granite-embedding-97m-multilingual-r2

모델 소개

**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. > Granite Embedding 97M Multilingual R2 scores **60.3** on Multilingual MTEB Retrieval (18 tasks) — the highest retrieval score of any open multilingual embedding model under 100M parameters, outperforming the next-best model in its size class (multilingual-e5-small at 50.9) by **+9.4 points** — while being roughly **3× smaller** than the full-size granite-embedding-311m-multilingual-r2. The multilingual R2 model shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, SiLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Compact multilingual vocabulary:** A purpose-trained 180K-token tokenizer preserves broad multilingual coverage while reducing model size. - **Model pruning:** Layer pruning (22 → 12 layers) from the full-size multilingual model, followed by continued distillation training to recover quality. - **Broader code coverage:** Code retrieval training set that includes Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers and contrastive fine-tuning yield a **+14.6 point** average gain over the previous-generation granite-embedding-107m-multilingual. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

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출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。