value: 86.6 |
|
- type: manhattan_precision |
|
value: 86.6 |
|
- type: manhattan_recall |
|
value: 86.6 |
|
- type: max_accuracy |
|
value: 99.74455445544554 |
|
- type: max_ap |
|
value: 92.57836032673468 |
|
- type: max_f1 |
|
value: 87.0471464019851 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
metrics: |
|
- type: v_measure |
|
value: 60.85593925770172 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
metrics: |
|
- type: v_measure |
|
value: 32.356772998237496 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
metrics: |
|
- type: map |
|
value: 49.320607035290735 |
|
- type: mrr |
|
value: 50.09196481622952 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 25.57602918901377 |
|
- type: cos_sim_spearman |
|
value: 25.440272876996694 |
|
- type: dot_pearson |
|
value: 24.909680980895065 |
|
- type: dot_spearman |
|
value: 24.032627570006824 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.22100000000000003 |
|
- type: map_at_10 |
|
value: 1.7229999999999999 |
|
- type: map_at_100 |
|
value: 9.195 |
|
- type: map_at_1000 |
|
value: 21.999 |
|
- type: map_at_3 |
|
value: 0.6479999999999999 |
|
- type: map_at_5 |
|
value: 0.964 |
|
- type: mrr_at_1 |
|
value: 86.0 |
|
- type: mrr_at_10 |
|
value: 90.667 |
|
- type: mrr_at_100 |
|
value: 90.858 |
|
- type: mrr_at_1000 |
|
value: 90.858 |
|
- type: mrr_at_3 |
|
value: 90.667 |
|
- type: mrr_at_5 |
|
value: 90.667 |
|
- type: ndcg_at_1 |
|
value: 82.0 |
|
- type: ndcg_at_10 |
|
value: 72.98 |
|
- type: ndcg_at_100 |
|
value: 52.868 |
|
- type: ndcg_at_1000 |
|
value: 46.541 |
|
- type: ndcg_at_3 |
|
value: 80.39699999999999 |
|
- type: ndcg_at_5 |
|
value: 76.303 |
|
- type: precision_at_1 |
|
value: 86.0 |
|
- type: precision_at_10 |
|
value: 75.8 |
|
- type: precision_at_100 |
|
value: 53.5 |
|
- type: precision_at_1000 |
|
value: 20.946 |
|
- type: precision_at_3 |
|
value: 85.333 |
|
- type: precision_at_5 |
|
value: 79.2 |
|
- type: recall_at_1 |
|
value: 0.22100000000000003 |
|
- type: recall_at_10 |
|
value: 1.9109999999999998 |
|
- type: recall_at_100 |
|
value: 12.437 |
|
- type: recall_at_1000 |
|
value: 43.606 |
|
- type: recall_at_3 |
|
value: 0.681 |
|
- type: recall_at_5 |
|
value: 1.023 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.5 |
|
- type: map_at_10 |
|
value: 9.568999999999999 |
|
- type: map_at_100 |
|
value: 15.653 |
|
- type: map_at_1000 |
|
value: 17.188 |
|
- type: map_at_3 |
|
value: 5.335999999999999 |
|
- type: map_at_5 |
|
value: 6.522 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 49.184 |
|
- type: mrr_at_100 |
|
value: 50.512 |
|
- type: mrr_at_1000 |
|
value: 50.512 |
|
- type: mrr_at_3 |
|
value: 46.259 |
|
- type: mrr_at_5 |
|
value: 48.299 |
|
- type: ndcg_at_1 |
|
value: 30.612000000000002 |
|
- type: ndcg_at_10 |
|
value: 24.45 |
|
- type: ndcg_at_100 |
|
value: 35.870999999999995 |
|
- type: ndcg_at_1000 |
|
value: 47.272999999999996 |
|
- type: ndcg_at_3 |
|
value: 28.528 |
|
- type: ndcg_at_5 |
|
value: 25.768 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 21.429000000000002 |
|
- type: precision_at_100 |
|
value: 7.265000000000001 |
|
- type: precision_at_1000 |
|
value: 1.504 |
|
- type: precision_at_3 |
|
value: 29.252 |
|
- type: precision_at_5 |
|
value: 24.898 |
|
- type: recall_at_1 |
|
value: 2.5 |
|
- type: recall_at_10 |
|
value: 15.844 |
|
- type: recall_at_100 |
|
value: 45.469 |
|
- type: recall_at_1000 |
|
value: 81.148 |
|
- type: recall_at_3 |
|
value: 6.496 |
|
- type: recall_at_5 |
|
value: 8.790000000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
metrics: |
|
- type: accuracy |
|
value: 68.7272 |
|
- type: ap |
|
value: 13.156450706152686 |
|
- type: f1 |
|
value: 52.814703437064395 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
metrics: |
|
- type: accuracy |
|
value: 55.6677985285795 |
|
- type: f1 |
|
value: 55.9373937514999 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
metrics: |
|
- type: v_measure |
|
value: 40.05809562275603 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.76807534124099 |
|
- type: cos_sim_ap |
|
value: 62.37052608803734 |
|
- type: cos_sim_f1 |
|
value: 59.077414934916646 |
|
- type: cos_sim_precision |
|
value: 52.07326892109501 |
|
- type: cos_sim_recall |
|
value: 68.25857519788919 |
|
- type: dot_accuracy |
|
value: 80.56267509089825 |
|
- type: dot_ap |
|
value: 54.75349561321037 |
|
- type: dot_f1 |
|
value: 54.75483794372552 |
|
- type: dot_precision |
|
value: 49.77336499028707 |
|
- type: dot_recall |
|
value: 60.844327176781 |
|
- type: euclidean_accuracy |
|
value: 82.476008821601 |
|
- type: euclidean_ap |
|
value: 61.17417554210511 |
|
- type: euclidean_f1 |
|
value: 57.80318696022382 |
|
- type: euclidean_precision |
|
value: 53.622207176709544 |
|
- type: euclidean_recall |
|
value: 62.69129287598945 |
|
- type: manhattan_accuracy |
|
value: 82.48792990403528 |
|
- type: manhattan_ap |
|
value: 61.044816292966544 |
|
- type: manhattan_f1 |
|
value: 58.03033951360462 |
|
- type: manhattan_precision |
|
value: 53.36581045172719 |
|
- type: manhattan_recall |
|
value: 63.58839050131926 |
|
- type: max_accuracy |
|
value: 82.76807534124099 |
|
- type: max_ap |
|
value: 62.37052608803734 |
|
- type: max_f1 |
|
value: 59.077414934916646 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.97881010594946 |
|
- type: cos_sim_ap |
|
value: 83.78748636891035 |
|
- type: cos_sim_f1 |
|
value: 75.94113995691386 |
|
- type: cos_sim_precision |
|
value: 72.22029307590805 |
|
- type: cos_sim_recall |
|
value: 80.06621496766245 |
|
- type: dot_accuracy |
|
value: 85.69294058291614 |
|
- type: dot_ap |
|
value: 78.15363722278026 |
|
- type: dot_f1 |
|
value: 72.08894926888564 |
|
- type: dot_precision |
|
value: 67.28959487419075 |
|
- type: dot_recall |
|
value: 77.62550046196489 |
|
- type: euclidean_accuracy |
|
value: 87.73625179493149 |
|
- type: euclidean_ap |
|
value: 83.19012184470559 |
|
- type: euclidean_f1 |
|
value: 75.5148064623461 |
|
- type: euclidean_precision |
|
value: 72.63352535381551 |
|
- type: euclidean_recall |
|
value: 78.6341238065907 |
|
- type: manhattan_accuracy |
|
value: 87.74013272790779 |
|
- type: manhattan_ap |
|
value: 83.23305405113403 |
|
- type: manhattan_f1 |
|
value: 75.63960775639607 |
|
- type: manhattan_precision |
|
value: 72.563304569246 |
|
- type: manhattan_recall |
|
value: 78.9882968894364 |
|
- type: max_accuracy |
|
value: 87.97881010594946 |
|
- type: max_ap |
|
value: 83.78748636891035 |
|
- type: max_f1 |
|
value: 75.94113995691386 |
|
--- |
|
|
|
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit |
|
|
|
## Usage |
|
|
|
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt |
|
|
|
## Evaluation Results |
|
|
|
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 |
|
|
|
## Training |
|
The model was trained with the parameters: |
|
|
|
**DataLoader**: |
|
|
|
`torch.utils.data.dataloader.DataLoader` of length 62398 with parameters: |
|
``` |
|
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
|
``` |
|
|
|
**Loss**: |
|
|
|
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
|
``` |
|
{'scale': 20.0, 'similarity_fct': 'cos_sim'} |
|
``` |
|
|
|
Parameters of the fit()-Method: |
|
``` |
|
{ |
|
"epochs": 10, |
|
"evaluation_steps": 0, |
|
"evaluator": "NoneType", |
|
"max_grad_norm": 1, |
|
"optimizer_class": "<class 'transformers.optimization.AdamW'>", |
|
"optimizer_params": { |
|
"lr": 0.0002 |
|
}, |
|
"scheduler": "WarmupLinear", |
|
"steps_per_epoch": null, |
|
"warmup_steps": 1000, |
|
"weight_decay": 0.01 |
|
} |
|
``` |
|
|
|
|
|
## Full Model Architecture |
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel |
|
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) |
|
) |
|
``` |
|
|
|
## Citing & Authors |
|
|
|
```bibtex |
|
@article{muennighoff2022sgpt, |
|
title={SGPT: GPT Sentence Embeddings for Semantic Search}, |
|
author={Muennighoff, Niklas}, |
|
journal={arXiv preprint arXiv:2202.08904}, |
|
year={2022} |
|
} |
|
``` |
|
|
