Text Retrieval
Transformers
Safetensors
sentence-transformers
English
kpr-bert
feature-extraction
custom_code
Instructions to use knowledgeable-ai/kpr-bge-large-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgeable-ai/kpr-bge-large-en-v1.5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("knowledgeable-ai/kpr-bge-large-en-v1.5", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use knowledgeable-ai/kpr-bge-large-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("knowledgeable-ai/kpr-bge-large-en-v1.5", 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
Add pipeline tag and Hugging Face paper link
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tag: text-retrievalto the metadata, which ensures the model can be discovered via the Hugging Face Hub's pipeline filters (e.g., at https://huggingface.co/models?pipeline_tag=text-retrieval). This accurately reflects the model's function as a dense retriever. - Adding a prominent link to the official Hugging Face paper page (
https://huggingface.co/papers/2507.03922) at the top of the model card for easy access. - Clarifying the existing paper references in the
IntroductionandModel Listsections to explicitly mention "on arXiv" without replacing the original arXiv links.
The existing sample usage, GitHub link, and license information are preserved as they are already correctly provided.
ikuyamada changed pull request status to merged