Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
new
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use NovaSearch/stella_en_400M_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NovaSearch/stella_en_400M_v5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NovaSearch/stella_en_400M_v5", 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 NovaSearch/stella_en_400M_v5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NovaSearch/stella_en_400M_v5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "3.0.1", | |
| "transformers": "4.42.3", | |
| "pytorch": "2.3.1+cu121" | |
| }, | |
| "prompts": { | |
| "s2p_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ", | |
| "s2s_query": "Instruct: Retrieve semantically similar text.\nQuery: " | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |