Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
feature-extraction
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use OrcaDB/qwen2-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OrcaDB/qwen2-1.5b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrcaDB/qwen2-1.5b", 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 OrcaDB/qwen2-1.5b with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OrcaDB/qwen2-1.5b", trust_remote_code=True) model = AutoModel.from_pretrained("OrcaDB/qwen2-1.5b", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbd464a2b807a15b64c2c3122e5e2b0af33cda89fe891092febb94980f20c42d
- Size of remote file:
- 11.4 MB
- SHA256:
- 7ec960d486f3fa63477b6795d78b86385b9360be44c4c788e85205bbf0780a6c
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