Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:50881
loss:TripletLoss
text-embeddings-inference
Instructions to use along26/distilroberta-base-sentence-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use along26/distilroberta-base-sentence-transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("along26/distilroberta-base-sentence-transformer") sentences = [ "How much do real hair extensions cost?", "Where you can buy cheap human hair extensions?", "How can I learn communication skills?", "How much to hair extensions cost?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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