1207 lines
32 KiB
Markdown
1207 lines
32 KiB
Markdown
---
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pipeline_tag: sentence-similarity
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tags:
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- mteb
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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model-index:
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- name: acge_text_embedding
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results:
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- task:
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type: STS
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dataset:
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type: C-MTEB/AFQMC
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name: MTEB AFQMC
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config: default
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split: validation
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revision: b44c3b011063adb25877c13823db83bb193913c4
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metrics:
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- type: cos_sim_pearson
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value: 54.03434872650919
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- type: cos_sim_spearman
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value: 58.80730796688325
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- type: euclidean_pearson
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||
value: 57.47231387497989
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||
- type: euclidean_spearman
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||
value: 58.80775026351807
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- type: manhattan_pearson
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||
value: 57.46332720141574
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- type: manhattan_spearman
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value: 58.80196022940078
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- task:
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type: STS
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dataset:
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type: C-MTEB/ATEC
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name: MTEB ATEC
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config: default
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split: test
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revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
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metrics:
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- type: cos_sim_pearson
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value: 53.52621290548175
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- type: cos_sim_spearman
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value: 57.945227768312144
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- type: euclidean_pearson
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||
value: 61.17041394151802
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||
- type: euclidean_spearman
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||
value: 57.94553287835657
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||
- type: manhattan_pearson
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||
value: 61.168327500057885
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- type: manhattan_spearman
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value: 57.94477516925043
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_reviews_multi
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name: MTEB AmazonReviewsClassification (zh)
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config: zh
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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value: 48.538000000000004
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- type: f1
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value: 46.59920995594044
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- task:
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type: STS
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dataset:
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type: C-MTEB/BQ
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name: MTEB BQ
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config: default
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split: test
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revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
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metrics:
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- type: cos_sim_pearson
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value: 68.27529991817154
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- type: cos_sim_spearman
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value: 70.37095914176643
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- type: euclidean_pearson
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||
value: 69.42690712802727
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- type: euclidean_spearman
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value: 70.37017971889912
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- type: manhattan_pearson
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value: 69.40264877917839
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- type: manhattan_spearman
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value: 70.34786744049524
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- task:
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type: Clustering
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dataset:
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type: C-MTEB/CLSClusteringP2P
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name: MTEB CLSClusteringP2P
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config: default
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split: test
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revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
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metrics:
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- type: v_measure
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value: 47.08027536192709
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- task:
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type: Clustering
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dataset:
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type: C-MTEB/CLSClusteringS2S
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name: MTEB CLSClusteringS2S
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config: default
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split: test
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revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
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metrics:
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- type: v_measure
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value: 44.0526024940363
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- task:
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type: Reranking
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dataset:
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type: C-MTEB/CMedQAv1-reranking
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name: MTEB CMedQAv1
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config: default
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split: test
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revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
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metrics:
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- type: map
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value: 88.65974993133156
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- type: mrr
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value: 90.64761904761905
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- task:
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type: Reranking
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dataset:
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type: C-MTEB/CMedQAv2-reranking
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name: MTEB CMedQAv2
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config: default
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split: test
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revision: 23d186750531a14a0357ca22cd92d712fd512ea0
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metrics:
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- type: map
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value: 88.90396838907245
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- type: mrr
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value: 90.90932539682541
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- task:
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type: Retrieval
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dataset:
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type: C-MTEB/CmedqaRetrieval
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name: MTEB CmedqaRetrieval
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config: default
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split: dev
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revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
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metrics:
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- type: map_at_1
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value: 26.875
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- type: map_at_10
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||
value: 39.995999999999995
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- type: map_at_100
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||
value: 41.899
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||
- type: map_at_1000
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||
value: 42.0
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- type: map_at_3
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value: 35.414
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- type: map_at_5
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||
value: 38.019
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- type: mrr_at_1
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||
value: 40.635
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- type: mrr_at_10
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||
value: 48.827
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- type: mrr_at_100
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value: 49.805
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- type: mrr_at_1000
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||
value: 49.845
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- type: mrr_at_3
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value: 46.145
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- type: mrr_at_5
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value: 47.693999999999996
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- type: ndcg_at_1
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||
value: 40.635
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- type: ndcg_at_10
|
||
value: 46.78
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- type: ndcg_at_100
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||
value: 53.986999999999995
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- type: ndcg_at_1000
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||
value: 55.684
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- type: ndcg_at_3
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value: 41.018
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- type: ndcg_at_5
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value: 43.559
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- type: precision_at_1
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value: 40.635
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- type: precision_at_10
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value: 10.427999999999999
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- type: precision_at_100
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value: 1.625
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- type: precision_at_1000
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value: 0.184
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- type: precision_at_3
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value: 23.139000000000003
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- type: precision_at_5
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value: 17.004
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- type: recall_at_1
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value: 26.875
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- type: recall_at_10
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value: 57.887
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- type: recall_at_100
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value: 87.408
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- type: recall_at_1000
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value: 98.721
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- type: recall_at_3
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value: 40.812
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- type: recall_at_5
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value: 48.397
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- task:
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type: PairClassification
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dataset:
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type: C-MTEB/CMNLI
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name: MTEB Cmnli
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config: default
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split: validation
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revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
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metrics:
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- type: cos_sim_accuracy
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value: 83.43956704750451
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- type: cos_sim_ap
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value: 90.49172854352659
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- type: cos_sim_f1
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value: 84.28475486903963
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- type: cos_sim_precision
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value: 80.84603822203135
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- type: cos_sim_recall
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value: 88.02899228431144
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- type: dot_accuracy
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value: 83.43956704750451
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- type: dot_ap
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value: 90.46317132695233
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- type: dot_f1
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value: 84.28794294628929
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- type: dot_precision
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value: 80.51948051948052
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- type: dot_recall
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value: 88.4264671498714
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- type: euclidean_accuracy
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value: 83.43956704750451
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- type: euclidean_ap
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value: 90.49171785256486
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- type: euclidean_f1
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value: 84.28235820561584
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- type: euclidean_precision
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value: 80.8022308022308
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- type: euclidean_recall
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value: 88.07575403320084
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- type: manhattan_accuracy
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value: 83.55983162958509
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- type: manhattan_ap
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value: 90.48046779812815
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- type: manhattan_f1
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value: 84.45354259069714
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- type: manhattan_precision
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value: 82.21877767936226
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- type: manhattan_recall
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value: 86.81318681318682
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- type: max_accuracy
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value: 83.55983162958509
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- type: max_ap
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value: 90.49172854352659
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- type: max_f1
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value: 84.45354259069714
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- task:
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type: Retrieval
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dataset:
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type: C-MTEB/CovidRetrieval
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name: MTEB CovidRetrieval
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config: default
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split: dev
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revision: 1271c7809071a13532e05f25fb53511ffce77117
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metrics:
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- type: map_at_1
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value: 68.54599999999999
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- type: map_at_10
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value: 77.62400000000001
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- type: map_at_100
|
||
value: 77.886
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- type: map_at_1000
|
||
value: 77.89
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- type: map_at_3
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||
value: 75.966
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- type: map_at_5
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||
value: 76.995
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- type: mrr_at_1
|
||
value: 68.915
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- type: mrr_at_10
|
||
value: 77.703
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- type: mrr_at_100
|
||
value: 77.958
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- type: mrr_at_1000
|
||
value: 77.962
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- type: mrr_at_3
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value: 76.08
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- type: mrr_at_5
|
||
value: 77.118
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- type: ndcg_at_1
|
||
value: 68.809
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- type: ndcg_at_10
|
||
value: 81.563
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||
- type: ndcg_at_100
|
||
value: 82.758
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- type: ndcg_at_1000
|
||
value: 82.864
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- type: ndcg_at_3
|
||
value: 78.29
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- type: ndcg_at_5
|
||
value: 80.113
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- type: precision_at_1
|
||
value: 68.809
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- type: precision_at_10
|
||
value: 9.463000000000001
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- type: precision_at_100
|
||
value: 1.001
|
||
- type: precision_at_1000
|
||
value: 0.101
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||
- type: precision_at_3
|
||
value: 28.486
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- type: precision_at_5
|
||
value: 18.019
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- type: recall_at_1
|
||
value: 68.54599999999999
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- type: recall_at_10
|
||
value: 93.625
|
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- type: recall_at_100
|
||
value: 99.05199999999999
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- type: recall_at_1000
|
||
value: 99.895
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- type: recall_at_3
|
||
value: 84.879
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- type: recall_at_5
|
||
value: 89.252
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- task:
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type: Retrieval
|
||
dataset:
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type: C-MTEB/DuRetrieval
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name: MTEB DuRetrieval
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||
config: default
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||
split: dev
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revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
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metrics:
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- type: map_at_1
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value: 25.653
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- type: map_at_10
|
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value: 79.105
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- type: map_at_100
|
||
value: 81.902
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||
- type: map_at_1000
|
||
value: 81.947
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||
- type: map_at_3
|
||
value: 54.54599999999999
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- type: map_at_5
|
||
value: 69.226
|
||
- type: mrr_at_1
|
||
value: 89.35
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||
- type: mrr_at_10
|
||
value: 92.69
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||
- type: mrr_at_100
|
||
value: 92.77
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- type: mrr_at_1000
|
||
value: 92.774
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- type: mrr_at_3
|
||
value: 92.425
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- type: mrr_at_5
|
||
value: 92.575
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||
- type: ndcg_at_1
|
||
value: 89.35
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- type: ndcg_at_10
|
||
value: 86.55199999999999
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||
- type: ndcg_at_100
|
||
value: 89.35300000000001
|
||
- type: ndcg_at_1000
|
||
value: 89.782
|
||
- type: ndcg_at_3
|
||
value: 85.392
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- type: ndcg_at_5
|
||
value: 84.5
|
||
- type: precision_at_1
|
||
value: 89.35
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- type: precision_at_10
|
||
value: 41.589999999999996
|
||
- type: precision_at_100
|
||
value: 4.781
|
||
- type: precision_at_1000
|
||
value: 0.488
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||
- type: precision_at_3
|
||
value: 76.683
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||
- type: precision_at_5
|
||
value: 65.06
|
||
- type: recall_at_1
|
||
value: 25.653
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- type: recall_at_10
|
||
value: 87.64999999999999
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- type: recall_at_100
|
||
value: 96.858
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- type: recall_at_1000
|
||
value: 99.13300000000001
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- type: recall_at_3
|
||
value: 56.869
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- type: recall_at_5
|
||
value: 74.024
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- task:
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type: Retrieval
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dataset:
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type: C-MTEB/EcomRetrieval
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name: MTEB EcomRetrieval
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config: default
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||
split: dev
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revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
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metrics:
|
||
- type: map_at_1
|
||
value: 52.1
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||
- type: map_at_10
|
||
value: 62.629999999999995
|
||
- type: map_at_100
|
||
value: 63.117000000000004
|
||
- type: map_at_1000
|
||
value: 63.134
|
||
- type: map_at_3
|
||
value: 60.267
|
||
- type: map_at_5
|
||
value: 61.777
|
||
- type: mrr_at_1
|
||
value: 52.1
|
||
- type: mrr_at_10
|
||
value: 62.629999999999995
|
||
- type: mrr_at_100
|
||
value: 63.117000000000004
|
||
- type: mrr_at_1000
|
||
value: 63.134
|
||
- type: mrr_at_3
|
||
value: 60.267
|
||
- type: mrr_at_5
|
||
value: 61.777
|
||
- type: ndcg_at_1
|
||
value: 52.1
|
||
- type: ndcg_at_10
|
||
value: 67.596
|
||
- type: ndcg_at_100
|
||
value: 69.95
|
||
- type: ndcg_at_1000
|
||
value: 70.33500000000001
|
||
- type: ndcg_at_3
|
||
value: 62.82600000000001
|
||
- type: ndcg_at_5
|
||
value: 65.546
|
||
- type: precision_at_1
|
||
value: 52.1
|
||
- type: precision_at_10
|
||
value: 8.309999999999999
|
||
- type: precision_at_100
|
||
value: 0.941
|
||
- type: precision_at_1000
|
||
value: 0.097
|
||
- type: precision_at_3
|
||
value: 23.400000000000002
|
||
- type: precision_at_5
|
||
value: 15.36
|
||
- type: recall_at_1
|
||
value: 52.1
|
||
- type: recall_at_10
|
||
value: 83.1
|
||
- type: recall_at_100
|
||
value: 94.1
|
||
- type: recall_at_1000
|
||
value: 97.0
|
||
- type: recall_at_3
|
||
value: 70.19999999999999
|
||
- type: recall_at_5
|
||
value: 76.8
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/IFlyTek-classification
|
||
name: MTEB IFlyTek
|
||
config: default
|
||
split: validation
|
||
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
|
||
metrics:
|
||
- type: accuracy
|
||
value: 51.773759138130046
|
||
- type: f1
|
||
value: 40.341407912920054
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/JDReview-classification
|
||
name: MTEB JDReview
|
||
config: default
|
||
split: test
|
||
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
|
||
metrics:
|
||
- type: accuracy
|
||
value: 86.69793621013133
|
||
- type: ap
|
||
value: 55.46718958939327
|
||
- type: f1
|
||
value: 81.48228915952436
|
||
- task:
|
||
type: STS
|
||
dataset:
|
||
type: C-MTEB/LCQMC
|
||
name: MTEB LCQMC
|
||
config: default
|
||
split: test
|
||
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
|
||
metrics:
|
||
- type: cos_sim_pearson
|
||
value: 71.1397780205448
|
||
- type: cos_sim_spearman
|
||
value: 78.17368193033309
|
||
- type: euclidean_pearson
|
||
value: 77.4849177602368
|
||
- type: euclidean_spearman
|
||
value: 78.17369079663212
|
||
- type: manhattan_pearson
|
||
value: 77.47344305182406
|
||
- type: manhattan_spearman
|
||
value: 78.16454335155387
|
||
- task:
|
||
type: Reranking
|
||
dataset:
|
||
type: C-MTEB/Mmarco-reranking
|
||
name: MTEB MMarcoReranking
|
||
config: default
|
||
split: dev
|
||
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
|
||
metrics:
|
||
- type: map
|
||
value: 27.76160559006673
|
||
- type: mrr
|
||
value: 28.02420634920635
|
||
- task:
|
||
type: Retrieval
|
||
dataset:
|
||
type: C-MTEB/MMarcoRetrieval
|
||
name: MTEB MMarcoRetrieval
|
||
config: default
|
||
split: dev
|
||
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
|
||
metrics:
|
||
- type: map_at_1
|
||
value: 65.661
|
||
- type: map_at_10
|
||
value: 74.752
|
||
- type: map_at_100
|
||
value: 75.091
|
||
- type: map_at_1000
|
||
value: 75.104
|
||
- type: map_at_3
|
||
value: 72.997
|
||
- type: map_at_5
|
||
value: 74.119
|
||
- type: mrr_at_1
|
||
value: 67.923
|
||
- type: mrr_at_10
|
||
value: 75.376
|
||
- type: mrr_at_100
|
||
value: 75.673
|
||
- type: mrr_at_1000
|
||
value: 75.685
|
||
- type: mrr_at_3
|
||
value: 73.856
|
||
- type: mrr_at_5
|
||
value: 74.82799999999999
|
||
- type: ndcg_at_1
|
||
value: 67.923
|
||
- type: ndcg_at_10
|
||
value: 78.424
|
||
- type: ndcg_at_100
|
||
value: 79.95100000000001
|
||
- type: ndcg_at_1000
|
||
value: 80.265
|
||
- type: ndcg_at_3
|
||
value: 75.101
|
||
- type: ndcg_at_5
|
||
value: 76.992
|
||
- type: precision_at_1
|
||
value: 67.923
|
||
- type: precision_at_10
|
||
value: 9.474
|
||
- type: precision_at_100
|
||
value: 1.023
|
||
- type: precision_at_1000
|
||
value: 0.105
|
||
- type: precision_at_3
|
||
value: 28.319
|
||
- type: precision_at_5
|
||
value: 17.986
|
||
- type: recall_at_1
|
||
value: 65.661
|
||
- type: recall_at_10
|
||
value: 89.09899999999999
|
||
- type: recall_at_100
|
||
value: 96.023
|
||
- type: recall_at_1000
|
||
value: 98.455
|
||
- type: recall_at_3
|
||
value: 80.314
|
||
- type: recall_at_5
|
||
value: 84.81
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: mteb/amazon_massive_intent
|
||
name: MTEB MassiveIntentClassification (zh-CN)
|
||
config: zh-CN
|
||
split: test
|
||
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
||
metrics:
|
||
- type: accuracy
|
||
value: 75.86751849361131
|
||
- type: f1
|
||
value: 73.04918450508
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: mteb/amazon_massive_scenario
|
||
name: MTEB MassiveScenarioClassification (zh-CN)
|
||
config: zh-CN
|
||
split: test
|
||
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
||
metrics:
|
||
- type: accuracy
|
||
value: 78.4364492266308
|
||
- type: f1
|
||
value: 78.120686034844
|
||
- task:
|
||
type: Retrieval
|
||
dataset:
|
||
type: C-MTEB/MedicalRetrieval
|
||
name: MTEB MedicalRetrieval
|
||
config: default
|
||
split: dev
|
||
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
|
||
metrics:
|
||
- type: map_at_1
|
||
value: 55.00000000000001
|
||
- type: map_at_10
|
||
value: 61.06399999999999
|
||
- type: map_at_100
|
||
value: 61.622
|
||
- type: map_at_1000
|
||
value: 61.663000000000004
|
||
- type: map_at_3
|
||
value: 59.583
|
||
- type: map_at_5
|
||
value: 60.373
|
||
- type: mrr_at_1
|
||
value: 55.2
|
||
- type: mrr_at_10
|
||
value: 61.168
|
||
- type: mrr_at_100
|
||
value: 61.726000000000006
|
||
- type: mrr_at_1000
|
||
value: 61.767
|
||
- type: mrr_at_3
|
||
value: 59.683
|
||
- type: mrr_at_5
|
||
value: 60.492999999999995
|
||
- type: ndcg_at_1
|
||
value: 55.00000000000001
|
||
- type: ndcg_at_10
|
||
value: 64.098
|
||
- type: ndcg_at_100
|
||
value: 67.05
|
||
- type: ndcg_at_1000
|
||
value: 68.262
|
||
- type: ndcg_at_3
|
||
value: 61.00600000000001
|
||
- type: ndcg_at_5
|
||
value: 62.439
|
||
- type: precision_at_1
|
||
value: 55.00000000000001
|
||
- type: precision_at_10
|
||
value: 7.37
|
||
- type: precision_at_100
|
||
value: 0.881
|
||
- type: precision_at_1000
|
||
value: 0.098
|
||
- type: precision_at_3
|
||
value: 21.7
|
||
- type: precision_at_5
|
||
value: 13.719999999999999
|
||
- type: recall_at_1
|
||
value: 55.00000000000001
|
||
- type: recall_at_10
|
||
value: 73.7
|
||
- type: recall_at_100
|
||
value: 88.1
|
||
- type: recall_at_1000
|
||
value: 97.8
|
||
- type: recall_at_3
|
||
value: 65.10000000000001
|
||
- type: recall_at_5
|
||
value: 68.60000000000001
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/MultilingualSentiment-classification
|
||
name: MTEB MultilingualSentiment
|
||
config: default
|
||
split: validation
|
||
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
|
||
metrics:
|
||
- type: accuracy
|
||
value: 77.52666666666667
|
||
- type: f1
|
||
value: 77.49784731367215
|
||
- task:
|
||
type: PairClassification
|
||
dataset:
|
||
type: C-MTEB/OCNLI
|
||
name: MTEB Ocnli
|
||
config: default
|
||
split: validation
|
||
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
|
||
metrics:
|
||
- type: cos_sim_accuracy
|
||
value: 81.10449377368705
|
||
- type: cos_sim_ap
|
||
value: 85.17742765935606
|
||
- type: cos_sim_f1
|
||
value: 83.00094966761633
|
||
- type: cos_sim_precision
|
||
value: 75.40983606557377
|
||
- type: cos_sim_recall
|
||
value: 92.29144667370645
|
||
- type: dot_accuracy
|
||
value: 81.10449377368705
|
||
- type: dot_ap
|
||
value: 85.17143850809614
|
||
- type: dot_f1
|
||
value: 83.01707779886148
|
||
- type: dot_precision
|
||
value: 75.36606373815677
|
||
- type: dot_recall
|
||
value: 92.39704329461456
|
||
- type: euclidean_accuracy
|
||
value: 81.10449377368705
|
||
- type: euclidean_ap
|
||
value: 85.17856775343333
|
||
- type: euclidean_f1
|
||
value: 83.00094966761633
|
||
- type: euclidean_precision
|
||
value: 75.40983606557377
|
||
- type: euclidean_recall
|
||
value: 92.29144667370645
|
||
- type: manhattan_accuracy
|
||
value: 81.05035192203573
|
||
- type: manhattan_ap
|
||
value: 85.14464459395809
|
||
- type: manhattan_f1
|
||
value: 82.96155671570953
|
||
- type: manhattan_precision
|
||
value: 75.3448275862069
|
||
- type: manhattan_recall
|
||
value: 92.29144667370645
|
||
- type: max_accuracy
|
||
value: 81.10449377368705
|
||
- type: max_ap
|
||
value: 85.17856775343333
|
||
- type: max_f1
|
||
value: 83.01707779886148
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/OnlineShopping-classification
|
||
name: MTEB OnlineShopping
|
||
config: default
|
||
split: test
|
||
revision: e610f2ebd179a8fda30ae534c3878750a96db120
|
||
metrics:
|
||
- type: accuracy
|
||
value: 93.71000000000001
|
||
- type: ap
|
||
value: 91.83202232349356
|
||
- type: f1
|
||
value: 93.69900560334331
|
||
- task:
|
||
type: STS
|
||
dataset:
|
||
type: C-MTEB/PAWSX
|
||
name: MTEB PAWSX
|
||
config: default
|
||
split: test
|
||
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
|
||
metrics:
|
||
- type: cos_sim_pearson
|
||
value: 39.175047651512415
|
||
- type: cos_sim_spearman
|
||
value: 45.51434675777896
|
||
- type: euclidean_pearson
|
||
value: 44.864110004132286
|
||
- type: euclidean_spearman
|
||
value: 45.516433048896076
|
||
- type: manhattan_pearson
|
||
value: 44.87153627706517
|
||
- type: manhattan_spearman
|
||
value: 45.52862617925012
|
||
- task:
|
||
type: STS
|
||
dataset:
|
||
type: C-MTEB/QBQTC
|
||
name: MTEB QBQTC
|
||
config: default
|
||
split: test
|
||
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
|
||
metrics:
|
||
- type: cos_sim_pearson
|
||
value: 34.249579701429084
|
||
- type: cos_sim_spearman
|
||
value: 37.30903127368978
|
||
- type: euclidean_pearson
|
||
value: 35.129438425253355
|
||
- type: euclidean_spearman
|
||
value: 37.308544018709085
|
||
- type: manhattan_pearson
|
||
value: 35.08936153503652
|
||
- type: manhattan_spearman
|
||
value: 37.25582901077839
|
||
- task:
|
||
type: STS
|
||
dataset:
|
||
type: mteb/sts22-crosslingual-sts
|
||
name: MTEB STS22 (zh)
|
||
config: zh
|
||
split: test
|
||
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
||
metrics:
|
||
- type: cos_sim_pearson
|
||
value: 61.29309637460004
|
||
- type: cos_sim_spearman
|
||
value: 65.85136090376717
|
||
- type: euclidean_pearson
|
||
value: 64.04783990953557
|
||
- type: euclidean_spearman
|
||
value: 65.85036859610366
|
||
- type: manhattan_pearson
|
||
value: 63.995852552712186
|
||
- type: manhattan_spearman
|
||
value: 65.86508416749417
|
||
- task:
|
||
type: STS
|
||
dataset:
|
||
type: C-MTEB/STSB
|
||
name: MTEB STSB
|
||
config: default
|
||
split: test
|
||
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
|
||
metrics:
|
||
- type: cos_sim_pearson
|
||
value: 81.5595940455587
|
||
- type: cos_sim_spearman
|
||
value: 82.72654634579749
|
||
- type: euclidean_pearson
|
||
value: 82.4892721061365
|
||
- type: euclidean_spearman
|
||
value: 82.72678504228253
|
||
- type: manhattan_pearson
|
||
value: 82.4770861422454
|
||
- type: manhattan_spearman
|
||
value: 82.71137469783162
|
||
- task:
|
||
type: Reranking
|
||
dataset:
|
||
type: C-MTEB/T2Reranking
|
||
name: MTEB T2Reranking
|
||
config: default
|
||
split: dev
|
||
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
|
||
metrics:
|
||
- type: map
|
||
value: 66.6159547610527
|
||
- type: mrr
|
||
value: 76.35739406347057
|
||
- task:
|
||
type: Retrieval
|
||
dataset:
|
||
type: C-MTEB/T2Retrieval
|
||
name: MTEB T2Retrieval
|
||
config: default
|
||
split: dev
|
||
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
|
||
metrics:
|
||
- type: map_at_1
|
||
value: 27.878999999999998
|
||
- type: map_at_10
|
||
value: 77.517
|
||
- type: map_at_100
|
||
value: 81.139
|
||
- type: map_at_1000
|
||
value: 81.204
|
||
- type: map_at_3
|
||
value: 54.728
|
||
- type: map_at_5
|
||
value: 67.128
|
||
- type: mrr_at_1
|
||
value: 90.509
|
||
- type: mrr_at_10
|
||
value: 92.964
|
||
- type: mrr_at_100
|
||
value: 93.045
|
||
- type: mrr_at_1000
|
||
value: 93.048
|
||
- type: mrr_at_3
|
||
value: 92.551
|
||
- type: mrr_at_5
|
||
value: 92.81099999999999
|
||
- type: ndcg_at_1
|
||
value: 90.509
|
||
- type: ndcg_at_10
|
||
value: 85.075
|
||
- type: ndcg_at_100
|
||
value: 88.656
|
||
- type: ndcg_at_1000
|
||
value: 89.25699999999999
|
||
- type: ndcg_at_3
|
||
value: 86.58200000000001
|
||
- type: ndcg_at_5
|
||
value: 85.138
|
||
- type: precision_at_1
|
||
value: 90.509
|
||
- type: precision_at_10
|
||
value: 42.05
|
||
- type: precision_at_100
|
||
value: 5.013999999999999
|
||
- type: precision_at_1000
|
||
value: 0.516
|
||
- type: precision_at_3
|
||
value: 75.551
|
||
- type: precision_at_5
|
||
value: 63.239999999999995
|
||
- type: recall_at_1
|
||
value: 27.878999999999998
|
||
- type: recall_at_10
|
||
value: 83.941
|
||
- type: recall_at_100
|
||
value: 95.568
|
||
- type: recall_at_1000
|
||
value: 98.55000000000001
|
||
- type: recall_at_3
|
||
value: 56.374
|
||
- type: recall_at_5
|
||
value: 70.435
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/TNews-classification
|
||
name: MTEB TNews
|
||
config: default
|
||
split: validation
|
||
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
|
||
metrics:
|
||
- type: accuracy
|
||
value: 53.687
|
||
- type: f1
|
||
value: 51.86911933364655
|
||
- task:
|
||
type: Clustering
|
||
dataset:
|
||
type: C-MTEB/ThuNewsClusteringP2P
|
||
name: MTEB ThuNewsClusteringP2P
|
||
config: default
|
||
split: test
|
||
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
|
||
metrics:
|
||
- type: v_measure
|
||
value: 74.65887489872564
|
||
- task:
|
||
type: Clustering
|
||
dataset:
|
||
type: C-MTEB/ThuNewsClusteringS2S
|
||
name: MTEB ThuNewsClusteringS2S
|
||
config: default
|
||
split: test
|
||
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
|
||
metrics:
|
||
- type: v_measure
|
||
value: 69.00410995984436
|
||
- task:
|
||
type: Retrieval
|
||
dataset:
|
||
type: C-MTEB/VideoRetrieval
|
||
name: MTEB VideoRetrieval
|
||
config: default
|
||
split: dev
|
||
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
|
||
metrics:
|
||
- type: map_at_1
|
||
value: 59.4
|
||
- type: map_at_10
|
||
value: 69.214
|
||
- type: map_at_100
|
||
value: 69.72699999999999
|
||
- type: map_at_1000
|
||
value: 69.743
|
||
- type: map_at_3
|
||
value: 67.717
|
||
- type: map_at_5
|
||
value: 68.782
|
||
- type: mrr_at_1
|
||
value: 59.4
|
||
- type: mrr_at_10
|
||
value: 69.214
|
||
- type: mrr_at_100
|
||
value: 69.72699999999999
|
||
- type: mrr_at_1000
|
||
value: 69.743
|
||
- type: mrr_at_3
|
||
value: 67.717
|
||
- type: mrr_at_5
|
||
value: 68.782
|
||
- type: ndcg_at_1
|
||
value: 59.4
|
||
- type: ndcg_at_10
|
||
value: 73.32300000000001
|
||
- type: ndcg_at_100
|
||
value: 75.591
|
||
- type: ndcg_at_1000
|
||
value: 75.98700000000001
|
||
- type: ndcg_at_3
|
||
value: 70.339
|
||
- type: ndcg_at_5
|
||
value: 72.246
|
||
- type: precision_at_1
|
||
value: 59.4
|
||
- type: precision_at_10
|
||
value: 8.59
|
||
- type: precision_at_100
|
||
value: 0.96
|
||
- type: precision_at_1000
|
||
value: 0.099
|
||
- type: precision_at_3
|
||
value: 25.967000000000002
|
||
- type: precision_at_5
|
||
value: 16.5
|
||
- type: recall_at_1
|
||
value: 59.4
|
||
- type: recall_at_10
|
||
value: 85.9
|
||
- type: recall_at_100
|
||
value: 96.0
|
||
- type: recall_at_1000
|
||
value: 99.1
|
||
- type: recall_at_3
|
||
value: 77.9
|
||
- type: recall_at_5
|
||
value: 82.5
|
||
- task:
|
||
type: Classification
|
||
dataset:
|
||
type: C-MTEB/waimai-classification
|
||
name: MTEB Waimai
|
||
config: default
|
||
split: test
|
||
revision: 339287def212450dcaa9df8c22bf93e9980c7023
|
||
metrics:
|
||
- type: accuracy
|
||
value: 88.53
|
||
- type: ap
|
||
value: 73.56216166534062
|
||
- type: f1
|
||
value: 87.06093694294485
|
||
---
|
||
|
||
<div align="center">
|
||
<img src="./img/logo.png" alt="icon" width="300px"/>
|
||
</div>
|
||
|
||
|
||
|
||
## acge model
|
||
|
||
acge模型来自于[合合信息](https://www.intsig.com/)技术团队，对外技术试用平台[TextIn](https://www.textin.com/), github开源链接为[github](https://github.com/intsig-textin)。合合信息是行业领先的人工智能及大数据科技企业，致力于通过智能文字识别及商业大数据领域的核心技术、C端和B端产品以及行业解决方案为全球企业和个人用户提供创新的数字化、智能化服务。
|
||
|
||
技术交流请联系<yanhui_he@intsig.net>，商务合作请联系<simon_liu@intsig.net>，可以[点击图片](https://huggingface.co/aspire/acge_text_embedding/blob/main/img/wx.jpg)，扫面二维码来加入我们的微信社群。想加入合合信息，做“文档解析”、“文档检索”、“文档预研”的同学可以投简历给min_du@intsig.net，也可直接添加[HR微信](https://huggingface.co/aspire/acge_text_embedding/blob/main/img/hr.jpg)详聊岗位内容。
|
||
|
||
acge是一个通用的文本编码模型，是一个可变长度的向量化模型，使用了[Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147)，如图所示：
|
||
|
||
|
||
|
||
建议使用的维度为1024或者1792
|
||
|
||
|
||
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|
||
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
|
||
| acge-text-embedding | 0.65 | [1024, 1792] | 1024 | Chinese | NO |
|
||
|
||
|
||
## Metric
|
||
|
||
#### C-MTEB leaderboard (Chinese)
|
||
|
||
测试的时候因为数据的随机性、显卡、推理的数据类型导致每次推理的结果不一致，我总共测试了4次，不同的显卡(A10 A100)，不同的数据类型，测试结果放在了result文件夹中，选取了一个精度最低的测试作为最终的精度测试。
|
||
根据[infgrad](https://huggingface.co/infgrad)的建议，选取不用的输入的长度作为测试，Sequence Length为512时测试最佳。
|
||
|
||
| Model Name | GPU | tensor-type | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) |
|
||
|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:|:-------:|:-------:|
|
||
| acge_text_embedding | NVIDIA TESLA A10 | bfloat16 | 0.65 | 1792 | 1024 | 68.91 | 72.76 | 58.22 | 87.82 | 67.67 | 72.48 | 62.24 |
|
||
| acge_text_embedding | NVIDIA TESLA A100 | bfloat16 | 0.65 | 1792 | 1024 | 68.91 | 72.77 | 58.35 | 87.82 | 67.53 | 72.48 | 62.24 |
|
||
| acge_text_embedding | NVIDIA TESLA A100 | float16 | 0.65 | 1792 | 1024 | 68.99 | 72.76 | 58.68 | 87.84 | 67.89 | 72.49 | 62.24 |
|
||
| acge_text_embedding | NVIDIA TESLA A100 | float32 | 0.65 | 1792 | 1024 | 68.98 | 72.76 | 58.58 | 87.83 | 67.91 | 72.49 | 62.24 |
|
||
| acge_text_embedding | NVIDIA TESLA A100 | float16 | 0.65 | 1792 | 768 | 68.95 | 72.76 | 58.68 | 87.84 | 67.86 | 72.48 | 62.07 |
|
||
| acge_text_embedding | NVIDIA TESLA A100 | float16 | 0.65 | 1792 | 512 | 69.07 | 72.75 | 58.7 | 87.84 | 67.99 | 72.93 | 62.09 |
|
||
|
||
#### Reproduce our results
|
||
|
||
**C-MTEB:**
|
||
|
||
```python
|
||
import torch
|
||
import argparse
|
||
import functools
|
||
from C_MTEB.tasks import *
|
||
from typing import List, Dict
|
||
from sentence_transformers import SentenceTransformer
|
||
from mteb import MTEB, DRESModel
|
||
|
||
|
||
class RetrievalModel(DRESModel):
|
||
def __init__(self, encoder, **kwargs):
|
||
self.encoder = encoder
|
||
|
||
def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:
|
||
input_texts = ['{}'.format(q) for q in queries]
|
||
return self._do_encode(input_texts)
|
||
|
||
def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs) -> np.ndarray:
|
||
input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]
|
||
input_texts = ['{}'.format(t) for t in input_texts]
|
||
return self._do_encode(input_texts)
|
||
|
||
@torch.no_grad()
|
||
def _do_encode(self, input_texts: List[str]) -> np.ndarray:
|
||
return self.encoder.encode(
|
||
sentences=input_texts,
|
||
batch_size=512,
|
||
normalize_embeddings=True,
|
||
convert_to_numpy=True
|
||
)
|
||
|
||
|
||
def get_args():
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--model_name_or_path', default="acge_text_embedding", type=str)
|
||
parser.add_argument('--task_type', default=None, type=str)
|
||
parser.add_argument('--pooling_method', default='cls', type=str)
|
||
parser.add_argument('--output_dir', default='zh_results',
|
||
type=str, help='output directory')
|
||
parser.add_argument('--max_len', default=1024, type=int, help='max length')
|
||
return parser.parse_args()
|
||
|
||
|
||
if __name__ == '__main__':
|
||
args = get_args()
|
||
encoder = SentenceTransformer(args.model_name_or_path).half()
|
||
encoder.encode = functools.partial(encoder.encode, normalize_embeddings=True)
|
||
encoder.max_seq_length = int(args.max_len)
|
||
|
||
task_names = [t.description["name"] for t in MTEB(task_types=args.task_type,
|
||
task_langs=['zh', 'zh-CN']).tasks]
|
||
TASKS_WITH_PROMPTS = ["T2Retrieval", "MMarcoRetrieval", "DuRetrieval", "CovidRetrieval", "CmedqaRetrieval",
|
||
"EcomRetrieval", "MedicalRetrieval", "VideoRetrieval"]
|
||
for task in task_names:
|
||
evaluation = MTEB(tasks=[task], task_langs=['zh', 'zh-CN'])
|
||
if task in TASKS_WITH_PROMPTS:
|
||
evaluation.run(RetrievalModel(encoder), output_folder=args.output_dir, overwrite_results=False)
|
||
else:
|
||
evaluation.run(encoder, output_folder=args.output_dir, overwrite_results=False)
|
||
|
||
|
||
```
|
||
|
||
|
||
## Usage
|
||
|
||
#### acge 中文系列模型
|
||
|
||
在sentence-transformer库中的使用方法：
|
||
|
||
```python
|
||
from sentence_transformers import SentenceTransformer
|
||
|
||
sentences = ["数据1", "数据2"]
|
||
model = SentenceTransformer('acge_text_embedding')
|
||
print(model.max_seq_length)
|
||
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
|
||
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
|
||
similarity = embeddings_1 @ embeddings_2.T
|
||
print(similarity)
|
||
```
|
||
在sentence-transformer库中的使用方法，选取不同的维度：
|
||
|
||
```python
|
||
from sklearn.preprocessing import normalize
|
||
from sentence_transformers import SentenceTransformer
|
||
|
||
sentences = ["数据1", "数据2"]
|
||
model = SentenceTransformer('acge_text_embedding')
|
||
embeddings = model.encode(sentences, normalize_embeddings=False)
|
||
matryoshka_dim = 1024
|
||
embeddings = embeddings[..., :matryoshka_dim] # Shrink the embedding dimensions
|
||
embeddings = normalize(embeddings, norm="l2", axis=1)
|
||
print(embeddings.shape)
|
||
# => (2, 1024)
|
||
|
||
```
|
||
|
||
|
||
|