Simcse Model Roberta Base Thai

🚀 {mrp/simcse-model-roberta-base-thai}

This model is designed for sentence similarity tasks. It maps sentences and paragraphs into a 768 - dimensional dense vector space, which can be applied to tasks such as clustering and semantic search.

🚀 Quick Start

This is a sentence - transformers model that uses XLM - R as the baseline. It maps sentences and paragraphs to a 768 - dimensional dense vector space and can be used for tasks like clustering or semantic search.

We utilize SimCSE here and train the model with Thai Wikipedia [here](https://github.com/PyThaiNLP/ThaiWiki - clean/releases/tag/20210620?fbclid=IwAR1YcmZkb - xd1ibTWCJOcu98_FQ5x3ioZaGW1ME - VHy9fAQLhEr5tXTJygA).

📦 Installation

Using this model becomes easy when you have sentence - transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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

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