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  1. [Introduction](#introduction)
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  2. [Pretrain model](#models)
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  3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
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- - [Installation](#install1)
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- - [Example usage](#usage1)
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  4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
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- - [Installation](#install2)
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- - [Example usage](#usage2)
 
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  # <a name="introduction"></a> SimeCSE_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
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  Pre-trained SimeCSE_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
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  ## Pre-trained models <a name="models"></a>
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- Model | #params | Arch.
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  ---|---|---
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  [`VoVanPhuc/sup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) | 135M | base
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  [`VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base) | 135M | base
@@ -30,8 +31,8 @@ Model | #params | Arch.
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  ### Installation <a name="install1"></a>
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  - Install `sentence-transformers`:
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- - `pip install -U sentence-transformers`
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-
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  ### Example usage <a name="usage1"></a>
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@@ -58,8 +59,8 @@ embeddings = model.encode(sentences)
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  ### Installation <a name="install2"></a>
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  - Install `transformers`:
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- - `pip install -U transformers`
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-
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  ### Example usage <a name="usage2"></a>
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@@ -93,12 +94,12 @@ with torch.no_grad():
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  ## Citation
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- @article{gao2021simcse,
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- title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
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- author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
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- journal={arXiv preprint arXiv:2104.08821},
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- year={2021}
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- }
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  @inproceedings{phobert,
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  title = {{PhoBERT: Pre-trained language models for Vietnamese}},
 
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  1. [Introduction](#introduction)
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  2. [Pretrain model](#models)
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  3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
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+ \t- [Installation](#install1)
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+ \t- [Example usage](#usage1)
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  4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
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+ \t- [Installation](#install2)
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+ \t- [Example usage](#usage2)
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+
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  # <a name="introduction"></a> SimeCSE_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
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  Pre-trained SimeCSE_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
 
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  ## Pre-trained models <a name="models"></a>
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+ Model | #params | Arch.\t
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  ---|---|---
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  [`VoVanPhuc/sup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) | 135M | base
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  [`VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base) | 135M | base
 
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  ### Installation <a name="install1"></a>
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  - Install `sentence-transformers`:
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+ \t- `pip install -U sentence-transformers`
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+ \t
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  ### Example usage <a name="usage1"></a>
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  ### Installation <a name="install2"></a>
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  - Install `transformers`:
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+ \t- `pip install -U transformers`
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+ \t
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  ### Example usage <a name="usage2"></a>
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  ## Citation
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+ \t@article{gao2021simcse,
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+ \t title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
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+ \t author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
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+ \t journal={arXiv preprint arXiv:2104.08821},
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+ \t year={2021}
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+ \t}
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  @inproceedings{phobert,
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  title = {{PhoBERT: Pre-trained language models for Vietnamese}},