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- ---
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- license: mit
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- ---
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  # Code Qualiy Evaluation Dataset
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  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.
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  ## Contents
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  This repository contains the following:
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- - License
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- - Dataset (https://github.com/tisage/codeQuality)
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  - Fine-tuned Model
 
 
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  ## Model Info
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  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:
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  **Loading the Model**
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  To utilize the bert_lc100_regression model within TensorFlow, follow these steps:
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  ```
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  import tensorflow as tf
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  import tensorflow_text as text
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  model = tf.keras.models.load_model('saved_model/bert_lc100_regression/', compile=False)
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  ```
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  **Making Predictions**
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  doi={10.1109/BigData59044.2023.10386406}
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  }
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  ```
 
 
 
 
 
 
 
 
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  # Code Qualiy Evaluation Dataset
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  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.
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  ## Contents
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  This repository contains the following:
 
 
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  - Fine-tuned Model
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+ - Dataset (https://github.com/tisage/codeQuality)
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+ - License
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  ## Model Info
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  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:
 
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  **Loading the Model**
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  To utilize the bert_lc100_regression model within TensorFlow, follow these steps:
 
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  ```
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  import tensorflow as tf
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  import tensorflow_text as text
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  model = tf.keras.models.load_model('saved_model/bert_lc100_regression/', compile=False)
 
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  ```
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  **Making Predictions**
 
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  doi={10.1109/BigData59044.2023.10386406}
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  }
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  ```
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+
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+ ---
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+ license: mit
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+ ---