Sentence Similarity
sentence-transformers
Safetensors
Ancient Greek (to 1453)
Latin
English
roberta
feature-extraction
cross-lingual
historical-nlp
knowledge-distillation
dataset_size:480534
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sebastian-reichbauer/DistilSPhilBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sebastian-reichbauer/DistilSPhilBERTa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sebastian-reichbauer/DistilSPhilBERTa") sentences = [ "ἐκνεύει πάλιν.", "I found out that he was accused concerning questions of their law, but had nothing charged against him deserving of death or chains.", "he is drawing back.", "In this book I have fully set forth the mechanical methods which I could furnish, and which I thought most useful in times of peace and war." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K