Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:105
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Aju360/ats-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Aju360/ats-mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Aju360/ats-mpnet") sentences = [ "Healthcare Analyst with 8 years of experience. Skilled in Healthcare Data, SQL, Reporting, Analytics. Delivered projects and collaborated across teams.", "Data Scientist position. Required skills include Python, Machine Learning, B2B Sales, CRM. Looking for a candidate with strong communication and execution skills.", "Sales Manager position. Required skills include B2B Sales, CRM, Negotiation, Leadership. Looking for a candidate with strong communication and execution skills.", "DevOps Engineer position. Required skills include AWS, Kubernetes, Docker, CI/CD. Looking for a candidate with strong communication and execution skills." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 429 Bytes
94f2ca4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
}
] |