Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:5579240
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use TechWolf/JobBERT-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TechWolf/JobBERT-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TechWolf/JobBERT-v2") sentences = [ "Program Coordinator RN", "discuss the medical history of the healthcare user, evidence-based approach in general practice, apply various lifting techniques, establish daily priorities, manage time, demonstrate disciplinary expertise, tolerate sitting for long periods, think critically, provide professional care in nursing, attend meetings, represent union members, nursing science, manage a multidisciplinary team involved in patient care, implement nursing care, customer service, work under supervision in care, keep up-to-date with training subjects, evidence-based nursing care, operate lifting equipment, follow code of ethics for biomedical practices, coordinate care, provide learning support in healthcare", "provide written content, prepare visual data, design computer network, deliver visual presentation of data, communication, operate relational database management system, ICT communications protocols, document management, use threading techniques, search engines, computer science, analyse network bandwidth requirements, analyse network configuration and performance, develop architectural plans, conduct ICT code review, hardware architectures, computer engineering, video-games functionalities, conduct web searches, use databases, use online tools to collaborate", "nursing science, administer appointments, administrative tasks in a medical environment, intravenous infusion, plan nursing care, prepare intravenous packs, work with nursing staff, supervise nursing staff, clinical perfusion" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Update model.onnx with 1024-dim output (includes Asym layer)
Browse filesRe-exported ONNX model with the Asym projection layer included.
This export now produces 1024-dimensional embeddings (matching the original SentenceTransformer output) instead of 768-dim.
The previous export was missing the asymmetric projection layer that transforms 768→1024 dimensions.
- onnx/model.onnx +2 -2
onnx/model.onnx
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