Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
historical-text
early-modern-english
economic-history
history-of-science
text-embeddings-inference
Instructions to use juliuskoschnick/SteamBERTh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use juliuskoschnick/SteamBERTh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("juliuskoschnick/SteamBERTh") 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] - Notebooks
- Google Colab
- Kaggle
Ctrl+K
Usage example: name SteamBERTh explicitly (innovfromtext 0.2.0 defaults to all-MiniLM-L6-v2)
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