Simcse Model M Bert Thai Cased

🚀 {mrp/simcse-model-m-bert-thai-cased}

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, useful for tasks such as clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It maps sentences and paragraphs to a 768-dimensional dense vector space and can be applied to tasks like clustering or semantic search.

We utilize SimCSE here, using mBERT as the baseline model and training it with Thai Wikipedia here.

📦 Installation

Using this model is straightforward when you have sentence-transformers installed. You can install it with the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

After installation, you can use the model as follows:

from sentence_transformers import SentenceTransformer
sentences = ["ฉันนะคือคนรักชาติยังไงละ!", "พวกสามกีบล้มเจ้า!"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)