Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
job-matching
philippines
bpo
information-technology
healthcare
text-embeddings-inference
Instructions to use Sneki04/Juanployment-JobMatching-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Sneki04/Juanployment-JobMatching-Model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sneki04/Juanployment-JobMatching-Model") sentences = [ "Job Title: Software Developer. Skills Required: Python, JavaScript, React. Education Level: Bachelor of Science in Computer Science. Industry: Information Technology. Location: Makati City. Job Type: Full-time.", "Skills: Python, JavaScript, React, SQL. Experience: Software Developer at Accenture Philippines. Education: Bachelor of Science in Computer Science. Preferences - Industry: Information Technology, Location: Makati City, Job Type: Full-time.", "Skills: Cooking, Food Preparation. Experience: Cook at Jollibee. Education: High School Graduate. Preferences - Industry: Food and Beverage, Location: Manila City, Job Type: Part-time.", "Skills: Customer Service, Communication Skills. Experience: Customer Service Representative at Concentrix. Education: College Graduate. Preferences - Industry: BPO, Location: BGC Taguig, Job Type: Full-time." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| "type": "sentence_transformers.models.Pooling" | |
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