Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Haixx/relation_retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Haixx/relation_retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Haixx/relation_retriever") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Haixx/relation_retriever with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Haixx/relation_retriever") model = AutoModel.from_pretrained("Haixx/relation_retriever") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c66909c23bb76b385a9756d11563816343adf0a83d277d8671b4c818a86fd8a
- Size of remote file:
- 438 MB
- SHA256:
- dd962190b8f7457232eecfa43eb3ddfd4f78bf6d2d3d55bdaafefa19746f9b24
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