Update README.md

README.md

@@ -45,8 +45,8 @@ Use the code below to get started with the model.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("your-model-repo/DM-QWEN-2-7B-AVOCADO")
-model = AutoModelForCausalLM.from_pretrained("your-model-repo/DM-QWEN-2-7B-AVOCADO")
+tokenizer = AutoTokenizer.from_pretrained("2billionbeats/DM-QWEN-2-7B-AVOCADO")
+model = AutoModelForCausalLM.from_pretrained("2billionbeats/DM-QWEN-2-7B-AVOCADO")
 
 input_text = "Your input text here"
 inputs = tokenizer(input_text, return_tensors="pt")
@@ -86,10 +86,6 @@ The evaluation considered various subpopulations and domains within the medical
 
 The evaluation metrics included accuracy, precision, recall, and F1 score, chosen for their relevance in assessing the model's performance in text classification tasks.
 
-### Results
-
-The model achieved an accuracy of [X]%, precision of [Y]%, recall of [Z]%, and F1 score of [W]%.
-
 #### Summary
 
 The model demonstrates strong performance in mapping Chinese medicine concepts to evidence-based medicine, with high accuracy and balanced precision and recall.