Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:6000
loss:CoSENTLoss
text-embeddings-inference
Instructions to use moshew/gist_small_ft_gooaq_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use moshew/gist_small_ft_gooaq_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("moshew/gist_small_ft_gooaq_v1") sentences = [ "are paris metro tickets one way?", "The two big differences between the 2.4 GHz and 5 GHz frequencies are speed and range. A wireless transmission at 2.4 GHz provides internet to a larger area but sacrifices speed, while 5 GHz provides faster speeds to a smaller area.", "The State of Rhode Island has adopted the income shares model to determine the weekly child support order. It is based upon the philosophy that children are entitled to the standard of living based upon both parents monthly income. ... Weekly gross income of both parents before taxes and before any other deductions.", "Insulin NPH may be administered in 2 divided doses daily (either as equally divided doses, or as ~2/3 of the dose before the morning meal and ~1/3 of the dose before the evening meal or at bedtime)." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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