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
- Xet hash:
- d580f519cd27be6d0406cdb5fc758fb050c601d928c2281e9bdcd5a5f11a0afc
- Size of remote file:
- 4.99 GB
- SHA256:
- 7f468b2dbdb4aa429f9e193ae22b0e5fe8cd341eb0dded96f62d1708a32ced55
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