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Cataloged from RedHatAI/granite-embedding-english-r2
All models by RedHatAI βModel Summary: Granite-embedding-english-r2 is a 149M 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 768 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-english-r2 is optimized to ensure strong alignment between query and passage embeddings.
The latest granite embedding r2 release introduces two English embedding models, both based on the ModernBERT architecture:
ModelCar: registry.redhat.io/rhai/modelcar-granite-embedding-english-r2
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.
For efficient decoding, these models use Flash Attention 2. Installing it is optional, but can lead to faster inference.
Usage with Sentence Transformers:
The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
The model can then be used to encode pairs of text and find the similarity between their representations
Usage with Huggingface Transformers:
This is a simple example of how to use the granite-embedding-english-r2 model with the Transformers library and PyTorch.
First, install the required libraries
The model can then be used to encode pairs of text
Granite embedding r2 models show a strong performance across tasks diverse tasks.
Performance of the granite models on MTEB Retrieval (i.e., BEIR), MTEB-v2, code retrieval (CoIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), benchmarks is reported in the below tables.
The r2 models demonstrates speed and efficiency, while mainintaining competitive performance. The average speed to encode documents on a single H100 GPU using a sliding window with 512 context length chunks is also reported.
The latest granite embedding r2 release introduces two English embedding models, both based on the ModernBERT architecture:
The following table shows the structure of the two models:
The granite embedding r2 models incorporate key enhancements from the ModernBERT architecture, including:
Granite embedding r2 models are trained using data from four key sources:
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license (many open-source models use this dataset due to its high quality).
The underlying encoder models using GneissWeb, an IBM-curated dataset composed exclusively of open, commercial-friendly sources.
For governance, all our data undergoes a data clearance process subject to technical, business, and governance review. This comprehensive process captures critical information about the data, including but not limited to their content description ownership, intended use, data classification, licensing information, usage restrictions, how the data will be acquired, as well as an assessment of sensitive information (i.e, personal information).
We trained the granite embedding english r2 models using IBM's computing cluster, BlueVela Cluster, which is outfitted with NVIDIA H100 80GB GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
Granite-embedding-english-r2 leverages both permissively licensed open-source and select proprietary data for enhanced performance. The training data for the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-embedding-english-r2 is trained only for English texts, and has a context length of 8192 tokens (longer texts will be truncated to this size).
pip install flash_attn==2.6.1pip install sentence_transformersfrom sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-english-r2"# Load the Sentence Transformer modelmodel = SentenceTransformer(model_path)
input_queries = [ ' Who made the song My achy breaky heart? ', 'summit define' ]
input_passages = [ "Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ]
# encode queries and passages. The model produces unnormalized vectors. If your task requires normalized embeddings pass normalize_embeddings=True to encode as below.query_embeddings = model.encode(input_queries)passage_embeddings = model.encode(input_passages)
# calculate cosine similarityprint(util.cos_sim(query_embeddings, passage_embeddings))pip install transformers torchimport torchfrom transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-english-r2"
# Load the model and tokenizermodel = AutoModel.from_pretrained(model_path)tokenizer = AutoTokenizer.from_pretrained(model_path)model.eval()
input_queries = [ ' Who made the song My achy breaky heart? ', 'summit define' ]
# tokenize inputstokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode querieswith torch.no_grad(): # Queries model_output = model(**tokenized_queries) # Perform pooling. granite-embedding-278m-multilingual uses CLS Pooling query_embeddings = model_output[0][:, 0]
# normalize the embeddingsquery_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)@misc{awasthy2025graniteembeddingr2models, title={Granite Embedding R2 Models}, author={Parul Awasthy and Aashka Trivedi and Yulong Li and Meet Doshi and Riyaz Bhat and Vignesh P and Vishwajeet Kumar and Yushu Yang and Bhavani Iyer and Abraham Daniels and Rudra Murthy and Ken Barker and Martin Franz and Madison Lee and Todd Ward and Salim Roukos and David Cox and Luis Lastras and Jaydeep Sen and Radu Florian}, year={2025}, eprint={2508.21085}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2508.21085}, }