Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
new
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use biggunnyso4/stella_en_400M_v5_cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use biggunnyso4/stella_en_400M_v5_cpu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("biggunnyso4/stella_en_400M_v5_cpu", 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 biggunnyso4/stella_en_400M_v5_cpu with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biggunnyso4/stella_en_400M_v5_cpu", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Downloads last month
- 50
Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported92.358
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported70.813
- ap_weighted on MTEB AmazonCounterfactualClassification (en)test set self-reported70.813
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported88.951
- f1_weighted on MTEB AmazonCounterfactualClassification (en)test set self-reported92.686
- main_score on MTEB AmazonCounterfactualClassification (en)test set self-reported92.358
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported97.195
- ap on MTEB AmazonPolarityClassificationtest set self-reported96.082