Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
feature-extraction
Qwen2
custom_code
text-embeddings-inference
Instructions to use Qodo/Qodo-Embed-1-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qodo/Qodo-Embed-1-1.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qodo/Qodo-Embed-1-1.5B", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Qodo/Qodo-Embed-1-1.5B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qodo/Qodo-Embed-1-1.5B", trust_remote_code=True) model = AutoModel.from_pretrained("Qodo/Qodo-Embed-1-1.5B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
fix typo
Browse files
README.md
CHANGED
|
@@ -19,7 +19,7 @@ base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
|
|
| 19 |
## Qodo-Embed-1
|
| 20 |
**Qodo-Embed-1 is a state-of-the-art** code embedding model designed for retrieval tasks in the software development domain.
|
| 21 |
It is offered in two sizes: lite (1.5B) and medium (7B). The model is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search, retrieval-augmented generation (RAG), and contextual understanding of programming languages.
|
| 22 |
-
This model outperforms all previous open-source models in the COIR and
|
| 23 |
|
| 24 |
### Languages Supported:
|
| 25 |
* Python
|
|
|
|
| 19 |
## Qodo-Embed-1
|
| 20 |
**Qodo-Embed-1 is a state-of-the-art** code embedding model designed for retrieval tasks in the software development domain.
|
| 21 |
It is offered in two sizes: lite (1.5B) and medium (7B). The model is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search, retrieval-augmented generation (RAG), and contextual understanding of programming languages.
|
| 22 |
+
This model outperforms all previous open-source models in the COIR and MTEB leaderboards, achieving best-in-class performance with a significantly smaller size compared to competing models.
|
| 23 |
|
| 24 |
### Languages Supported:
|
| 25 |
* Python
|