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--- |
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language: |
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- vi |
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thumbnail: "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png" |
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tags: |
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- transfomer |
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- sbert |
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- legaltext |
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- vietnamese |
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license: "mit" |
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datasets: |
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- vietnamese-legal-text |
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--- |
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# Vietnamese Legal Text BERT |
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#### Table of contents |
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1. [Introduction](#introduction) |
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2. [Using Vietnamese Legal Text BERT](#transformers) |
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- [Installation](#install2) |
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- [Pre-trained models](#models2) |
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- [Example usage](#usage2) |
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# <a name="introduction"></a> Using Vietnamese Legal Text BERT `hmthanh/VietnamLegalText-SBERT` |
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## <a name="transformers"></a> Using Vietnamese Legal Text BERT `transformers` |
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### Installation <a name="install2"></a> |
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- Install `transformers` with pip: |
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```pip install transformers```<br /> |
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- Install `tokenizers` with pip: |
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```pip install tokenizers``` |
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### Pre-trained models <a name="models2"></a> |
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Model | #params | Arch. | Max length | Pre-training data |
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---|---|---|---|--- |
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`hmthanh/VietnamLegalText-SBERT` | 135M | base | 256 | 20GB of texts |
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### Example usage <a name="usage2"></a> |
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```python |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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phobert = AutoModel.from_pretrained("hmthanh/VietnamLegalText-SBERT") |
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tokenizer = AutoTokenizer.from_pretrained("hmthanh/VietnamLegalText-SBERT") |
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sentence = 'Vượt đèn đỏ bị phạt bao nhiêu tiền?' |
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input_ids = torch.tensor([tokenizer.encode(sentence)]) |
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with torch.no_grad(): |
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features = phobert(input_ids) # Models outputs are now tuples |
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``` |
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