AI is happening. The future is updating.granite-embedding-97m-multilingual-r2-ONNX
This is an ONNX version of ibm-granite/granite-embedding-97m-multilingual-r2. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for `feature-extraction`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline **Model Summary:** Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings at minimal compute cost. It produces 384-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with **enhanced support for 52 languages and programming code** that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets.
This is an ONNX version of ibm-granite/granite-embedding-97m-multilingual-r2. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for `feature-extraction`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline **Model Summary:** Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings at minimal compute cost. It produces 384-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with **enhanced support for 52 languages and programming code** that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets.