Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:72
loss:ContrastiveLoss
text-embeddings-inference
Instructions to use Marco127/D1_finetuned_2_test_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Marco127/D1_finetuned_2_test_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Marco127/D1_finetuned_2_test_1") sentences = [ "What was the original purpose of the Basilica di San Lorenzo's design by Filippo Brunelleschi in 1419?", " It is one of several churches that claim to be the oldest in Florence, having been consecrated in 393 AD, at which time it stood outside the city walls.", "The Palazzo Pitti, in English sometimes called the Pitti Palace, is a vast, mainly Renaissance, palace in Florence, Italy. It is situated on the south side of the River Arno in Pitti Square, a short distance from the Ponte Vecchio.", " The architects were Mariano Falcini, Professor Vincenzo Micheli, and Marco Treves, who was Jewish. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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