Feature Extraction
sentence-transformers
Safetensors
English
code
roberta
python
javascript
go
java
ruby
php
code-search
code-embeddings
multilingual
matryoshka
unixcoder
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use divakantpandey/code-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use divakantpandey/code-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("divakantpandey/code-embed-v2") 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
Download 3_Dense/config.json from divakantpandey/code-embed-v2: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/3_Dense/config.json
- Command line
-
hf download hf://divakantpandey/code-embed-v2/3_Dense/config.json
-
curl -L -o config.json https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/3_Dense/config.json
229 Bytes
| { | |
| "in_features": 1536, | |
| "out_features": 1536, | |
| "bias": true, | |
| "activation_function": "torch.nn.modules.linear.Identity", | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |