--- language: - pt thumbnail: "Portugues BERT for the Legal Domain" pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers datasets: - assin - assin2 - rufimelo/PortugueseLegalSentences-v0 widget: - source_sentence: "O advogado apresentou as provas ao juíz." sentences: - "O juíz leu as provas." - "O juíz leu o recurso." - "O juíz atirou uma pedra." example_title: "Example 1" model-index: - name: BERTimbau results: - task: name: STS type: STS metrics: - name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.71457 - name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.73545 - name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.72383 --- This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-large which derives from BERTimbau base. It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. Using this model becomes easy when you have sentence-transformers installed: rufimelo/Legal-BERTimbau-sts-base is based on Legal-BERTimbau-largewhich derives from BERTimbau base. It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin and assin2 datasets.

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Legal-BERTimbau-sts-large

rufimelo

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Legal-BERTimbau-sts-large-ma-v3

rufimelo

This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from BERTimbau large. It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from BERTimbau large. Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation. For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese.

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