Sentence Similarity
sentence-transformers
Safetensors
bert
multilingual
layer-pruning
vocab-pruning
knowledge-distillation
minilm-l12
text-embeddings-inference
Instructions to use gomyk/minilm-student-L6_bottom_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/minilm-student-L6_bottom_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/minilm-student-L6_bottom_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:
- 0c3306a86dd019c914d559ddde0857636c1f36ff9f8ce91609562a5cd1a49711
- Size of remote file:
- 103 MB
- SHA256:
- 004eb19de9c420e7c08f3ec6152ae317953b127b3f50d3111b2c5dad8c12b556
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.