Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
qwen2
text-generation
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use NovaSearch/stella_en_1.5B_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NovaSearch/stella_en_1.5B_v5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NovaSearch/stella_en_1.5B_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_1.5B_v5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NovaSearch/stella_en_1.5B_v5", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("NovaSearch/stella_en_1.5B_v5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
How to cite or reference this work?
#36
by Lux1997 - opened
Thank you for sharing this fantastic work! I am currently working on a paper in which I plan to compare the SOTA models in MTEB. However, I noticed that there isn’t a provided citation or associated paper for this work.
You can cite our arxiv report,Thanks !
@misc {zhang2025jasperstelladistillationsota,
title={Jasper and Stella: distillation of SOTA embedding models},
author={Dun Zhang and Jiacheng Li and Ziyang Zeng and Fulong Wang},
year={2025},
eprint={2412.19048},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2412.19048},
}