SimeCSE_Vietnamese stands out as an innovative tool that enables users to work with Vietnamese sentence embeddings effectively. In this article, we will guide you through the entire process, from installation to usage. Whether you’re dealing with labeled or unlabeled data, SimeCSE_Vietnamese optimizes performance in both cases.

Table of Contents

• Introduction
• Pretrained Model
• Using SimeCSE_Vietnamese with Sentence Transformers
  • Installation
  • Example Usage
• Using SimeCSE_Vietnamese with Transformers
  • Installation
  • Example Usage

Introduction

The SimeCSE_Vietnamese model provides state-of-the-art performance for encoding Vietnamese sentences. It is built on the foundation of the SimCSE pre-training process which enhances its robustness and accuracy in understanding language nuances.

Pre-trained Models

Below are the available pre-trained models:

• VoVanPhuc/sup-SimCSE-Vietnamese-phobert-base – 135M parameters, base architecture
• VoVanPhuc/unsup-SimCSE-Vietnamese-phobert-base – 135M parameters, base architecture

Using SimeCSE_Vietnamese with Sentence Transformers

Installation

To get started, perform the following installations:

• Install Sentence Transformers:

pip install -U sentence-transformers

• Install pyvi for word segmentation:

pip install pyvi

Example Usage

Here’s an analogy to help you understand how the code works. Think of your sentences as ingredients in a recipe where each ingredient needs to be chopped up and prepared before you can cook them into a delicious dish. The model here is like a high-quality chef who takes your prepared ingredients (sentences) and transforms them into a tasty dish (embeddings).

Now, let’s look at the code:

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."
]

sentences = tokenize(sentences)
embeddings = model.encode(sentences)

Using SimeCSE_Vietnamese with Transformers

Installation

To utilize the transformers, follow these steps:

• Install Transformers:

pip install -U transformers

• Install pyvi for word segmentation:

pip install pyvi

Example Usage

Once again, think of this code as a series of well-orchestrated musical notes that come together to create a symphony—you’re organizing your sentences and music (data) into a harmonious output (embeddings).

The following Python code snippet illustrates this process:

import torch
from transformers import AutoModel, AutoTokenizer
from pyvi.ViTokenizer import tokenize

PhobertTokenizer = AutoTokenizer.from_pretrained('VoVanPhuc/sup-SimCSE-Vietnamese-phobert-base')
model = AutoModel.from_pretrained('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."
]

sentences = tokenize(sentences)
inputs = PhobertTokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output

Troubleshooting

If you encounter issues during the installation or execution of your code, consider the following tips:

• Ensure your Python and pip are up to date.
• Check for any typos in the model names or paths.
• If you receive errors regarding missing modules, recheck your installation commands.
• Look into the console for detailed error messages to better understand the issue.

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