Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta-custom
feature-extraction
custom_code
Instructions to use michaelfeil/stsb-roberta-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaelfeil/stsb-roberta-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/stsb-roberta-base-v2", 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 michaelfeil/stsb-roberta-base-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("michaelfeil/stsb-roberta-base-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
this model is derrived from
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/stsb-roberta-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
You need to have ibm-fms installed:
https://github.com/foundation-model-stack/foundation-model-stack/issues/169
- Downloads last month
- 11