ibm-granite/granite-embedding-311m-multilingual-r2
feature-extraction model by ibm-granite
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Model card & documentation
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Model Summary: Granite-Embedding-311M-Multilingual-R2 is a 311M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings. It produces 768-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. The model uses a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, code, and documents, enabling seamless comparison through cosine similarity. Built using contrastive fine-tuning, knowledge distillation, and model merging, the Granite Embedding 311M Multilingual R2 model is optimized to ensure strong alignment between query and passage embeddings across many languages. The Granite Embedding Multilingual R2 release consists of two multilingual embedding models, both based on the ModernBERT architecture:…
Read the full model card ↗ · Preview checked 2026-11-01T09:23:53.144Z
Inside the original model card — Document outline
- Granite-Embedding-311M-Multilingual-R2
- What's New in R2
- Model Details
- Supported Languages
- When to Use This Model
- Usage
- Optimized Inference and Deployment
- Evaluation Results
- Multilingual Retrieval Performance
- Matryoshka Embeddings Performance
- Model Architecture and Key Features
- Training and Optimization
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
ibm-granite/granite-embedding-311m-multilingual-r2 is a feature-extraction model repository published by ibm-granite on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- sentence-transformers
- Recent downloads (30 days)
- 98,222
- Cumulative likes
- 143
- Hugging Face trending score
- 2
- Reported safetensors parameters
- 311,664,384
- Architecture
- ModernBertModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-04-20T15:16:51.000Z
- Last modified
- 2026-05-18T20:15:41.000Z
Compatibility and lineage
Reported languages: ar, az, bg, bn, ca, cs, da, de, el, en, es, et, fa, fi, fr, he, hi, hr, hu, id, is, it, ja, ka, kk, km, ko, lt, lv, mr, ms, nl, no, pl, pt, ro, ru, sk, sl, sq
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.