> ## Documentation Index > Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt > Use this file to discover all available pages before exploring further. # Sentence Transformers > Configure PipesHub to use Sentence Transformer embedding models # Sentence Transformers Embeddings Configuration Sentence Transformer Embeddings Configuration Interface *The Sentence Transformers configuration screen in PipesHub where you'll enter your Model Name* PipesHub allows you to use Sentence Transformer embedding models to enable semantic search and document retrieval in your workspace. These models run locally inside PipesHub — no external API, no API key, and no usage costs. ## Required Fields ### Model Name \* The Model Name is the only required field. It defines which Sentence Transformer model you want to use. **Popular Sentence Transformer models include:** * `all-MiniLM-L6-v2` - Lightweight general-purpose model, good balance of speed and quality * `all-mpnet-base-v2` - Higher accuracy general-purpose model * `paraphrase-multilingual-mpnet-base-v2` - Supports 50+ languages for multilingual applications **How to choose a model:** * For general purpose use, select `all-MiniLM-L6-v2` * For higher accuracy when speed is less critical, select `all-mpnet-base-v2` * For multilingual content, select `paraphrase-multilingual-mpnet-base-v2` * Check the [Sentence Transformers documentation](https://www.sbert.net/docs/pretrained_models.html) for the full list of available models ## Configuration Steps As shown in the image above: 1. Click **Configure** on the Sentence Transformers provider card 2. Specify your desired Model Name (marked with \*) 3. Click **Add Model** to save and validate > Model Name is the only required field. No API key or external service is needed — the model runs locally inside PipesHub. ## Usage Considerations * No external API calls are made — all embedding happens locally inside PipesHub * No API key or billing setup required * Larger models provide better quality but require more memory and are slower to load * Most models support a maximum sequence length of 128–512 tokens per document chunk ## Troubleshooting * If you encounter errors, verify the model name is spelled correctly * For memory issues, consider switching to a smaller model such as `all-MiniLM-L6-v2` * For multilingual applications, ensure you are using a model that supports your target languages For additional support, refer to the [Sentence Transformers documentation](https://www.sbert.net/) or contact PipesHub support.