Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:3
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use aimanfadillah/standardized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aimanfadillah/standardized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aimanfadillah/standardized") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Replace model with new fine-tuned weights (clean upload)
Browse files- .gitattributes +2 -0
.gitattributes
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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onnx/model.onnx filter=lfs diff=lfs merge=lfs -text
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