--- dataset_info: features: - name: text dtype: string - name: embedding list: float32 length: 768 splits: - name: train num_examples: 13028 dataset_size: 13028 download_size: 13028 configs: - config_name: default data_files: - split: train path: train/* metadata: model_name: Alibaba-NLP/gte-modernbert-base dataset_name: mteb/lotte --- # Embedpress: Alibaba-NLP/gte-modernbert-base on the mteb/lotte dataset This is the [mteb/lotte](https://huggingface.co/datasets/mteb/lotte) dataset, embedded with [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). For each example, we embed the text directly (no additional instruction prompt). Embeddings have dimensionality **768**. These embeddings are intended for tasks like large-scale distillation, retrieval, and similarity search. Because the raw text may exceed the model’s limit, we recommend truncating to the model’s maximum token length at build time. ## Schema - `text` *(string)* — the query text used for embedding - `embedding` *(float32[768])* — the vector representation from `Alibaba-NLP/gte-modernbert-base` ## Split - `train` — **13028 examples** ## Notes - Produced with `Alibaba-NLP/gte-modernbert-base` from Hugging Face Hub. - If you need a smaller embedding size (e.g., matryoshka/truncated vectors), you can safely slice the embeddings without re-embedding. ## Acknowledgments Thanks [Mixedbread AI](https://www.mixedbread.com/) for a GPU grant for research into small retrieval models.