Sentence Similarity
sentence-transformers
Safetensors
code
bert
feature-extraction
code-retrieval
code-search
linux-kernel
c
text-embeddings-inference
Instructions to use nethunter2023/kernel-code-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nethunter2023/kernel-code-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nethunter2023/kernel-code-embed") 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
File size: 296 Bytes
185fab4 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 512,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |