Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:21927
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use lengocquangLAB/fine-tuned-skill-jd-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lengocquangLAB/fine-tuned-skill-jd-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lengocquangLAB/fine-tuned-skill-jd-embed") sentences = [ "nextjs", "Familiarity with project management software (e.g., JIRA, Trello).", ", contributing to group projects with an understanding of Git workflows", "Ưu tiên ứng viên có kinh nghiệm với TypeScript, Next.js hoặc các công cụ tối ưu hóa hiệu suất Front-end." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |