sup-SimCSE-VietNamese-phobert-base EU AI Act Compliance Profile

Table of contents

1. Introduction
2. Pretrain model
3. Using SimeCSE_Vietnamese with sentences-transformers
   • Installation
   • Example usage
4. Using SimeCSE_Vietnamese with transformers
   • Installation
   • Example usage

<a name="introduction"></a> SimeCSE_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese

Pre-trained SimeCSE_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :

• SimeCSE_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE_Vietnamese pre-training procedure for more robust performance.
• SimeCSE_Vietnamese encode input sentences using a pre-trained language model such as PhoBert
• SimeCSE_Vietnamese works with both unlabeled and labeled data.

Pre-trained models <a name="models"></a>

Model | #params | Arch. ---|---|--- VoVanPhuc/sup-SimCSE-VietNamese-phobert-base | 135M | base VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base | 135M | base

<a name="sentences-transformers"></a> Using SimeCSE_Vietnamese with sentences-transformers

Installation <a name="install1"></a>

• Install sentence-transformers:

  • pip install -U sentence-transformers

• Install pyvi to word segment:

  • pip install pyvi

Example usage <a name="usage1"></a>

from sentence_transformers import SentenceTransformer
from pyvi.ViTokenizer import tokenize

model = SentenceTransformer('VoVanPhuc/sup-SimCSE-VietNamese-phobert-base')

sentences = ['Kẻ đánh bom đinh tồi tệ nhất nước Anh.',
          'Nghệ sĩ làm thiện nguyện - minh bạch là việc cấp thiết.',
          'Bắc Giang tăng khả

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