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README.md

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+# Code Qualiy Evaluation Dataset
+Welcome to the repository for our research paper: T. Wang and Z. Chen, "Analyzing Code Text Strings for Code Evaluation," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 5619-5628, doi: 10.1109/BigData59044.2023.10386406.
+
+## Contents
+This repository contains the following:
+- License
+- Dataset
+- Fine-tuned Model
+
+## Model Info
+There are three BERT models, each fine-tuned on a dataset of 70,000 Python 3 solutions submitted by users for problems #1 through #100 on LeetCode:
+- `bert_lc100_hp25`: This model classifies code based on the 25th percentile as its threshold. It is designed for identifying lower quartile code solutions in terms of quality or performance.
+- `bert_lc100_hp50`: Operating with a median-based approach, this model uses the 50th percentile as its classification threshold. It is suitable for general assessments, providing a balanced view of code quality.
+- `bert_lc100_regression`: Unlike the others, this is a regression model. It provides a nuanced prediction of the overall code quality score, offering a more detailed evaluation compared to the binary classification approach.
+
+## Model Usage
+**Installation**
+First, ensure you have the latest version of the tf-models-official package. You can install it using the following command:```
+pip install -q tf-models-official
+```
+
+**Loading the Model**
+To utilize the bert_lc100_regression model within TensorFlow, follow these steps:
+
+```
+import tensorflow as tf
+import tensorflow_text as text
+model =  tf.keras.models.load_model('saved_model/bert_lc100_regression/', compile=False)
+
+```
+
+**Making Predictions**
+To assess the quality of code, given that `X_test` contains a list of code strings, use the model to predict as follows:
+```
+y_pred = model.predict(X_test)
+```
+
+## Reference
+If you found the dataset useful in your research or applications, please cite using the following BibTeX:
+```
+@INPROCEEDINGS{10386406,
+  author={Wang, Tianyu and Chen, Zhixiong},
+  booktitle={2023 IEEE International Conference on Big Data (BigData)}, 
+  title={Analyzing Code Text Strings for Code Evaluation}, 
+  year={2023},
+  volume={},
+  number={},
+  pages={5619-5628},
+  keywords={Measurement;Deep learning;Codes;Bidirectional control;Organizations;Transformers;Software;code assessment;code annotation;deep learning;nature language processing;software assurance;code security},
+  doi={10.1109/BigData59044.2023.10386406}
+}
+```