This is the Cantonese model of BART base. It is obtained by a second-stage pre-training on the LIHKG dataset based on the fnlp/bart-base-chinese model.

This project is supported by Cloud TPUs from Google's TPU Research Cloud (TRC).

Note: To avoid any copyright issues, please do not use this model for any purpose.

GitHub Links

Dataset: ayaka14732/lihkg-scraper
Tokeniser: ayaka14732/bert-tokenizer-cantonese
Base model: ayaka14732/bart-base-jax
Pre-training: ayaka14732/bart-base-cantonese

Usage

python

from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
tokenizer = BertTokenizer.from_pretrained('Ayaka/bart-base-cantonese')
model = BartForConditionalGeneration.from_pretrained('Ayaka/bart-base-cantonese')
text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
output = text2text_generator('聽日就要返香港，我激動到[MASK]唔着', max_length=50, do_sample=False)
print(output[0]['generated_text'].replace(' ', ''))
# output: 聽日就要返香港，我激動到瞓唔着

Note: Please use the BertTokenizer for the model vocabulary. DO NOT use the original BartTokenizer.

Training Details

Optimiser: SGD 0.03 + Adaptive Gradient Clipping 0.1
Dataset: 172937863 sentences, pad or truncate to 64 tokens
Batch size: 640
Number of epochs: 7 epochs + 61440 steps
Time: 44.0 hours on Google Cloud TPU v4-16

WandB link: 1j7zs802