pawan2411/address-emnet

Address Embedding Model

This model generates embeddings for addresses, designed to facilitate address matching, deduplication, and standardization tasks.

Model description

The Address Matching Embedding Model is designed to create vector representations of addresses that capture semantic similarities, making it easier to match and deduplicate addresses across different formats and styles.

• —Model Type: Sentence Transformer
• —Base model: pawan2411/address_net <!-- at revision 59a25ad94c91cf025ae8d44f21e404c387065b4b -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pawan2411/address-emnet")
# Run inference
sentences = [
    '60 Ratchadaphisek Rd, Khwaeng Khlong Toei, Khet Khlong Toei, Krung Thep Maha Nakhon 10110',
    '60 Ratchadaphisek Road, Krung Thep Maha Nakhon, Thailand',
    '61 Ratchadaphisek Road, Krung Thep Maha Nakhon, Thailand'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)

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Direct Usage (Transformers)

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Downstream Usage (Sentence Transformers)

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<!-- * Size: 4,008 training samples

• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 16.73 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 27 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:------------------------------------------------------------------------------------|:------------------------------------------------| | <code>1-7-1 Konan, Minato City, Tokyo 108-0075, Japan</code> | <code>1-7-1 Konan, Tokyo 108-0075, Japan</code> | | <code>Avenida Paulista, 1000 - Bela Vista, São Paulo - SP, 01310-100, Brazil</code> | <code>Bela Vista 01310-100</code> | | <code>Strada Lipscani 25, București 030031, Romania</code> | <code>Strada Lipscani București</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

-->

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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