Sentence Similarity
sentence-transformers
Safetensors
bert
multilingual
model-compression
layer-pruning
vocab-pruning
knowledge-distillation
me5-small
text-embeddings-inference
Instructions to use gomyk/me5s-student-me5s_compressed_distilled 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_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/me5s-student-me5s_compressed_distilled") 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:
- c4e653bad553b525d40468b30df85193fc1fb21d0f58c75d274f4fbb5ceaab1a
- Size of remote file:
- 53.1 MB
- SHA256:
- 248b7dfb9bb4c1e024b364077af53bc31b35b853f6050510d4a89dd74d0803de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.