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bart-base-cantonese

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Transformers
PyTorch
JAX
Yue Chinese
bart
feature-extraction
cantonese
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bart-base-cantonese / README.md
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metadata
language:
  - yue
tags:
  - cantonese
license: other
library_name: transformers
co2_eq_emissions:
  emissions: 6.29
  source: estimated by using ML CO2 Calculator
  training_type: second-stage pre-training
  hardware_used: Google Cloud TPU v4-16
pipeline_tag: fill-mask

bart-base-cantonese

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

Usage 

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