Sentence Similarity
sentence-transformers
Safetensors
bert
multilingual
model-compression
layer-pruning
vocab-pruning
me5-small
text-embeddings-inference
Instructions to use gomyk/me5s-student-me5s_compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/me5s-student-me5s_compressed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/me5s-student-me5s_compressed") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60e95156bf6618ff5010a27af0f364913df0b99f68e4811956e30eaf3f818fd0
- Size of remote file:
- 53.1 MB
- SHA256:
- 0f3325327b3f6b1a47e0147ba24482ffebadb034b543f766f92957b099aa267e
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