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--- |
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tags: |
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- ctranslate2 |
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- int8 |
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- float16 |
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- mteb |
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- sentence-similarity |
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- sentence-transformers |
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- Sentence Transformers |
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model-index: |
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- name: gte-base |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
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- type: accuracy |
|
value: 74.17910447761193 |
|
- type: ap |
|
value: 36.827146398068926 |
|
- type: f1 |
|
value: 68.11292888046363 |
|
- task: |
|
type: Classification |
|
dataset: |
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type: mteb/amazon_polarity |
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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: 91.77345000000001 |
|
- type: ap |
|
value: 88.33530426691347 |
|
- type: f1 |
|
value: 91.76549906404642 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
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- type: accuracy |
|
value: 48.964 |
|
- type: f1 |
|
value: 48.22995586184998 |
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- 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 |
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metrics: |
|
- type: map_at_1 |
|
value: 32.147999999999996 |
|
- type: map_at_10 |
|
value: 48.253 |
|
- type: map_at_100 |
|
value: 49.038 |
|
- type: map_at_1000 |
|
value: 49.042 |
|
- type: map_at_3 |
|
value: 43.433 |
|
- type: map_at_5 |
|
value: 46.182 |
|
- type: mrr_at_1 |
|
value: 32.717 |
|
- type: mrr_at_10 |
|
value: 48.467 |
|
- type: mrr_at_100 |
|
value: 49.252 |
|
- type: mrr_at_1000 |
|
value: 49.254999999999995 |
|
- type: mrr_at_3 |
|
value: 43.599 |
|
- type: mrr_at_5 |
|
value: 46.408 |
|
- type: ndcg_at_1 |
|
value: 32.147999999999996 |
|
- type: ndcg_at_10 |
|
value: 57.12199999999999 |
|
- type: ndcg_at_100 |
|
value: 60.316 |
|
- type: ndcg_at_1000 |
|
value: 60.402 |
|
- type: ndcg_at_3 |
|
value: 47.178 |
|
- type: ndcg_at_5 |
|
value: 52.146 |
|
- type: precision_at_1 |
|
value: 32.147999999999996 |
|
- type: precision_at_10 |
|
value: 8.542 |
|
- type: precision_at_100 |
|
value: 0.9900000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 19.346 |
|
- type: precision_at_5 |
|
value: 14.026 |
|
- type: recall_at_1 |
|
value: 32.147999999999996 |
|
- type: recall_at_10 |
|
value: 85.42 |
|
- type: recall_at_100 |
|
value: 99.004 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 58.037000000000006 |
|
- type: recall_at_5 |
|
value: 70.128 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
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name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
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metrics: |
|
- type: v_measure |
|
value: 48.59706013699614 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
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name: MTEB ArxivClusteringS2S |
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config: default |
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split: test |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
|
- type: v_measure |
|
value: 43.01463593002057 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
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- type: map |
|
value: 61.80250355752458 |
|
- type: mrr |
|
value: 74.79455216989844 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
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name: MTEB BIOSSES |
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config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 89.87448576082345 |
|
- type: cos_sim_spearman |
|
value: 87.64235843637468 |
|
- type: euclidean_pearson |
|
value: 88.4901825511062 |
|
- type: euclidean_spearman |
|
value: 87.74537283182033 |
|
- type: manhattan_pearson |
|
value: 88.39040638362911 |
|
- type: manhattan_spearman |
|
value: 87.62669542888003 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
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- type: accuracy |
|
value: 85.06818181818183 |
|
- type: f1 |
|
value: 85.02524460098233 |
|
- task: |
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type: Clustering |
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dataset: |
|
type: mteb/biorxiv-clustering-p2p |
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name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
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metrics: |
|
- type: v_measure |
|
value: 38.20471092679967 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 36.58967592147641 |
|
- 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 |
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metrics: |
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- type: map_at_1 |
|
value: 32.411 |
|
- type: map_at_10 |
|
value: 45.162 |
|
- type: map_at_100 |
|
value: 46.717 |
|
- type: map_at_1000 |
|
value: 46.836 |
|
- type: map_at_3 |
|
value: 41.428 |
|
- type: map_at_5 |
|
value: 43.54 |
|
- type: mrr_at_1 |
|
value: 39.914 |
|
- type: mrr_at_10 |
|
value: 51.534 |
|
- type: mrr_at_100 |
|
value: 52.185 |
|
- type: mrr_at_1000 |
|
value: 52.22 |
|
- type: mrr_at_3 |
|
value: 49.046 |
|
- type: mrr_at_5 |
|
value: 50.548 |
|
- type: ndcg_at_1 |
|
value: 39.914 |
|
- type: ndcg_at_10 |
|
value: 52.235 |
|
- type: ndcg_at_100 |
|
value: 57.4 |
|
- type: ndcg_at_1000 |
|
value: 58.982 |
|
- type: ndcg_at_3 |
|
value: 47.332 |
|
- type: ndcg_at_5 |
|
value: 49.62 |
|
- type: precision_at_1 |
|
value: 39.914 |
|
- type: precision_at_10 |
|
value: 10.258000000000001 |
|
- type: precision_at_100 |
|
value: 1.6219999999999999 |
|
- type: precision_at_1000 |
|
value: 0.20500000000000002 |
|
- type: precision_at_3 |
|
value: 23.462 |
|
- type: precision_at_5 |
|
value: 16.71 |
|
- type: recall_at_1 |
|
value: 32.411 |
|
- type: recall_at_10 |
|
value: 65.408 |
|
- type: recall_at_100 |
|
value: 87.248 |
|
- type: recall_at_1000 |
|
value: 96.951 |
|
- type: recall_at_3 |
|
value: 50.349999999999994 |
|
- type: recall_at_5 |
|
value: 57.431 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
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config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.911 |
|
- type: map_at_10 |
|
value: 42.608000000000004 |
|
- type: map_at_100 |
|
value: 43.948 |
|
- type: map_at_1000 |
|
value: 44.089 |
|
- type: map_at_3 |
|
value: 39.652 |
|
- type: map_at_5 |
|
value: 41.236 |
|
- type: mrr_at_1 |
|
value: 40.064 |
|
- type: mrr_at_10 |
|
value: 48.916 |
|
- type: mrr_at_100 |
|
value: 49.539 |
|
- type: mrr_at_1000 |
|
value: 49.583 |
|
- type: mrr_at_3 |
|
value: 46.741 |
|
- type: mrr_at_5 |
|
value: 48.037 |
|
- type: ndcg_at_1 |
|
value: 40.064 |
|
- type: ndcg_at_10 |
|
value: 48.442 |
|
- type: ndcg_at_100 |
|
value: 52.798 |
|
- type: ndcg_at_1000 |
|
value: 54.871 |
|
- type: ndcg_at_3 |
|
value: 44.528 |
|
- type: ndcg_at_5 |
|
value: 46.211 |
|
- type: precision_at_1 |
|
value: 40.064 |
|
- type: precision_at_10 |
|
value: 9.178 |
|
- type: precision_at_100 |
|
value: 1.452 |
|
- type: precision_at_1000 |
|
value: 0.193 |
|
- type: precision_at_3 |
|
value: 21.614 |
|
- type: precision_at_5 |
|
value: 15.185 |
|
- type: recall_at_1 |
|
value: 31.911 |
|
- type: recall_at_10 |
|
value: 58.155 |
|
- type: recall_at_100 |
|
value: 76.46300000000001 |
|
- type: recall_at_1000 |
|
value: 89.622 |
|
- type: recall_at_3 |
|
value: 46.195 |
|
- type: recall_at_5 |
|
value: 51.288999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.597 |
|
- type: map_at_10 |
|
value: 54.290000000000006 |
|
- type: map_at_100 |
|
value: 55.340999999999994 |
|
- type: map_at_1000 |
|
value: 55.388999999999996 |
|
- type: map_at_3 |
|
value: 50.931000000000004 |
|
- type: map_at_5 |
|
value: 52.839999999999996 |
|
- type: mrr_at_1 |
|
value: 46.646 |
|
- type: mrr_at_10 |
|
value: 57.524 |
|
- type: mrr_at_100 |
|
value: 58.225 |
|
- type: mrr_at_1000 |
|
value: 58.245999999999995 |
|
- type: mrr_at_3 |
|
value: 55.235 |
|
- type: mrr_at_5 |
|
value: 56.589 |
|
- type: ndcg_at_1 |
|
value: 46.646 |
|
- type: ndcg_at_10 |
|
value: 60.324999999999996 |
|
- type: ndcg_at_100 |
|
value: 64.30900000000001 |
|
- type: ndcg_at_1000 |
|
value: 65.19 |
|
- type: ndcg_at_3 |
|
value: 54.983000000000004 |
|
- type: ndcg_at_5 |
|
value: 57.621 |
|
- type: precision_at_1 |
|
value: 46.646 |
|
- type: precision_at_10 |
|
value: 9.774 |
|
- type: precision_at_100 |
|
value: 1.265 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 24.911 |
|
- type: precision_at_5 |
|
value: 16.977999999999998 |
|
- type: recall_at_1 |
|
value: 40.597 |
|
- type: recall_at_10 |
|
value: 74.773 |
|
- type: recall_at_100 |
|
value: 91.61200000000001 |
|
- type: recall_at_1000 |
|
value: 97.726 |
|
- type: recall_at_3 |
|
value: 60.458 |
|
- type: recall_at_5 |
|
value: 66.956 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.122 |
|
- type: map_at_10 |
|
value: 36.711 |
|
- type: map_at_100 |
|
value: 37.775 |
|
- type: map_at_1000 |
|
value: 37.842999999999996 |
|
- type: map_at_3 |
|
value: 33.693 |
|
- type: map_at_5 |
|
value: 35.607 |
|
- type: mrr_at_1 |
|
value: 29.153000000000002 |
|
- type: mrr_at_10 |
|
value: 38.873999999999995 |
|
- type: mrr_at_100 |
|
value: 39.739000000000004 |
|
- type: mrr_at_1000 |
|
value: 39.794000000000004 |
|
- type: mrr_at_3 |
|
value: 36.102000000000004 |
|
- type: mrr_at_5 |
|
value: 37.876 |
|
- type: ndcg_at_1 |
|
value: 29.153000000000002 |
|
- type: ndcg_at_10 |
|
value: 42.048 |
|
- type: ndcg_at_100 |
|
value: 47.144999999999996 |
|
- type: ndcg_at_1000 |
|
value: 48.901 |
|
- type: ndcg_at_3 |
|
value: 36.402 |
|
- type: ndcg_at_5 |
|
value: 39.562999999999995 |
|
- type: precision_at_1 |
|
value: 29.153000000000002 |
|
- type: precision_at_10 |
|
value: 6.4750000000000005 |
|
- type: precision_at_100 |
|
value: 0.951 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 15.479999999999999 |
|
- type: precision_at_5 |
|
value: 11.028 |
|
- type: recall_at_1 |
|
value: 27.122 |
|
- type: recall_at_10 |
|
value: 56.279999999999994 |
|
- type: recall_at_100 |
|
value: 79.597 |
|
- type: recall_at_1000 |
|
value: 92.804 |
|
- type: recall_at_3 |
|
value: 41.437000000000005 |
|
- type: recall_at_5 |
|
value: 49.019 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.757 |
|
- type: map_at_10 |
|
value: 26.739 |
|
- type: map_at_100 |
|
value: 28.015 |
|
- type: map_at_1000 |
|
value: 28.127999999999997 |
|
- type: map_at_3 |
|
value: 23.986 |
|
- type: map_at_5 |
|
value: 25.514 |
|
- type: mrr_at_1 |
|
value: 22.015 |
|
- type: mrr_at_10 |
|
value: 31.325999999999997 |
|
- type: mrr_at_100 |
|
value: 32.368 |
|
- type: mrr_at_1000 |
|
value: 32.426 |
|
- type: mrr_at_3 |
|
value: 28.897000000000002 |
|
- type: mrr_at_5 |
|
value: 30.147000000000002 |
|
- type: ndcg_at_1 |
|
value: 22.015 |
|
- type: ndcg_at_10 |
|
value: 32.225 |
|
- type: ndcg_at_100 |
|
value: 38.405 |
|
- type: ndcg_at_1000 |
|
value: 40.932 |
|
- type: ndcg_at_3 |
|
value: 27.403 |
|
- type: ndcg_at_5 |
|
value: 29.587000000000003 |
|
- type: precision_at_1 |
|
value: 22.015 |
|
- type: precision_at_10 |
|
value: 5.9830000000000005 |
|
- type: precision_at_100 |
|
value: 1.051 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 13.391 |
|
- type: precision_at_5 |
|
value: 9.602 |
|
- type: recall_at_1 |
|
value: 17.757 |
|
- type: recall_at_10 |
|
value: 44.467 |
|
- type: recall_at_100 |
|
value: 71.53699999999999 |
|
- type: recall_at_1000 |
|
value: 89.281 |
|
- type: recall_at_3 |
|
value: 31.095 |
|
- type: recall_at_5 |
|
value: 36.818 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.354 |
|
- type: map_at_10 |
|
value: 42.134 |
|
- type: map_at_100 |
|
value: 43.429 |
|
- type: map_at_1000 |
|
value: 43.532 |
|
- type: map_at_3 |
|
value: 38.491 |
|
- type: map_at_5 |
|
value: 40.736 |
|
- type: mrr_at_1 |
|
value: 37.247 |
|
- type: mrr_at_10 |
|
value: 47.775 |
|
- type: mrr_at_100 |
|
value: 48.522999999999996 |
|
- type: mrr_at_1000 |
|
value: 48.567 |
|
- type: mrr_at_3 |
|
value: 45.059 |
|
- type: mrr_at_5 |
|
value: 46.811 |
|
- type: ndcg_at_1 |
|
value: 37.247 |
|
- type: ndcg_at_10 |
|
value: 48.609 |
|
- type: ndcg_at_100 |
|
value: 53.782 |
|
- type: ndcg_at_1000 |
|
value: 55.666000000000004 |
|
- type: ndcg_at_3 |
|
value: 42.866 |
|
- type: ndcg_at_5 |
|
value: 46.001 |
|
- type: precision_at_1 |
|
value: 37.247 |
|
- type: precision_at_10 |
|
value: 8.892999999999999 |
|
- type: precision_at_100 |
|
value: 1.341 |
|
- type: precision_at_1000 |
|
value: 0.168 |
|
- type: precision_at_3 |
|
value: 20.5 |
|
- type: precision_at_5 |
|
value: 14.976 |
|
- type: recall_at_1 |
|
value: 30.354 |
|
- type: recall_at_10 |
|
value: 62.273 |
|
- type: recall_at_100 |
|
value: 83.65599999999999 |
|
- type: recall_at_1000 |
|
value: 95.82000000000001 |
|
- type: recall_at_3 |
|
value: 46.464 |
|
- type: recall_at_5 |
|
value: 54.225 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.949 |
|
- type: map_at_10 |
|
value: 37.230000000000004 |
|
- type: map_at_100 |
|
value: 38.644 |
|
- type: map_at_1000 |
|
value: 38.751999999999995 |
|
- type: map_at_3 |
|
value: 33.816 |
|
- type: map_at_5 |
|
value: 35.817 |
|
- type: mrr_at_1 |
|
value: 33.446999999999996 |
|
- type: mrr_at_10 |
|
value: 42.970000000000006 |
|
- type: mrr_at_100 |
|
value: 43.873 |
|
- type: mrr_at_1000 |
|
value: 43.922 |
|
- type: mrr_at_3 |
|
value: 40.467999999999996 |
|
- type: mrr_at_5 |
|
value: 41.861 |
|
- type: ndcg_at_1 |
|
value: 33.446999999999996 |
|
- type: ndcg_at_10 |
|
value: 43.403000000000006 |
|
- type: ndcg_at_100 |
|
value: 49.247 |
|
- type: ndcg_at_1000 |
|
value: 51.361999999999995 |
|
- type: ndcg_at_3 |
|
value: 38.155 |
|
- type: ndcg_at_5 |
|
value: 40.643 |
|
- type: precision_at_1 |
|
value: 33.446999999999996 |
|
- type: precision_at_10 |
|
value: 8.128 |
|
- type: precision_at_100 |
|
value: 1.274 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 18.493000000000002 |
|
- type: precision_at_5 |
|
value: 13.333 |
|
- type: recall_at_1 |
|
value: 26.949 |
|
- type: recall_at_10 |
|
value: 56.006 |
|
- type: recall_at_100 |
|
value: 80.99199999999999 |
|
- type: recall_at_1000 |
|
value: 95.074 |
|
- type: recall_at_3 |
|
value: 40.809 |
|
- type: recall_at_5 |
|
value: 47.57 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.243583333333333 |
|
- type: map_at_10 |
|
value: 37.193250000000006 |
|
- type: map_at_100 |
|
value: 38.44833333333334 |
|
- type: map_at_1000 |
|
value: 38.56083333333333 |
|
- type: map_at_3 |
|
value: 34.06633333333333 |
|
- type: map_at_5 |
|
value: 35.87858333333334 |
|
- type: mrr_at_1 |
|
value: 32.291583333333335 |
|
- type: mrr_at_10 |
|
value: 41.482749999999996 |
|
- type: mrr_at_100 |
|
value: 42.33583333333333 |
|
- type: mrr_at_1000 |
|
value: 42.38683333333333 |
|
- type: mrr_at_3 |
|
value: 38.952999999999996 |
|
- type: mrr_at_5 |
|
value: 40.45333333333333 |
|
- type: ndcg_at_1 |
|
value: 32.291583333333335 |
|
- type: ndcg_at_10 |
|
value: 42.90533333333334 |
|
- type: ndcg_at_100 |
|
value: 48.138666666666666 |
|
- type: ndcg_at_1000 |
|
value: 50.229083333333335 |
|
- type: ndcg_at_3 |
|
value: 37.76133333333334 |
|
- type: ndcg_at_5 |
|
value: 40.31033333333334 |
|
- type: precision_at_1 |
|
value: 32.291583333333335 |
|
- type: precision_at_10 |
|
value: 7.585583333333333 |
|
- type: precision_at_100 |
|
value: 1.2045000000000001 |
|
- type: precision_at_1000 |
|
value: 0.15733333333333335 |
|
- type: precision_at_3 |
|
value: 17.485416666666666 |
|
- type: precision_at_5 |
|
value: 12.5145 |
|
- type: recall_at_1 |
|
value: 27.243583333333333 |
|
- type: recall_at_10 |
|
value: 55.45108333333334 |
|
- type: recall_at_100 |
|
value: 78.25858333333335 |
|
- type: recall_at_1000 |
|
value: 92.61716666666665 |
|
- type: recall_at_3 |
|
value: 41.130583333333334 |
|
- type: recall_at_5 |
|
value: 47.73133333333334 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.325 |
|
- type: map_at_10 |
|
value: 32.795 |
|
- type: map_at_100 |
|
value: 33.96 |
|
- type: map_at_1000 |
|
value: 34.054 |
|
- type: map_at_3 |
|
value: 30.64 |
|
- type: map_at_5 |
|
value: 31.771 |
|
- type: mrr_at_1 |
|
value: 29.908 |
|
- type: mrr_at_10 |
|
value: 35.83 |
|
- type: mrr_at_100 |
|
value: 36.868 |
|
- type: mrr_at_1000 |
|
value: 36.928 |
|
- type: mrr_at_3 |
|
value: 33.896 |
|
- type: mrr_at_5 |
|
value: 34.893 |
|
- type: ndcg_at_1 |
|
value: 29.908 |
|
- type: ndcg_at_10 |
|
value: 36.746 |
|
- type: ndcg_at_100 |
|
value: 42.225 |
|
- type: ndcg_at_1000 |
|
value: 44.523 |
|
- type: ndcg_at_3 |
|
value: 32.82 |
|
- type: ndcg_at_5 |
|
value: 34.583000000000006 |
|
- type: precision_at_1 |
|
value: 29.908 |
|
- type: precision_at_10 |
|
value: 5.6129999999999995 |
|
- type: precision_at_100 |
|
value: 0.9079999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 13.753000000000002 |
|
- type: precision_at_5 |
|
value: 9.417 |
|
- type: recall_at_1 |
|
value: 26.325 |
|
- type: recall_at_10 |
|
value: 45.975 |
|
- type: recall_at_100 |
|
value: 70.393 |
|
- type: recall_at_1000 |
|
value: 87.217 |
|
- type: recall_at_3 |
|
value: 35.195 |
|
- type: recall_at_5 |
|
value: 39.69 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.828 |
|
- type: map_at_10 |
|
value: 25.759 |
|
- type: map_at_100 |
|
value: 26.961000000000002 |
|
- type: map_at_1000 |
|
value: 27.094 |
|
- type: map_at_3 |
|
value: 23.166999999999998 |
|
- type: map_at_5 |
|
value: 24.610000000000003 |
|
- type: mrr_at_1 |
|
value: 21.61 |
|
- type: mrr_at_10 |
|
value: 29.605999999999998 |
|
- type: mrr_at_100 |
|
value: 30.586000000000002 |
|
- type: mrr_at_1000 |
|
value: 30.664 |
|
- type: mrr_at_3 |
|
value: 27.214 |
|
- type: mrr_at_5 |
|
value: 28.571 |
|
- type: ndcg_at_1 |
|
value: 21.61 |
|
- type: ndcg_at_10 |
|
value: 30.740000000000002 |
|
- type: ndcg_at_100 |
|
value: 36.332 |
|
- type: ndcg_at_1000 |
|
value: 39.296 |
|
- type: ndcg_at_3 |
|
value: 26.11 |
|
- type: ndcg_at_5 |
|
value: 28.297 |
|
- type: precision_at_1 |
|
value: 21.61 |
|
- type: precision_at_10 |
|
value: 5.643 |
|
- type: precision_at_100 |
|
value: 1.0 |
|
- type: precision_at_1000 |
|
value: 0.14400000000000002 |
|
- type: precision_at_3 |
|
value: 12.4 |
|
- type: precision_at_5 |
|
value: 9.119 |
|
- type: recall_at_1 |
|
value: 17.828 |
|
- type: recall_at_10 |
|
value: 41.876000000000005 |
|
- type: recall_at_100 |
|
value: 66.648 |
|
- type: recall_at_1000 |
|
value: 87.763 |
|
- type: recall_at_3 |
|
value: 28.957 |
|
- type: recall_at_5 |
|
value: 34.494 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.921000000000003 |
|
- type: map_at_10 |
|
value: 37.156 |
|
- type: map_at_100 |
|
value: 38.399 |
|
- type: map_at_1000 |
|
value: 38.498 |
|
- type: map_at_3 |
|
value: 34.134 |
|
- type: map_at_5 |
|
value: 35.936 |
|
- type: mrr_at_1 |
|
value: 32.649 |
|
- type: mrr_at_10 |
|
value: 41.19 |
|
- type: mrr_at_100 |
|
value: 42.102000000000004 |
|
- type: mrr_at_1000 |
|
value: 42.157 |
|
- type: mrr_at_3 |
|
value: 38.464 |
|
- type: mrr_at_5 |
|
value: 40.148 |
|
- type: ndcg_at_1 |
|
value: 32.649 |
|
- type: ndcg_at_10 |
|
value: 42.679 |
|
- type: ndcg_at_100 |
|
value: 48.27 |
|
- type: ndcg_at_1000 |
|
value: 50.312 |
|
- type: ndcg_at_3 |
|
value: 37.269000000000005 |
|
- type: ndcg_at_5 |
|
value: 40.055 |
|
- type: precision_at_1 |
|
value: 32.649 |
|
- type: precision_at_10 |
|
value: 7.155 |
|
- type: precision_at_100 |
|
value: 1.124 |
|
- type: precision_at_1000 |
|
value: 0.14100000000000001 |
|
- type: precision_at_3 |
|
value: 16.791 |
|
- type: precision_at_5 |
|
value: 12.015 |
|
- type: recall_at_1 |
|
value: 27.921000000000003 |
|
- type: recall_at_10 |
|
value: 55.357 |
|
- type: recall_at_100 |
|
value: 79.476 |
|
- type: recall_at_1000 |
|
value: 93.314 |
|
- type: recall_at_3 |
|
value: 40.891 |
|
- type: recall_at_5 |
|
value: 47.851 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.524 |
|
- type: map_at_10 |
|
value: 35.135 |
|
- type: map_at_100 |
|
value: 36.665 |
|
- type: map_at_1000 |
|
value: 36.886 |
|
- type: map_at_3 |
|
value: 31.367 |
|
- type: map_at_5 |
|
value: 33.724 |
|
- type: mrr_at_1 |
|
value: 30.631999999999998 |
|
- type: mrr_at_10 |
|
value: 39.616 |
|
- type: mrr_at_100 |
|
value: 40.54 |
|
- type: mrr_at_1000 |
|
value: 40.585 |
|
- type: mrr_at_3 |
|
value: 36.462 |
|
- type: mrr_at_5 |
|
value: 38.507999999999996 |
|
- type: ndcg_at_1 |
|
value: 30.631999999999998 |
|
- type: ndcg_at_10 |
|
value: 41.61 |
|
- type: ndcg_at_100 |
|
value: 47.249 |
|
- type: ndcg_at_1000 |
|
value: 49.662 |
|
- type: ndcg_at_3 |
|
value: 35.421 |
|
- type: ndcg_at_5 |
|
value: 38.811 |
|
- type: precision_at_1 |
|
value: 30.631999999999998 |
|
- type: precision_at_10 |
|
value: 8.123 |
|
- type: precision_at_100 |
|
value: 1.5810000000000002 |
|
- type: precision_at_1000 |
|
value: 0.245 |
|
- type: precision_at_3 |
|
value: 16.337 |
|
- type: precision_at_5 |
|
value: 12.568999999999999 |
|
- type: recall_at_1 |
|
value: 25.524 |
|
- type: recall_at_10 |
|
value: 54.994 |
|
- type: recall_at_100 |
|
value: 80.03099999999999 |
|
- type: recall_at_1000 |
|
value: 95.25099999999999 |
|
- type: recall_at_3 |
|
value: 37.563 |
|
- type: recall_at_5 |
|
value: 46.428999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.224 |
|
- type: map_at_10 |
|
value: 30.599999999999998 |
|
- type: map_at_100 |
|
value: 31.526 |
|
- type: map_at_1000 |
|
value: 31.629 |
|
- type: map_at_3 |
|
value: 27.491 |
|
- type: map_at_5 |
|
value: 29.212 |
|
- type: mrr_at_1 |
|
value: 24.214 |
|
- type: mrr_at_10 |
|
value: 32.632 |
|
- type: mrr_at_100 |
|
value: 33.482 |
|
- type: mrr_at_1000 |
|
value: 33.550000000000004 |
|
- type: mrr_at_3 |
|
value: 29.852 |
|
- type: mrr_at_5 |
|
value: 31.451 |
|
- type: ndcg_at_1 |
|
value: 24.214 |
|
- type: ndcg_at_10 |
|
value: 35.802 |
|
- type: ndcg_at_100 |
|
value: 40.502 |
|
- type: ndcg_at_1000 |
|
value: 43.052 |
|
- type: ndcg_at_3 |
|
value: 29.847 |
|
- type: ndcg_at_5 |
|
value: 32.732 |
|
- type: precision_at_1 |
|
value: 24.214 |
|
- type: precision_at_10 |
|
value: 5.804 |
|
- type: precision_at_100 |
|
value: 0.885 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 12.692999999999998 |
|
- type: precision_at_5 |
|
value: 9.242 |
|
- type: recall_at_1 |
|
value: 22.224 |
|
- type: recall_at_10 |
|
value: 49.849 |
|
- type: recall_at_100 |
|
value: 71.45 |
|
- type: recall_at_1000 |
|
value: 90.583 |
|
- type: recall_at_3 |
|
value: 34.153 |
|
- type: recall_at_5 |
|
value: 41.004000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.386999999999999 |
|
- type: map_at_10 |
|
value: 20.182 |
|
- type: map_at_100 |
|
value: 21.86 |
|
- type: map_at_1000 |
|
value: 22.054000000000002 |
|
- type: map_at_3 |
|
value: 17.165 |
|
- type: map_at_5 |
|
value: 18.643 |
|
- type: mrr_at_1 |
|
value: 26.906000000000002 |
|
- type: mrr_at_10 |
|
value: 37.907999999999994 |
|
- type: mrr_at_100 |
|
value: 38.868 |
|
- type: mrr_at_1000 |
|
value: 38.913 |
|
- type: mrr_at_3 |
|
value: 34.853 |
|
- type: mrr_at_5 |
|
value: 36.567 |
|
- type: ndcg_at_1 |
|
value: 26.906000000000002 |
|
- type: ndcg_at_10 |
|
value: 28.103 |
|
- type: ndcg_at_100 |
|
value: 35.073 |
|
- type: ndcg_at_1000 |
|
value: 38.653 |
|
- type: ndcg_at_3 |
|
value: 23.345 |
|
- type: ndcg_at_5 |
|
value: 24.828 |
|
- type: precision_at_1 |
|
value: 26.906000000000002 |
|
- type: precision_at_10 |
|
value: 8.547 |
|
- type: precision_at_100 |
|
value: 1.617 |
|
- type: precision_at_1000 |
|
value: 0.22799999999999998 |
|
- type: precision_at_3 |
|
value: 17.025000000000002 |
|
- type: precision_at_5 |
|
value: 12.834000000000001 |
|
- type: recall_at_1 |
|
value: 12.386999999999999 |
|
- type: recall_at_10 |
|
value: 33.306999999999995 |
|
- type: recall_at_100 |
|
value: 57.516 |
|
- type: recall_at_1000 |
|
value: 77.74799999999999 |
|
- type: recall_at_3 |
|
value: 21.433 |
|
- type: recall_at_5 |
|
value: 25.915 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.322 |
|
- type: map_at_10 |
|
value: 20.469 |
|
- type: map_at_100 |
|
value: 28.638 |
|
- type: map_at_1000 |
|
value: 30.433 |
|
- type: map_at_3 |
|
value: 14.802000000000001 |
|
- type: map_at_5 |
|
value: 17.297 |
|
- type: mrr_at_1 |
|
value: 68.75 |
|
- type: mrr_at_10 |
|
value: 76.29599999999999 |
|
- type: mrr_at_100 |
|
value: 76.62400000000001 |
|
- type: mrr_at_1000 |
|
value: 76.633 |
|
- type: mrr_at_3 |
|
value: 75.083 |
|
- type: mrr_at_5 |
|
value: 75.771 |
|
- type: ndcg_at_1 |
|
value: 54.87499999999999 |
|
- type: ndcg_at_10 |
|
value: 41.185 |
|
- type: ndcg_at_100 |
|
value: 46.400000000000006 |
|
- type: ndcg_at_1000 |
|
value: 54.223 |
|
- type: ndcg_at_3 |
|
value: 45.489000000000004 |
|
- type: ndcg_at_5 |
|
value: 43.161 |
|
- type: precision_at_1 |
|
value: 68.75 |
|
- type: precision_at_10 |
|
value: 32.300000000000004 |
|
- type: precision_at_100 |
|
value: 10.607999999999999 |
|
- type: precision_at_1000 |
|
value: 2.237 |
|
- type: precision_at_3 |
|
value: 49.083 |
|
- type: precision_at_5 |
|
value: 41.6 |
|
- type: recall_at_1 |
|
value: 9.322 |
|
- type: recall_at_10 |
|
value: 25.696 |
|
- type: recall_at_100 |
|
value: 52.898 |
|
- type: recall_at_1000 |
|
value: 77.281 |
|
- type: recall_at_3 |
|
value: 15.943 |
|
- type: recall_at_5 |
|
value: 19.836000000000002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 48.650000000000006 |
|
- type: f1 |
|
value: 43.528467245539396 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 66.56 |
|
- type: map_at_10 |
|
value: 76.767 |
|
- type: map_at_100 |
|
value: 77.054 |
|
- type: map_at_1000 |
|
value: 77.068 |
|
- type: map_at_3 |
|
value: 75.29299999999999 |
|
- type: map_at_5 |
|
value: 76.24 |
|
- type: mrr_at_1 |
|
value: 71.842 |
|
- type: mrr_at_10 |
|
value: 81.459 |
|
- type: mrr_at_100 |
|
value: 81.58800000000001 |
|
- type: mrr_at_1000 |
|
value: 81.59100000000001 |
|
- type: mrr_at_3 |
|
value: 80.188 |
|
- type: mrr_at_5 |
|
value: 81.038 |
|
- type: ndcg_at_1 |
|
value: 71.842 |
|
- type: ndcg_at_10 |
|
value: 81.51899999999999 |
|
- type: ndcg_at_100 |
|
value: 82.544 |
|
- type: ndcg_at_1000 |
|
value: 82.829 |
|
- type: ndcg_at_3 |
|
value: 78.92 |
|
- type: ndcg_at_5 |
|
value: 80.406 |
|
- type: precision_at_1 |
|
value: 71.842 |
|
- type: precision_at_10 |
|
value: 10.066 |
|
- type: precision_at_100 |
|
value: 1.076 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 30.703000000000003 |
|
- type: precision_at_5 |
|
value: 19.301 |
|
- type: recall_at_1 |
|
value: 66.56 |
|
- type: recall_at_10 |
|
value: 91.55 |
|
- type: recall_at_100 |
|
value: 95.67099999999999 |
|
- type: recall_at_1000 |
|
value: 97.539 |
|
- type: recall_at_3 |
|
value: 84.46900000000001 |
|
- type: recall_at_5 |
|
value: 88.201 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.087 |
|
- type: map_at_10 |
|
value: 32.830999999999996 |
|
- type: map_at_100 |
|
value: 34.814 |
|
- type: map_at_1000 |
|
value: 34.999 |
|
- type: map_at_3 |
|
value: 28.198 |
|
- type: map_at_5 |
|
value: 30.779 |
|
- type: mrr_at_1 |
|
value: 38.889 |
|
- type: mrr_at_10 |
|
value: 48.415 |
|
- type: mrr_at_100 |
|
value: 49.187 |
|
- type: mrr_at_1000 |
|
value: 49.226 |
|
- type: mrr_at_3 |
|
value: 45.705 |
|
- type: mrr_at_5 |
|
value: 47.225 |
|
- type: ndcg_at_1 |
|
value: 38.889 |
|
- type: ndcg_at_10 |
|
value: 40.758 |
|
- type: ndcg_at_100 |
|
value: 47.671 |
|
- type: ndcg_at_1000 |
|
value: 50.744 |
|
- type: ndcg_at_3 |
|
value: 36.296 |
|
- type: ndcg_at_5 |
|
value: 37.852999999999994 |
|
- type: precision_at_1 |
|
value: 38.889 |
|
- type: precision_at_10 |
|
value: 11.466 |
|
- type: precision_at_100 |
|
value: 1.8499999999999999 |
|
- type: precision_at_1000 |
|
value: 0.24 |
|
- type: precision_at_3 |
|
value: 24.126 |
|
- type: precision_at_5 |
|
value: 18.21 |
|
- type: recall_at_1 |
|
value: 20.087 |
|
- type: recall_at_10 |
|
value: 48.042 |
|
- type: recall_at_100 |
|
value: 73.493 |
|
- type: recall_at_1000 |
|
value: 91.851 |
|
- type: recall_at_3 |
|
value: 32.694 |
|
- type: recall_at_5 |
|
value: 39.099000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.096000000000004 |
|
- type: map_at_10 |
|
value: 56.99999999999999 |
|
- type: map_at_100 |
|
value: 57.914 |
|
- type: map_at_1000 |
|
value: 57.984 |
|
- type: map_at_3 |
|
value: 53.900999999999996 |
|
- type: map_at_5 |
|
value: 55.827000000000005 |
|
- type: mrr_at_1 |
|
value: 76.19200000000001 |
|
- type: mrr_at_10 |
|
value: 81.955 |
|
- type: mrr_at_100 |
|
value: 82.164 |
|
- type: mrr_at_1000 |
|
value: 82.173 |
|
- type: mrr_at_3 |
|
value: 80.963 |
|
- type: mrr_at_5 |
|
value: 81.574 |
|
- type: ndcg_at_1 |
|
value: 76.19200000000001 |
|
- type: ndcg_at_10 |
|
value: 65.75 |
|
- type: ndcg_at_100 |
|
value: 68.949 |
|
- type: ndcg_at_1000 |
|
value: 70.342 |
|
- type: ndcg_at_3 |
|
value: 61.29 |
|
- type: ndcg_at_5 |
|
value: 63.747 |
|
- type: precision_at_1 |
|
value: 76.19200000000001 |
|
- type: precision_at_10 |
|
value: 13.571 |
|
- type: precision_at_100 |
|
value: 1.6070000000000002 |
|
- type: precision_at_1000 |
|
value: 0.179 |
|
- type: precision_at_3 |
|
value: 38.663 |
|
- type: precision_at_5 |
|
value: 25.136999999999997 |
|
- type: recall_at_1 |
|
value: 38.096000000000004 |
|
- type: recall_at_10 |
|
value: 67.853 |
|
- type: recall_at_100 |
|
value: 80.365 |
|
- type: recall_at_1000 |
|
value: 89.629 |
|
- type: recall_at_3 |
|
value: 57.995 |
|
- type: recall_at_5 |
|
value: 62.843 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 85.95200000000001 |
|
- type: ap |
|
value: 80.73847277002109 |
|
- type: f1 |
|
value: 85.92406135678594 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.916999999999998 |
|
- type: map_at_10 |
|
value: 33.23 |
|
- type: map_at_100 |
|
value: 34.427 |
|
- type: map_at_1000 |
|
value: 34.477000000000004 |
|
- type: map_at_3 |
|
value: 29.292 |
|
- type: map_at_5 |
|
value: 31.6 |
|
- type: mrr_at_1 |
|
value: 21.547 |
|
- type: mrr_at_10 |
|
value: 33.839999999999996 |
|
- type: mrr_at_100 |
|
value: 34.979 |
|
- type: mrr_at_1000 |
|
value: 35.022999999999996 |
|
- type: mrr_at_3 |
|
value: 29.988 |
|
- type: mrr_at_5 |
|
value: 32.259 |
|
- type: ndcg_at_1 |
|
value: 21.519 |
|
- type: ndcg_at_10 |
|
value: 40.209 |
|
- type: ndcg_at_100 |
|
value: 45.954 |
|
- type: ndcg_at_1000 |
|
value: 47.187 |
|
- type: ndcg_at_3 |
|
value: 32.227 |
|
- type: ndcg_at_5 |
|
value: 36.347 |
|
- type: precision_at_1 |
|
value: 21.519 |
|
- type: precision_at_10 |
|
value: 6.447 |
|
- type: precision_at_100 |
|
value: 0.932 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 13.877999999999998 |
|
- type: precision_at_5 |
|
value: 10.404 |
|
- type: recall_at_1 |
|
value: 20.916999999999998 |
|
- type: recall_at_10 |
|
value: 61.7 |
|
- type: recall_at_100 |
|
value: 88.202 |
|
- type: recall_at_1000 |
|
value: 97.588 |
|
- type: recall_at_3 |
|
value: 40.044999999999995 |
|
- type: recall_at_5 |
|
value: 49.964999999999996 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.02781577747379 |
|
- type: f1 |
|
value: 92.83653922768306 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 72.04286365709075 |
|
- type: f1 |
|
value: 53.43867658525793 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 71.47276395427035 |
|
- type: f1 |
|
value: 69.77017399597342 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.3819771351715 |
|
- type: f1 |
|
value: 76.8484533435409 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.16515993299593 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.77145323314774 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.53637706586391 |
|
- type: mrr |
|
value: 33.7312926288863 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.063999999999999 |
|
- type: map_at_10 |
|
value: 15.046999999999999 |
|
- type: map_at_100 |
|
value: 19.116 |
|
- type: map_at_1000 |
|
value: 20.702 |
|
- type: map_at_3 |
|
value: 10.932 |
|
- type: map_at_5 |
|
value: 12.751999999999999 |
|
- type: mrr_at_1 |
|
value: 50.464 |
|
- type: mrr_at_10 |
|
value: 58.189 |
|
- type: mrr_at_100 |
|
value: 58.733999999999995 |
|
- type: mrr_at_1000 |
|
value: 58.769000000000005 |
|
- type: mrr_at_3 |
|
value: 56.24400000000001 |
|
- type: mrr_at_5 |
|
value: 57.68299999999999 |
|
- type: ndcg_at_1 |
|
value: 48.142 |
|
- type: ndcg_at_10 |
|
value: 37.897 |
|
- type: ndcg_at_100 |
|
value: 35.264 |
|
- type: ndcg_at_1000 |
|
value: 44.033 |
|
- type: ndcg_at_3 |
|
value: 42.967 |
|
- type: ndcg_at_5 |
|
value: 40.815 |
|
- type: precision_at_1 |
|
value: 50.15500000000001 |
|
- type: precision_at_10 |
|
value: 28.235 |
|
- type: precision_at_100 |
|
value: 8.994 |
|
- type: precision_at_1000 |
|
value: 2.218 |
|
- type: precision_at_3 |
|
value: 40.041 |
|
- type: precision_at_5 |
|
value: 35.046 |
|
- type: recall_at_1 |
|
value: 7.063999999999999 |
|
- type: recall_at_10 |
|
value: 18.598 |
|
- type: recall_at_100 |
|
value: 35.577999999999996 |
|
- type: recall_at_1000 |
|
value: 67.43 |
|
- type: recall_at_3 |
|
value: 11.562999999999999 |
|
- type: recall_at_5 |
|
value: 14.771 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.046 |
|
- type: map_at_10 |
|
value: 44.808 |
|
- type: map_at_100 |
|
value: 45.898 |
|
- type: map_at_1000 |
|
value: 45.927 |
|
- type: map_at_3 |
|
value: 40.19 |
|
- type: map_at_5 |
|
value: 42.897 |
|
- type: mrr_at_1 |
|
value: 32.706 |
|
- type: mrr_at_10 |
|
value: 47.275 |
|
- type: mrr_at_100 |
|
value: 48.075 |
|
- type: mrr_at_1000 |
|
value: 48.095 |
|
- type: mrr_at_3 |
|
value: 43.463 |
|
- type: mrr_at_5 |
|
value: 45.741 |
|
- type: ndcg_at_1 |
|
value: 32.706 |
|
- type: ndcg_at_10 |
|
value: 52.835 |
|
- type: ndcg_at_100 |
|
value: 57.345 |
|
- type: ndcg_at_1000 |
|
value: 57.985 |
|
- type: ndcg_at_3 |
|
value: 44.171 |
|
- type: ndcg_at_5 |
|
value: 48.661 |
|
- type: precision_at_1 |
|
value: 32.706 |
|
- type: precision_at_10 |
|
value: 8.895999999999999 |
|
- type: precision_at_100 |
|
value: 1.143 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 20.238999999999997 |
|
- type: precision_at_5 |
|
value: 14.728 |
|
- type: recall_at_1 |
|
value: 29.046 |
|
- type: recall_at_10 |
|
value: 74.831 |
|
- type: recall_at_100 |
|
value: 94.192 |
|
- type: recall_at_1000 |
|
value: 98.897 |
|
- type: recall_at_3 |
|
value: 52.37500000000001 |
|
- type: recall_at_5 |
|
value: 62.732 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.38799999999999 |
|
- type: map_at_10 |
|
value: 84.315 |
|
- type: map_at_100 |
|
value: 84.955 |
|
- type: map_at_1000 |
|
value: 84.971 |
|
- type: map_at_3 |
|
value: 81.33399999999999 |
|
- type: map_at_5 |
|
value: 83.21300000000001 |
|
- type: mrr_at_1 |
|
value: 81.03 |
|
- type: mrr_at_10 |
|
value: 87.395 |
|
- type: mrr_at_100 |
|
value: 87.488 |
|
- type: mrr_at_1000 |
|
value: 87.48899999999999 |
|
- type: mrr_at_3 |
|
value: 86.41499999999999 |
|
- type: mrr_at_5 |
|
value: 87.074 |
|
- type: ndcg_at_1 |
|
value: 81.04 |
|
- type: ndcg_at_10 |
|
value: 88.151 |
|
- type: ndcg_at_100 |
|
value: 89.38199999999999 |
|
- type: ndcg_at_1000 |
|
value: 89.479 |
|
- type: ndcg_at_3 |
|
value: 85.24000000000001 |
|
- type: ndcg_at_5 |
|
value: 86.856 |
|
- type: precision_at_1 |
|
value: 81.04 |
|
- type: precision_at_10 |
|
value: 13.372 |
|
- type: precision_at_100 |
|
value: 1.526 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.217 |
|
- type: precision_at_5 |
|
value: 24.502 |
|
- type: recall_at_1 |
|
value: 70.38799999999999 |
|
- type: recall_at_10 |
|
value: 95.452 |
|
- type: recall_at_100 |
|
value: 99.59700000000001 |
|
- type: recall_at_1000 |
|
value: 99.988 |
|
- type: recall_at_3 |
|
value: 87.11 |
|
- type: recall_at_5 |
|
value: 91.662 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 59.334991029213235 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 62.586500854616666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.153 |
|
- type: map_at_10 |
|
value: 14.277000000000001 |
|
- type: map_at_100 |
|
value: 16.922 |
|
- type: map_at_1000 |
|
value: 17.302999999999997 |
|
- type: map_at_3 |
|
value: 9.961 |
|
- type: map_at_5 |
|
value: 12.257 |
|
- type: mrr_at_1 |
|
value: 25.4 |
|
- type: mrr_at_10 |
|
value: 37.458000000000006 |
|
- type: mrr_at_100 |
|
value: 38.681 |
|
- type: mrr_at_1000 |
|
value: 38.722 |
|
- type: mrr_at_3 |
|
value: 34.1 |
|
- type: mrr_at_5 |
|
value: 36.17 |
|
- type: ndcg_at_1 |
|
value: 25.4 |
|
- type: ndcg_at_10 |
|
value: 23.132 |
|
- type: ndcg_at_100 |
|
value: 32.908 |
|
- type: ndcg_at_1000 |
|
value: 38.754 |
|
- type: ndcg_at_3 |
|
value: 21.82 |
|
- type: ndcg_at_5 |
|
value: 19.353 |
|
- type: precision_at_1 |
|
value: 25.4 |
|
- type: precision_at_10 |
|
value: 12.1 |
|
- type: precision_at_100 |
|
value: 2.628 |
|
- type: precision_at_1000 |
|
value: 0.402 |
|
- type: precision_at_3 |
|
value: 20.732999999999997 |
|
- type: precision_at_5 |
|
value: 17.34 |
|
- type: recall_at_1 |
|
value: 5.153 |
|
- type: recall_at_10 |
|
value: 24.54 |
|
- type: recall_at_100 |
|
value: 53.293 |
|
- type: recall_at_1000 |
|
value: 81.57 |
|
- type: recall_at_3 |
|
value: 12.613 |
|
- type: recall_at_5 |
|
value: 17.577 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.86284404925333 |
|
- type: cos_sim_spearman |
|
value: 78.85870555294795 |
|
- type: euclidean_pearson |
|
value: 82.20105295276093 |
|
- type: euclidean_spearman |
|
value: 78.92125617009592 |
|
- type: manhattan_pearson |
|
value: 82.15840025289069 |
|
- type: manhattan_spearman |
|
value: 78.85955732900803 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.98747423389027 |
|
- type: cos_sim_spearman |
|
value: 75.71298531799367 |
|
- type: euclidean_pearson |
|
value: 81.59709559192291 |
|
- type: euclidean_spearman |
|
value: 75.40622749225653 |
|
- type: manhattan_pearson |
|
value: 81.55553547608804 |
|
- type: manhattan_spearman |
|
value: 75.39380235424899 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.76861330695503 |
|
- type: cos_sim_spearman |
|
value: 85.72991921531624 |
|
- type: euclidean_pearson |
|
value: 84.84504307397536 |
|
- type: euclidean_spearman |
|
value: 86.02679162824732 |
|
- type: manhattan_pearson |
|
value: 84.79969439220142 |
|
- type: manhattan_spearman |
|
value: 85.99238837291625 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.31929747511796 |
|
- type: cos_sim_spearman |
|
value: 81.50806522502528 |
|
- type: euclidean_pearson |
|
value: 82.93936686512777 |
|
- type: euclidean_spearman |
|
value: 81.54403447993224 |
|
- type: manhattan_pearson |
|
value: 82.89696981900828 |
|
- type: manhattan_spearman |
|
value: 81.52817825470865 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.14413295332908 |
|
- type: cos_sim_spearman |
|
value: 88.81032027008195 |
|
- type: euclidean_pearson |
|
value: 88.19205563407645 |
|
- type: euclidean_spearman |
|
value: 88.89738339479216 |
|
- type: manhattan_pearson |
|
value: 88.11075942004189 |
|
- type: manhattan_spearman |
|
value: 88.8297061675564 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.15980075557017 |
|
- type: cos_sim_spearman |
|
value: 83.81896308594801 |
|
- type: euclidean_pearson |
|
value: 83.11195254311338 |
|
- type: euclidean_spearman |
|
value: 84.10479481755407 |
|
- type: manhattan_pearson |
|
value: 83.13915225100556 |
|
- type: manhattan_spearman |
|
value: 84.09895591027859 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.93669480147919 |
|
- type: cos_sim_spearman |
|
value: 87.89861394614361 |
|
- type: euclidean_pearson |
|
value: 88.37316413202339 |
|
- type: euclidean_spearman |
|
value: 88.18033817842569 |
|
- type: manhattan_pearson |
|
value: 88.39427578879469 |
|
- type: manhattan_spearman |
|
value: 88.09185009236847 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.62215083348255 |
|
- type: cos_sim_spearman |
|
value: 67.33243665716736 |
|
- type: euclidean_pearson |
|
value: 67.60871701996284 |
|
- type: euclidean_spearman |
|
value: 66.75929225238659 |
|
- type: manhattan_pearson |
|
value: 67.63907838970992 |
|
- type: manhattan_spearman |
|
value: 66.79313656754846 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.65549191934764 |
|
- type: cos_sim_spearman |
|
value: 85.73266847750143 |
|
- type: euclidean_pearson |
|
value: 85.75609932254318 |
|
- type: euclidean_spearman |
|
value: 85.9452287759371 |
|
- type: manhattan_pearson |
|
value: 85.69717413063573 |
|
- type: manhattan_spearman |
|
value: 85.86546318377046 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.08164129085783 |
|
- type: mrr |
|
value: 96.2877273416489 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 62.09400000000001 |
|
- type: map_at_10 |
|
value: 71.712 |
|
- type: map_at_100 |
|
value: 72.128 |
|
- type: map_at_1000 |
|
value: 72.14399999999999 |
|
- type: map_at_3 |
|
value: 68.93 |
|
- type: map_at_5 |
|
value: 70.694 |
|
- type: mrr_at_1 |
|
value: 65.0 |
|
- type: mrr_at_10 |
|
value: 72.572 |
|
- type: mrr_at_100 |
|
value: 72.842 |
|
- type: mrr_at_1000 |
|
value: 72.856 |
|
- type: mrr_at_3 |
|
value: 70.44399999999999 |
|
- type: mrr_at_5 |
|
value: 71.744 |
|
- type: ndcg_at_1 |
|
value: 65.0 |
|
- type: ndcg_at_10 |
|
value: 76.178 |
|
- type: ndcg_at_100 |
|
value: 77.887 |
|
- type: ndcg_at_1000 |
|
value: 78.227 |
|
- type: ndcg_at_3 |
|
value: 71.367 |
|
- type: ndcg_at_5 |
|
value: 73.938 |
|
- type: precision_at_1 |
|
value: 65.0 |
|
- type: precision_at_10 |
|
value: 10.033 |
|
- type: precision_at_100 |
|
value: 1.097 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 27.667 |
|
- type: precision_at_5 |
|
value: 18.4 |
|
- type: recall_at_1 |
|
value: 62.09400000000001 |
|
- type: recall_at_10 |
|
value: 89.022 |
|
- type: recall_at_100 |
|
value: 96.833 |
|
- type: recall_at_1000 |
|
value: 99.333 |
|
- type: recall_at_3 |
|
value: 75.922 |
|
- type: recall_at_5 |
|
value: 82.428 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.82178217821782 |
|
- type: cos_sim_ap |
|
value: 95.71282508220798 |
|
- type: cos_sim_f1 |
|
value: 90.73120494335737 |
|
- type: cos_sim_precision |
|
value: 93.52441613588111 |
|
- type: cos_sim_recall |
|
value: 88.1 |
|
- type: dot_accuracy |
|
value: 99.73960396039604 |
|
- type: dot_ap |
|
value: 92.98534606529098 |
|
- type: dot_f1 |
|
value: 86.83024536805209 |
|
- type: dot_precision |
|
value: 86.96088264794383 |
|
- type: dot_recall |
|
value: 86.7 |
|
- type: euclidean_accuracy |
|
value: 99.82475247524752 |
|
- type: euclidean_ap |
|
value: 95.72927039014849 |
|
- type: euclidean_f1 |
|
value: 90.89974293059126 |
|
- type: euclidean_precision |
|
value: 93.54497354497354 |
|
- type: euclidean_recall |
|
value: 88.4 |
|
- type: manhattan_accuracy |
|
value: 99.82574257425742 |
|
- type: manhattan_ap |
|
value: 95.72142177390405 |
|
- type: manhattan_f1 |
|
value: 91.00152516522625 |
|
- type: manhattan_precision |
|
value: 92.55429162357808 |
|
- type: manhattan_recall |
|
value: 89.5 |
|
- type: max_accuracy |
|
value: 99.82574257425742 |
|
- type: max_ap |
|
value: 95.72927039014849 |
|
- type: max_f1 |
|
value: 91.00152516522625 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 66.63957663468679 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 36.003307257923964 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 53.005825525863905 |
|
- type: mrr |
|
value: 53.854683919022165 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.503611569974098 |
|
- type: cos_sim_spearman |
|
value: 31.17155564248449 |
|
- type: dot_pearson |
|
value: 26.740428413981306 |
|
- type: dot_spearman |
|
value: 26.55727635469746 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.23600000000000002 |
|
- type: map_at_10 |
|
value: 1.7670000000000001 |
|
- type: map_at_100 |
|
value: 10.208 |
|
- type: map_at_1000 |
|
value: 25.997999999999998 |
|
- type: map_at_3 |
|
value: 0.605 |
|
- type: map_at_5 |
|
value: 0.9560000000000001 |
|
- type: mrr_at_1 |
|
value: 84.0 |
|
- type: mrr_at_10 |
|
value: 90.167 |
|
- type: mrr_at_100 |
|
value: 90.167 |
|
- type: mrr_at_1000 |
|
value: 90.167 |
|
- type: mrr_at_3 |
|
value: 89.667 |
|
- type: mrr_at_5 |
|
value: 90.167 |
|
- type: ndcg_at_1 |
|
value: 77.0 |
|
- type: ndcg_at_10 |
|
value: 68.783 |
|
- type: ndcg_at_100 |
|
value: 54.196 |
|
- type: ndcg_at_1000 |
|
value: 52.077 |
|
- type: ndcg_at_3 |
|
value: 71.642 |
|
- type: ndcg_at_5 |
|
value: 70.45700000000001 |
|
- type: precision_at_1 |
|
value: 84.0 |
|
- type: precision_at_10 |
|
value: 73.0 |
|
- type: precision_at_100 |
|
value: 55.48 |
|
- type: precision_at_1000 |
|
value: 23.102 |
|
- type: precision_at_3 |
|
value: 76.0 |
|
- type: precision_at_5 |
|
value: 74.8 |
|
- type: recall_at_1 |
|
value: 0.23600000000000002 |
|
- type: recall_at_10 |
|
value: 1.9869999999999999 |
|
- type: recall_at_100 |
|
value: 13.749 |
|
- type: recall_at_1000 |
|
value: 50.157 |
|
- type: recall_at_3 |
|
value: 0.633 |
|
- type: recall_at_5 |
|
value: 1.0290000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.437 |
|
- type: map_at_10 |
|
value: 8.791 |
|
- type: map_at_100 |
|
value: 15.001999999999999 |
|
- type: map_at_1000 |
|
value: 16.549 |
|
- type: map_at_3 |
|
value: 3.8080000000000003 |
|
- type: map_at_5 |
|
value: 5.632000000000001 |
|
- type: mrr_at_1 |
|
value: 20.408 |
|
- type: mrr_at_10 |
|
value: 36.96 |
|
- type: mrr_at_100 |
|
value: 37.912 |
|
- type: mrr_at_1000 |
|
value: 37.912 |
|
- type: mrr_at_3 |
|
value: 29.592000000000002 |
|
- type: mrr_at_5 |
|
value: 34.489999999999995 |
|
- type: ndcg_at_1 |
|
value: 19.387999999999998 |
|
- type: ndcg_at_10 |
|
value: 22.554 |
|
- type: ndcg_at_100 |
|
value: 35.197 |
|
- type: ndcg_at_1000 |
|
value: 46.58 |
|
- type: ndcg_at_3 |
|
value: 20.285 |
|
- type: ndcg_at_5 |
|
value: 21.924 |
|
- type: precision_at_1 |
|
value: 20.408 |
|
- type: precision_at_10 |
|
value: 21.837 |
|
- type: precision_at_100 |
|
value: 7.754999999999999 |
|
- type: precision_at_1000 |
|
value: 1.537 |
|
- type: precision_at_3 |
|
value: 21.769 |
|
- type: precision_at_5 |
|
value: 23.673 |
|
- type: recall_at_1 |
|
value: 1.437 |
|
- type: recall_at_10 |
|
value: 16.314999999999998 |
|
- type: recall_at_100 |
|
value: 47.635 |
|
- type: recall_at_1000 |
|
value: 82.963 |
|
- type: recall_at_3 |
|
value: 4.955 |
|
- type: recall_at_5 |
|
value: 8.805 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.6128 |
|
- type: ap |
|
value: 14.279639861175664 |
|
- type: f1 |
|
value: 54.922292491204274 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 57.01188455008489 |
|
- type: f1 |
|
value: 57.377953019225515 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 52.306769136544254 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.64701674912082 |
|
- type: cos_sim_ap |
|
value: 72.46600945328552 |
|
- type: cos_sim_f1 |
|
value: 67.96572367648784 |
|
- type: cos_sim_precision |
|
value: 61.21801649397336 |
|
- type: cos_sim_recall |
|
value: 76.38522427440633 |
|
- type: dot_accuracy |
|
value: 82.33295583238957 |
|
- type: dot_ap |
|
value: 62.54843443071716 |
|
- type: dot_f1 |
|
value: 60.38378562507096 |
|
- type: dot_precision |
|
value: 52.99980067769583 |
|
- type: dot_recall |
|
value: 70.15831134564644 |
|
- type: euclidean_accuracy |
|
value: 85.7423854085951 |
|
- type: euclidean_ap |
|
value: 72.76873850945174 |
|
- type: euclidean_f1 |
|
value: 68.23556960543262 |
|
- type: euclidean_precision |
|
value: 61.3344559040202 |
|
- type: euclidean_recall |
|
value: 76.88654353562005 |
|
- type: manhattan_accuracy |
|
value: 85.74834594981225 |
|
- type: manhattan_ap |
|
value: 72.66825372446462 |
|
- type: manhattan_f1 |
|
value: 68.21539194662853 |
|
- type: manhattan_precision |
|
value: 62.185056472632496 |
|
- type: manhattan_recall |
|
value: 75.54089709762533 |
|
- type: max_accuracy |
|
value: 85.74834594981225 |
|
- type: max_ap |
|
value: 72.76873850945174 |
|
- type: max_f1 |
|
value: 68.23556960543262 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.73171110334924 |
|
- type: cos_sim_ap |
|
value: 85.51855542063649 |
|
- type: cos_sim_f1 |
|
value: 77.95706775700934 |
|
- type: cos_sim_precision |
|
value: 74.12524298805887 |
|
- type: cos_sim_recall |
|
value: 82.20665229442562 |
|
- type: dot_accuracy |
|
value: 86.94842240074514 |
|
- type: dot_ap |
|
value: 80.90995345771762 |
|
- type: dot_f1 |
|
value: 74.20765027322403 |
|
- type: dot_precision |
|
value: 70.42594385285575 |
|
- type: dot_recall |
|
value: 78.41854019094548 |
|
- type: euclidean_accuracy |
|
value: 88.73753250281368 |
|
- type: euclidean_ap |
|
value: 85.54712254033734 |
|
- type: euclidean_f1 |
|
value: 78.07565728654365 |
|
- type: euclidean_precision |
|
value: 75.1120597652081 |
|
- type: euclidean_recall |
|
value: 81.282722513089 |
|
- type: manhattan_accuracy |
|
value: 88.72588970388482 |
|
- type: manhattan_ap |
|
value: 85.52118291594071 |
|
- type: manhattan_f1 |
|
value: 78.04428724070593 |
|
- type: manhattan_precision |
|
value: 74.83219105490002 |
|
- type: manhattan_recall |
|
value: 81.54450261780106 |
|
- type: max_accuracy |
|
value: 88.73753250281368 |
|
- type: max_ap |
|
value: 85.54712254033734 |
|
- type: max_f1 |
|
value: 78.07565728654365 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
# # Fast-Inference with Ctranslate2 |
|
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. |
|
|
|
quantized version of [thenlper/gte-base](https://huggingface.co./thenlper/gte-base) |
|
```bash |
|
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 |
|
``` |
|
|
|
```python |
|
# from transformers import AutoTokenizer |
|
model_name = "michaelfeil/ct2fast-gte-base" |
|
model_name_orig="thenlper/gte-base" |
|
|
|
from hf_hub_ctranslate2 import EncoderCT2fromHfHub |
|
model = EncoderCT2fromHfHub( |
|
# load in int8 on CUDA |
|
model_name_or_path=model_name, |
|
device="cuda", |
|
compute_type="int8_float16" |
|
) |
|
outputs = model.generate( |
|
text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"], |
|
max_length=64, |
|
) # perform downstream tasks on outputs |
|
outputs["pooler_output"] |
|
outputs["last_hidden_state"] |
|
outputs["attention_mask"] |
|
|
|
# alternative, use SentenceTransformer Mix-In |
|
# for end-to-end Sentence embeddings generation |
|
# (not pulling from this CT2fast-HF repo) |
|
|
|
from hf_hub_ctranslate2 import CT2SentenceTransformer |
|
model = CT2SentenceTransformer( |
|
model_name_orig, compute_type="int8_float16", device="cuda" |
|
) |
|
embeddings = model.encode( |
|
["I like soccer", "I like tennis", "The eiffel tower is in Paris"], |
|
batch_size=32, |
|
convert_to_numpy=True, |
|
normalize_embeddings=True, |
|
) |
|
print(embeddings.shape, embeddings) |
|
scores = (embeddings @ embeddings.T) * 100 |
|
|
|
# Hint: you can also host this code via REST API and |
|
# via github.com/michaelfeil/infinity |
|
|
|
|
|
``` |
|
|
|
Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) |
|
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) |
|
- `compute_type=int8_float16` for `device="cuda"` |
|
- `compute_type=int8` for `device="cpu"` |
|
|
|
Converted on 2023-10-13 using |
|
``` |
|
LLama-2 -> removed <pad> token. |
|
``` |
|
|
|
# Licence and other remarks: |
|
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. |
|
|
|
# Original description |
|
|
|
|
|
# gte-base |
|
|
|
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) |
|
|
|
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co./thenlper/gte-large), [GTE-base](https://huggingface.co./thenlper/gte-base), and [GTE-small](https://huggingface.co./thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. |
|
|
|
## Metrics |
|
|
|
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co./spaces/mteb/leaderboard). |
|
|
|
|
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**gte-large**](https://huggingface.co./thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | |
|
| [**gte-base**](https://huggingface.co./thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co./intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | |
|
| [e5-base-v2](https://huggingface.co./intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | |
|
| [**gte-small**](https://huggingface.co./thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co./intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co./sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co./bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | |
|
| [all-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | |
|
| [all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | |
|
| [contriever-base-msmarco](https://huggingface.co./nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | |
|
| [sentence-t5-base](https://huggingface.co./sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | |
|
|
|
|
|
## Usage |
|
|
|
Code example |
|
|
|
```python |
|
import torch.nn.functional as F |
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
def average_pool(last_hidden_states: Tensor, |
|
attention_mask: Tensor) -> Tensor: |
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
|
|
input_texts = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base") |
|
model = AutoModel.from_pretrained("thenlper/gte-base") |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
|
|
|
# (Optionally) normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
Use with sentence-transformers: |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['That is a happy person', 'That is a very happy person'] |
|
|
|
model = SentenceTransformer('thenlper/gte-base') |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
### Limitation |
|
|
|
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. |
|
|
|
### Citation |
|
|
|
If you find our paper or models helpful, please consider citing them as follows: |
|
|
|
``` |
|
@misc{li2023general, |
|
title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, |
|
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang}, |
|
year={2023}, |
|
eprint={2308.03281}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
