Update README.md

README.md

@@ -42,17 +42,24 @@ with torch.no_grad():
 predictions = outputs.logits.squeeze()
 
 predicted_scores = predictions.numpy()  
-item_names = ["cohesion", "syntax", "vocabulary", "phraseology", "grammar",  "conventions"]
-for item, score in zip(item_names, predicted_scores):
-    print(f"{item}: {score:.4f}")
-
-##"output" (values raning from 1 to 5): 
-#cohesion: 3.5399
-#syntax: 3.6380
-#vocabulary: 3.9250
-#phraseology: 3.8381
-#grammar: 3.9194
-#conventions: 3.6819
+item_names = ["cohesion", "syntax", "vocabulary", "phraseology", "grammar", "conventions"]
+
+# Scale predictions from the raw output to the range [1, 5]
+scaled_scores = 1 + 4 * (predicted_scores - np.min(predicted_scores)) / (np.max(predicted_scores) - np.min(predicted_scores))
+
+# Round scores to the nearest 0.5
+rounded_scores = np.round(scaled_scores * 2) / 2
+
+for item, score in zip(item_names, rounded_scores):
+    print(f"{item}: {score:.1f}")
+
+# Example output:
+# cohesion: 3.5
+# syntax: 3.5
+# vocabulary: 4.0
+# phraseology: 4.0
+# grammar: 4.0
+# conventions: 3.5
 
 ```
 
@@ -76,18 +83,20 @@ predictions = outputs.logits.squeeze()
 predicted_scores = predictions.numpy()  # Convert to numpy array
 item_names = ["cohesion", "syntax", "vocabulary", "phraseology", "grammar", "conventions"]
 
-# Scale predictions from 1 to 10
+# Scale predictions from 1 to 10 and round to the nearest 0.5
 scaled_scores = 2.25 * predicted_scores - 1.25
-for item, score in zip(item_names, scaled_scores):
-    print(f"{trait}: {score:.4f}")
+rounded_scores = [round(score * 2) / 2 for score in scaled_scores]  # Round to nearest 0.5
 
-##"ouput" (values between 1-10)
-#cohesion: 6.7147
-#syntax: 6.9354
-#vocabulary: 7.5814
-#phraseology: 7.3856
-#grammar: 7.5687
-#conventions: 7.0344
+for item, score in zip(item_names, rounded_scores):
+    print(f"{item}: {score:.1f}")
+
+# Example output:
+# cohesion: 6.5
+# syntax: 7.0
+# vocabulary: 7.5
+# phraseology: 7.5
+# grammar: 7.5
+# conventions: 7.0
 
 ```
 
@@ -98,24 +107,35 @@ Examples:
 
 new_text ="Dear Mauro, Thank you for agreeing to take a care of my house and my pets in my absence. This is my daily routine. Every day I water the plants, I walk the my dog in the morning and in the evening. I feed food it twice a day, I check water's dog twice a week. I take out trash every Friday. I sweep the floor and clean house on Monday and on Wednesday. In your free time you can watch TV and play video games.  In the fridge I left coca cola and ice-cream for you  Have a nice week. "
 ##ouput
-cohesion: 5.0837
-syntax: 4.9995
-vocabulary: 5.4083
-phraseology: 5.1587
-grammar: 4.9063
-conventions: 5.6578
+cohesion: 5.0
+syntax: 5.0
+vocabulary: 5.5
+phraseology: 5.0
+grammar: 5.0
+conventions: 6.0
 
 
 # the second example (C1 level)
 
 new_text = " Dear Mr. Tromps It was so good to hear from you and your group of international buyers are visiting our company next month. And in response to your question, I would like to recommend some suggestions about business etiquette in my country. Firstly, you'll need to make hotel's reservations with anticipation, especially when the group is numerous. There are several five starts hotels in the commercial center of the Guayaquil city, very close to our offices. Business appointments well in advance and don't be late. Usually, at those meetings the persons exchange presentation cards. Some places include tipping by services in restaurant bills, but if any not the tip is 10% of the bill. The people is very kind here, surely you'll be invited to a meal at a house, you can take a small gift as flowers, candy or wine. Finally, remember it's a beautiful summer here, especially in our city is always warm, then you might include appropriate clothes for this weather. If you have any questions, please just let me know. Have you a nice and safe trip.  Sincerely,  JG Marketing Dpt. LP Representations."
 ##output:
-cohesion: 8.0674
-syntax: 7.9963
-vocabulary: 8.0226
-phraseology: 8.1645
-grammar: 8.3697
-conventions: 8.2797
+cohesion: 8.0
+syntax: 8.0
+vocabulary: 8.0
+phraseology: 8.5
+grammar: 8.5
+conventions: 8.5
+
+
+```
 
+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},
+  year={2024},
+  journal={ArXiv},
+  url={https://arxiv.org/abs/5634515}
+}
+