Vietnamese Address Embedding

🚀 CaoHaiNam/vietnamese-address-embedding

This is a model that maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for address standardization.

🚀 Quick Start

This is a sentence-transformers model. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for address standardization.

💻 Usage Examples

Basic Usage

Detail about how It works can be found here: https://github.com/CaoHaiNam/Vietnamese-Address-Standardization

🔧 Technical Details

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 8626 with parameters:

{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

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

Parameters of the fit()-Method:

{
    "epochs": 10,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 100,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)

📄 License

No license information provided in the original document.

📚 Documentation

No further detailed documentation provided in the original document.

📦 Installation

No installation steps provided in the original document.

✨ Features

• Tags: sentence-transformers, sentence-similarity, transformers, address standardization
• Pipeline tag: sentence-similarity