Sentence Similarity
sentence-transformers
Safetensors
English
electra
feature-extraction
Generated from Trainer
dataset_size:7004
loss:SoftmaxLoss
Eval Results (legacy)
Instructions to use Deehan1866/Finetuned-electra-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Deehan1866/Finetuned-electra-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Deehan1866/Finetuned-electra-large") sentences = [ "Google SEO expert Matt Cutts had a similar experience, of the eight magazines and newspapers Cutts tried to order, he received zero.", "He dissolved the services of her guards and her court attendants and seized an expansive reach of properties belonging to her.", "Google SEO expert Matt Cutts had a comparable occurrence, of the eight magazines and newspapers Cutts tried to order, he received zero.", "bill's newest solo play, \"all over the map\", premiered off broadway in april 2016, produced by all for an individual cinema." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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