phobert large

Introduction

PhoBERT is a pre-trained language model specifically designed for the Vietnamese language. It is based on the RoBERTa architecture and offers two versions: "base" and "large". These models achieve state-of-the-art performance in various Vietnamese NLP tasks, including part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.

Architecture

PhoBERT utilizes the RoBERTa architecture, an optimized version of the BERT model. The RoBERTa framework enhances BERT's pre-training procedure, providing more robust performance for language tasks. PhoBERT is tailored for the Vietnamese language, making it a monolingual model that excels in understanding and processing Vietnamese text.

Training

The PhoBERT models were trained using a large-scale Vietnamese text corpus. The training approach builds on the RoBERTa methodology, which involves training with longer sequences, larger batches, and more data to improve the model's understanding of linguistic patterns specific to Vietnamese.

Guide: Running Locally

1. Environment Setup: Ensure you have Python and PyTorch installed. You can do this via package managers like pip.
2. Install Transformers Library: Use the command pip install transformers to install the Hugging Face Transformers library.
3. Download PhoBERT: Use the transformers library to load the PhoBERT model by specifying vinai/phobert-large.
4. Run Inference: Use the model for tasks such as text classification or tokenization within your Python environment.

For optimal performance, consider using cloud-based GPUs from providers like AWS, GCP, or Azure, which offer scalable solutions for model training and inference.

License

PhoBERT is released under the MIT License, allowing for wide usage and distribution.