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**Note:** Check out my new, updated models: German_Semantic_V3 and V3b! 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. Special thanks to deepset for providing the model gBERT-large and also to Philip May for the Translation of the dataset and chats about the topic.

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This is a sentence-transformers model finetuned with wandb-ai from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. For this case, it uses for column standardization. - **Model Type:** Sentence Transformer - **Base model:** sentence-transformers/all-MiniLM-L6-v2 - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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