Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:5000
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use scr17/fyp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use scr17/fyp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("scr17/fyp") sentences = [ "looking Product Manager expertise AWS Cybersecurity JavaScript Cloud Architecture candidate responsible designing implementing maintaining solutions using modern technologies", "Emily Barry professional skilled JavaScript Machine Learning Kubernetes Computer Vision Experienced working multiple projects involving cloud technologies modern software development practices", "Stephen Baker professional skilled React AWS Node.js NLP Experienced working multiple projects involving cloud technologies modern software development practices", "James Jackson professional skilled Node.js Cybersecurity Kubernetes Docker Experienced working multiple projects involving cloud technologies modern software development practices" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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