Update README.md

README.md

@@ -7,6 +7,11 @@ metrics:
 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
@@ -14,7 +19,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bart-translation-zh-yue
 
-This model is a fine-tuned version of [indiejoseph/bart-base-cantonese](https://huggingface.co/indiejoseph/bart-base-cantonese) on the LLMs generated dataset.
+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
@@ -23,11 +28,19 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+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 & limitations
+## Intended uses
 
-More information needed
+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
 
@@ -67,4 +80,4 @@ The following hyperparameters were used during training:
 - Transformers 4.35.0.dev0
 - Pytorch 2.1.1+cu121
 - Datasets 2.14.6
-- Tokenizers 0.14.1
+- Tokenizers 0.14.1