65 lines
1.7 KiB
Markdown
65 lines
1.7 KiB
Markdown
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- transformers
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- address standardization
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---
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# CaoHaiNam/vietnamese-address-embedding
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for address standardization.
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## Usage (Sentence-Transformers)
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Detail about how It works can be found here: https://github.com/CaoHaiNam/Vietnamese-Address-Standardization
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 8626 with parameters:
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```
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{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'cos_sim'}
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 10,
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 100,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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