Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:86648
loss:MSELoss
Eval Results (legacy)
Instructions to use pj-mathematician/JobGTE-multilingual-base-pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pj-mathematician/JobGTE-multilingual-base-pruned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-pruned") sentences = [ "Familienberaterin", "electric power station operator", "venue booker & promoter", "betrieblicher Aus- und Weiterbildner/betriebliche Aus- und Weiterbildnerin" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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