Update README.md

README.md

@@ -4,7 +4,7 @@ language:
 - en
 ---
 
-We trained a language model to **automatically score the IELTS essays** by using massive the training dataset by human raters. 
+We trained a language model to **automatically score the IELTS (International English Language Testing System) essays** by using massive the training dataset by human raters. 
 
 The impressive result in the test dataset is as follows: **Accuracy = 0.82, F1 Score = 0.81**.
 
@@ -13,7 +13,7 @@ The following is the code to implement the model for scoring new IELTS essays.
 In the following example, an essay is taken from the test dataset with the overall score 8.
 
 ```
-# Import necessary packages
+
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 import numpy as np
@@ -43,31 +43,30 @@ new_text = (
     "for people of every society, so government should build parks in every city and town."
 )
 
-# Encode the text using the same tokenizer used during training
+
 encoded_input = tokenizer(new_text, return_tensors='pt', padding=True, truncation=True, max_length=512)
 
-# Set the model to evaluation mode
+
 model.eval()
 
 # Perform the prediction
 with torch.no_grad():
     outputs = model(**encoded_input)
 
-# Get the predictions (the output here depends on whether you are doing regression or classification)
 predictions = outputs.logits.squeeze()
 
-# Assuming the model is a regression model and outputs raw scores
-predicted_scores = predictions.numpy()  # Convert to numpy array if necessary
+
+predicted_scores = predictions.numpy()  
 
 # Normalize the scores
 normalized_scores = (predicted_scores / predicted_scores.max()) * 9  # Scale to 9
 
-# Round the scores to the nearest 0.5 increment
+
 rounded_scores = np.round(normalized_scores * 2) / 2
 
 item_names = ["Task Achievement", "Coherence and Cohesion", "Vocabulary", "Grammar", "Overall"]
 
-# Print the predicted scores
+
 for item, score in zip(item_names, rounded_scores):
     print(f"{item}: {score:.1f}")