Upload 2 files

README.md

@@ -16829,13 +16829,19 @@ pipeline_tag: sentence-similarity
 ---
 # Granite-Embedding-125m-English
 
+**News:** 
+Granite Embedding R2 models with 8192 context length released.
+
+- [granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-english-r2) (149M parameters): with an output embedding size of 768, replacing granite-embedding-125m-english.
+- [granite-embedding-small-english-r2](https://huggingface.co/ibm-granite/granite-embedding-small-english-r2) (47M parameters): A first-of-its-kind reduced-size model, with fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english.
+
 **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.
 
 - **Developers:** Granite Embedding Team, IBM
 - **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
 - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
-- **Paper:** Coming Soon
+- **Paper:** [Technical Report](https://arxiv.org/abs/2502.20204)
 - **Release Date**: December 18th, 2024
 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5a8fd811d55d90cbefb7b7434628629d69d06f9a8ad803fe941d75f44f66c62
+size 498821859