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:
- 18aa0d6d95c7eb6c708bfbdd427144e31584609e5600b2d902b6c3c8d1191d1a
- Size of remote file:
- 1.18 GB
- SHA256:
- bba07c2aa6d8981db23b38c8cf10665ad30dadbcbdc6ecaa495a7a93c24ffd58
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