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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:30 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-large-en-v1.5 |
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widget: |
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- source_sentence: O NAT foi criado em 13 de setembro de 2012 pelo Ato n.º 25 da Procuradoria-Geral |
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de Justiça do MPAC. |
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sentences: |
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- Quando o NAT foi criado? |
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- O que significa NAT? |
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- Quem instituiu o NAT? |
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- source_sentence: A Lei Complementar n.º 291 de 2014 regulamentou o NAT como um órgão |
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auxiliar do MPAC, fortalecendo seu papel de apoio técnico e científico. |
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sentences: |
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- O NAT é parte de qual organização? |
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- Qual é a função do NAT no MPAC? |
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- Como o NAT foi regulamentado pela Lei Complementar 291? |
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- source_sentence: O NAT é o Núcleo de Apoio Técnico do MPAC, criado para prestar |
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apoio em inteligência, segurança e operações técnico-científicas aos órgãos de |
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execução, especialmente ao GAECO. |
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sentences: |
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- Quem são os coordenadores do NAT? |
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- Qual é a função do NAT no LAB-LD? |
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- Me explique o que é o NAT no Ministério Público. |
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- source_sentence: O NAT é responsável por fornecer inteligência, suporte técnico-científico |
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e segurança ao MPAC, além de gerenciar o SIMBA e o LAB-LD. |
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sentences: |
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- O que é o SIMBA, gerenciado pelo NAT? |
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- Quais são as responsabilidades do NAT? |
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- Para que foi criado o NAT? |
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- source_sentence: NAT é o Núcleo de Apoio Técnico do Ministério Público do Estado |
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do Acre, criado para fornecer suporte especializado em inteligência, segurança |
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institucional e operações técnico-científicas. |
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sentences: |
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- Explique o que é o NAT no MPAC. |
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- O NAT trabalha com o GAECO? |
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- O que significa NAT no Ministério Público? |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.75 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.25 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.2 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.75 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7544456402014998 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.675 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.675 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 1.0 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.3333333333333333 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.2 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 1.0 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7827324383928644 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7083333333333333 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7083333333333333 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.75 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.25 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.2 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.75 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7544456402014998 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.675 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.675 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.75 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.25 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.2 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.75 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7326691395183482 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.6458333333333333 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6458333333333333 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.16666666666666666 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.2 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
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value: 1.0 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7043823413269836 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6125 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6125 |
|
name: Cosine Map@100 |
|
--- |
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|
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# BGE base Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
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``` |
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|
|
Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("antonioanerao/mpac-bge-large") |
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# Run inference |
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sentences = [ |
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'NAT é o Núcleo de Apoio Técnico do Ministério Público do Estado do Acre, criado para fornecer suporte especializado em inteligência, segurança institucional e operações técnico-científicas.', |
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'O que significa NAT no Ministério Público?', |
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'O NAT trabalha com o GAECO?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
|
<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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</details> |
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--> |
|
|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
|
|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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|
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| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | |
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| cosine_accuracy@3 | 0.75 | 1.0 | 0.75 | 0.75 | 0.5 | |
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| cosine_accuracy@5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| cosine_accuracy@10 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| cosine_precision@1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | |
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| cosine_precision@3 | 0.25 | 0.3333 | 0.25 | 0.25 | 0.1667 | |
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| cosine_precision@5 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | |
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| cosine_precision@10 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | |
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| cosine_recall@1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | |
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| cosine_recall@3 | 0.75 | 1.0 | 0.75 | 0.75 | 0.5 | |
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| cosine_recall@5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| cosine_recall@10 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| **cosine_ndcg@10** | **0.7544** | **0.7827** | **0.7544** | **0.7327** | **0.7044** | |
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| cosine_mrr@10 | 0.675 | 0.7083 | 0.675 | 0.6458 | 0.6125 | |
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| cosine_map@100 | 0.675 | 0.7083 | 0.675 | 0.6458 | 0.6125 | |
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|
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<!-- |
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## Bias, Risks and Limitations |
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|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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|
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<!-- |
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### Recommendations |
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|
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
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## Training Details |
|
|
|
### Training Dataset |
|
|
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#### json |
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|
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* Dataset: json |
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* Size: 30 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 30 samples: |
|
| | positive | anchor | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 38 tokens</li><li>mean: 54.3 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.5 tokens</li><li>max: 18 tokens</li></ul> | |
|
* Samples: |
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| positive | anchor | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------| |
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| <code>O NAT foi criado em 13 de setembro de 2012 pelo Ato n.º 25 da Procuradoria-Geral de Justiça do MPAC.</code> | <code>Quando o NAT foi criado?</code> | |
|
| <code>O NAT é vinculado à Procuradoria-Geral de Justiça e presta apoio técnico especializado ao MPAC.</code> | <code>O NAT é vinculado a qual órgão?</code> | |
|
| <code>Os coordenadores do Núcleo de Apoio Técnico (NAT) são Marcela Cristina Ozório, como Coordenadora Geral e Bernardo Fiterman Albano, como Coordenador Adjunto </code> | <code>Quem são os coordenadores do NAT?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
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- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 1.0 | 1 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | |
|
| **2.0** | **2** | **0.7468** | **0.7468** | **0.6967** | **0.7654** | **0.6973** | |
|
| 3.0 | 3 | 0.7544 | 0.7654 | 0.7544 | 0.7327 | 0.6967 | |
|
| 4.0 | 4 | 0.7544 | 0.7827 | 0.7544 | 0.7327 | 0.7044 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.7 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.1.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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