Text Retrieval
Transformers
Safetensors
sentence-transformers
English
kpr-bert
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
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -25,7 +25,7 @@ Although RAG typically relies on embedding-based retrieval, the embedding models
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**Knowledgeable Embedding** addresses this challenge by injecting real-world entity knowledge into embeddings, making them more *knowledgeable*.
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**The entity knowledge is pluggable and can be dynamically updated
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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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**Knowledgeable Embedding** addresses this challenge by injecting real-world entity knowledge into embeddings, making them more *knowledgeable*.
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**The entity knowledge is pluggable and can be dynamically updated.**
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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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