|
--- |
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pipeline_tag: sentence-similarity |
|
tags: |
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- text-embedding |
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- embeddings |
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- information-retrieval |
|
- beir |
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- text-classification |
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- language-model |
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- text-clustering |
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- text-semantic-similarity |
|
- text-evaluation |
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- prompt-retrieval |
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- text-reranking |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
|
- transformers |
|
- t5 |
|
- English |
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- Sentence Similarity |
|
- natural_questions |
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- ms_marco |
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- fever |
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- hotpot_qa |
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- mteb |
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language: en |
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inference: false |
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license: apache-2.0 |
|
model-index: |
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- name: final_base_results |
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results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 86.2089552238806 |
|
- type: ap |
|
value: 55.76273850794966 |
|
- type: f1 |
|
value: 81.26104211414781 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
|
- type: accuracy |
|
value: 88.35995000000001 |
|
- type: ap |
|
value: 84.18839957309655 |
|
- type: f1 |
|
value: 88.317619250081 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 44.64 |
|
- type: f1 |
|
value: 42.48663956478136 |
|
- task: |
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type: Retrieval |
|
dataset: |
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type: arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.383000000000003 |
|
- type: map_at_10 |
|
value: 43.024 |
|
- type: map_at_100 |
|
value: 44.023 |
|
- type: map_at_1000 |
|
value: 44.025999999999996 |
|
- type: map_at_3 |
|
value: 37.684 |
|
- type: map_at_5 |
|
value: 40.884 |
|
- type: mrr_at_1 |
|
value: 28.094 |
|
- type: mrr_at_10 |
|
value: 43.315 |
|
- type: mrr_at_100 |
|
value: 44.313 |
|
- type: mrr_at_1000 |
|
value: 44.317 |
|
- type: mrr_at_3 |
|
value: 37.862 |
|
- type: mrr_at_5 |
|
value: 41.155 |
|
- type: ndcg_at_1 |
|
value: 27.383000000000003 |
|
- type: ndcg_at_10 |
|
value: 52.032000000000004 |
|
- type: ndcg_at_100 |
|
value: 56.19499999999999 |
|
- type: ndcg_at_1000 |
|
value: 56.272 |
|
- type: ndcg_at_3 |
|
value: 41.166000000000004 |
|
- type: ndcg_at_5 |
|
value: 46.92 |
|
- type: precision_at_1 |
|
value: 27.383000000000003 |
|
- type: precision_at_10 |
|
value: 8.087 |
|
- type: precision_at_100 |
|
value: 0.989 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 17.093 |
|
- type: precision_at_5 |
|
value: 13.044 |
|
- type: recall_at_1 |
|
value: 27.383000000000003 |
|
- type: recall_at_10 |
|
value: 80.868 |
|
- type: recall_at_100 |
|
value: 98.86200000000001 |
|
- type: recall_at_1000 |
|
value: 99.431 |
|
- type: recall_at_3 |
|
value: 51.28 |
|
- type: recall_at_5 |
|
value: 65.22 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 39.68441054431849 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 29.188539728343844 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
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config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 63.173362687519784 |
|
- type: mrr |
|
value: 76.18860748362133 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 82.30789953771232 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 77.03571428571428 |
|
- type: f1 |
|
value: 75.87384305045917 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 32.98041170516364 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 25.71652988451154 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.739999999999995 |
|
- type: map_at_10 |
|
value: 46.197 |
|
- type: map_at_100 |
|
value: 47.814 |
|
- type: map_at_1000 |
|
value: 47.934 |
|
- type: map_at_3 |
|
value: 43.091 |
|
- type: map_at_5 |
|
value: 44.81 |
|
- type: mrr_at_1 |
|
value: 41.059 |
|
- type: mrr_at_10 |
|
value: 52.292 |
|
- type: mrr_at_100 |
|
value: 52.978 |
|
- type: mrr_at_1000 |
|
value: 53.015 |
|
- type: mrr_at_3 |
|
value: 49.976 |
|
- type: mrr_at_5 |
|
value: 51.449999999999996 |
|
- type: ndcg_at_1 |
|
value: 41.059 |
|
- type: ndcg_at_10 |
|
value: 52.608 |
|
- type: ndcg_at_100 |
|
value: 57.965 |
|
- type: ndcg_at_1000 |
|
value: 59.775999999999996 |
|
- type: ndcg_at_3 |
|
value: 48.473 |
|
- type: ndcg_at_5 |
|
value: 50.407999999999994 |
|
- type: precision_at_1 |
|
value: 41.059 |
|
- type: precision_at_10 |
|
value: 9.943 |
|
- type: precision_at_100 |
|
value: 1.6070000000000002 |
|
- type: precision_at_1000 |
|
value: 0.20500000000000002 |
|
- type: precision_at_3 |
|
value: 23.413999999999998 |
|
- type: precision_at_5 |
|
value: 16.481 |
|
- type: recall_at_1 |
|
value: 33.739999999999995 |
|
- type: recall_at_10 |
|
value: 63.888999999999996 |
|
- type: recall_at_100 |
|
value: 85.832 |
|
- type: recall_at_1000 |
|
value: 97.475 |
|
- type: recall_at_3 |
|
value: 51.953 |
|
- type: recall_at_5 |
|
value: 57.498000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.169999999999998 |
|
- type: map_at_10 |
|
value: 41.455 |
|
- type: map_at_100 |
|
value: 42.716 |
|
- type: map_at_1000 |
|
value: 42.847 |
|
- type: map_at_3 |
|
value: 38.568999999999996 |
|
- type: map_at_5 |
|
value: 40.099000000000004 |
|
- type: mrr_at_1 |
|
value: 39.427 |
|
- type: mrr_at_10 |
|
value: 47.818 |
|
- type: mrr_at_100 |
|
value: 48.519 |
|
- type: mrr_at_1000 |
|
value: 48.558 |
|
- type: mrr_at_3 |
|
value: 45.86 |
|
- type: mrr_at_5 |
|
value: 46.936 |
|
- type: ndcg_at_1 |
|
value: 39.427 |
|
- type: ndcg_at_10 |
|
value: 47.181 |
|
- type: ndcg_at_100 |
|
value: 51.737 |
|
- type: ndcg_at_1000 |
|
value: 53.74 |
|
- type: ndcg_at_3 |
|
value: 43.261 |
|
- type: ndcg_at_5 |
|
value: 44.891 |
|
- type: precision_at_1 |
|
value: 39.427 |
|
- type: precision_at_10 |
|
value: 8.847 |
|
- type: precision_at_100 |
|
value: 1.425 |
|
- type: precision_at_1000 |
|
value: 0.189 |
|
- type: precision_at_3 |
|
value: 20.785999999999998 |
|
- type: precision_at_5 |
|
value: 14.560999999999998 |
|
- type: recall_at_1 |
|
value: 31.169999999999998 |
|
- type: recall_at_10 |
|
value: 56.971000000000004 |
|
- type: recall_at_100 |
|
value: 76.31400000000001 |
|
- type: recall_at_1000 |
|
value: 88.93900000000001 |
|
- type: recall_at_3 |
|
value: 45.208 |
|
- type: recall_at_5 |
|
value: 49.923 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.682 |
|
- type: map_at_10 |
|
value: 52.766000000000005 |
|
- type: map_at_100 |
|
value: 53.84100000000001 |
|
- type: map_at_1000 |
|
value: 53.898 |
|
- type: map_at_3 |
|
value: 49.291000000000004 |
|
- type: map_at_5 |
|
value: 51.365 |
|
- type: mrr_at_1 |
|
value: 45.266 |
|
- type: mrr_at_10 |
|
value: 56.093 |
|
- type: mrr_at_100 |
|
value: 56.763 |
|
- type: mrr_at_1000 |
|
value: 56.793000000000006 |
|
- type: mrr_at_3 |
|
value: 53.668000000000006 |
|
- type: mrr_at_5 |
|
value: 55.1 |
|
- type: ndcg_at_1 |
|
value: 45.266 |
|
- type: ndcg_at_10 |
|
value: 58.836 |
|
- type: ndcg_at_100 |
|
value: 62.863 |
|
- type: ndcg_at_1000 |
|
value: 63.912 |
|
- type: ndcg_at_3 |
|
value: 53.19199999999999 |
|
- type: ndcg_at_5 |
|
value: 56.125 |
|
- type: precision_at_1 |
|
value: 45.266 |
|
- type: precision_at_10 |
|
value: 9.492 |
|
- type: precision_at_100 |
|
value: 1.236 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 23.762 |
|
- type: precision_at_5 |
|
value: 16.414 |
|
- type: recall_at_1 |
|
value: 39.682 |
|
- type: recall_at_10 |
|
value: 73.233 |
|
- type: recall_at_100 |
|
value: 90.335 |
|
- type: recall_at_1000 |
|
value: 97.452 |
|
- type: recall_at_3 |
|
value: 58.562000000000005 |
|
- type: recall_at_5 |
|
value: 65.569 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.743 |
|
- type: map_at_10 |
|
value: 34.016000000000005 |
|
- type: map_at_100 |
|
value: 35.028999999999996 |
|
- type: map_at_1000 |
|
value: 35.113 |
|
- type: map_at_3 |
|
value: 31.763 |
|
- type: map_at_5 |
|
value: 33.013999999999996 |
|
- type: mrr_at_1 |
|
value: 28.927000000000003 |
|
- type: mrr_at_10 |
|
value: 36.32 |
|
- type: mrr_at_100 |
|
value: 37.221 |
|
- type: mrr_at_1000 |
|
value: 37.281 |
|
- type: mrr_at_3 |
|
value: 34.105000000000004 |
|
- type: mrr_at_5 |
|
value: 35.371 |
|
- type: ndcg_at_1 |
|
value: 28.927000000000003 |
|
- type: ndcg_at_10 |
|
value: 38.474000000000004 |
|
- type: ndcg_at_100 |
|
value: 43.580000000000005 |
|
- type: ndcg_at_1000 |
|
value: 45.64 |
|
- type: ndcg_at_3 |
|
value: 34.035 |
|
- type: ndcg_at_5 |
|
value: 36.186 |
|
- type: precision_at_1 |
|
value: 28.927000000000003 |
|
- type: precision_at_10 |
|
value: 5.74 |
|
- type: precision_at_100 |
|
value: 0.8710000000000001 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 14.124 |
|
- type: precision_at_5 |
|
value: 9.74 |
|
- type: recall_at_1 |
|
value: 26.743 |
|
- type: recall_at_10 |
|
value: 49.955 |
|
- type: recall_at_100 |
|
value: 73.904 |
|
- type: recall_at_1000 |
|
value: 89.133 |
|
- type: recall_at_3 |
|
value: 38.072 |
|
- type: recall_at_5 |
|
value: 43.266 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.928 |
|
- type: map_at_10 |
|
value: 23.549 |
|
- type: map_at_100 |
|
value: 24.887 |
|
- type: map_at_1000 |
|
value: 25.018 |
|
- type: map_at_3 |
|
value: 21.002000000000002 |
|
- type: map_at_5 |
|
value: 22.256 |
|
- type: mrr_at_1 |
|
value: 21.02 |
|
- type: mrr_at_10 |
|
value: 27.898 |
|
- type: mrr_at_100 |
|
value: 29.018 |
|
- type: mrr_at_1000 |
|
value: 29.099999999999998 |
|
- type: mrr_at_3 |
|
value: 25.456 |
|
- type: mrr_at_5 |
|
value: 26.625 |
|
- type: ndcg_at_1 |
|
value: 21.02 |
|
- type: ndcg_at_10 |
|
value: 28.277 |
|
- type: ndcg_at_100 |
|
value: 34.54 |
|
- type: ndcg_at_1000 |
|
value: 37.719 |
|
- type: ndcg_at_3 |
|
value: 23.707 |
|
- type: ndcg_at_5 |
|
value: 25.482 |
|
- type: precision_at_1 |
|
value: 21.02 |
|
- type: precision_at_10 |
|
value: 5.361 |
|
- type: precision_at_100 |
|
value: 0.9809999999999999 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 11.401 |
|
- type: precision_at_5 |
|
value: 8.209 |
|
- type: recall_at_1 |
|
value: 16.928 |
|
- type: recall_at_10 |
|
value: 38.601 |
|
- type: recall_at_100 |
|
value: 65.759 |
|
- type: recall_at_1000 |
|
value: 88.543 |
|
- type: recall_at_3 |
|
value: 25.556 |
|
- type: recall_at_5 |
|
value: 30.447000000000003 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.549000000000003 |
|
- type: map_at_10 |
|
value: 38.426 |
|
- type: map_at_100 |
|
value: 39.845000000000006 |
|
- type: map_at_1000 |
|
value: 39.956 |
|
- type: map_at_3 |
|
value: 35.372 |
|
- type: map_at_5 |
|
value: 37.204 |
|
- type: mrr_at_1 |
|
value: 35.034 |
|
- type: mrr_at_10 |
|
value: 44.041000000000004 |
|
- type: mrr_at_100 |
|
value: 44.95 |
|
- type: mrr_at_1000 |
|
value: 44.997 |
|
- type: mrr_at_3 |
|
value: 41.498000000000005 |
|
- type: mrr_at_5 |
|
value: 43.077 |
|
- type: ndcg_at_1 |
|
value: 35.034 |
|
- type: ndcg_at_10 |
|
value: 44.218 |
|
- type: ndcg_at_100 |
|
value: 49.958000000000006 |
|
- type: ndcg_at_1000 |
|
value: 52.019000000000005 |
|
- type: ndcg_at_3 |
|
value: 39.34 |
|
- type: ndcg_at_5 |
|
value: 41.892 |
|
- type: precision_at_1 |
|
value: 35.034 |
|
- type: precision_at_10 |
|
value: 7.911 |
|
- type: precision_at_100 |
|
value: 1.26 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 18.511 |
|
- type: precision_at_5 |
|
value: 13.205 |
|
- type: recall_at_1 |
|
value: 28.549000000000003 |
|
- type: recall_at_10 |
|
value: 56.035999999999994 |
|
- type: recall_at_100 |
|
value: 79.701 |
|
- type: recall_at_1000 |
|
value: 93.149 |
|
- type: recall_at_3 |
|
value: 42.275 |
|
- type: recall_at_5 |
|
value: 49.097 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.391000000000002 |
|
- type: map_at_10 |
|
value: 39.48 |
|
- type: map_at_100 |
|
value: 40.727000000000004 |
|
- type: map_at_1000 |
|
value: 40.835 |
|
- type: map_at_3 |
|
value: 36.234 |
|
- type: map_at_5 |
|
value: 37.877 |
|
- type: mrr_at_1 |
|
value: 35.959 |
|
- type: mrr_at_10 |
|
value: 44.726 |
|
- type: mrr_at_100 |
|
value: 45.531 |
|
- type: mrr_at_1000 |
|
value: 45.582 |
|
- type: mrr_at_3 |
|
value: 42.047000000000004 |
|
- type: mrr_at_5 |
|
value: 43.611 |
|
- type: ndcg_at_1 |
|
value: 35.959 |
|
- type: ndcg_at_10 |
|
value: 45.303 |
|
- type: ndcg_at_100 |
|
value: 50.683 |
|
- type: ndcg_at_1000 |
|
value: 52.818 |
|
- type: ndcg_at_3 |
|
value: 39.987 |
|
- type: ndcg_at_5 |
|
value: 42.243 |
|
- type: precision_at_1 |
|
value: 35.959 |
|
- type: precision_at_10 |
|
value: 8.241999999999999 |
|
- type: precision_at_100 |
|
value: 1.274 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 18.836 |
|
- type: precision_at_5 |
|
value: 13.196 |
|
- type: recall_at_1 |
|
value: 29.391000000000002 |
|
- type: recall_at_10 |
|
value: 57.364000000000004 |
|
- type: recall_at_100 |
|
value: 80.683 |
|
- type: recall_at_1000 |
|
value: 94.918 |
|
- type: recall_at_3 |
|
value: 42.263 |
|
- type: recall_at_5 |
|
value: 48.634 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.791749999999997 |
|
- type: map_at_10 |
|
value: 35.75541666666667 |
|
- type: map_at_100 |
|
value: 37.00791666666667 |
|
- type: map_at_1000 |
|
value: 37.12408333333333 |
|
- type: map_at_3 |
|
value: 33.02966666666667 |
|
- type: map_at_5 |
|
value: 34.56866666666667 |
|
- type: mrr_at_1 |
|
value: 31.744333333333337 |
|
- type: mrr_at_10 |
|
value: 39.9925 |
|
- type: mrr_at_100 |
|
value: 40.86458333333333 |
|
- type: mrr_at_1000 |
|
value: 40.92175000000001 |
|
- type: mrr_at_3 |
|
value: 37.68183333333334 |
|
- type: mrr_at_5 |
|
value: 39.028499999999994 |
|
- type: ndcg_at_1 |
|
value: 31.744333333333337 |
|
- type: ndcg_at_10 |
|
value: 40.95008333333334 |
|
- type: ndcg_at_100 |
|
value: 46.25966666666667 |
|
- type: ndcg_at_1000 |
|
value: 48.535333333333334 |
|
- type: ndcg_at_3 |
|
value: 36.43333333333333 |
|
- type: ndcg_at_5 |
|
value: 38.602333333333334 |
|
- type: precision_at_1 |
|
value: 31.744333333333337 |
|
- type: precision_at_10 |
|
value: 7.135166666666666 |
|
- type: precision_at_100 |
|
value: 1.1535833333333334 |
|
- type: precision_at_1000 |
|
value: 0.15391666666666665 |
|
- type: precision_at_3 |
|
value: 16.713 |
|
- type: precision_at_5 |
|
value: 11.828416666666666 |
|
- type: recall_at_1 |
|
value: 26.791749999999997 |
|
- type: recall_at_10 |
|
value: 51.98625 |
|
- type: recall_at_100 |
|
value: 75.30358333333334 |
|
- type: recall_at_1000 |
|
value: 91.05433333333333 |
|
- type: recall_at_3 |
|
value: 39.39583333333333 |
|
- type: recall_at_5 |
|
value: 45.05925 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.219 |
|
- type: map_at_10 |
|
value: 29.162 |
|
- type: map_at_100 |
|
value: 30.049999999999997 |
|
- type: map_at_1000 |
|
value: 30.144 |
|
- type: map_at_3 |
|
value: 27.204 |
|
- type: map_at_5 |
|
value: 28.351 |
|
- type: mrr_at_1 |
|
value: 25.153 |
|
- type: mrr_at_10 |
|
value: 31.814999999999998 |
|
- type: mrr_at_100 |
|
value: 32.573 |
|
- type: mrr_at_1000 |
|
value: 32.645 |
|
- type: mrr_at_3 |
|
value: 29.934 |
|
- type: mrr_at_5 |
|
value: 30.946 |
|
- type: ndcg_at_1 |
|
value: 25.153 |
|
- type: ndcg_at_10 |
|
value: 33.099000000000004 |
|
- type: ndcg_at_100 |
|
value: 37.768 |
|
- type: ndcg_at_1000 |
|
value: 40.331 |
|
- type: ndcg_at_3 |
|
value: 29.473 |
|
- type: ndcg_at_5 |
|
value: 31.206 |
|
- type: precision_at_1 |
|
value: 25.153 |
|
- type: precision_at_10 |
|
value: 5.183999999999999 |
|
- type: precision_at_100 |
|
value: 0.8170000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 12.831999999999999 |
|
- type: precision_at_5 |
|
value: 8.895999999999999 |
|
- type: recall_at_1 |
|
value: 22.219 |
|
- type: recall_at_10 |
|
value: 42.637 |
|
- type: recall_at_100 |
|
value: 64.704 |
|
- type: recall_at_1000 |
|
value: 83.963 |
|
- type: recall_at_3 |
|
value: 32.444 |
|
- type: recall_at_5 |
|
value: 36.802 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.427999999999997 |
|
- type: map_at_10 |
|
value: 24.029 |
|
- type: map_at_100 |
|
value: 25.119999999999997 |
|
- type: map_at_1000 |
|
value: 25.257 |
|
- type: map_at_3 |
|
value: 22.016 |
|
- type: map_at_5 |
|
value: 23.143 |
|
- type: mrr_at_1 |
|
value: 21.129 |
|
- type: mrr_at_10 |
|
value: 27.750000000000004 |
|
- type: mrr_at_100 |
|
value: 28.666999999999998 |
|
- type: mrr_at_1000 |
|
value: 28.754999999999995 |
|
- type: mrr_at_3 |
|
value: 25.849 |
|
- type: mrr_at_5 |
|
value: 26.939999999999998 |
|
- type: ndcg_at_1 |
|
value: 21.129 |
|
- type: ndcg_at_10 |
|
value: 28.203 |
|
- type: ndcg_at_100 |
|
value: 33.44 |
|
- type: ndcg_at_1000 |
|
value: 36.61 |
|
- type: ndcg_at_3 |
|
value: 24.648999999999997 |
|
- type: ndcg_at_5 |
|
value: 26.316 |
|
- type: precision_at_1 |
|
value: 21.129 |
|
- type: precision_at_10 |
|
value: 5.055 |
|
- type: precision_at_100 |
|
value: 0.909 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 11.666 |
|
- type: precision_at_5 |
|
value: 8.3 |
|
- type: recall_at_1 |
|
value: 17.427999999999997 |
|
- type: recall_at_10 |
|
value: 36.923 |
|
- type: recall_at_100 |
|
value: 60.606 |
|
- type: recall_at_1000 |
|
value: 83.19 |
|
- type: recall_at_3 |
|
value: 26.845000000000002 |
|
- type: recall_at_5 |
|
value: 31.247000000000003 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.457000000000004 |
|
- type: map_at_10 |
|
value: 35.228 |
|
- type: map_at_100 |
|
value: 36.475 |
|
- type: map_at_1000 |
|
value: 36.585 |
|
- type: map_at_3 |
|
value: 32.444 |
|
- type: map_at_5 |
|
value: 34.046 |
|
- type: mrr_at_1 |
|
value: 30.784 |
|
- type: mrr_at_10 |
|
value: 39.133 |
|
- type: mrr_at_100 |
|
value: 40.11 |
|
- type: mrr_at_1000 |
|
value: 40.169 |
|
- type: mrr_at_3 |
|
value: 36.692 |
|
- type: mrr_at_5 |
|
value: 38.17 |
|
- type: ndcg_at_1 |
|
value: 30.784 |
|
- type: ndcg_at_10 |
|
value: 40.358 |
|
- type: ndcg_at_100 |
|
value: 46.119 |
|
- type: ndcg_at_1000 |
|
value: 48.428 |
|
- type: ndcg_at_3 |
|
value: 35.504000000000005 |
|
- type: ndcg_at_5 |
|
value: 37.864 |
|
- type: precision_at_1 |
|
value: 30.784 |
|
- type: precision_at_10 |
|
value: 6.800000000000001 |
|
- type: precision_at_100 |
|
value: 1.083 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 15.920000000000002 |
|
- type: precision_at_5 |
|
value: 11.437 |
|
- type: recall_at_1 |
|
value: 26.457000000000004 |
|
- type: recall_at_10 |
|
value: 51.845 |
|
- type: recall_at_100 |
|
value: 77.046 |
|
- type: recall_at_1000 |
|
value: 92.892 |
|
- type: recall_at_3 |
|
value: 38.89 |
|
- type: recall_at_5 |
|
value: 44.688 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.378999999999998 |
|
- type: map_at_10 |
|
value: 37.373 |
|
- type: map_at_100 |
|
value: 39.107 |
|
- type: map_at_1000 |
|
value: 39.317 |
|
- type: map_at_3 |
|
value: 34.563 |
|
- type: map_at_5 |
|
value: 36.173 |
|
- type: mrr_at_1 |
|
value: 35.178 |
|
- type: mrr_at_10 |
|
value: 42.44 |
|
- type: mrr_at_100 |
|
value: 43.434 |
|
- type: mrr_at_1000 |
|
value: 43.482 |
|
- type: mrr_at_3 |
|
value: 39.987 |
|
- type: mrr_at_5 |
|
value: 41.370000000000005 |
|
- type: ndcg_at_1 |
|
value: 35.178 |
|
- type: ndcg_at_10 |
|
value: 42.82 |
|
- type: ndcg_at_100 |
|
value: 48.935 |
|
- type: ndcg_at_1000 |
|
value: 51.28 |
|
- type: ndcg_at_3 |
|
value: 38.562999999999995 |
|
- type: ndcg_at_5 |
|
value: 40.687 |
|
- type: precision_at_1 |
|
value: 35.178 |
|
- type: precision_at_10 |
|
value: 7.945 |
|
- type: precision_at_100 |
|
value: 1.524 |
|
- type: precision_at_1000 |
|
value: 0.242 |
|
- type: precision_at_3 |
|
value: 17.721 |
|
- type: precision_at_5 |
|
value: 12.925 |
|
- type: recall_at_1 |
|
value: 29.378999999999998 |
|
- type: recall_at_10 |
|
value: 52.141999999999996 |
|
- type: recall_at_100 |
|
value: 79.49000000000001 |
|
- type: recall_at_1000 |
|
value: 93.782 |
|
- type: recall_at_3 |
|
value: 39.579 |
|
- type: recall_at_5 |
|
value: 45.462 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.814999999999998 |
|
- type: map_at_10 |
|
value: 27.383999999999997 |
|
- type: map_at_100 |
|
value: 28.483999999999998 |
|
- type: map_at_1000 |
|
value: 28.585 |
|
- type: map_at_3 |
|
value: 24.807000000000002 |
|
- type: map_at_5 |
|
value: 26.485999999999997 |
|
- type: mrr_at_1 |
|
value: 21.996 |
|
- type: mrr_at_10 |
|
value: 29.584 |
|
- type: mrr_at_100 |
|
value: 30.611 |
|
- type: mrr_at_1000 |
|
value: 30.684 |
|
- type: mrr_at_3 |
|
value: 27.11 |
|
- type: mrr_at_5 |
|
value: 28.746 |
|
- type: ndcg_at_1 |
|
value: 21.996 |
|
- type: ndcg_at_10 |
|
value: 32.024 |
|
- type: ndcg_at_100 |
|
value: 37.528 |
|
- type: ndcg_at_1000 |
|
value: 40.150999999999996 |
|
- type: ndcg_at_3 |
|
value: 27.016000000000002 |
|
- type: ndcg_at_5 |
|
value: 29.927999999999997 |
|
- type: precision_at_1 |
|
value: 21.996 |
|
- type: precision_at_10 |
|
value: 5.102 |
|
- type: precision_at_100 |
|
value: 0.856 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 11.583 |
|
- type: precision_at_5 |
|
value: 8.577 |
|
- type: recall_at_1 |
|
value: 19.814999999999998 |
|
- type: recall_at_10 |
|
value: 44.239 |
|
- type: recall_at_100 |
|
value: 69.269 |
|
- type: recall_at_1000 |
|
value: 89.216 |
|
- type: recall_at_3 |
|
value: 31.102999999999998 |
|
- type: recall_at_5 |
|
value: 38.078 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 11.349 |
|
- type: map_at_10 |
|
value: 19.436 |
|
- type: map_at_100 |
|
value: 21.282999999999998 |
|
- type: map_at_1000 |
|
value: 21.479 |
|
- type: map_at_3 |
|
value: 15.841 |
|
- type: map_at_5 |
|
value: 17.558 |
|
- type: mrr_at_1 |
|
value: 25.863000000000003 |
|
- type: mrr_at_10 |
|
value: 37.218 |
|
- type: mrr_at_100 |
|
value: 38.198 |
|
- type: mrr_at_1000 |
|
value: 38.236 |
|
- type: mrr_at_3 |
|
value: 33.409 |
|
- type: mrr_at_5 |
|
value: 35.602000000000004 |
|
- type: ndcg_at_1 |
|
value: 25.863000000000003 |
|
- type: ndcg_at_10 |
|
value: 27.953 |
|
- type: ndcg_at_100 |
|
value: 35.327 |
|
- type: ndcg_at_1000 |
|
value: 38.708999999999996 |
|
- type: ndcg_at_3 |
|
value: 21.985 |
|
- type: ndcg_at_5 |
|
value: 23.957 |
|
- type: precision_at_1 |
|
value: 25.863000000000003 |
|
- type: precision_at_10 |
|
value: 8.99 |
|
- type: precision_at_100 |
|
value: 1.6889999999999998 |
|
- type: precision_at_1000 |
|
value: 0.232 |
|
- type: precision_at_3 |
|
value: 16.308 |
|
- type: precision_at_5 |
|
value: 12.912 |
|
- type: recall_at_1 |
|
value: 11.349 |
|
- type: recall_at_10 |
|
value: 34.581 |
|
- type: recall_at_100 |
|
value: 60.178 |
|
- type: recall_at_1000 |
|
value: 78.88199999999999 |
|
- type: recall_at_3 |
|
value: 20.041999999999998 |
|
- type: recall_at_5 |
|
value: 25.458 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.893 |
|
- type: map_at_10 |
|
value: 15.457 |
|
- type: map_at_100 |
|
value: 20.905 |
|
- type: map_at_1000 |
|
value: 22.116 |
|
- type: map_at_3 |
|
value: 11.593 |
|
- type: map_at_5 |
|
value: 13.134 |
|
- type: mrr_at_1 |
|
value: 57.49999999999999 |
|
- type: mrr_at_10 |
|
value: 65.467 |
|
- type: mrr_at_100 |
|
value: 66.022 |
|
- type: mrr_at_1000 |
|
value: 66.039 |
|
- type: mrr_at_3 |
|
value: 63.458000000000006 |
|
- type: mrr_at_5 |
|
value: 64.546 |
|
- type: ndcg_at_1 |
|
value: 45.875 |
|
- type: ndcg_at_10 |
|
value: 33.344 |
|
- type: ndcg_at_100 |
|
value: 36.849 |
|
- type: ndcg_at_1000 |
|
value: 44.03 |
|
- type: ndcg_at_3 |
|
value: 37.504 |
|
- type: ndcg_at_5 |
|
value: 34.892 |
|
- type: precision_at_1 |
|
value: 57.49999999999999 |
|
- type: precision_at_10 |
|
value: 25.95 |
|
- type: precision_at_100 |
|
value: 7.89 |
|
- type: precision_at_1000 |
|
value: 1.669 |
|
- type: precision_at_3 |
|
value: 40.333000000000006 |
|
- type: precision_at_5 |
|
value: 33.050000000000004 |
|
- type: recall_at_1 |
|
value: 7.893 |
|
- type: recall_at_10 |
|
value: 20.724999999999998 |
|
- type: recall_at_100 |
|
value: 42.516 |
|
- type: recall_at_1000 |
|
value: 65.822 |
|
- type: recall_at_3 |
|
value: 12.615000000000002 |
|
- type: recall_at_5 |
|
value: 15.482000000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 51.760000000000005 |
|
- type: f1 |
|
value: 45.51690565701713 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 53.882 |
|
- type: map_at_10 |
|
value: 65.902 |
|
- type: map_at_100 |
|
value: 66.33 |
|
- type: map_at_1000 |
|
value: 66.348 |
|
- type: map_at_3 |
|
value: 63.75999999999999 |
|
- type: map_at_5 |
|
value: 65.181 |
|
- type: mrr_at_1 |
|
value: 58.041 |
|
- type: mrr_at_10 |
|
value: 70.133 |
|
- type: mrr_at_100 |
|
value: 70.463 |
|
- type: mrr_at_1000 |
|
value: 70.47 |
|
- type: mrr_at_3 |
|
value: 68.164 |
|
- type: mrr_at_5 |
|
value: 69.465 |
|
- type: ndcg_at_1 |
|
value: 58.041 |
|
- type: ndcg_at_10 |
|
value: 71.84700000000001 |
|
- type: ndcg_at_100 |
|
value: 73.699 |
|
- type: ndcg_at_1000 |
|
value: 74.06700000000001 |
|
- type: ndcg_at_3 |
|
value: 67.855 |
|
- type: ndcg_at_5 |
|
value: 70.203 |
|
- type: precision_at_1 |
|
value: 58.041 |
|
- type: precision_at_10 |
|
value: 9.427000000000001 |
|
- type: precision_at_100 |
|
value: 1.049 |
|
- type: precision_at_1000 |
|
value: 0.11 |
|
- type: precision_at_3 |
|
value: 27.278000000000002 |
|
- type: precision_at_5 |
|
value: 17.693 |
|
- type: recall_at_1 |
|
value: 53.882 |
|
- type: recall_at_10 |
|
value: 85.99 |
|
- type: recall_at_100 |
|
value: 94.09100000000001 |
|
- type: recall_at_1000 |
|
value: 96.612 |
|
- type: recall_at_3 |
|
value: 75.25 |
|
- type: recall_at_5 |
|
value: 80.997 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.165 |
|
- type: map_at_10 |
|
value: 31.845000000000002 |
|
- type: map_at_100 |
|
value: 33.678999999999995 |
|
- type: map_at_1000 |
|
value: 33.878 |
|
- type: map_at_3 |
|
value: 27.881 |
|
- type: map_at_5 |
|
value: 30.049999999999997 |
|
- type: mrr_at_1 |
|
value: 38.272 |
|
- type: mrr_at_10 |
|
value: 47.04 |
|
- type: mrr_at_100 |
|
value: 47.923 |
|
- type: mrr_at_1000 |
|
value: 47.973 |
|
- type: mrr_at_3 |
|
value: 44.985 |
|
- type: mrr_at_5 |
|
value: 46.150000000000006 |
|
- type: ndcg_at_1 |
|
value: 38.272 |
|
- type: ndcg_at_10 |
|
value: 39.177 |
|
- type: ndcg_at_100 |
|
value: 45.995000000000005 |
|
- type: ndcg_at_1000 |
|
value: 49.312 |
|
- type: ndcg_at_3 |
|
value: 36.135 |
|
- type: ndcg_at_5 |
|
value: 36.936 |
|
- type: precision_at_1 |
|
value: 38.272 |
|
- type: precision_at_10 |
|
value: 10.926 |
|
- type: precision_at_100 |
|
value: 1.809 |
|
- type: precision_at_1000 |
|
value: 0.23700000000000002 |
|
- type: precision_at_3 |
|
value: 24.331 |
|
- type: precision_at_5 |
|
value: 17.747 |
|
- type: recall_at_1 |
|
value: 19.165 |
|
- type: recall_at_10 |
|
value: 45.103 |
|
- type: recall_at_100 |
|
value: 70.295 |
|
- type: recall_at_1000 |
|
value: 90.592 |
|
- type: recall_at_3 |
|
value: 32.832 |
|
- type: recall_at_5 |
|
value: 37.905 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.397 |
|
- type: map_at_10 |
|
value: 44.83 |
|
- type: map_at_100 |
|
value: 45.716 |
|
- type: map_at_1000 |
|
value: 45.797 |
|
- type: map_at_3 |
|
value: 41.955999999999996 |
|
- type: map_at_5 |
|
value: 43.736999999999995 |
|
- type: mrr_at_1 |
|
value: 64.794 |
|
- type: mrr_at_10 |
|
value: 71.866 |
|
- type: mrr_at_100 |
|
value: 72.22 |
|
- type: mrr_at_1000 |
|
value: 72.238 |
|
- type: mrr_at_3 |
|
value: 70.416 |
|
- type: mrr_at_5 |
|
value: 71.304 |
|
- type: ndcg_at_1 |
|
value: 64.794 |
|
- type: ndcg_at_10 |
|
value: 54.186 |
|
- type: ndcg_at_100 |
|
value: 57.623000000000005 |
|
- type: ndcg_at_1000 |
|
value: 59.302 |
|
- type: ndcg_at_3 |
|
value: 49.703 |
|
- type: ndcg_at_5 |
|
value: 52.154999999999994 |
|
- type: precision_at_1 |
|
value: 64.794 |
|
- type: precision_at_10 |
|
value: 11.219 |
|
- type: precision_at_100 |
|
value: 1.394 |
|
- type: precision_at_1000 |
|
value: 0.16199999999999998 |
|
- type: precision_at_3 |
|
value: 30.767 |
|
- type: precision_at_5 |
|
value: 20.397000000000002 |
|
- type: recall_at_1 |
|
value: 32.397 |
|
- type: recall_at_10 |
|
value: 56.096999999999994 |
|
- type: recall_at_100 |
|
value: 69.696 |
|
- type: recall_at_1000 |
|
value: 80.88499999999999 |
|
- type: recall_at_3 |
|
value: 46.150999999999996 |
|
- type: recall_at_5 |
|
value: 50.993 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 81.1744 |
|
- type: ap |
|
value: 75.44973697032414 |
|
- type: f1 |
|
value: 81.09901117955782 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.519000000000002 |
|
- type: map_at_10 |
|
value: 31.025000000000002 |
|
- type: map_at_100 |
|
value: 32.275999999999996 |
|
- type: map_at_1000 |
|
value: 32.329 |
|
- type: map_at_3 |
|
value: 27.132 |
|
- type: map_at_5 |
|
value: 29.415999999999997 |
|
- type: mrr_at_1 |
|
value: 20.115 |
|
- type: mrr_at_10 |
|
value: 31.569000000000003 |
|
- type: mrr_at_100 |
|
value: 32.768 |
|
- type: mrr_at_1000 |
|
value: 32.816 |
|
- type: mrr_at_3 |
|
value: 27.748 |
|
- type: mrr_at_5 |
|
value: 29.956 |
|
- type: ndcg_at_1 |
|
value: 20.115 |
|
- type: ndcg_at_10 |
|
value: 37.756 |
|
- type: ndcg_at_100 |
|
value: 43.858000000000004 |
|
- type: ndcg_at_1000 |
|
value: 45.199 |
|
- type: ndcg_at_3 |
|
value: 29.818 |
|
- type: ndcg_at_5 |
|
value: 33.875 |
|
- type: precision_at_1 |
|
value: 20.115 |
|
- type: precision_at_10 |
|
value: 6.122 |
|
- type: precision_at_100 |
|
value: 0.919 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 12.794 |
|
- type: precision_at_5 |
|
value: 9.731 |
|
- type: recall_at_1 |
|
value: 19.519000000000002 |
|
- type: recall_at_10 |
|
value: 58.62500000000001 |
|
- type: recall_at_100 |
|
value: 86.99 |
|
- type: recall_at_1000 |
|
value: 97.268 |
|
- type: recall_at_3 |
|
value: 37.002 |
|
- type: recall_at_5 |
|
value: 46.778 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.71865025079799 |
|
- type: f1 |
|
value: 93.38906173610519 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 70.2576379388965 |
|
- type: f1 |
|
value: 49.20405830249464 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.48486886348351 |
|
- type: f1 |
|
value: 64.92199176095157 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 72.59246805648958 |
|
- type: f1 |
|
value: 72.1222026389164 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 30.887642595096825 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.3764418784054 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.81544126336991 |
|
- type: mrr |
|
value: 32.82666576268031 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.185 |
|
- type: map_at_10 |
|
value: 11.158 |
|
- type: map_at_100 |
|
value: 14.041 |
|
- type: map_at_1000 |
|
value: 15.360999999999999 |
|
- type: map_at_3 |
|
value: 8.417 |
|
- type: map_at_5 |
|
value: 9.378 |
|
- type: mrr_at_1 |
|
value: 44.582 |
|
- type: mrr_at_10 |
|
value: 53.083999999999996 |
|
- type: mrr_at_100 |
|
value: 53.787 |
|
- type: mrr_at_1000 |
|
value: 53.824000000000005 |
|
- type: mrr_at_3 |
|
value: 51.187000000000005 |
|
- type: mrr_at_5 |
|
value: 52.379 |
|
- type: ndcg_at_1 |
|
value: 42.57 |
|
- type: ndcg_at_10 |
|
value: 31.593 |
|
- type: ndcg_at_100 |
|
value: 29.093999999999998 |
|
- type: ndcg_at_1000 |
|
value: 37.909 |
|
- type: ndcg_at_3 |
|
value: 37.083 |
|
- type: ndcg_at_5 |
|
value: 34.397 |
|
- type: precision_at_1 |
|
value: 43.963 |
|
- type: precision_at_10 |
|
value: 23.498 |
|
- type: precision_at_100 |
|
value: 7.6160000000000005 |
|
- type: precision_at_1000 |
|
value: 2.032 |
|
- type: precision_at_3 |
|
value: 34.572 |
|
- type: precision_at_5 |
|
value: 29.412 |
|
- type: recall_at_1 |
|
value: 5.185 |
|
- type: recall_at_10 |
|
value: 15.234 |
|
- type: recall_at_100 |
|
value: 29.49 |
|
- type: recall_at_1000 |
|
value: 62.273999999999994 |
|
- type: recall_at_3 |
|
value: 9.55 |
|
- type: recall_at_5 |
|
value: 11.103 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.803 |
|
- type: map_at_10 |
|
value: 38.183 |
|
- type: map_at_100 |
|
value: 39.421 |
|
- type: map_at_1000 |
|
value: 39.464 |
|
- type: map_at_3 |
|
value: 33.835 |
|
- type: map_at_5 |
|
value: 36.327 |
|
- type: mrr_at_1 |
|
value: 26.68 |
|
- type: mrr_at_10 |
|
value: 40.439 |
|
- type: mrr_at_100 |
|
value: 41.415 |
|
- type: mrr_at_1000 |
|
value: 41.443999999999996 |
|
- type: mrr_at_3 |
|
value: 36.612 |
|
- type: mrr_at_5 |
|
value: 38.877 |
|
- type: ndcg_at_1 |
|
value: 26.68 |
|
- type: ndcg_at_10 |
|
value: 45.882 |
|
- type: ndcg_at_100 |
|
value: 51.227999999999994 |
|
- type: ndcg_at_1000 |
|
value: 52.207 |
|
- type: ndcg_at_3 |
|
value: 37.511 |
|
- type: ndcg_at_5 |
|
value: 41.749 |
|
- type: precision_at_1 |
|
value: 26.68 |
|
- type: precision_at_10 |
|
value: 7.9750000000000005 |
|
- type: precision_at_100 |
|
value: 1.0959999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 17.449 |
|
- type: precision_at_5 |
|
value: 12.897 |
|
- type: recall_at_1 |
|
value: 23.803 |
|
- type: recall_at_10 |
|
value: 67.152 |
|
- type: recall_at_100 |
|
value: 90.522 |
|
- type: recall_at_1000 |
|
value: 97.743 |
|
- type: recall_at_3 |
|
value: 45.338 |
|
- type: recall_at_5 |
|
value: 55.106 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.473 |
|
- type: map_at_10 |
|
value: 84.452 |
|
- type: map_at_100 |
|
value: 85.101 |
|
- type: map_at_1000 |
|
value: 85.115 |
|
- type: map_at_3 |
|
value: 81.435 |
|
- type: map_at_5 |
|
value: 83.338 |
|
- type: mrr_at_1 |
|
value: 81.19 |
|
- type: mrr_at_10 |
|
value: 87.324 |
|
- type: mrr_at_100 |
|
value: 87.434 |
|
- type: mrr_at_1000 |
|
value: 87.435 |
|
- type: mrr_at_3 |
|
value: 86.31 |
|
- type: mrr_at_5 |
|
value: 87.002 |
|
- type: ndcg_at_1 |
|
value: 81.21000000000001 |
|
- type: ndcg_at_10 |
|
value: 88.19 |
|
- type: ndcg_at_100 |
|
value: 89.44 |
|
- type: ndcg_at_1000 |
|
value: 89.526 |
|
- type: ndcg_at_3 |
|
value: 85.237 |
|
- type: ndcg_at_5 |
|
value: 86.892 |
|
- type: precision_at_1 |
|
value: 81.21000000000001 |
|
- type: precision_at_10 |
|
value: 13.417000000000002 |
|
- type: precision_at_100 |
|
value: 1.537 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.31 |
|
- type: precision_at_5 |
|
value: 24.59 |
|
- type: recall_at_1 |
|
value: 70.473 |
|
- type: recall_at_10 |
|
value: 95.367 |
|
- type: recall_at_100 |
|
value: 99.616 |
|
- type: recall_at_1000 |
|
value: 99.996 |
|
- type: recall_at_3 |
|
value: 86.936 |
|
- type: recall_at_5 |
|
value: 91.557 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 59.25776525253911 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 63.22135271663078 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.003 |
|
- type: map_at_10 |
|
value: 10.062999999999999 |
|
- type: map_at_100 |
|
value: 11.854000000000001 |
|
- type: map_at_1000 |
|
value: 12.145999999999999 |
|
- type: map_at_3 |
|
value: 7.242 |
|
- type: map_at_5 |
|
value: 8.652999999999999 |
|
- type: mrr_at_1 |
|
value: 19.7 |
|
- type: mrr_at_10 |
|
value: 29.721999999999998 |
|
- type: mrr_at_100 |
|
value: 30.867 |
|
- type: mrr_at_1000 |
|
value: 30.944 |
|
- type: mrr_at_3 |
|
value: 26.683 |
|
- type: mrr_at_5 |
|
value: 28.498 |
|
- type: ndcg_at_1 |
|
value: 19.7 |
|
- type: ndcg_at_10 |
|
value: 17.095 |
|
- type: ndcg_at_100 |
|
value: 24.375 |
|
- type: ndcg_at_1000 |
|
value: 29.831000000000003 |
|
- type: ndcg_at_3 |
|
value: 16.305 |
|
- type: ndcg_at_5 |
|
value: 14.291 |
|
- type: precision_at_1 |
|
value: 19.7 |
|
- type: precision_at_10 |
|
value: 8.799999999999999 |
|
- type: precision_at_100 |
|
value: 1.9349999999999998 |
|
- type: precision_at_1000 |
|
value: 0.32399999999999995 |
|
- type: precision_at_3 |
|
value: 15.2 |
|
- type: precision_at_5 |
|
value: 12.540000000000001 |
|
- type: recall_at_1 |
|
value: 4.003 |
|
- type: recall_at_10 |
|
value: 17.877000000000002 |
|
- type: recall_at_100 |
|
value: 39.217 |
|
- type: recall_at_1000 |
|
value: 65.862 |
|
- type: recall_at_3 |
|
value: 9.242 |
|
- type: recall_at_5 |
|
value: 12.715000000000002 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 80.25888668589654 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 77.02037527837669 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 86.58432681008449 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 81.31697756099051 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 88.18867599667057 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 84.87853941747623 |
|
- 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_spearman |
|
value: 89.46479925383916 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 66.45272113649146 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_spearman |
|
value: 86.43357313527851 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 78.82761687254882 |
|
- type: mrr |
|
value: 93.46223674655047 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 44.583 |
|
- type: map_at_10 |
|
value: 52.978 |
|
- type: map_at_100 |
|
value: 53.803 |
|
- type: map_at_1000 |
|
value: 53.839999999999996 |
|
- type: map_at_3 |
|
value: 50.03300000000001 |
|
- type: map_at_5 |
|
value: 51.939 |
|
- type: mrr_at_1 |
|
value: 47.0 |
|
- type: mrr_at_10 |
|
value: 54.730000000000004 |
|
- type: mrr_at_100 |
|
value: 55.31399999999999 |
|
- type: mrr_at_1000 |
|
value: 55.346 |
|
- type: mrr_at_3 |
|
value: 52.0 |
|
- type: mrr_at_5 |
|
value: 53.783 |
|
- type: ndcg_at_1 |
|
value: 47.0 |
|
- type: ndcg_at_10 |
|
value: 57.82899999999999 |
|
- type: ndcg_at_100 |
|
value: 61.49400000000001 |
|
- type: ndcg_at_1000 |
|
value: 62.676 |
|
- type: ndcg_at_3 |
|
value: 52.373000000000005 |
|
- type: ndcg_at_5 |
|
value: 55.481 |
|
- type: precision_at_1 |
|
value: 47.0 |
|
- type: precision_at_10 |
|
value: 7.867 |
|
- type: precision_at_100 |
|
value: 0.997 |
|
- type: precision_at_1000 |
|
value: 0.11 |
|
- type: precision_at_3 |
|
value: 20.556 |
|
- type: precision_at_5 |
|
value: 14.066999999999998 |
|
- type: recall_at_1 |
|
value: 44.583 |
|
- type: recall_at_10 |
|
value: 71.172 |
|
- type: recall_at_100 |
|
value: 87.7 |
|
- type: recall_at_1000 |
|
value: 97.333 |
|
- type: recall_at_3 |
|
value: 56.511 |
|
- type: recall_at_5 |
|
value: 64.206 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.66237623762376 |
|
- type: cos_sim_ap |
|
value: 90.35465126226322 |
|
- type: cos_sim_f1 |
|
value: 82.44575936883628 |
|
- type: cos_sim_precision |
|
value: 81.32295719844358 |
|
- type: cos_sim_recall |
|
value: 83.6 |
|
- type: dot_accuracy |
|
value: 99.66237623762376 |
|
- type: dot_ap |
|
value: 90.35464287920453 |
|
- type: dot_f1 |
|
value: 82.44575936883628 |
|
- type: dot_precision |
|
value: 81.32295719844358 |
|
- type: dot_recall |
|
value: 83.6 |
|
- type: euclidean_accuracy |
|
value: 99.66237623762376 |
|
- type: euclidean_ap |
|
value: 90.3546512622632 |
|
- type: euclidean_f1 |
|
value: 82.44575936883628 |
|
- type: euclidean_precision |
|
value: 81.32295719844358 |
|
- type: euclidean_recall |
|
value: 83.6 |
|
- type: manhattan_accuracy |
|
value: 99.65940594059406 |
|
- type: manhattan_ap |
|
value: 90.29220174849843 |
|
- type: manhattan_f1 |
|
value: 82.4987605354487 |
|
- type: manhattan_precision |
|
value: 81.80924287118977 |
|
- type: manhattan_recall |
|
value: 83.2 |
|
- type: max_accuracy |
|
value: 99.66237623762376 |
|
- type: max_ap |
|
value: 90.35465126226322 |
|
- type: max_f1 |
|
value: 82.4987605354487 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 65.0394225901397 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 35.27954189859326 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.99055979974896 |
|
- type: mrr |
|
value: 51.82745257193787 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.21655465344237 |
|
- type: cos_sim_spearman |
|
value: 29.853205339630172 |
|
- type: dot_pearson |
|
value: 30.216540628083564 |
|
- type: dot_spearman |
|
value: 29.868978894753027 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.2 |
|
- type: map_at_10 |
|
value: 1.398 |
|
- type: map_at_100 |
|
value: 7.406 |
|
- type: map_at_1000 |
|
value: 18.401 |
|
- type: map_at_3 |
|
value: 0.479 |
|
- type: map_at_5 |
|
value: 0.772 |
|
- type: mrr_at_1 |
|
value: 70.0 |
|
- type: mrr_at_10 |
|
value: 79.25999999999999 |
|
- type: mrr_at_100 |
|
value: 79.25999999999999 |
|
- type: mrr_at_1000 |
|
value: 79.25999999999999 |
|
- type: mrr_at_3 |
|
value: 77.333 |
|
- type: mrr_at_5 |
|
value: 78.133 |
|
- type: ndcg_at_1 |
|
value: 63.0 |
|
- type: ndcg_at_10 |
|
value: 58.548 |
|
- type: ndcg_at_100 |
|
value: 45.216 |
|
- type: ndcg_at_1000 |
|
value: 41.149 |
|
- type: ndcg_at_3 |
|
value: 60.641999999999996 |
|
- type: ndcg_at_5 |
|
value: 61.135 |
|
- type: precision_at_1 |
|
value: 70.0 |
|
- type: precision_at_10 |
|
value: 64.0 |
|
- type: precision_at_100 |
|
value: 46.92 |
|
- type: precision_at_1000 |
|
value: 18.642 |
|
- type: precision_at_3 |
|
value: 64.667 |
|
- type: precision_at_5 |
|
value: 66.4 |
|
- type: recall_at_1 |
|
value: 0.2 |
|
- type: recall_at_10 |
|
value: 1.6729999999999998 |
|
- type: recall_at_100 |
|
value: 10.856 |
|
- type: recall_at_1000 |
|
value: 38.964999999999996 |
|
- type: recall_at_3 |
|
value: 0.504 |
|
- type: recall_at_5 |
|
value: 0.852 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.6629999999999998 |
|
- type: map_at_10 |
|
value: 8.601 |
|
- type: map_at_100 |
|
value: 14.354 |
|
- type: map_at_1000 |
|
value: 15.927 |
|
- type: map_at_3 |
|
value: 4.1930000000000005 |
|
- type: map_at_5 |
|
value: 5.655 |
|
- type: mrr_at_1 |
|
value: 18.367 |
|
- type: mrr_at_10 |
|
value: 34.466 |
|
- type: mrr_at_100 |
|
value: 35.235 |
|
- type: mrr_at_1000 |
|
value: 35.27 |
|
- type: mrr_at_3 |
|
value: 28.571 |
|
- type: mrr_at_5 |
|
value: 31.531 |
|
- type: ndcg_at_1 |
|
value: 14.285999999999998 |
|
- type: ndcg_at_10 |
|
value: 20.374 |
|
- type: ndcg_at_100 |
|
value: 33.532000000000004 |
|
- type: ndcg_at_1000 |
|
value: 45.561 |
|
- type: ndcg_at_3 |
|
value: 18.442 |
|
- type: ndcg_at_5 |
|
value: 18.076 |
|
- type: precision_at_1 |
|
value: 18.367 |
|
- type: precision_at_10 |
|
value: 20.204 |
|
- type: precision_at_100 |
|
value: 7.489999999999999 |
|
- type: precision_at_1000 |
|
value: 1.5630000000000002 |
|
- type: precision_at_3 |
|
value: 21.769 |
|
- type: precision_at_5 |
|
value: 20.408 |
|
- type: recall_at_1 |
|
value: 1.6629999999999998 |
|
- type: recall_at_10 |
|
value: 15.549 |
|
- type: recall_at_100 |
|
value: 47.497 |
|
- type: recall_at_1000 |
|
value: 84.524 |
|
- type: recall_at_3 |
|
value: 5.289 |
|
- type: recall_at_5 |
|
value: 8.035 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.8194 |
|
- type: ap |
|
value: 14.447702451658554 |
|
- type: f1 |
|
value: 55.13659412856185 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 63.310696095076416 |
|
- type: f1 |
|
value: 63.360434851097814 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 51.30677907335145 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.12386004649221 |
|
- type: cos_sim_ap |
|
value: 73.99096426215495 |
|
- type: cos_sim_f1 |
|
value: 68.18416968442834 |
|
- type: cos_sim_precision |
|
value: 66.86960933536275 |
|
- type: cos_sim_recall |
|
value: 69.55145118733509 |
|
- type: dot_accuracy |
|
value: 86.12386004649221 |
|
- type: dot_ap |
|
value: 73.99096813038672 |
|
- type: dot_f1 |
|
value: 68.18416968442834 |
|
- type: dot_precision |
|
value: 66.86960933536275 |
|
- type: dot_recall |
|
value: 69.55145118733509 |
|
- type: euclidean_accuracy |
|
value: 86.12386004649221 |
|
- type: euclidean_ap |
|
value: 73.99095984980165 |
|
- type: euclidean_f1 |
|
value: 68.18416968442834 |
|
- type: euclidean_precision |
|
value: 66.86960933536275 |
|
- type: euclidean_recall |
|
value: 69.55145118733509 |
|
- type: manhattan_accuracy |
|
value: 86.09405734040651 |
|
- type: manhattan_ap |
|
value: 73.96825745608601 |
|
- type: manhattan_f1 |
|
value: 68.13888179729383 |
|
- type: manhattan_precision |
|
value: 65.99901088031652 |
|
- type: manhattan_recall |
|
value: 70.42216358839049 |
|
- type: max_accuracy |
|
value: 86.12386004649221 |
|
- type: max_ap |
|
value: 73.99096813038672 |
|
- type: max_f1 |
|
value: 68.18416968442834 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.99367407924865 |
|
- type: cos_sim_ap |
|
value: 86.19720829843081 |
|
- type: cos_sim_f1 |
|
value: 78.39889075384951 |
|
- type: cos_sim_precision |
|
value: 74.5110278818144 |
|
- type: cos_sim_recall |
|
value: 82.71481367416075 |
|
- type: dot_accuracy |
|
value: 88.99367407924865 |
|
- type: dot_ap |
|
value: 86.19718471454047 |
|
- type: dot_f1 |
|
value: 78.39889075384951 |
|
- type: dot_precision |
|
value: 74.5110278818144 |
|
- type: dot_recall |
|
value: 82.71481367416075 |
|
- type: euclidean_accuracy |
|
value: 88.99367407924865 |
|
- type: euclidean_ap |
|
value: 86.1972021422436 |
|
- type: euclidean_f1 |
|
value: 78.39889075384951 |
|
- type: euclidean_precision |
|
value: 74.5110278818144 |
|
- type: euclidean_recall |
|
value: 82.71481367416075 |
|
- type: manhattan_accuracy |
|
value: 88.95680521597392 |
|
- type: manhattan_ap |
|
value: 86.16659921351506 |
|
- type: manhattan_f1 |
|
value: 78.39125971550081 |
|
- type: manhattan_precision |
|
value: 74.82502799552073 |
|
- type: manhattan_recall |
|
value: 82.31444410224823 |
|
- type: max_accuracy |
|
value: 88.99367407924865 |
|
- type: max_ap |
|
value: 86.19720829843081 |
|
- type: max_f1 |
|
value: 78.39889075384951 |
|
--- |
|
|
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# hkunlp/instructor-base |
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We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks! |
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The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! |
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**************************** **Updates** **************************** |
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* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-base) trained with hard negatives, which gives better performance. |
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* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-base) and [project page](https://instructor-embedding.github.io/)! Check them out! |
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## Quick start |
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<hr /> |
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## Installation |
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```bash |
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pip install InstructorEmbedding |
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``` |
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## Compute your customized embeddings |
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Then you can use the model like this to calculate domain-specific and task-aware embeddings: |
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```python |
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from InstructorEmbedding import INSTRUCTOR |
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model = INSTRUCTOR('hkunlp/instructor-base') |
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sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" |
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instruction = "Represent the Science title:" |
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embeddings = model.encode([[instruction,sentence]]) |
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print(embeddings) |
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``` |
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## Use cases |
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<hr /> |
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## Calculate embeddings for your customized texts |
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If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: |
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Represent the `domain` `text_type` for `task_objective`: |
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* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. |
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* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. |
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* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. |
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## Calculate Sentence similarities |
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You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. |
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```python |
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from sklearn.metrics.pairwise import cosine_similarity |
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sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], |
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['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] |
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sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], |
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['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] |
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embeddings_a = model.encode(sentences_a) |
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embeddings_b = model.encode(sentences_b) |
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similarities = cosine_similarity(embeddings_a,embeddings_b) |
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print(similarities) |
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``` |
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## Information Retrieval |
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You can also use **customized embeddings** for information retrieval. |
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```python |
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import numpy as np |
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from sklearn.metrics.pairwise import cosine_similarity |
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query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] |
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corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], |
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['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], |
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['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] |
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query_embeddings = model.encode(query) |
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corpus_embeddings = model.encode(corpus) |
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similarities = cosine_similarity(query_embeddings,corpus_embeddings) |
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retrieved_doc_id = np.argmax(similarities) |
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print(retrieved_doc_id) |
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``` |
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## Clustering |
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Use **customized embeddings** for clustering texts in groups. |
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```python |
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import sklearn.cluster |
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sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], |
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['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], |
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['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], |
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['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], |
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['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] |
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embeddings = model.encode(sentences) |
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clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) |
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clustering_model.fit(embeddings) |
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cluster_assignment = clustering_model.labels_ |
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print(cluster_assignment) |
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``` |
