Sentence Similarity
sentence-transformers
Safetensors
bert
multilingual
layer-pruning
vocab-pruning
minilm-l12
text-embeddings-inference
Instructions to use gomyk/minilm-student-L6_uniform with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/minilm-student-L6_uniform with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/minilm-student-L6_uniform") 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:
- d979b37d643feaa5044edebbafa629d9954651c618953ef1b7e1ad72810a4c4a
- Size of remote file:
- 103 MB
- SHA256:
- ceb00b87c0d6be77f61085e0601848645837c7f021ef10442be67a7a51d89a98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.