ibm-granite/granite-embedding-125m-english
sentence-similarity model by ibm-granite
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Model card & documentation
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News: Granite Embedding R2 models with 8192 context length released. Model Summary: Granite-Embedding-125m-English is a 125M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768. 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. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation. Supported Languages: English. Intended use: The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications. Usage with Sentence Transformers: The model is compatible with SentenceTransformer library and is very easy to use: First,…
Read the full model card ↗ · Preview checked 2026-10-30T23:58:06.736Z
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Model overview
ibm-granite/granite-embedding-125m-english is a sentence-similarity 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
- sentence-similarity
- Library
- sentence-transformers
- Recent downloads (30 days)
- 202,471
- Cumulative likes
- 38
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 124,645,632
- Architecture
- RobertaModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-12-04T20:28:08.000Z
- Last modified
- 2025-08-19T01:31:48.000Z
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
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:31.802Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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