![]() |
---|
Vietnam Reunification Day 30-4-1975 |
This is the source code of the project "Vietnamese Poem Generator" of the course "Natural Language Processing" Summer 2023.
Vietnamese poetry has a rich history that dates back to the 10th century, with the emergence of the "Lục Bát" (six-eight) form, which is characterized by alternating lines of six and eight syllables. Since then, Vietnamese poetry has undergone various transformations and has been influenced by different cultural and historical periods, such as Chinese Confucianism, French colonialism, and modernization. Vietnamese poetry often includes themes of love, nature, patriotism, and social issues, and follows strict rules and structures, such as tonal patterns, rhyming schemes, and word choice.
This project proposes a Vietnamese poem generator that utilizes a combination of state-of-the-art natural language processing techniques, including diffusion, transformer, GPT2, and LLMs. The proposed model generates high-quality Vietnamese poems that adhere to the traditional rules and structures of Vietnamese poetry while also incorporating modern themes and language. The generator is based on a large corpus of Vietnamese poetry and uses the diffusion technique to enhance the coherence and fluency of the generated poems. The transformer-based architecture is used for encoding and decoding, while the GPT2 and LLMs techniques are employed for language modeling and improving the diversity of the generated poems.
The performance of the proposed model is evaluated through a set of quantitative and qualitative metrics, including perplexity, rhyme, and coherence. The experimental results demonstrate the effectiveness of the proposed model in generating high-quality Vietnamese poems that are both linguistically and aesthetically pleasing. The proposed model has potential applications in various fields, including literature, education, and art.
nlps23/
├── configs/ - training config
| ├── README.md - config name style
│ ├── */README.md - abstract and experiment results model
| ├── api-key/ - wandb api key for monitoring
|
├── tools/ - script to downloading data, training, testing, inference and web interface
|
├── trainer/ - trainer classes
|
├── model/
| ├── architecture/ - model architectures
| ├── README.md - losses and metrics definition
|
├── base/ - abstract base classes
│
├── data/ - storing input data
|
├── data_loader/ - custom dataset and dataloader
│
├── saved/ - trained models config, log-dir and logging output
│
├── logger/ - module for wandb visualization and logging
|
├── utils/ - utility functions
Traditional approach | ||||
Beam Search | HMM | |||
Deep Learning approach | ||||
SP-GPT2 (ICMLA'2021) | BARTpho (INTERSPEECH'2022) | LD4LG (ACCV'2022) |
Install the required packages:
pip install -r requirements.txt
Running private repository on Kaggle:
- Generate your token
- Get repo address from
github.com/.../...git
:
git clone https://your_personal_token@your_repo_address.git
cd CVP
Config files are in YAML format
name: U2NetFull_scratch_1gpu-bs4_KNC_size320x320
n_gpu: 1
arch:
type: u2net_full
args: {}
data_loader:
type: KNC_DataLoader
args:
batch_size: 4
shuffle: true
num_workers: 1
validation_split: 0.1
output_size: 320
crop_size: 288
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
eps: 1.e-8
betas:
- 0.9
- 0.999
loss: multi_bce_fusion
metrics:
- mae
- sm
lr_scheduler:
type: StepLR
args:
step_size: 50
gamma: 0.1
trainer:
type: Trainer
epochs: 1000
save_dir: saved/
save_period: 10
verbosity: 1
visual_tool: wandb
project: cvps23
name: U2NetLite_scratch_1gpu-bs4_KNC_size320x320
# Edit *username for tracking WandB multi-accounts
api_key_file: ./configs/api-key/tuanlda78202
entity: tuanlda78202
test:
save_dir: saved/generated
n_sample: 1000
batch_size: 32
Modify the configurations in .yaml
config files, then run:
python tools/train.py [CONFIG] [RESUME] [DEVICE] [BATCH_SIZE] [EPOCHS]
You can resume from a previously saved checkpoint by:
sh tools/train.py --resume path/to/the/ckpt
python tools/eval.py
-
Running demo on notebook
inference.ipynb
in -
Web Interface: Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo