Sentence Similarity
sentence-transformers
Safetensors
new
multilingual
layer-pruning
vocab-pruning
knowledge-distillation
gte-multilingual
custom_code
text-embeddings-inference
Instructions to use gomyk/gte-student-gte_L6_uniform_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/gte-student-gte_L6_uniform_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/gte-student-gte_L6_uniform_distilled", trust_remote_code=True) 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:
- e1b852e6ef3a9fce40da8e3281ee4f2a24a8127178b89738f7a827355e70cc3c
- Size of remote file:
- 367 MB
- SHA256:
- 6ef2520ef112e10887b36e187c5c5a9c54676ac3f4504f1a2b886b5a6cfc516a
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