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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:34 |
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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: Quais são as iniciativas do Seringal Lab? |
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sentences: |
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- O objetivo do Seringal Lab é atuar como um catalisador da transformação interna |
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do Ministério Público do Acre, promovendo melhorias contínuas que otimizam o funcionamento |
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da instituição e geram um impacto positivo direto para a sociedade. |
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- O NAT é vinculado à Procuradoria-Geral de Justiça e presta apoio técnico especializado |
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ao MPAC. |
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- Algumas das iniciativas do Seringal Lab incluem a Anton.IA, o TranscreveAI e o |
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Simplifica. |
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- source_sentence: Em que ano o NAT foi instituído? |
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sentences: |
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- O SIMBA é o Sistema de Investigação de Movimentação Bancária, gerenciado pelo |
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NAT, para monitoramento de atividades financeiras suspeitas no Acre. |
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- O NAT foi criado em 2012 pelo Ato n.º 25, visando oferecer apoio técnico-científico |
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e de segurança institucional ao MPAC. |
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- O NAT foi instituído no ano de 2012 como uma unidade de suporte técnico e segurança |
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ao MPAC. |
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- source_sentence: Qual o impacto do NAT no combate ao crime organizado? |
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sentences: |
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- NAT é o Núcleo de Apoio Técnico do Ministério Público do Estado do Acre, criado |
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para fornecer suporte especializado em inteligência, segurança institucional e |
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operações técnico-científicas. |
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- O NAT fortalece o combate ao crime organizado ao fornecer suporte técnico e científico |
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ao GAECO e outros órgãos do MPAC. |
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- O NAT foi criado para oferecer suporte especializado ao MPAC, garantindo apoio |
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em áreas técnico-científicas e de segurança para facilitar as operações de investigação |
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e combate ao crime. |
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- source_sentence: Quem regulamenta o NAT? |
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sentences: |
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- O escopo do NAT envolve oferecer apoio de inteligência, segurança institucional, |
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e suporte técnico-científico ao MPAC, especialmente nas operações do GAECO. |
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- NAT significa Núcleo de Apoio Técnico, uma unidade de suporte técnico e de segurança |
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ao Ministério Público do Acre. |
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- O NAT é regulamentado pelo Ministério Público do Estado do Acre e foi formalizado |
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pela Lei Complementar n.º 291 de 2014. |
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- source_sentence: Qual a importância do NAT para o MPAC? |
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sentences: |
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- O TranscreveAI transforma áudios em textos de maneira automática e precisa, além |
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de registrar o tempo exato do início e do fim de cada fala (timestamp). |
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- O NAT é essencial para o MPAC, fornecendo apoio técnico especializado e segurança, |
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fortalecendo as operações de investigação e combate ao crime. |
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- A Lei Complementar n.º 291 de 2014 regulamentou o NAT como um órgão auxiliar do |
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MPAC, fortalecendo seu papel de apoio técnico e científico. |
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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: MPAC BGE Large |
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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: 0.7777777777777778 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7777777777777778 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2962962962962963 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17777777777777778 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08888888888888889 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7777777777777778 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8888888888888888 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8888888888888888 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8888888888888888 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8333333333333334 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8148148148148149 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8249158249158248 |
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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: 0.7777777777777778 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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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: 0.7777777777777778 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2962962962962963 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17777777777777778 |
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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: 0.7777777777777778 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8888888888888888 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8888888888888888 |
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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: 0.8813288610261599 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.845679012345679 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.845679012345679 |
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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: 0.7777777777777778 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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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: 0.7777777777777778 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2962962962962963 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17777777777777778 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.7777777777777778 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8888888888888888 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.8888888888888888 |
|
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: 0.884918120767199 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8492063492063493 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8492063492063492 |
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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: 0.7777777777777778 |
|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.8888888888888888 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
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value: 0.7777777777777778 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2962962962962963 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17777777777777778 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.7777777777777778 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8888888888888888 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8888888888888888 |
|
name: Cosine Recall@5 |
|
- 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: 0.8813288610261599 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.845679012345679 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
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value: 0.845679012345679 |
|
name: Cosine Map@100 |
|
- 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 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8888888888888888 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8888888888888888 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2962962962962963 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17777777777777778 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8888888888888888 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8888888888888888 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.884918120767199 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8492063492063493 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8492063492063492 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# MPAC BGE Large |
|
|
|
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. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
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- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
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}) |
|
(2): Normalize() |
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) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("mp-ac/mpac-bge-large-v1.2") |
|
# Run inference |
|
sentences = [ |
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'Qual a importância do NAT para o MPAC?', |
|
'O NAT é essencial para o MPAC, fornecendo apoio técnico especializado e segurança, fortalecendo as operações de investigação e combate ao crime.', |
|
'A Lei Complementar n.º 291 de 2014 regulamentou o NAT como um órgão auxiliar do MPAC, fortalecendo seu papel de apoio técnico e científico.', |
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] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
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|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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|
|
<details><summary>Click to expand</summary> |
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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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|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
|
|
|
## Evaluation |
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|
|
### Metrics |
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|
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#### Information Retrieval |
|
|
|
* 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) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | |
|
| cosine_accuracy@3 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | |
|
| cosine_accuracy@5 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | |
|
| cosine_accuracy@10 | 0.8889 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
| cosine_precision@1 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | |
|
| cosine_precision@3 | 0.2963 | 0.2963 | 0.2963 | 0.2963 | 0.2963 | |
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| cosine_precision@5 | 0.1778 | 0.1778 | 0.1778 | 0.1778 | 0.1778 | |
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| cosine_precision@10 | 0.0889 | 0.1 | 0.1 | 0.1 | 0.1 | |
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| cosine_recall@1 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | 0.7778 | |
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| cosine_recall@3 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | |
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| cosine_recall@5 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | 0.8889 | |
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| cosine_recall@10 | 0.8889 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| **cosine_ndcg@10** | **0.8333** | **0.8813** | **0.8849** | **0.8813** | **0.8849** | |
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| cosine_mrr@10 | 0.8148 | 0.8457 | 0.8492 | 0.8457 | 0.8492 | |
|
| cosine_map@100 | 0.8249 | 0.8457 | 0.8492 | 0.8457 | 0.8492 | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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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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|
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## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 34 training samples |
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
* Approximate statistics based on the first 34 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 13.85 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 53.62 tokens</li><li>max: 76 tokens</li></ul> | |
|
* Samples: |
|
| anchor | positive | |
|
|:-----------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Qual é o objetivo do Simplifica?</code> | <code>O objetivo do Simplifica é implementar e disseminar a Linguagem Simples no Ministério Público do Estado do Acre, tornando a comunicação institucional mais acessível, clara e objetiva para todos os cidadãos.</code> | |
|
| <code>Qual é a função do NAT no LAB-LD?</code> | <code>O NAT gerencia o LAB-LD, oferecendo suporte especializado em investigações financeiras para combater a lavagem de dinheiro.</code> | |
|
| <code>O que é o NAT?</code> | <code>O NAT, Núcleo de Apoio Técnico, é uma unidade do Ministério Público do Estado do Acre criada em 2012 para oferecer apoio técnico, científico e de segurança aos órgãos de execução do MPAC.</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 5 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `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`: 5 |
|
- `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 | 0.7368 | 0.7368 | 0.7222 | 0.6686 | 0.7222 | |
|
| 2.0 | 2 | 0.8128 | 0.7738 | 0.7292 | 0.7738 | 0.7702 | |
|
| 3.0 | 3 | 0.8256 | 0.8258 | 0.8542 | 0.8800 | 0.8591 | |
|
| **4.0** | **4** | **0.8333** | **0.8258** | **0.8704** | **0.8813** | **0.8829** | |
|
| 5.0 | 5 | 0.8333 | 0.8813 | 0.8849 | 0.8813 | 0.8849 | |
|
|
|
* 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: 1.1.1 |
|
- Datasets: 3.1.0 |
|
- 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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