Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:4372
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use lealdaniel/comp-embedding-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lealdaniel/comp-embedding-matching with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lealdaniel/comp-embedding-matching") sentences = [ "analista de produtos pl", "product management", "business operations", "logistic management generalist" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d5ff2be0c2ad82184564bb57cf8938f2e0f3836141c8d50f9f5737e292e8164
- Size of remote file:
- 438 MB
- SHA256:
- b12db7f02b40be2f96f0917beaaf9462baea0bc46b6ca85a26613d5db4d792d4
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