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type: dot_accuracy_threshold value: 86.3375 - type: dot_f1 value: 79.5103 - type: dot_f1_threshold value: 85.09649999999999 - type: dot_precision value: 77.381 - type: dot_recall value: 81.76010000000001 - type: dot_ap value: 87.07770000000001 - type: max_accuracy value: 89.5195 - type: max_f1 value: 79.5103 - type: max_precision value: 77.381 - type: max_recall value: 83.8928 - type: max_ap value: 87.0889 - type: main_score value: 87.0889 task: type: PairClassification tags: - mteb --- # Gemma Embeddings v1.0 GemmaEmbed is a dense-vector embedding model, trained especially for retrieval. As of December 12, 2024, GemmaEmbed achieves the #1 position overall on the MTEB leaderboard, with a score of 72.72. # Important Notes * This is not an official Google product. * This is a research project. # Results summary Results comparing with BGE-EN-ICL and NV-Embed-v2 on each task in [MTEB](https://huggingface.co./spaces/mteb/leaderboard): Model | Total (56) |Classification (12) | Classification Pair (3) | STS (10) |Clustering (11) | Reranking (4) | Retrieval (15) | Summary (1) -- | -- | -- | -- | -- | -- | -- | -- | -- bge-en-icl | 0.7167 | 0.8895 | 0.8814 | 0.8425 | 0.5789 | 0.5986 | 0.6216 | 0.3077 NV-Embed-v2 | 0.7231 | 0.9037 | 0.8867 | 0.8431 | 0.5846 | 0.6065 | 0.6265 | 0.3070 Gemma-Embeddings-v1.0 | 0.7272 | 0.9000 | 0.8809 | 0.8423 | 0.5826 | 0.6214 | 0.6371 | 0.4052 # Model & Data Our base encoder model is [Gemma2 9B](https://huggingface.co./google/gemma-2-9b). We use the [BGE-EN-ICL training data](https://huggingface.co./datasets/cfli/bge-full-data). # Research Team * Nicholas Monath * Michael Boratko * Seungyeon Kim * Andrew McCallum * Rob Fergus * Manzil Zaheer