Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:4828
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Education
Retrieval
Syllabus
text-embeddings-inference
Instructions to use rsajja/Fine-tuned-Educational-Model-Dual-Loss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rsajja/Fine-tuned-Educational-Model-Dual-Loss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rsajja/Fine-tuned-Educational-Model-Dual-Loss") sentences = [ "What is the credit hour load?", "Received: 4 credit hours.", "Part-time: typically 6–8 credits per term, this class is 2.", "Credit hour load: 1.5 hours." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K