Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:93
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Rezaq234r3/apex-custom-e5-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Rezaq234r3/apex-custom-e5-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Rezaq234r3/apex-custom-e5-model") sentences = [ "query: Best of luck.", "passage: Corporate Sales | 100 pair, bulk order, quantity", "passage: Apex Reward Point | use point, order, redeem", "passage: Feedback Received | wishes, luck, encouragement" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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