Update README.md

README.md

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 Ancient Chinese Translator + Phonology Model (SimaQian)
 
 Name Origin:
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 The origin of the model name comes from famous ancient chinese historian Qian Sima (司馬遷), known for his Records of the Grand Historian, a general history of China covering more than two thousand years.
 
 This model combines two key functionalities for Ancient Chinese texts:
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 	1.	Translation: Converts Ancient Chinese passages into modern Chinese.
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 	2.	Phonological Reconstruction: Provides historical pronunciations for characters or entire sentences across multiple eras (e.g., Middle Tang, Song, Yuan, Ming/Qing).
 
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 Model Description
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 	•	Architecture: Fine-tuned on top of Google’s Gemma 2 model using LoRA.
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 	•	Input Format: Special tokens <start_of_turn> / <end_of_turn> define user vs. model turns.
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 	•	Output: Era identification (optional), phonetic renderings, and modern Chinese translations.
 
 Training Data
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 Usage
 
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   from transformers import AutoTokenizer, AutoModelForCausalLM
   
   tokenizer = AutoTokenizer.from_pretrained("username/ancient-chinese-phonology")
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   model = AutoModelForCausalLM.from_pretrained("username/ancient-chinese-phonology")
   
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   prompt = """
   <start_of_turn>user
   Given the ancient text: 「子曰：學而時習之，不亦說乎？」
@@ -46,11 +56,18 @@ Usage
   <end_of_turn>
   <start_of_turn>model
   """
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   inputs = tokenizer(prompt, return_tensors="pt")
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   outputs = model.generate(**inputs, max_length=256)
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   print(tokenizer.decode(outputs[0]))
 
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 Limitations and Biases
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 	•	Era Estimation: Model may not always correctly guess the historical era.
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 	•	Pronunciations: Reconstructions are approximate and can vary by scholarly consensus.
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 	•	Contextual Accuracy: For highly contextual Ancient Chinese passages, translations may need further review by domain experts.