Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:4008
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use pawan2411/address-emnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pawan2411/address-emnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pawan2411/address-emnet") sentences = [ "Orchard Road 313, Singapore 238895", "Orchard Rd 313, Singapore 238895", "15 Rue de la Paix/75002/France", "NY, 5th Avenue and 57th Street" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
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### Training Dataset
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* Size: 4,008 training samples
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* Columns: <code>sentence_0</code> and <code>sentence_1</code>
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* Approximate statistics based on the first 1000 samples:
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"similarity_fct": "cos_sim"
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```
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## Citation
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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<!-- * Size: 4,008 training samples
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* Columns: <code>sentence_0</code> and <code>sentence_1</code>
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* Approximate statistics based on the first 1000 samples:
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"similarity_fct": "cos_sim"
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}
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```
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## Citation
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