Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:5700
loss:TripletLoss
text-embeddings-inference
Instructions to use Alexhuou/embedder_model_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alexhuou/embedder_model_FT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alexhuou/embedder_model_FT") sentences = [ "Perchloric acid (HClO4) is considered one of the stronger acids in existence. Which of the following statements corresponds most accurately with strong acids?", "Who argued that if an organization did not affect a public then there was no need for a practitioner to consider that public in its communications?", "Glycogen breakdown in exercising muscle is activated by:", "The collision theory of reaction rates does not include" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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