AI is happening. The future is updating.EmbeddingGemma-Scandi-300m
EmbeddingGemma-Scandi-300m is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using google/embeddinggemma-300m as the base model via the sentence-transformers framework. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** google/embeddinggemma-300m - **Maximum Sequence Length:** 2048 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - nordic-embedding-training-data - **Languages:** da, no, sw, sv - **License:** apache-2.0
EmbeddingGemma-Scandi-300m is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using google/embeddinggemma-300m as the base model via the sentence-transformers framework. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** google/embeddinggemma-300m - **Maximum Sequence Length:** 2048 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - nordic-embedding-training-data - **Languages:** da, no, sw, sv - **License:** apache-2.0