ibm-granite/granite-embedding-small-english-r2
feature-extraction model by ibm-granite
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Model card & documentation
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Model Summary: Granite-embedding-small-english-r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. The r2 models show strong performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases. These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-small-english-r2 is optimized to ensure strong alignment…
Read the full model card ↗ · Preview checked 2026-10-28T19:41:11.751Z
Inside the original model card — Document outline
- Granite-Embedding-Small-English-R2
- Model Details
- Usage
- Evaluation Results
- Model Architecture and Key Features
- Training and Optimization
- Data Collection
- Infrastructure
- Ethical Considerations and Limitations
- Citation
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Model overview
ibm-granite/granite-embedding-small-english-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)
- 6,749,640
- Cumulative likes
- 113
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 47,662,464
- Architecture
- ModernBertModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2025-07-17T20:41:53.000Z
- Last modified
- 2026-01-21T18:38:43.000Z
Compatibility and lineage
Reported languages: en
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.