Create README.md

README.md

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+---
+license: mit
+language:
+- en
+---
+
+We trained a language model to **automatically score the IELTS 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**.
+
+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
+
+# Load the pre-trained model and tokenizer
+model_path = "./ielts_scoring_model"
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", use_fast=True)
+
+# Example text to be evaluated, the essay with the score by human rater (= 8.5) in the test dataset.
+
+new_text = (
+    "It is important for all towns and cities to have large public spaces such as squares and parks. "
+    "Do you agree or disagree with this statement? It is crucial for all metropolitan cities and towns to "
+    "have some recreational facilities like parks and squares because of their numerous benefits. A number of "
+    "arguments surround my opinion, and I will discuss it in upcoming paragraphs. To commence with, the first "
+    "and the foremost merit is that it is beneficial for the health of people because in morning time they can "
+    "go for walking as well as in the evenings, also older people can spend their free time with their loved ones, "
+    "and they can discuss about their daily happenings. In addition, young people do lot of exercise in parks and "
+    "gardens to keep their health fit and healthy, otherwise if there is no park they glue with electronic gadgets "
+    "like mobile phones and computers and many more. Furthermore, little children get best place to play, they play "
+    "with their friends in parks if any garden or square is not available for kids then they use roads and streets "
+    "for playing it can lead to serious incidents. Moreover, parks have some educational value too, in schools, "
+    "students learn about environment protection in their studies and teachers can take their pupils to parks because "
+    "students can see those pictures so lively which they see in their school books and they know about importance "
+    "and protection of trees and flowers. In recapitulate, parks holds immense importance regarding education, health "
+    "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
+
+# 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}")
+
+##the output:
+#Task Achievement: 9.0
+#Coherence and Cohesion: 7.5
+#Vocabulary: 8.0
+#Grammar: 7.5
+#Overall: 8.5
+```