Update README.md

README.md

@@ -31,7 +31,7 @@ taiwan_llm_tokenizer = LlamaTokenizer.from_pretrained("yentinglin/Taiwan-LLM_v3_
 original_llama_tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
 ```
 
-Once the tokenizer is loaded, you can use it to tokenize both English and Traditional Chinese text:
+Once the tokenizer is loaded, you can use it to tokenize both English and Traditional Mandarin text:
 
 ```python
 text_en = """During the recent GTC (GPU Technology Conference), Nvidia CEO Jensen Huang took time out of his busy schedule to dine with the Taiwanese community in Silicon Valley. In his speech at the gathering, Huang referred to himself as a "great ambassador for Taiwan," expressing his gratitude for the island nation's role in Nvidia's growth and success."""
@@ -47,28 +47,28 @@ print(f"English text:")
 print(f"Taiwan-LLM_v3_tokenizer: {len(taiwan_llm_tokens_en)} tokens")
 print(f"Original LLaMA tokenizer: {len(original_llama_tokens_en)} tokens")
 
-print(f"\nTraditional Chinese text:")
+print(f"\nTraditional Mandarin text:")
 print(f"Taiwan-LLM_v3_tokenizer: {len(taiwan_llm_tokens_zh)} tokens")
 print(f"Original LLaMA tokenizer: {len(original_llama_tokens_zh)} tokens")
 ```
 
 ## Training Data
 
-The Chinese SentencePiece model used in this tokenizer was trained on a diverse set of Traditional Mandarin text data, including:
+The Mandarin SentencePiece model used in this tokenizer was trained on a diverse set of Traditional Mandarin text data, including:
 
 - Wikipedia articles
 - Legal documents
 - Online forum discussions
 - Cultural and historical texts
 
-This ensures that the tokenizer is well-suited for a wide range of Traditional Chinese language applications.
+This ensures that the tokenizer is well-suited for a wide range of Traditional Mandarin language applications.
 
 ## Tokenizer Merging Process
 
 The tokenizer was created by following these steps:
 
 1. Load and preprocess the Traditional Mandarin text data
-2. Train a Chinese SentencePiece model using the preprocessed text data
+2. Train a Mandarin SentencePiece model using the preprocessed text data
 3. Merge the Mandarin SentencePiece model with the LLaMA tokenizer
 
 ## Acknowledgements