ViDeBERTa: A powerful pre-trained language model for Vietnamese, EACL 2023

Paper: https://aclanthology.org/2023.findings-eacl.79.pdf

Contributors

• Tran Cong Dao
• Pham Nhut Huy
• Nguyen Tuan Anh
• Hy Truong Son (Correspondent / PI)

Main components

1. Pre-training
2. Model
3. Fine-tuning

Pre-training

Code architecture

1. bash: bash scripts to run the pipeline
2. config: model_config (json files)
3. dataset: datasets folder (both store original txt dataset and the pointer to memory of datasets.load_from_disk)
4. source: main python files to run pre-training tokenizers
5. tokenizer: folder to store tokenizers

Pre-tokenizer

• Split the original txt datasets into train, validation and test sets with 90%, 5%, 5%.
• Using the PyVi library to segment the datasets
• Save datasets to disk

Pre-train_tokenizer

• Load datasets
• Train the tokenizers with SentencePiece models
• Save tokenizers

Pre-train_model

• Load datasets
• Load tokenizers
• Pre-train DeBERTa-v3

Model

Fine-tuning

Code architecture

1. POS tagging and NER (POS_NER)
2. Question Answering (QA and QA2)
3. Open-domain Question Answering (OPQA)