Text Classification
Transformers
PyTorch
Safetensors
Vietnamese
roberta
transfomer
sbert
legaltext
vietnamese
Instructions to use hmthanh/VietnamLegalText-SBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hmthanh/VietnamLegalText-SBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hmthanh/VietnamLegalText-SBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hmthanh/VietnamLegalText-SBERT") model = AutoModelForSequenceClassification.from_pretrained("hmthanh/VietnamLegalText-SBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# <a name="introduction"></a> Using Vietnamese Legal Text BERT `hmthanh/VietnamLegalText-SBERT`
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Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
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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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- Install `tokenizers` with pip:
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### Pre-trained models <a name="models2"></a>
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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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