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
Update code-embed-v2: Fast Lane fine-tuned weights (+21.52% macro gain across 17 MTEB benchmarks)
d7759ca verified Download code_embed_config.json from divakantpandey/code-embed-v2: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/code_embed_config.json
- Command line
-
hf download hf://divakantpandey/code-embed-v2/code_embed_config.json
-
curl -L -o code_embed_config.json https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/code_embed_config.json
212 Bytes
| { | |
| "base_model_name_or_path": "microsoft/unixcoder-base", | |
| "backbone_hidden_size": 768, | |
| "embedding_dim": 1536, | |
| "projection_hidden_dim": 1536, | |
| "pooling": "cls", | |
| "max_seq_length": 8192, | |
| "dropout": 0.1 | |
| } |