Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:7851
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use myfi/semantic-embedding_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use myfi/semantic-embedding_2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("myfi/semantic-embedding_2") sentences = [ "did I gain any profits over the past 10 days", "Which stocks have a strong potential to see a 10% increase in the next 10 months?", "Did I make any money from trading in the last 10 days", "Which stocks have a strong potential to go up by 10% in the next 10 months?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model.onnx'
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by Ryvken - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:a9e7cd2bcf115015d56368dc2ef389bb58cbc3e0a99e8d855e3b4c4bb0ad5db9
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size 435826548
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