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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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- financial |
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- fiqa |
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- finance |
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- retrieval |
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- rag |
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- esg |
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- fixed-income |
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- equity |
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model-index: |
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- name: fin-mpnet-base-v0.1 |
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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_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 |
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value: 29.128 |
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- type: f1 |
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value: 28.657401543151707 |
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- task: |
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type: Retrieval |
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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: |
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- type: map_at_1 |
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value: 24.111 |
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- type: map_at_10 |
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value: 40.083 |
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- type: map_at_100 |
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value: 41.201 |
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- type: map_at_1000 |
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value: 41.215 |
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- type: map_at_3 |
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value: 35.325 |
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- type: map_at_5 |
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value: 37.796 |
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- type: mrr_at_1 |
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value: 25.036 |
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- type: mrr_at_10 |
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value: 40.436 |
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- type: mrr_at_100 |
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value: 41.554 |
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- type: mrr_at_1000 |
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value: 41.568 |
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- type: mrr_at_3 |
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value: 35.644999999999996 |
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- type: mrr_at_5 |
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value: 38.141000000000005 |
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- type: ndcg_at_1 |
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value: 24.111 |
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- type: ndcg_at_10 |
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value: 49.112 |
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- type: ndcg_at_100 |
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value: 53.669999999999995 |
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- type: ndcg_at_1000 |
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value: 53.944 |
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- type: ndcg_at_3 |
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value: 39.035 |
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- type: ndcg_at_5 |
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value: 43.503 |
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- type: precision_at_1 |
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value: 24.111 |
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- type: precision_at_10 |
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value: 7.817 |
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- type: precision_at_100 |
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value: 0.976 |
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- type: precision_at_1000 |
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value: 0.1 |
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- type: precision_at_3 |
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value: 16.596 |
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- type: precision_at_5 |
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value: 12.134 |
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- type: recall_at_1 |
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value: 24.111 |
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- type: recall_at_10 |
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value: 78.16499999999999 |
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- type: recall_at_100 |
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value: 97.58200000000001 |
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- type: recall_at_1000 |
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value: 99.57300000000001 |
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- type: recall_at_3 |
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value: 49.787 |
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- type: recall_at_5 |
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value: 60.669 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/banking77 |
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name: MTEB Banking77Classification |
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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 |
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value: 80.25 |
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- type: f1 |
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value: 79.64999520103544 |
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- task: |
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type: Retrieval |
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dataset: |
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type: fiqa |
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name: MTEB FiQA2018 |
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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 |
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value: 37.747 |
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- type: map_at_10 |
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value: 72.223 |
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- type: map_at_100 |
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value: 73.802 |
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- type: map_at_1000 |
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value: 73.80499999999999 |
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- type: map_at_3 |
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value: 61.617999999999995 |
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- type: map_at_5 |
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value: 67.92200000000001 |
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- type: mrr_at_1 |
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value: 71.914 |
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- type: mrr_at_10 |
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value: 80.71000000000001 |
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- type: mrr_at_100 |
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value: 80.901 |
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- type: mrr_at_1000 |
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value: 80.901 |
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- type: mrr_at_3 |
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value: 78.935 |
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- type: mrr_at_5 |
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value: 80.193 |
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- type: ndcg_at_1 |
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value: 71.914 |
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- type: ndcg_at_10 |
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value: 79.912 |
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- type: ndcg_at_100 |
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value: 82.675 |
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- type: ndcg_at_1000 |
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value: 82.702 |
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- type: ndcg_at_3 |
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value: 73.252 |
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- type: ndcg_at_5 |
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value: 76.36 |
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- type: precision_at_1 |
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value: 71.914 |
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- type: precision_at_10 |
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value: 23.071 |
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- type: precision_at_100 |
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value: 2.62 |
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- type: precision_at_1000 |
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value: 0.263 |
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- type: precision_at_3 |
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value: 51.235 |
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- type: precision_at_5 |
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value: 38.117000000000004 |
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- type: recall_at_1 |
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value: 37.747 |
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- type: recall_at_10 |
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value: 91.346 |
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- type: recall_at_100 |
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value: 99.776 |
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- type: recall_at_1000 |
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value: 99.897 |
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- type: recall_at_3 |
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value: 68.691 |
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- type: recall_at_5 |
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value: 80.742 |
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- task: |
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type: Retrieval |
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dataset: |
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type: nfcorpus |
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name: MTEB NFCorpus |
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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 |
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value: 4.124 |
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- type: map_at_10 |
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value: 10.206999999999999 |
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- type: map_at_100 |
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value: 13.181000000000001 |
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- type: map_at_1000 |
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value: 14.568 |
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- type: map_at_3 |
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value: 7.2620000000000005 |
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- type: map_at_5 |
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value: 8.622 |
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- type: mrr_at_1 |
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value: 39.009 |
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- type: mrr_at_10 |
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value: 48.144 |
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- type: mrr_at_100 |
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value: 48.746 |
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- type: mrr_at_1000 |
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value: 48.789 |
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- type: mrr_at_3 |
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value: 45.356 |
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- type: mrr_at_5 |
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value: 47.152 |
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- type: ndcg_at_1 |
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value: 36.533 |
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- type: ndcg_at_10 |
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value: 29.643000000000004 |
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- type: ndcg_at_100 |
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value: 27.893 |
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- type: ndcg_at_1000 |
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value: 37.307 |
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- type: ndcg_at_3 |
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value: 33.357 |
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- type: ndcg_at_5 |
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value: 32.25 |
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- type: precision_at_1 |
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value: 38.7 |
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- type: precision_at_10 |
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value: 22.941 |
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- type: precision_at_100 |
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value: 7.303 |
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- type: precision_at_1000 |
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value: 2.028 |
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- type: precision_at_3 |
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value: 31.889 |
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- type: precision_at_5 |
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value: 29.04 |
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- type: recall_at_1 |
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value: 4.124 |
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- type: recall_at_10 |
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value: 14.443 |
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- type: recall_at_100 |
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value: 29.765000000000004 |
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- type: recall_at_1000 |
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value: 63.074 |
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- type: recall_at_3 |
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value: 8.516 |
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- type: recall_at_5 |
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value: 10.979 |
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- task: |
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type: Retrieval |
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dataset: |
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type: scifact |
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name: MTEB SciFact |
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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 |
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value: 49.010999999999996 |
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- type: map_at_10 |
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value: 60.094 |
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- type: map_at_100 |
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value: 60.79900000000001 |
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- type: map_at_1000 |
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value: 60.828 |
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- type: map_at_3 |
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value: 57.175 |
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- type: map_at_5 |
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value: 58.748 |
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- type: mrr_at_1 |
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value: 51.666999999999994 |
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- type: mrr_at_10 |
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value: 61.312 |
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- type: mrr_at_100 |
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value: 61.821000000000005 |
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- type: mrr_at_1000 |
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value: 61.85000000000001 |
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- type: mrr_at_3 |
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value: 59.0 |
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- type: mrr_at_5 |
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value: 60.199999999999996 |
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- type: ndcg_at_1 |
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value: 51.666999999999994 |
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- type: ndcg_at_10 |
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value: 65.402 |
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- type: ndcg_at_100 |
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value: 68.377 |
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- type: ndcg_at_1000 |
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value: 69.094 |
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- type: ndcg_at_3 |
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value: 60.153999999999996 |
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- type: ndcg_at_5 |
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value: 62.455000000000005 |
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- type: precision_at_1 |
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value: 51.666999999999994 |
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- type: precision_at_10 |
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value: 9.067 |
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- type: precision_at_100 |
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value: 1.0670000000000002 |
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- type: precision_at_1000 |
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value: 0.11199999999999999 |
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- type: precision_at_3 |
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value: 24.0 |
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- type: precision_at_5 |
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value: 15.933 |
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- type: recall_at_1 |
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value: 49.010999999999996 |
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- type: recall_at_10 |
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value: 80.511 |
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- type: recall_at_100 |
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value: 94.0 |
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- type: recall_at_1000 |
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value: 99.5 |
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- type: recall_at_3 |
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value: 66.2 |
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- type: recall_at_5 |
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value: 71.944 |
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--- |
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full evaluation not complete |
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# Fin-MPNET-Base (v0.1) |
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This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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This model aims to be very strong on Financial Document Retrieval Tasks, while trying to maintain as much generalized performance as possible. |
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| | FiQA | SciFact | AmazonReviews | OnlineBankingIntent | ArguAna | |
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|-------------------|-------|---------|---------------|---------------------|---------| |
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| fin-mpnet-base | 79.91 | 65.40 | 29.12 | 80.25 | 49.11 | |
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| all-mpnet-base-v2 | 49.96 | 65.57 | 31.92 | 81.86 | 46.52 | |
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| previous SoTA | 56.59 | - | - | - | - | |
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v0.1 shows SoTA results on FiQA Test set while other non-financial benchmarks only drop a few small % and improvement in others. |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('mukaj/fin-mpnet-base') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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Model was evaluated during training only on the new finance QA examples, as such only financial relevant benchmarks were evaluated on for v0.1 [FiQA-2018, BankingClassification77] |
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The model currently shows the highest FiQA Retrieval score on the test set, on the MTEB Leaderboard (https://huggingface.co/spaces/mteb/leaderboard) |
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The model will have likely suffered some performance on other benchmarks, i.e. BankingClassification77 has dropped from 81.6 to 80.25, this will be addressed for v0.2 and full evaluation on all sets will be run. |
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## Training |
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"sentence-transformers/all-mpnet-base-v2" was fine-tuned on 150k+ financial document QA examples using MNR Loss. |
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