Update README.md

README.md

@@ -8,13 +8,14 @@ each scored for overall holistic language proficiency as well as analytic scores
 phraseology, grammar, and conventions. The scores were obtained through assessments by a number of professional English teachers 
 adhering to rigorous procedures. The training dataset guarantees that our model acuqires high practicality and accuracy, closely emulating professional grading standards.
 
-The model's performance on the test dataset, which includes around 980 English essays, is summarized by the following metrics: 'accuracy'= 0.87 and 'f1 score' = 0.85.
+The model's performance on the test dataset, which includes around 980 English essays, 
+is summarized by the following metrics: 'mean accuracy'= 0.91 and 'mean f1 score' = 0.9, mean Quadratic Weighted Kappa (QWK) =0.85.
 
 Upon inputting an essay, the model outputs six scores corresponding to cohesion, syntax, vocabulary, phraseology, grammar, and conventions. Each score ranges from 1 to 5, with higher scores indicating greater proficiency within the essay. These dimensions collectively assess the quality of the input essay from multiple perspectives. The model serves as a valuable tool for EFL teachers and researchers, and it is also beneficial for English L2 learners and parents for self-evaluating their composition skills.
 
 To test the model, run the following code or paste your essay into the API interface:
 
-1) Please use the following Python code if you want to get the ouput values ranging from 1 to 5.
+1) Please use the following Python code if you want to get the ouput values ranging from **1 to 5**.
 
 ```
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -63,7 +64,7 @@ for item, score in zip(item_names, rounded_scores):
 
 ```
 
-2) However, implement the following code if you expect to obtain the output values between 1 to 10.
+2) However, implement the following code if you expect to obtain the output values between **1 to 10**.
 
 ```
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -101,7 +102,7 @@ for item, score in zip(item_names, rounded_scores):
 ```
 
 
-Examples:
+**Examples**:
 ```
 # the first example (A1 level)
 
@@ -129,8 +130,8 @@ conventions: 8.5
 
 ```
 
-Please cite the following paper if you use this model:
-
+Please **cite** the following paper if you use this model:
+```
 @article{sun2024automatic,
   title={Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression},
   author={Kun Sun and Rong Wang},
@@ -138,4 +139,4 @@ Please cite the following paper if you use this model:
   journal={ArXiv},
   url={https://arxiv.org/abs/5634515}
 }
- 
+'''