---
base_model: indiejoseph/bart-base-cantonese
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: bart-translation-zh-yue
  results: []
language:
- zh
- yue
license: apache-2.0
pipeline_tag: translation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bart-translation-zh-yue

Cantonese to Simplified Chinese translation model fine-tuned on [indiejoseph/bart-base-cantonese](https://huggingface.co/indiejoseph/bart-base-cantonese) using the LLMs generated dataset.

It achieves the following results on the evaluation set:
- Loss: 0.5042
- Bleu: 36.3458
- Gen Len: 19.8785

## Model description

Since the base model [indiejoseph/bart-base-cantonese](https://huggingface.co/indiejoseph/bart-base-cantonese) is further pre-trained based on [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese), However, it inherits the issue of its whitespace tokenizer, which results in space delimiters between every individual Chinese character in the outputs. To address this problem, I have created a translation pipeline that mitigates the inconsistent output of Simplified Chinese with SequenceBiasLogitsProcessor from the transformers library.

## Intended uses

1. Cantonese Chinese Translation: The model can be utilized to translate text from Cantonese Chinese to other languages, enabling communication and understanding across different linguistic backgrounds.
2. Language Learning: The model can assist language learners in understanding and translating Cantonese Chinese texts, aiding in the acquisition of Cantonese language skills.

## Limitations

1. Domain Specificity: The model's performance may vary when translating texts that contain domain-specific or technical terminology. It is trained on general language data and may struggle with specialized vocabulary.
2. Accuracy and Fluency: While the model strives to provide accurate and fluent translations, it may occasionally produce errors or less natural-sounding output. Post-editing or human review may be necessary for critical or high-stakes translations.
3. Cultural Nuances: Translations generated by the model might not capture the full range of cultural nuances and contextual meanings present in the original text. Human interpretation and cultural understanding are essential for accurate translations in sensitive or culturally specific contexts.
4. Potential for Harmful or Hate Speech: The training dataset was generated from Language Models (LLMs), which may inadvertently include instances of harmful or hate speech. While efforts have been made to filter and mitigate such content, the model's output may still occasionally contain offensive or inappropriate language. It is essential to exercise caution and implement appropriate content moderation measures when utilizing the model to ensure the generated translations align with ethical standards and community guidelines.

## Training and evaluation data

The training and evaludation dataset are generated by ChatGPT and Palm2.

Leverage over 4,000 Chinese and Cantonese phrase pairs meticulously gathered from diverse websites and dictionaries as the foundation for generating initial seed sentences in Chinese using ChatGPT. Subsequently, employ the Palm2 API to translate all Chinese sentences into Cantonese, while dedicating attention to manually rectifying any typos and enhancing overall fluency and linguistic variety.

Utilizing the collected Chinese and Cantonese phrase pairs, each phrase is employed to generate ten unique sentences, resulting in a comprehensive dataset size of approximately 40,000 sentences. These sentences serve as the basis for training and refining the translation model, ensuring a robust and diverse language understanding.

Similarly, the evaluation dataset is meticulously crafted using a comparable methodology to the training dataset. This ensures that the evaluation data reflects the same level of quality, diversity, and linguistic nuances, providing a reliable benchmark for assessing the performance and effectiveness of the translation model.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.135         | 1.0   | 3521  | 0.4865          | 35.3577 | 19.8859 |
| 0.0983        | 2.0   | 7042  | 0.4813          | 36.0938 | 19.8796 |
| 0.072         | 3.0   | 10563 | 0.4847          | 36.193  | 19.8817 |
| 0.0552        | 4.0   | 14084 | 0.5042          | 36.3458 | 19.8785 |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1