Update README.md

README.md

@@ -70,7 +70,7 @@ config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 
 
-# 4bit量化OneKE
+# 4-bit Quantized OneKE
 quantization_config=BitsAndBytesConfig(     
     load_in_4bit=True,
     llm_int8_threshold=6.0,
@@ -270,7 +270,10 @@ split_num_mapper = {
 
 Since predicting all schemas in the label set at once is too challenging and not easily scalable, OneKE uses a batched approach during training. It divides the number of schemas asked in the instructions, querying a fixed number of schemas at a time. Hence, if the label set of a piece of data is too long, it will be split into multiple instructions that the model will address in turns.
 
-**schema格式**:
+
+**Schema Format**:
+
+
 
 ```python
 NER: ["Person Name", "Education", "Position", "Nationality"]   # List of strings