Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
skill-extraction
job-description
skill-matching
workforce-analytics
hr-tech
talent-management
semantic-search
text-embedding
skills-taxonomy
skillsfuture
singapore
dense
Generated from Trainer
dataset_size:21958
loss:CosineSimilarityLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use imocha-ai-org/ssf-skill-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use imocha-ai-org/ssf-skill-extractor with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("imocha-ai-org/ssf-skill-extractor", trust_remote_code=True) sentences = [ "Analyze tax liabilities, identify applicable rates, and apply corrections to ensure proper calculation and reporting.", "Tax Computation", "Cloud Infrastructure Management", "Asian Cold Dish and Dessert Preparation" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "5.2.2", | |
| "transformers": "4.57.3", | |
| "pytorch": "2.9.1+cu128" | |
| }, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |