Feature Extraction
sentence-transformers
ONNX
Safetensors
English
code
roberta
python
code-search
code-embeddings
unixcoder
matryoshka
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use divakantpandey/code-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use divakantpandey/code-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("divakantpandey/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
fix: strictly cap max_seq_length and model_max_length to 8192 for RoBERTa position embeddings
0f0286c verified Download sentence_bert_config.json from divakantpandey/code-embed: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/divakantpandey/code-embed/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://divakantpandey/code-embed/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/divakantpandey/code-embed/resolve/main/sentence_bert_config.json
239 Bytes
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings", | |
| "max_seq_length": 8192 | |
| } |