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This is a sentence-transformers model finetuned from Alibaba-NLP/gte-modernbert-base on the Quora dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching. - **Model Type:** Sentence Transformer - **Base model:** Alibaba-NLP/gte-modernbert-base - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - Quora - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face

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This is a Japanese CLIP (Contrastive Language-Image Pre-Training) model trained by rinna Co., Ltd.. Please see japanese-clip for the other available models. The model was trained a ViT-B/16 Transformer architecture as an image encoder and uses a 12-layer BERT as a text encoder. The image encoder was initialized from the AugReg `vit-base-patch16-224` model.

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e5-v

royokong

We propose a framework, called E5-V, to adpat MLLMs for achieving multimodal embeddings. E5-V effectively bridges the modality gap between different types of inputs, demonstrating strong performance in multimodal embeddings even without fine-tuning. We also propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs, demonstrating better performance than multimodal training. The model uses a custom chat template that automatically wraps text inputs with the instruction "Summary above sentence in one word:" and image inputs with "Summary above image in one word:".

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LUAR-MUD

rrivera1849

Author Style Representations using LUAR. The LUAR training and evaluation repository can be found here. This model was trained on the Reddit Million User Dataset (MUD) found here.

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LUSR

rrivera1849

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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--- 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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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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