Text Retrieval
Transformers
Safetensors
sentence-transformers
English
kpr-bert
image-feature-extraction
custom_code
Instructions to use knowledgeable-ai/kpr-retromae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgeable-ai/kpr-retromae with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("knowledgeable-ai/kpr-retromae", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use knowledgeable-ai/kpr-retromae with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("knowledgeable-ai/kpr-retromae", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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README.md
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**The entity knowledge is pluggable and can be dynamically updated with ease.**
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## Model List
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## Citation
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If you use this model in your research, please cite the following paper:
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[Dynamic Injection of Entity Knowledge into Dense Retrievers](https://arxiv.org/abs/2507.03922)
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```bibtex
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**The entity knowledge is pluggable and can be dynamically updated with ease.**
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For further details, refer to [our paper](https://arxiv.org/abs/2507.03922) or [GitHub repository](https://github.com/knowledgeable-embedding/knowledgeable-embedding).
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## Model List
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## Citation
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If you use this model in your research, please cite the following paper:
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[Dynamic Injection of Entity Knowledge into Dense Retrievers](https://arxiv.org/abs/2507.03922)
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```bibtex
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